librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-12 00:41:42	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 555 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-12 00:40:24	card stringlengths 11 1.01M
PhongLe1311/my_awesome_billsum_model	PhongLe1311	2023-06-25T15:30:09Z	99	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:billsum", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-06-25T15:20:04Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - billsum metrics: - rouge model-index: - name: my_awesome_billsum_model results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: billsum type: billsum config: default split: ca_test args: default metrics: - name: Rouge1 type: rouge value: 0.1408 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5181 - Rouge1: 0.1408 - Rouge2: 0.0514 - Rougel: 0.1173 - Rougelsum: 0.1173 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 62 \| 2.8150 \| 0.1264 \| 0.0373 \| 0.1061 \| 0.1061 \| 19.0 \| \| No log \| 2.0 \| 124 \| 2.5989 \| 0.1379 \| 0.0501 \| 0.1164 \| 0.1165 \| 19.0 \| \| No log \| 3.0 \| 186 \| 2.5349 \| 0.1396 \| 0.0525 \| 0.1179 \| 0.1181 \| 19.0 \| \| No log \| 4.0 \| 248 \| 2.5181 \| 0.1408 \| 0.0514 \| 0.1173 \| 0.1173 \| 19.0 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
ahessamb/bertopic-test	ahessamb	2023-06-25T15:29:15Z	3	0	bertopic	[ "bertopic", "text-classification", "region:us" ]	text-classification	2023-06-25T15:29:09Z	--- tags: - bertopic library_name: bertopic pipeline_tag: text-classification --- # bertopic-test This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model. BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets. ## Usage To use this model, please install BERTopic: ``` pip install -U bertopic ``` You can use the model as follows: ```python from bertopic import BERTopic topic_model = BERTopic.load("ahessamb/bertopic-test") topic_model.get_topic_info() ``` ## Topic overview * Number of topics: 50 * Number of training documents: 1570 <details> <summary>Click here for an overview of all topics.</summary> \| Topic ID \| Topic Keywords \| Topic Frequency \| Label \| \|----------\|----------------\|-----------------\|-------\| \| 0 \| liquidations - forcefully - betting - liquidation - contracts \| 8 \| 0_liquidations_forcefully_betting_liquidation \| \| 1 \| litecoin - wsm - presale - 77 - near \| 94 \| 1_litecoin_wsm_presale_77 \| \| 2 \| sec - court - terraform - dismiss - lawyers \| 49 \| 2_sec_court_terraform_dismiss \| \| 3 \| huobi - hkvac - bsl - web3 - code \| 12 \| 3_huobi_hkvac_bsl_web3 \| \| 4 \| lucie - shiba - susbarium - puppynet - portals \| 3 \| 4_lucie_shiba_susbarium_puppynet \| \| 5 \| 000006819 - shiba - accuracy - finbold - estimates \| 27 \| 5_000006819_shiba_accuracy_finbold \| \| 6 \| tokens - sec - binance - securities - coinbase \| 45 \| 6_tokens_sec_binance_securities \| \| 7 \| mckinsey - ai - nanjing - productivity - diffusion \| 43 \| 7_mckinsey_ai_nanjing_productivity \| \| 8 \| resistance - swing - fib - zone - ltc \| 32 \| 8_resistance_swing_fib_zone \| \| 9 \| brinkman - tategpt - bitcoin - artists - wealth \| 26 \| 9_brinkman_tategpt_bitcoin_artists \| \| 10 \| stablecoin - stablecoins - decline - redemptions - tusd \| 2 \| 10_stablecoin_stablecoins_decline_redemptions \| \| 11 \| mutant - mayc - bayc - club - mcmullen \| 64 \| 11_mutant_mayc_bayc_club \| \| 12 \| xrp - ema - ripple - bullish - cryptocurrencies \| 43 \| 12_xrp_ema_ripple_bullish \| \| 13 \| tether - cbdcs - loans - federal - nafcu \| 27 \| 13_tether_cbdcs_loans_federal \| \| 14 \| rate - tradingview - bnb - breakout - coinmarketcap \| 85 \| 14_rate_tradingview_bnb_breakout \| \| 15 \| 26 - bulls - rsi - ceiling - 300 \| 2 \| 15_26_bulls_rsi_ceiling \| \| 16 \| lowest - jump - week - wallet - staggering \| 3 \| 16_lowest_jump_week_wallet \| \| 17 \| xrp - ripple - mekras - sbi - institutions \| 56 \| 17_xrp_ripple_mekras_sbi \| \| 18 \| debt - mortgages - trillion - government - suspends \| 3 \| 18_debt_mortgages_trillion_government \| \| 19 \| longitude - chronometer - bitcoin - ships - graffiti \| 2 \| 19_longitude_chronometer_bitcoin_ships \| \| 20 \| volumes - piggy - aud - xrp - usdt \| 15 \| 20_volumes_piggy_aud_xrp \| \| 21 \| root - ledger - stakers - sidechains - compatibility \| 4 \| 21_root_ledger_stakers_sidechains \| \| 22 \| astra - letter - concerns - investors - bitwise \| 4 \| 22_astra_letter_concerns_investors \| \| 23 \| gold - governments - manipulated - stocks - mined \| 10 \| 23_gold_governments_manipulated_stocks \| \| 24 \| tether - sygnum - documents - bank - coindesk \| 9 \| 24_tether_sygnum_documents_bank \| \| 25 \| rewards - governance - lido - proposal - june \| 45 \| 25_rewards_governance_lido_proposal \| \| 26 \| listings - coin - fairerc20 - bittrex - withdrawals \| 68 \| 26_listings_coin_fairerc20_bittrex \| \| 27 \| peaq - ordibots - cosmos - fetch - machine \| 81 \| 27_peaq_ordibots_cosmos_fetch \| \| 28 \| uniswap - v4 - orders - hooks - differing \| 23 \| 28_uniswap_v4_orders_hooks \| \| 29 \| price - neo - matic - rise - altcoin \| 92 \| 29_price_neo_matic_rise \| \| 30 \| emptydoc - staff - policy - binance - workspaces \| 2 \| 30_emptydoc_staff_policy_binance \| \| 31 \| lunc - synthetix - terra - perps - staking \| 33 \| 31_lunc_synthetix_terra_perps \| \| 32 \| tweet - dogecoin - chart - meme - negative \| 3 \| 32_tweet_dogecoin_chart_meme \| \| 33 \| binance - securities - exchange - cz - regulators \| 63 \| 33_binance_securities_exchange_cz \| \| 34 \| bitmart - sale - xrp - discount - event \| 4 \| 34_bitmart_sale_xrp_discount \| \| 35 \| yuan - event - olympics - canadians - organizers \| 49 \| 35_yuan_event_olympics_canadians \| \| 36 \| gusd - fidelity - bitcoin - proposal - blackrock \| 52 \| 36_gusd_fidelity_bitcoin_proposal \| \| 37 \| bills - mcglone - markets - stablecoins - liquidity \| 56 \| 37_bills_mcglone_markets_stablecoins \| \| 38 \| asset - gain - drop - trading - hours \| 2 \| 38_asset_gain_drop_trading \| \| 39 \| epstein - hamsterwheel - vulnerability - bounty - certick \| 28 \| 39_epstein_hamsterwheel_vulnerability_bounty \| \| 40 \| pyth - transparency - data - terra - oracle \| 19 \| 40_pyth_transparency_data_terra \| \| 41 \| shiba - inu - weighted - collapse - recovery \| 2 \| 41_shiba_inu_weighted_collapse \| \| 42 \| neo - opensea - carey - security - impersonators \| 24 \| 42_neo_opensea_carey_security \| \| 43 \| balancer - zkevm - liquidity - defi - 8020 \| 3 \| 43_balancer_zkevm_liquidity_defi \| \| 44 \| reed - battle - platform - argument - trading \| 22 \| 44_reed_battle_platform_argument \| \| 45 \| ada - cardano - whale - sell - investors \| 4 \| 45_ada_cardano_whale_sell \| \| 46 \| uk - coinbase - hong - crypto - regulatory \| 65 \| 46_uk_coinbase_hong_crypto \| \| 47 \| ethereum - tvl - defi - arbitrum - airdrop \| 54 \| 47_ethereum_tvl_defi_arbitrum \| \| 48 \| swyftx - shibarium - token - shibaswap - shiba \| 54 \| 48_swyftx_shibarium_token_shibaswap \| \| 49 \| bitcoin - mining - gain - miners - difficulty \| 54 \| 49_bitcoin_mining_gain_miners \| </details> ## Training hyperparameters * calculate_probabilities: False * language: None * low_memory: False * min_topic_size: 10 * n_gram_range: (1, 1) * nr_topics: None * seed_topic_list: None * top_n_words: 10 * verbose: False ## Framework versions * Numpy: 1.22.4 * HDBSCAN: 0.8.29 * UMAP: 0.5.3 * Pandas: 1.5.3 * Scikit-Learn: 1.2.2 * Sentence-transformers: 2.2.2 * Transformers: 4.30.2 * Numba: 0.56.4 * Plotly: 5.13.1 * Python: 3.10.12
SwampMan/ppo-Huggy	SwampMan	2023-06-25T15:20:32Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-06-25T15:20:22Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: SwampMan/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
cagmfr/q-FrozenLake-v1-4x4-noSlippery	cagmfr	2023-06-25T15:20:16Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-25T15:20:14Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="cagmfr/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
NasimB/gpt2-2-dp-mod-aochild-cut	NasimB	2023-06-25T15:09:04Z	22	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-25T07:34:36Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-2-dp-mod-aochild-cut results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-2-dp-mod-aochild-cut This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.4109 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.7147 \| 0.27 \| 500 \| 5.6451 \| \| 5.3609 \| 0.54 \| 1000 \| 5.2108 \| \| 5.0162 \| 0.81 \| 1500 \| 4.9585 \| \| 4.7627 \| 1.08 \| 2000 \| 4.8126 \| \| 4.5775 \| 1.35 \| 2500 \| 4.7013 \| \| 4.4856 \| 1.62 \| 3000 \| 4.6034 \| \| 4.4038 \| 1.89 \| 3500 \| 4.5175 \| \| 4.2252 \| 2.16 \| 4000 \| 4.4775 \| \| 4.1408 \| 2.42 \| 4500 \| 4.4236 \| \| 4.1136 \| 2.69 \| 5000 \| 4.3721 \| \| 4.0852 \| 2.96 \| 5500 \| 4.3281 \| \| 3.87 \| 3.23 \| 6000 \| 4.3418 \| \| 3.8651 \| 3.5 \| 6500 \| 4.3062 \| \| 3.8601 \| 3.77 \| 7000 \| 4.2781 \| \| 3.8091 \| 4.04 \| 7500 \| 4.2785 \| \| 3.5972 \| 4.31 \| 8000 \| 4.2888 \| \| 3.6301 \| 4.58 \| 8500 \| 4.2678 \| \| 3.6398 \| 4.85 \| 9000 \| 4.2396 \| \| 3.4906 \| 5.12 \| 9500 \| 4.2803 \| \| 3.3704 \| 5.39 \| 10000 \| 4.2849 \| \| 3.4008 \| 5.66 \| 10500 \| 4.2718 \| \| 3.4029 \| 5.93 \| 11000 \| 4.2491 \| \| 3.1804 \| 6.2 \| 11500 \| 4.3116 \| \| 3.1361 \| 6.47 \| 12000 \| 4.3119 \| \| 3.1532 \| 6.73 \| 12500 \| 4.3067 \| \| 3.1591 \| 7.0 \| 13000 \| 4.3072 \| \| 2.8974 \| 7.27 \| 13500 \| 4.3563 \| \| 2.9167 \| 7.54 \| 14000 \| 4.3589 \| \| 2.9248 \| 7.81 \| 14500 \| 4.3580 \| \| 2.8683 \| 8.08 \| 15000 \| 4.3791 \| \| 2.741 \| 8.35 \| 15500 \| 4.3939 \| \| 2.7503 \| 8.62 \| 16000 \| 4.3968 \| \| 2.7573 \| 8.89 \| 16500 \| 4.3983 \| \| 2.6961 \| 9.16 \| 17000 \| 4.4075 \| \| 2.6562 \| 9.43 \| 17500 \| 4.4101 \| \| 2.6653 \| 9.7 \| 18000 \| 4.4107 \| \| 2.667 \| 9.97 \| 18500 \| 4.4109 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
Smaraa/t5-text-simplification_1e4_adafactor_biendata	Smaraa	2023-06-25T15:07:10Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-06-25T12:37:38Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-text-simplification_1e4_adafactor_biendata results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-text-simplification_1e4_adafactor_biendata This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7562 - Rouge1: 10.4603 - Rouge2: 2.642 - Rougel: 9.6362 - Rougelsum: 9.6589 - Gen Len: 13.2838 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 464 \| 0.5489 \| 29.7693 \| 11.1997 \| 25.6091 \| 25.5979 \| 14.7281 \| \| 0.9314 \| 2.0 \| 928 \| 0.5392 \| 29.9099 \| 10.9645 \| 25.334 \| 25.3259 \| 14.7188 \| \| 0.5594 \| 3.0 \| 1392 \| 0.5342 \| 30.3194 \| 11.4204 \| 25.8248 \| 25.8255 \| 14.7666 \| \| 0.5333 \| 4.0 \| 1856 \| 0.5376 \| 30.8368 \| 11.6152 \| 26.3172 \| 26.3583 \| 14.1578 \| \| 0.5192 \| 5.0 \| 2320 \| 0.8890 \| 7.5517 \| 1.4313 \| 7.0971 \| 7.1064 \| 9.9191 \| \| 0.8897 \| 6.0 \| 2784 \| 0.8252 \| 6.9283 \| 1.3484 \| 6.5916 \| 6.5877 \| 10.9894 \| \| 0.9385 \| 7.0 \| 3248 \| 0.7971 \| 8.2401 \| 1.9957 \| 7.7693 \| 7.7675 \| 10.7732 \| \| 0.9089 \| 8.0 \| 3712 \| 0.7725 \| 9.7559 \| 2.2249 \| 9.0272 \| 9.0098 \| 10.7175 \| \| 0.8824 \| 9.0 \| 4176 \| 0.7552 \| 12.006 \| 2.8041 \| 11.0115 \| 10.992 \| 10.7838 \| \| 0.8658 \| 10.0 \| 4640 \| 0.7490 \| 13.311 \| 3.4159 \| 12.1933 \| 12.1551 \| 10.6499 \| \| 0.864 \| 11.0 \| 5104 \| 0.7448 \| 13.9983 \| 3.6176 \| 12.7712 \| 12.7347 \| 10.752 \| \| 0.868 \| 12.0 \| 5568 \| 0.7495 \| 12.318 \| 3.2975 \| 11.3451 \| 11.3218 \| 12.0252 \| \| 0.8844 \| 13.0 \| 6032 \| 0.7552 \| 10.6154 \| 2.7347 \| 9.8228 \| 9.8116 \| 13.191 \| \| 0.8844 \| 14.0 \| 6496 \| 0.7562 \| 10.4603 \| 2.642 \| 9.6362 \| 9.6589 \| 13.2838 \| \| 0.8971 \| 15.0 \| 6960 \| 0.7562 \| 10.4603 \| 2.642 \| 9.6362 \| 9.6589 \| 13.2838 \| \| 0.8981 \| 16.0 \| 7424 \| 0.7562 \| 10.4603 \| 2.642 \| 9.6362 \| 9.6589 \| 13.2838 \| \| 0.8956 \| 17.0 \| 7888 \| 0.7562 \| 10.4603 \| 2.642 \| 9.6362 \| 9.6589 \| 13.2838 \| \| 0.8984 \| 18.0 \| 8352 \| 0.7562 \| 10.4603 \| 2.642 \| 9.6362 \| 9.6589 \| 13.2838 \| \| 0.8959 \| 19.0 \| 8816 \| 0.7562 \| 10.4603 \| 2.642 \| 9.6362 \| 9.6589 \| 13.2838 \| \| 0.8977 \| 20.0 \| 9280 \| 0.7562 \| 10.4603 \| 2.642 \| 9.6362 \| 9.6589 \| 13.2838 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Smaraa/gpt2-text-simplification_1e4_adafactor_biendata	Smaraa	2023-06-25T14:56:13Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-25T12:42:47Z	--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-text-simplification_1e4_adafactor_biendata results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-text-simplification_1e4_adafactor_biendata This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9089 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 464 \| 0.7729 \| \| 1.0489 \| 2.0 \| 928 \| 0.7546 \| \| 0.754 \| 3.0 \| 1392 \| 0.7497 \| \| 0.7034 \| 4.0 \| 1856 \| 0.7530 \| \| 0.6619 \| 5.0 \| 2320 \| 0.7560 \| \| 0.6265 \| 6.0 \| 2784 \| 0.7639 \| \| 0.5921 \| 7.0 \| 3248 \| 0.7747 \| \| 0.5621 \| 8.0 \| 3712 \| 0.7848 \| \| 0.5359 \| 9.0 \| 4176 \| 0.7969 \| \| 0.5115 \| 10.0 \| 4640 \| 0.8113 \| \| 0.4879 \| 11.0 \| 5104 \| 0.8256 \| \| 0.4683 \| 12.0 \| 5568 \| 0.8373 \| \| 0.4491 \| 13.0 \| 6032 \| 0.8519 \| \| 0.4491 \| 14.0 \| 6496 \| 0.8642 \| \| 0.4324 \| 15.0 \| 6960 \| 0.8741 \| \| 0.4176 \| 16.0 \| 7424 \| 0.8841 \| \| 0.4054 \| 17.0 \| 7888 \| 0.8924 \| \| 0.3946 \| 18.0 \| 8352 \| 0.8994 \| \| 0.3868 \| 19.0 \| 8816 \| 0.9043 \| \| 0.3813 \| 20.0 \| 9280 \| 0.9089 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
sukantan/all-MiniLM-L6-v2-ftlegal-v1	sukantan	2023-06-25T14:44:18Z	8	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "dataset:sukantan/nyaya-st-training", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-06-25T14:44:14Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity datasets: - sukantan/nyaya-st-training --- # sukantan/all-MiniLM-L6-v2-ftlegal-v1 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sukantan/all-MiniLM-L6-v2-ftlegal-v1') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=sukantan/all-MiniLM-L6-v2-ftlegal-v1) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 391 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MegaBatchMarginLoss.MegaBatchMarginLoss` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 391, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
wza/llama-65b-qlora-fin-1epoch	wza	2023-06-25T14:09:07Z	2	0	peft	[ "peft", "region:us" ]	null	2023-06-23T01:52:13Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0
wza/llama-65b-qlora-fin-2epoch	wza	2023-06-25T14:04:59Z	1	0	peft	[ "peft", "region:us" ]	null	2023-06-25T12:56:22Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0
Smaraa/bart-text-simplification_1e4_adafactor_biendata	Smaraa	2023-06-25T14:04:43Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-06-25T12:33:30Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-text-simplification_1e4_adafactor_biendata results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-text-simplification_1e4_adafactor_biendata This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7599 - Rouge1: 29.7176 - Rouge2: 10.9512 - Rougel: 25.5101 - Rougelsum: 25.526 - Gen Len: 15.2029 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 232 \| 0.5813 \| 30.604 \| 12.4253 \| 26.5172 \| 26.4807 \| 15.2241 \| \| No log \| 2.0 \| 464 \| 0.5739 \| 31.9076 \| 12.798 \| 27.4728 \| 27.4929 \| 15.2241 \| \| 1.0176 \| 3.0 \| 696 \| 0.5700 \| 31.3776 \| 12.2852 \| 27.1116 \| 27.0878 \| 15.6459 \| \| 1.0176 \| 4.0 \| 928 \| 0.5762 \| 30.8731 \| 12.3014 \| 26.9196 \| 26.8301 \| 14.6353 \| \| 0.4798 \| 5.0 \| 1160 \| 0.5863 \| 29.927 \| 11.7166 \| 25.9447 \| 25.921 \| 14.4297 \| \| 0.4798 \| 6.0 \| 1392 \| 0.6003 \| 29.9528 \| 11.2098 \| 25.6908 \| 25.7209 \| 14.7414 \| \| 0.3855 \| 7.0 \| 1624 \| 0.6179 \| 30.1161 \| 11.2863 \| 26.1433 \| 26.1519 \| 15.1698 \| \| 0.3855 \| 8.0 \| 1856 \| 0.6290 \| 29.5566 \| 11.1229 \| 25.6003 \| 25.5754 \| 14.87 \| \| 0.3092 \| 9.0 \| 2088 \| 0.6538 \| 29.7844 \| 11.2434 \| 25.8222 \| 25.8067 \| 14.9708 \| \| 0.3092 \| 10.0 \| 2320 \| 0.6698 \| 28.9941 \| 10.6603 \| 25.0054 \| 25.0198 \| 15.0239 \| \| 0.247 \| 11.0 \| 2552 \| 0.6906 \| 28.732 \| 10.4525 \| 24.8897 \| 24.8953 \| 14.9721 \| \| 0.247 \| 12.0 \| 2784 \| 0.7023 \| 29.0609 \| 10.4762 \| 24.9678 \| 24.9893 \| 15.317 \| \| 0.198 \| 13.0 \| 3016 \| 0.7200 \| 29.9516 \| 11.2397 \| 25.7347 \| 25.7489 \| 15.1485 \| \| 0.198 \| 14.0 \| 3248 \| 0.7263 \| 29.1565 \| 10.7363 \| 25.2238 \| 25.203 \| 14.9761 \| \| 0.198 \| 15.0 \| 3480 \| 0.7376 \| 30.0068 \| 11.2078 \| 26.0012 \| 26.0235 \| 14.9589 \| \| 0.1602 \| 16.0 \| 3712 \| 0.7489 \| 29.8747 \| 11.0555 \| 25.7321 \| 25.7543 \| 15.2931 \| \| 0.1602 \| 17.0 \| 3944 \| 0.7487 \| 29.6901 \| 10.8692 \| 25.5467 \| 25.5808 \| 15.2798 \| \| 0.1342 \| 18.0 \| 4176 \| 0.7553 \| 29.5496 \| 10.8611 \| 25.2895 \| 25.3218 \| 15.3156 \| \| 0.1342 \| 19.0 \| 4408 \| 0.7590 \| 29.7733 \| 11.1577 \| 25.671 \| 25.6883 \| 15.1313 \| \| 0.1184 \| 20.0 \| 4640 \| 0.7599 \| 29.7176 \| 10.9512 \| 25.5101 \| 25.526 \| 15.2029 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
rdenadai/BR_BERTo	rdenadai	2023-06-25T14:02:18Z	180	3	transformers	[ "transformers", "pytorch", "jax", "safetensors", "roberta", "fill-mask", "portuguese", "brazil", "pt_BR", "pt", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- language: pt tags: - portuguese - brazil - pt_BR widget: - text: gostei muito dessa <mask> --- # BR_BERTo Portuguese (Brazil) model for text inference. ## Params Trained on a corpus of 6_993_330 sentences. - Vocab size: 150_000 - RobertaForMaskedLM size : 512 - Num train epochs: 3 - Time to train: ~10days (on GCP with a Nvidia T4) I follow the great tutorial from HuggingFace team: [How to train a new language model from scratch using Transformers and Tokenizers](https://huggingface.co/blog/how-to-train) More infor here: [BR_BERTo](https://github.com/rdenadai/BR-BERTo)
anas21/English1SpeechToTextModel	anas21	2023-06-25T13:48:32Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-06-25T13:34:09Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: English1SpeechToTextModel results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # English1SpeechToTextModel This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 10 - eval_batch_size: 8 - seed: 10 - gradient_accumulation_steps: 10 - total_train_batch_size: 100 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
findnitai/FaceGen	findnitai	2023-06-25T13:25:03Z	138	3	diffusers	[ "diffusers", "safetensors", "text-to-image", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-24T03:47:05Z	--- license: apache-2.0 pipeline_tag: text-to-image --- Few examples of unique faces generated by the model. Trained on FFHQ dataset. ![7qfdf0.gif](https://s3.amazonaws.com/moonup/production/uploads/6430e44437ee6d9b76cb8388/fqmUfSW6C9vB-YDIZyTfm.gif)
lucasbertola/q-FrozenLake-v1-8x8-noSlipper	lucasbertola	2023-06-25T13:23:29Z	0	0	null	[ "FrozenLake-v1-8x8-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "Lucas_is_the_best", "model-index", "region:us" ]	reinforcement-learning	2023-06-25T13:18:21Z	--- tags: - FrozenLake-v1-8x8-no_slippery - q-learning - reinforcement-learning - custom-implementation - Lucas_is_the_best model-index: - name: q-FrozenLake-v1-8x8-noSlipper results: - metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-8x8-no_slippery type: FrozenLake-v1-8x8-no_slippery --- # Q-Learning Agent playing1 This is a trained model of a Q-Learning agent playing ## Usage ```python model = load_from_hub(repo_id="lucasbertola/q-FrozenLake-v1-4x4-noSlipper", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
PaulineJamin/q-FrozenLake-v1-4x4-noSlippery	PaulineJamin	2023-06-25T13:16:52Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-25T12:25:16Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="PaulineJamin/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
S3S3/q-Taxi-v3	S3S3	2023-06-25T13:05:40Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-25T13:05:36Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.44 +/- 2.75 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="S3S3/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
paramrah/shoesv2	paramrah	2023-06-25T13:00:03Z	2	0	tf-keras	[ "tf-keras", "mobilenet", "image-classification", "region:us" ]	image-classification	2023-06-25T12:59:39Z	--- pipeline_tag: image-classification ---
bogdancazan/bart-base-newsela-biendata-with-domain-adaptation	bogdancazan	2023-06-25T12:57:32Z	105	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-06-19T14:35:21Z	training_args = TrainingArguments( output_dir='bart-base-newsela-biendata-with-domain-adaptation', num_train_epochs=20, warmup_steps=250, per_device_train_batch_size=BATCH_SIZE, weight_decay=0.01, learning_rate=2e-4, fp16=True, optim="adafactor", ) Step Training Loss 500 5.677000 1000 2.361900 1500 1.826000 2000 1.672900 2500 1.597900 3000 1.555700 3500 1.520600 4000 1.496300 4500 1.476800 TrainOutput(global_step=4640, training_loss=2.1116079396214977, metrics={'train_runtime': 1059.6025, 'train_samples_per_second': 279.992, 'train_steps_per_second': 4.379, 'total_flos': 0.0, 'train_loss': 2.1116079396214977, 'epoch': 20.0})
S3S3/q-FrozenLake-v1-4x4-noSlippery	S3S3	2023-06-25T12:53:11Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-25T12:53:07Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="S3S3/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
VilohitT/question_answering_majorproject_2nd	VilohitT	2023-06-25T12:47:34Z	0	0	peft	[ "peft", "region:us" ]	null	2023-06-25T12:47:29Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
AtomGradient/Adjust_ChatGLM_6B	AtomGradient	2023-06-25T12:45:31Z	104	0	transformers	[ "transformers", "pytorch", "chatglm", "feature-extraction", "custom_code", "license:other", "region:us" ]	feature-extraction	2023-06-25T12:04:00Z	--- license: other --- ``` from transformers import AutoConfig, AutoModel, AutoTokenizer import os import torch # 载入Tokenizer tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) config = AutoConfig.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True, pre_seq_len=128) model = AutoModel.from_pretrained("THUDM/chatglm-6b", config=config, trust_remote_code=True) prefix_state_dict = torch.load(os.path.join("./Adjust_ChatGLM_6B/", "pytorch_model.bin")) new_prefix_state_dict = {} for k, v in prefix_state_dict.items(): if k.startswith("transformer.prefix_encoder."): new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict) model = model.quantize(4) model = model.half().cuda() model.transformer.prefix_encoder.float() model = model.eval() response, history = model.chat(tokenizer, "生成衬衣的广告词", history=[]) print(response) ```
TheBloke/vicuna-13b-v1.3.0-GGML	TheBloke	2023-06-25T12:41:16Z	0	16	null	[ "arxiv:2302.13971", "arxiv:2306.05685", "license:other", "region:us" ]	null	2023-06-25T10:52:15Z	--- inference: false license: other --- <!-- header start --> <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <!-- header end --> # LmSys' Vicuna 13B v1.3 GGML These files are GGML format model files for [LmSys' Vicuna 13B v1.3](https://huggingface.co/lmsys/vicuna-13b-v1.3). NOTE: This model was recently updated by the LmSys Team. If you already downloaded Vicuna 13B v1.3 GPTQ or GGML, you may want to re-download it from this repo, as the weights were updated. The original model I uploaded has been renamed to v1.3-preview. GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as: * [text-generation-webui](https://github.com/oobabooga/text-generation-webui) * [KoboldCpp](https://github.com/LostRuins/koboldcpp) * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) * [ctransformers](https://github.com/marella/ctransformers) ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/vicuna-13b-v1.3.0-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/vicuna-13b-v1.3.0-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/lmsys/vicuna-13b-v1.3) ## Prompt template ``` A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: prompt ASSISTANT: ``` <!-- compatibility_ggml start --> ## Compatibility ### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0` I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit `2d5db48`. These are guaranteed to be compatbile with any UIs, tools and libraries released since late May. ### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K` These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`. They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python and ctransformers. Other tools and libraries may or may not be compatible - check their documentation if in doubt. ## Explanation of the new k-quant methods The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw * GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type. Refer to the Provided Files table below to see what files use which methods, and how. <!-- compatibility_ggml end --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| vicuna-13b-v1.3.0.ggmlv3.q2_K.bin \| q2_K \| 2 \| 5.51 GB \| 8.01 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. \| \| vicuna-13b-v1.3.0.ggmlv3.q3_K_L.bin \| q3_K_L \| 3 \| 6.93 GB \| 9.43 GB \| New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K \| \| vicuna-13b-v1.3.0.ggmlv3.q3_K_M.bin \| q3_K_M \| 3 \| 6.31 GB \| 8.81 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K \| \| vicuna-13b-v1.3.0.ggmlv3.q3_K_S.bin \| q3_K_S \| 3 \| 5.66 GB \| 8.16 GB \| New k-quant method. Uses GGML_TYPE_Q3_K for all tensors \| \| vicuna-13b-v1.3.0.ggmlv3.q4_0.bin \| q4_0 \| 4 \| 7.32 GB \| 9.82 GB \| Original llama.cpp quant method, 4-bit. \| \| vicuna-13b-v1.3.0.ggmlv3.q4_1.bin \| q4_1 \| 4 \| 8.14 GB \| 10.64 GB \| Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. \| \| vicuna-13b-v1.3.0.ggmlv3.q4_K_M.bin \| q4_K_M \| 4 \| 7.87 GB \| 10.37 GB \| New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K \| \| vicuna-13b-v1.3.0.ggmlv3.q4_K_S.bin \| q4_K_S \| 4 \| 7.37 GB \| 9.87 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for all tensors \| \| vicuna-13b-v1.3.0.ggmlv3.q5_0.bin \| q5_0 \| 5 \| 8.95 GB \| 11.45 GB \| Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. \| \| vicuna-13b-v1.3.0.ggmlv3.q5_1.bin \| q5_1 \| 5 \| 9.76 GB \| 12.26 GB \| Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. \| \| vicuna-13b-v1.3.0.ggmlv3.q5_K_M.bin \| q5_K_M \| 5 \| 9.23 GB \| 11.73 GB \| New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K \| \| vicuna-13b-v1.3.0.ggmlv3.q5_K_S.bin \| q5_K_S \| 5 \| 8.97 GB \| 11.47 GB \| New k-quant method. Uses GGML_TYPE_Q5_K for all tensors \| \| vicuna-13b-v1.3.0.ggmlv3.q6_K.bin \| q6_K \| 6 \| 10.68 GB \| 13.18 GB \| New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors \| \| vicuna-13b-v1.3.0.ggmlv3.q8_0.bin \| q8_0 \| 8 \| 13.83 GB \| 16.33 GB \| Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `llama.cpp` I use the following command line; adjust for your tastes and needs: ``` ./main -t 10 -ngl 32 -m vicuna-13b-v1.3.0.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "USER: Write a story about llamas\nASSISTANT:" ``` If you're able to use full GPU offloading, you should use `-t 1` to get best performance. If not able to fully offload to GPU, you should use more cores. Change `-t 10` to the number of physical CPU cores you have, or a lower number depending on what gives best performance. Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md). <!-- footer start --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov. Patreon special mentions: Pyrater, WelcomeToTheClub, Kalila, Mano Prime, Trenton Dambrowitz, Spiking Neurons AB, Pierre Kircher, Fen Risland, Kevin Schuppel, Luke, Rainer Wilmers, vamX, Gabriel Puliatti, Alex , Karl Bernard, Ajan Kanaga, Talal Aujan, Space Cruiser, ya boyyy, biorpg, Johann-Peter Hartmann, Asp the Wyvern, Ai Maven, Ghost , Preetika Verma, Nikolai Manek, trip7s trip, John Detwiler, Fred von Graf, Artur Olbinski, subjectnull, John Villwock, Junyu Yang, Rod A, Lone Striker, Chris McCloskey, Iucharbius , Matthew Berman, Illia Dulskyi, Khalefa Al-Ahmad, Imad Khwaja, chris gileta, Willem Michiel, Greatston Gnanesh, Derek Yates, K, Alps Aficionado, Oscar Rangel, David Flickinger, Luke Pendergrass, Deep Realms, Eugene Pentland, Cory Kujawski, terasurfer , Jonathan Leane, senxiiz, Joseph William Delisle, Sean Connelly, webtim, zynix , Nathan LeClaire. Thank you to all my generous patrons and donaters! <!-- footer end --> # Original model card: LmSys' Vicuna 13B v1.3 # Vicuna Model Card ## Model Details Vicuna is a chat assistant trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. - Developed by: [LMSYS](https://lmsys.org/) - Model type: An auto-regressive language model based on the transformer architecture. - License: Non-commercial license - Finetuned from model: [LLaMA](https://arxiv.org/abs/2302.13971). ### Model Sources - Repository: https://github.com/lm-sys/FastChat - Blog: https://lmsys.org/blog/2023-03-30-vicuna/ - Paper: https://arxiv.org/abs/2306.05685 - Demo: https://chat.lmsys.org/ ## Uses The primary use of Vicuna is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence. ## How to Get Started with the Model Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights. APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api. ## Training Details Vicuna v1.3 is fine-tuned from LLaMA with supervised instruction fine-tuning. The training data is around 140K conversations collected from ShareGPT.com. See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf). ## Evaluation Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf). ## Difference between different versions of Vicuna See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)
emilianJR/HRA_hyperrealism_art	emilianJR	2023-06-25T12:30:23Z	52	2	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-25T12:20:01Z	--- language: - en license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Diffuser model for this SD checkpoint: https://civitai.com/models/80515/hrahyperrealism-art emilianJR/HRA_hyperrealism_art is the HuggingFace diffuser that you can use with diffusers.StableDiffusionPipeline(). Examples \| Examples \| Examples ---- \| ---- \| ---- ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/57a93272-f14e-4252-b6c4-485264e07a9d/width=450/194806-2023-06-03-20230603034324-640-896.jpeg) \| ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/07069db6-0ec5-4d96-a9ac-cc3c8501a1d8/width=450/194800-2023-06-03-20230603034105-640-896.jpeg) \| ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/a3682d16-7fd3-4e7d-8698-e9c51dcdcbb6/width=450/194705-2023-06-03-20230603030320-640-896.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/23d67259-0a4f-4168-af5d-feeeffcf8101/width=450/194799-2023-06-03-20230603034104-640-896.jpeg) \| ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/1dff82b9-748e-477f-b0f3-f2b9860aa093/width=450/194790-2023-06-03-20230603033734-640-896.jpeg) \| ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/43d23920-55db-4c6b-abd6-33cdacb6d4eb/width=450/194690-2023-06-03-20230603025728-640-896.jpeg) ------- ## 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). ```python from diffusers import StableDiffusionPipeline import torch model_id = "emilianJR/HRA_hyperrealism_art" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "YOUR PROMPT" image = pipe(prompt).images[0] image.save("image.png") ``` ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
Luke537/image_classification_food_model	Luke537	2023-06-25T12:30:18Z	189	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:food101", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-06-24T19:15:08Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - food101 metrics: - accuracy model-index: - name: image_classification_food_model results: - task: name: Image Classification type: image-classification dataset: name: food101 type: food101 config: default split: train[:5000] args: default metrics: - name: Accuracy type: accuracy value: 0.893 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # image_classification_food_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 1.6474 - Accuracy: 0.893 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.7587 \| 0.99 \| 62 \| 2.5481 \| 0.844 \| \| 1.8903 \| 2.0 \| 125 \| 1.8096 \| 0.874 \| \| 1.6502 \| 2.98 \| 186 \| 1.6474 \| 0.893 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cpu - Datasets 2.13.0 - Tokenizers 0.13.3
emilianJR/majicMIX_realistic_v6	emilianJR	2023-06-25T12:26:15Z	69	14	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-18T12:42:51Z	--- language: - en license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Diffuser model for this SD checkpoint: https://civitai.com/models/43331/majicmix-realistic emilianJR/majicMIX_realistic_v6 is the HuggingFace diffuser that you can use with diffusers.StableDiffusionPipeline(). Examples \| Examples \| Examples ---- \| ---- \| ---- ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/55e308aa-aec9-4816-b76e-523d9235a6e1/width=450/00005-321001525.jpeg) \| ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/f1bb9271-3628-45c6-8b2c-05ee3b19af0f/width=450/00027-1961413425.jpeg) \| ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/18150a9d-1e07-494f-8498-cd0c033907c5/width=450/00042-2448422190.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d748fcfd-29f1-4fe1-95e2-d34765bccca9/width=450/00058-3698311310.jpeg) \| ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/a5b348c9-7b5b-4235-a943-834eec84a17a/width=450/00003-703532927.jpeg) \| ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/66820fd3-98f2-4fbb-b904-0507de39c36a/width=450/00002-140050360.jpeg) ------- ## 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). ```python from diffusers import StableDiffusionPipeline import torch model_id = "emilianJR/majicMIX_realistic_v6" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "YOUR PROMPT" image = pipe(prompt).images[0] image.save("image.png") ``` ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
bogdancazan/t5-base-newsela-biendata-with-domain-adaptation	bogdancazan	2023-06-25T12:24:30Z	105	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-06-19T13:46:06Z	training_args = TrainingArguments( output_dir='t5-base-wikilarge-newsela-with-domain-adaptation', num_train_epochs=20, warmup_steps=250, per_device_train_batch_size=BATCH_SIZE, weight_decay=0.01, learning_rate=2e-4, # fp16=True, optim="adafactor", ) Step Training Loss 500 4.184500 1000 2.470900 1500 2.128900 2000 1.951600 2500 1.834400 3000 1.755800 3500 1.701800 4000 1.656300 4500 1.628800 TrainOutput(global_step=4640, training_loss=2.1286644540984057, metrics={'train_runtime': 4090.6694, 'train_samples_per_second': 72.526, 'train_steps_per_second': 1.134, 'total_flos': 0.0, 'train_loss': 2.1286644540984057, 'epoch': 20.0})
Smaraa/bart-text-simplification_1e4_adafactor	Smaraa	2023-06-25T11:45:02Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-06-24T11:26:12Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-text-simplification_1e4_adafactor results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-text-simplification_1e4_adafactor This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8377 - Rouge1: 60.5348 - Rouge2: 41.6762 - Rougel: 55.5994 - Rougelsum: 55.5841 - Gen Len: 18.7487 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 0.1741 \| 1.0 \| 1163 \| 0.6416 \| 62.4 \| 44.1316 \| 57.9029 \| 57.8644 \| 18.8482 \| \| 0.1553 \| 2.0 \| 2326 \| 0.6504 \| 62.2879 \| 43.9281 \| 57.4714 \| 57.461 \| 18.8063 \| \| 0.1369 \| 3.0 \| 3489 \| 0.6656 \| 61.2481 \| 42.605 \| 56.5118 \| 56.4636 \| 18.733 \| \| 0.1286 \| 4.0 \| 4652 \| 0.6906 \| 61.3015 \| 42.1608 \| 56.2688 \| 56.1707 \| 18.7487 \| \| 0.1141 \| 5.0 \| 5815 \| 0.7082 \| 62.1771 \| 43.1481 \| 57.0231 \| 57.0673 \| 18.911 \| \| 0.1016 \| 6.0 \| 6978 \| 0.7188 \| 61.408 \| 42.2759 \| 56.1699 \| 56.1779 \| 18.8377 \| \| 0.0961 \| 7.0 \| 8141 \| 0.7334 \| 60.802 \| 41.9149 \| 56.0171 \| 56.0279 \| 18.8168 \| \| 0.0869 \| 8.0 \| 9304 \| 0.7509 \| 60.6564 \| 41.3587 \| 55.4436 \| 55.468 \| 18.7382 \| \| 0.0783 \| 9.0 \| 10467 \| 0.7713 \| 60.3551 \| 41.8074 \| 55.6856 \| 55.679 \| 18.7173 \| \| 0.0751 \| 10.0 \| 11630 \| 0.7785 \| 60.378 \| 41.6134 \| 55.5217 \| 55.505 \| 18.8325 \| \| 0.0679 \| 11.0 \| 12793 \| 0.7835 \| 60.5835 \| 41.6735 \| 55.5469 \| 55.5791 \| 18.7435 \| \| 0.0619 \| 12.0 \| 13956 \| 0.8012 \| 60.8152 \| 41.2014 \| 55.7186 \| 55.7233 \| 18.9424 \| \| 0.0611 \| 13.0 \| 15119 \| 0.8091 \| 60.8188 \| 41.8074 \| 55.6684 \| 55.8026 \| 18.7958 \| \| 0.0568 \| 14.0 \| 16282 \| 0.8175 \| 60.9209 \| 41.5689 \| 55.8838 \| 55.8642 \| 18.7277 \| \| 0.0527 \| 15.0 \| 17445 \| 0.8250 \| 61.0215 \| 41.9079 \| 55.9018 \| 55.8709 \| 18.9162 \| \| 0.0524 \| 16.0 \| 18608 \| 0.8317 \| 60.8214 \| 41.6554 \| 55.8053 \| 55.7947 \| 18.7277 \| \| 0.0504 \| 17.0 \| 19771 \| 0.8310 \| 60.6533 \| 41.6507 \| 55.9289 \| 55.9426 \| 18.7958 \| \| 0.0486 \| 18.0 \| 20934 \| 0.8345 \| 60.4722 \| 41.5319 \| 55.3384 \| 55.3655 \| 18.6859 \| \| 0.0491 \| 19.0 \| 22097 \| 0.8379 \| 60.4012 \| 41.2452 \| 55.5059 \| 55.5553 \| 18.8115 \| \| 0.0489 \| 20.0 \| 23260 \| 0.8377 \| 60.5348 \| 41.6762 \| 55.5994 \| 55.5841 \| 18.7487 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
alfredplpl/unlimited-1-0	alfredplpl	2023-06-25T11:44:51Z	34	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "text-to-image", "arxiv:2112.10752", "arxiv:2212.03860", "license:other", "autotrain_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-25T11:21:59Z	--- license: other tags: - stable-diffusion - text-to-image inference: false --- # Unlimited 1.0 Model Card ![eyecatch.jpg](eyecatch.jpg) Title: Unleash your limit. English version is [here](README_en.md). # はじめに Unlimitedは、情報漏洩したNovel AI Diffusionの代わりとなるように開発した、AIアートに特化した画像生成AIです。 # ライセンスについてライセンスについては、もとのライセンス CreativeML Open RAIL++-M License に例外を除き商用利用禁止を追加しただけです。例外を除き商用利用禁止を追加した理由は創作業界に悪影響を及ぼしかねないという懸念からです。営利企業にいる方は法務部にいる人と相談してください。趣味で利用する方はあまり気にしなくても一般常識を守り、お使いください。なお、商用利用したい方は別途こちら (ozaki.yasunori@outlook.com) にご相談ください。 # 法律について本モデルは日本にて作成されました。したがって、日本の法律が適用されます。本モデルの学習は、著作権法第30条の4に基づき、合法であると主張します。また、本モデルの配布については、著作権法や刑法175条に照らしてみても、正犯や幇助犯にも該当しないと主張します。詳しくは柿沼弁護士の[見解](https://twitter.com/tka0120/status/1601483633436393473?s=20&t=yvM9EX0Em-_7lh8NJln3IQ)を御覧ください。ただし、ライセンスにもある通り、本モデルの生成物は各種法令に従って取り扱って下さい。 # 使い方モデルは[safetensors形式](unlimited_1_0.safetensors)からダウンロードできます。以下、一般的なモデルカードの日本語訳です。 ## モデル詳細 - モデルタイプ: 拡散モデルベースの text-to-image 生成モデル - 言語: 日本語 - ライセンス: CreativeML Open RAIL++-M-NC License - モデルの説明: このモデルはプロンプトに応じて適切な画像を生成することができます。アルゴリズムは [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) と [OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip) です。 - 補足: - 参考文献: @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ## モデルの使用例 Stable Diffusion v2と同じ使い方です。たくさんの方法がありますが、２つのパターンを提供します。 - Web UI - Diffusers ### Web UIの場合 Stable Diffusion v2 の使い方と同じく、safetensor形式のモデルファイルをモデルフォルダに入れてください。詳しいインストール方法は、[こちらの記事](https://note.com/it_navi/n/n6ffb66513769)を参照してください。なお、xformersをインストールし、--xformers --disable-nan-checkオプションをオンにすることをおすすめします。そうでない場合は--no-halfオプションをオンにしてください。 ### Diffusersの場合 [🤗's Diffusers library](https://github.com/huggingface/diffusers) を使ってください。まずは、以下のスクリプトを実行し、ライブラリをいれてください。 ```bash pip install --upgrade git+https://github.com/huggingface/diffusers.git transformers accelerate scipy ``` 次のスクリプトを実行し、画像を生成してください。 ```python from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler import torch model_id = "alfredplpl/unlimited-1-0" scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "masterpiece, anime, close up, white short hair, red eyes, 1girl, solo, red roses" negative_prompt="lowres , kanji, monochrome, ((bad anatomy)), ((bad hands)), text, missing finger, extra digits, fewer digits, blurry, ((mutated hands and fingers)), (poorly drawn face), ((mutation)), ((deformed face)), (ugly), ((bad proportions)), ((extra limbs)), extra face, (double head), (extra head), ((extra feet)), monster, logo, cropped, jpeg, humpbacked, long body, long neck, ((jpeg artifacts)), ((censored)), ((bad aesthetic))" images = pipe(prompt,negative_prompt=negative_prompt, num_inference_steps=30).images images[0].save("girl.png") ``` 注意: - [xformers](https://github.com/facebookresearch/xformers) を使うと早くなります。 - GPUを使う際にGPUのメモリが少ない人は `pipe.enable_attention_slicing()` を使ってください。 #### 想定される用途 - 自己表現 - このAIを使い、「あなた」らしさを発信すること - 画像生成AIに関する報道 - 公共放送だけでなく、営利企業でも可能 - 画像合成AIに関する情報を「知る権利」は創作業界に悪影響を及ぼさないと判断したためです。また、報道の自由などを尊重しました。 - 研究開発 - Discord上でのモデルの利用 - プロンプトエンジニアリング - ファインチューニング（追加学習とも） - DreamBooth など - 他のモデルとのマージ - 本モデルの性能をFIDなどで調べること - 本モデルがStable Diffusion以外のモデルとは独立であることをチェックサムやハッシュ関数などで調べること - 教育 - 美大生や専門学校生の卒業制作 - 大学生の卒業論文や課題制作 - 先生が画像生成AIの現状を伝えること - Hugging Face の Community にかいてある用途 - 日本語か英語で質問してください #### 想定されない用途 - 物事を事実として表現するようなこと - 収益化されているYouTubeなどのコンテンツへの使用 - 商用のサービスとして直接提供すること - 先生を困らせるようなこと - その他、創作業界に悪影響を及ぼすこと # 使用してはいけない用途や悪意のある用途 - デジタル贋作 ([Digital Forgery](https://arxiv.org/abs/2212.03860)) は公開しないでください（著作権法に違反するおそれ） - 他人の作品を無断でImage-to-Imageしないでください（著作権法に違反するおそれ） - わいせつ物を頒布しないでください (刑法175条に違反するおそれ） - いわゆる業界のマナーを守らないようなこと - 事実に基づかないことを事実のように語らないようにしてください（威力業務妨害罪が適用されるおそれ） - フェイクニュース ## モデルの限界やバイアス ### モデルの限界 - 拡散モデルや大規模言語モデルは、いまだに未知の部分が多く、その限界は判明していない。 ### バイアス - 拡散モデルや大規模言語モデルは、いまだに未知の部分が多く、バイアスは判明していない。 ## 学習学習データ国内法に準拠したデータとモデル。学習プロセス - ハードウェア: A6000x2 ## 評価結果第三者による評価を求めています。 ## 環境への影響 - ハードウェアタイプ: A6000x2 - 使用時間（単位は時間）: 1000 - 学習した場所: 日本 ## 参考文献 @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } *このモデルカードは [Stable Diffusion v2](https://huggingface.co/stabilityai/stable-diffusion-2/raw/main/README.md) に基づいて書かれました。
PraveenJesu/openai-whisper-medium-peft-lora-colab	PraveenJesu	2023-06-25T11:43:36Z	0	0	peft	[ "peft", "region:us" ]	null	2023-06-25T11:43:33Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
Erfan2001/distilbert_NoTokenized	Erfan2001	2023-06-25T11:43:35Z	109	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-24T22:00:23Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: xxx results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xxx This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6856 - Accuracy: 0.7758 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7996 \| 1.0 \| 4284 \| 0.7921 \| 0.7287 \| \| 0.5539 \| 2.0 \| 8568 \| 0.6856 \| 0.7758 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
edfryo/bangkelser	edfryo	2023-06-25T11:39:27Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2023-05-09T11:58:00Z	--- license: bigscience-openrail-m ---
jondurbin/airoboros-13b-gpt4-1.4-fp16	jondurbin	2023-06-25T11:39:17Z	1,423	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "dataset:jondurbin/airoboros-gpt4-1.4", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-22T10:46:42Z	--- license: other datasets: - jondurbin/airoboros-gpt4-1.4 --- float16 version of https://huggingface.co/jondurbin/airoboros-13b-gpt4-1.4
Ryukijano/DialoGPT_med_model	Ryukijano	2023-06-25T11:38:19Z	118	1	transformers	[ "transformers", "pytorch", "safetensors", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-24T12:37:08Z	Hello there , this bot is trained on DialoGTP for an epoch of 45
jiyuanq/falcon-40b-instruct-gptq-128g-act	jiyuanq	2023-06-25T10:35:13Z	14	0	transformers	[ "transformers", "safetensors", "RefinedWeb", "text-generation", "custom_code", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-25T08:31:32Z	--- library_name: transformers pipeline_tag: text-generation --- falcon-40b-instruct quantized with GPTQ using the script in https://github.com/huggingface/text-generation-inference/pull/438 - group size: 128 - act order: true - nsamples: 128 - dataset: wikitext2
abhishek-kumar/dreambooth_test	abhishek-kumar	2023-06-25T10:34:42Z	30	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-24T16:02:54Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - abhishek-kumar/output This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
Omogo/xlm-roberta-base-finetuned-panx-de	Omogo	2023-06-25T10:27:58Z	124	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-06-25T07:39:34Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8602627537962806 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1355 - F1: 0.8603 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2574 \| 1.0 \| 525 \| 0.1627 \| 0.8221 \| \| 0.1295 \| 2.0 \| 1050 \| 0.1435 \| 0.8467 \| \| 0.0815 \| 3.0 \| 1575 \| 0.1355 \| 0.8603 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
TheBloke/orca_mini_3B-GGML	TheBloke	2023-06-25T10:25:04Z	0	59	transformers	[ "transformers", "en", "dataset:psmathur/alpaca_orca", "dataset:psmathur/dolly-v2_orca", "dataset:psmathur/WizardLM_Orca", "arxiv:2306.02707", "license:mit", "region:us" ]	null	2023-06-24T22:33:56Z	--- inference: false license: mit language: - en library_name: transformers datasets: - psmathur/alpaca_orca - psmathur/dolly-v2_orca - psmathur/WizardLM_Orca --- <!-- header start --> <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <!-- header end --> # Pankaj Mathur's Orca Mini 3B GGML These files are GGML format model files for [Pankaj Mathur's Orca Mini 3B](https://huggingface.co/psmathur/orca_mini_3b). GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as: * [text-generation-webui](https://github.com/oobabooga/text-generation-webui) * [KoboldCpp](https://github.com/LostRuins/koboldcpp) * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) * [ctransformers](https://github.com/marella/ctransformers) ## Repositories available * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/orca_mini_3B-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/psmathur/orca_mini_3b) ## Prompt template: ``` ### System: You are an AI assistant that follows instruction extremely well. Help as much as you can. ### User: prompt ### Response: ``` or ``` ### System: You are an AI assistant that follows instruction extremely well. Help as much as you can. ### User: prompt ### Input: input ### Response: ``` <!-- compatibility_ggml start --> ## Compatibility ### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0` I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit `2d5db48`. These are guaranteed to be compatbile with any UIs, tools and libraries released since late May. ### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K` These cannot be provided with Open Llama 3B models at this time, due to an issue in llama.cpp. This is being worked on in the llama.cpp repo. More issues here: https://github.com/ggerganov/llama.cpp/issues/1919 Refer to the Provided Files table below to see what files use which methods, and how. <!-- compatibility_ggml end --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| orca-mini-3b.ggmlv3.q4_0.bin \| q4_0 \| 4 \| 1.93 GB \| 4.43 GB \| Original llama.cpp quant method, 4-bit. \| \| orca-mini-3b.ggmlv3.q4_1.bin \| q4_1 \| 4 \| 2.14 GB \| 4.64 GB \| Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. \| \| orca-mini-3b.ggmlv3.q5_0.bin \| q5_0 \| 5 \| 2.36 GB \| 4.86 GB \| Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. \| \| orca-mini-3b.ggmlv3.q5_1.bin \| q5_1 \| 5 \| 2.57 GB \| 5.07 GB \| Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. \| \| orca-mini-3b.ggmlv3.q8_0.bin \| q8_0 \| 8 \| 3.64 GB \| 6.14 GB \| Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `llama.cpp` I use the following command line; adjust for your tastes and needs: ``` ./main -t 10 -ngl 32 -m orca-mini-3b.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### System:\nYou are an story writing assistant who writes very long, detailed and interesting stories\n\n### User:\nWrite a story about llamas\n\n### Input:\n{input}\n\n### Response:\n" ``` If you're able to use full GPU offloading, you should use `-t 1` to get best performance. If not able to fully offload to GPU, you should use more cores. Change `-t 10` to the number of physical CPU cores you have, or a lower number depending on what gives best performance. Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md). <!-- footer start --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov. Patreon special mentions: Pyrater, WelcomeToTheClub, Kalila, Mano Prime, Trenton Dambrowitz, Spiking Neurons AB, Pierre Kircher, Fen Risland, Kevin Schuppel, Luke, Rainer Wilmers, vamX, Gabriel Puliatti, Alex , Karl Bernard, Ajan Kanaga, Talal Aujan, Space Cruiser, ya boyyy, biorpg, Johann-Peter Hartmann, Asp the Wyvern, Ai Maven, Ghost , Preetika Verma, Nikolai Manek, trip7s trip, John Detwiler, Fred von Graf, Artur Olbinski, subjectnull, John Villwock, Junyu Yang, Rod A, Lone Striker, Chris McCloskey, Iucharbius , Matthew Berman, Illia Dulskyi, Khalefa Al-Ahmad, Imad Khwaja, chris gileta, Willem Michiel, Greatston Gnanesh, Derek Yates, K, Alps Aficionado, Oscar Rangel, David Flickinger, Luke Pendergrass, Deep Realms, Eugene Pentland, Cory Kujawski, terasurfer , Jonathan Leane, senxiiz, Joseph William Delisle, Sean Connelly, webtim, zynix , Nathan LeClaire. Thank you to all my generous patrons and donaters! <!-- footer end --> # Original model card: Pankaj Mathur's Orca Mini 3B # orca_mini_3b An [OpenLLaMa-3B model](https://github.com/openlm-research/open_llama) model trained on explain tuned datasets, created using Instructions and Input from WizardLM, Alpaca & Dolly-V2 datasets and applying Orca Research Paper dataset construction approaches. # Dataset We build explain tuned [WizardLM dataset ~70K](https://github.com/nlpxucan/WizardLM), [Alpaca dataset ~52K](https://crfm.stanford.edu/2023/03/13/alpaca.html) & [Dolly-V2 dataset ~15K](https://github.com/databrickslabs/dolly) created using approaches from [Orca Research Paper](https://arxiv.org/abs/2306.02707). We leverage all of the 15 system instructions provided in Orca Research Paper. to generate custom datasets, in contrast to vanilla instruction tuning approaches used by original datasets. This helps student model aka this model to learn *thought* process from teacher model, which is ChatGPT (gpt-3.5-turbo-0301 version). Please see below example usage how the System prompt is added before each instruction. # Training The training configurations are provided in the table below. The training takes on 8x A100(80G) GPUs and lasts for around 4 Hours for cost of $48 using [Lambda Labs](https://lambdalabs.com) We used DeepSpeed with fully sharded data parallelism, also know as [ZeRO stage 3](https://engineering.fb.com/2021/07/15/open-source/fsdp/) by writing our own fine tunning scripts plus leveraging some of the model training code provided by amazing [OpenAlpaca repo](https://github.com/yxuansu/OpenAlpaca) Here are some of params used during training: \|\|\| \|:-------------:\|:-------------:\| \|batch_size\|64\| \|train_micro_batch_size_per_gpu\|4\| \|gradient_accumulation_steps\|2\| \|Learning rate\|2e-5\| \|Max length\|1024\| \|Epochs\|3\| \|Optimizer\|AdamW\| # Example Usage Below shows an example on how to use this model ```python import torch from transformers import LlamaForCausalLM, LlamaTokenizer # Hugging Face model_path model_path = 'psmathur/orca_mini_3b' tokenizer = LlamaTokenizer.from_pretrained(model_path) model = LlamaForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map='auto', ) #generate text function def generate_text(system, instruction, input=None): if input: prompt = f"### System:\n{system}\n\n### User:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n" else: prompt = f"### System:\n{system}\n\n### User:\n{instruction}\n\n### Response:\n" tokens = tokenizer.encode(prompt) tokens = torch.LongTensor(tokens).unsqueeze(0) tokens = tokens.to('cuda') instance = {'input_ids': tokens,'top_p': 1.0, 'temperature':0.7, 'generate_len': 1024, 'top_k': 50} length = len(tokens[0]) with torch.no_grad(): rest = model.generate( input_ids=tokens, max_length=length+instance['generate_len'], use_cache=True, do_sample=True, top_p=instance['top_p'], temperature=instance['temperature'], top_k=instance['top_k'] ) output = rest[0][length:] string = tokenizer.decode(output, skip_special_tokens=True) return f'[!] Response: {string}' # Sample Test Instruction Used by Youtuber Sam Witteveen https://www.youtube.com/@samwitteveenai system = 'You are an AI assistant that follows instruction extremely well. Help as much as you can.' instruction = 'Write a letter to Sam Altman, CEO of OpenAI, requesting him to convert GPT4 a private model by OpenAI to an open source project' print(generate_text(system, instruction)) ``` ``` [!] Response: Dear Sam Altman, I am writing to request that you convert the GPT4 private model developed by OpenAI to an open source project. As a user of OpenAI, I have been waiting for the day when I can use the advanced natural language processing capabilities of GPT4 in a more open and accessible way. While OpenAI has made significant progress in developing AI applications, it has primarily focused on building private models that are not accessible to the general public. However, with the recent release of GPT-3, there is a growing demand for more open and accessible AI tools. Converting GPT4 to an open source project would allow for greater transparency, collaboration, and innovation. It would also help to build trust in the technology and ensure that it is used ethically and responsibly. I urge you to consider converting GPT4 to an open source project. This would be a significant contribution to the AI community and would help to create a more open and accessible future. Thank you for your consideration. Sincerely, [Your Name] ``` P.S. I am #opentowork and #collaboration, if you can help, please reach out to me at psmathur.public@gmail.com Next Goals: 1) Try more data like actually using FLAN-v2, just like Orka Research Paper (I am open for suggestions) 2) Provide more options for Text generation UI. (may be https://github.com/oobabooga/text-generation-webui) 3) Provide 4bit GGML/GPTQ quantized model (may be [TheBloke](https://huggingface.co/TheBloke) can help here) Limitations & Biases: This model can produce factually incorrect output, and should not be relied on to produce factually accurate information. This model was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Disclaimer: The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes. Citiation: If you found wizardlm_alpaca_dolly_orca_open_llama_3b useful in your research or applications, please kindly cite using the following BibTeX: ``` @misc{wizardlm_alpaca_dolly_orca_open_llama_3b, author = {Pankaj Mathur}, title = {wizardlm_alpaca_dolly_orca_open_llama_3b: An explain tuned OpenLLaMA-3b model on custom wizardlm, alpaca, & dolly datasets}, year = {2023}, publisher = {GitHub, HuggingFace}, journal = {GitHub repository, HuggingFace repository}, howpublished = {\url{https://github.com/pankajarm/wizardlm_alpaca_dolly_orca_open_llama_3b}, \url{https://https://huggingface.co/psmathur/wizardlm_alpaca_dolly_orca_open_llama_3b}}, } ``` ``` @software{openlm2023openllama, author = {Xinyang Geng and Hao Liu}, title = {OpenLLaMA: An Open Reproduction of LLaMA}, month = May, year = 2023, url = {https://github.com/openlm-research/open_llama} } ``` ``` @misc{openalpaca, author = {Yixuan Su and Tian Lan and Deng Cai}, title = {OpenAlpaca: A Fully Open-Source Instruction-Following Model Based On OpenLLaMA}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/yxuansu/OpenAlpaca}}, } ``` ``` @misc{alpaca, author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto }, title = {Stanford Alpaca: An Instruction-following LLaMA model}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}}, } ```
Sp1786/mutliclass-sentiment-analysis-bert	Sp1786	2023-06-25T10:22:55Z	4	0	transformers	[ "transformers", "bert", "code", "text-classification", "en", "dataset:Sp1786/multiclass-sentiment-analysis-dataset", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-classification	2023-06-21T11:23:59Z	--- license: apache-2.0 datasets: - Sp1786/multiclass-sentiment-analysis-dataset language: - en metrics: - bleu - sacrebleu library_name: transformers pipeline_tag: text-classification tags: - code ---
kbondar17/test-trainer	kbondar17	2023-06-25T10:12:41Z	103	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-25T10:06:32Z	--- license: mit tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: test-trainer results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # test-trainer This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4009 - F1: 0.6363 - Roc Auc: 0.7682 - Accuracy: 0.6079 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Roc Auc \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\|:--------:\| \| No log \| 1.0 \| 125 \| 0.2975 \| 0.5710 \| 0.7129 \| 0.4693 \| \| No log \| 2.0 \| 250 \| 0.3742 \| 0.6226 \| 0.7621 \| 0.6013 \| \| No log \| 3.0 \| 375 \| 0.4009 \| 0.6363 \| 0.7682 \| 0.6079 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
dhruvil237/userutterance_classification_verplus	dhruvil237	2023-06-25T10:05:26Z	13	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "doi:10.57967/hf/0811", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-05T12:20:52Z	--- license: mit tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: userutterance_classification_verplus results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos config: plus split: validation args: plus metrics: - name: Accuracy type: accuracy value: 0.9619354838709677 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # userutterance_classification_verplus This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.2270 - Accuracy: 0.9619 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 5.0219 \| 0.21 \| 200 \| 4.9813 \| 0.0077 \| \| 4.8915 \| 0.42 \| 400 \| 4.5741 \| 0.1155 \| \| 4.2736 \| 0.63 \| 600 \| 3.5359 \| 0.4719 \| \| 3.2701 \| 0.84 \| 800 \| 2.4291 \| 0.7429 \| \| 2.3578 \| 1.05 \| 1000 \| 1.5793 \| 0.8413 \| \| 1.5695 \| 1.26 \| 1200 \| 1.0029 \| 0.8994 \| \| 1.0412 \| 1.47 \| 1400 \| 0.6475 \| 0.9187 \| \| 0.7034 \| 1.68 \| 1600 \| 0.4439 \| 0.9303 \| \| 0.501 \| 1.89 \| 1800 \| 0.3400 \| 0.9381 \| \| 0.3187 \| 2.1 \| 2000 \| 0.2793 \| 0.9439 \| \| 0.2185 \| 2.31 \| 2200 \| 0.2538 \| 0.9490 \| \| 0.1669 \| 2.52 \| 2400 \| 0.2210 \| 0.9523 \| \| 0.1081 \| 2.73 \| 2600 \| 0.2225 \| 0.9519 \| \| 0.1004 \| 2.94 \| 2800 \| 0.2136 \| 0.9555 \| \| 0.0665 \| 3.14 \| 3000 \| 0.2078 \| 0.9561 \| \| 0.0509 \| 3.35 \| 3200 \| 0.2155 \| 0.9568 \| \| 0.05 \| 3.56 \| 3400 \| 0.2107 \| 0.9581 \| \| 0.0527 \| 3.77 \| 3600 \| 0.2171 \| 0.9568 \| \| 0.0447 \| 3.98 \| 3800 \| 0.2128 \| 0.9590 \| \| 0.0259 \| 4.19 \| 4000 \| 0.2099 \| 0.9587 \| \| 0.0279 \| 4.4 \| 4200 \| 0.2179 \| 0.9577 \| \| 0.0176 \| 4.61 \| 4400 \| 0.2191 \| 0.9574 \| \| 0.0288 \| 4.82 \| 4600 \| 0.2216 \| 0.9590 \| \| 0.0328 \| 5.03 \| 4800 \| 0.2237 \| 0.9606 \| \| 0.0154 \| 5.24 \| 5000 \| 0.2241 \| 0.9616 \| \| 0.0157 \| 5.45 \| 5200 \| 0.2265 \| 0.9603 \| \| 0.023 \| 5.66 \| 5400 \| 0.2276 \| 0.9613 \| \| 0.0178 \| 5.87 \| 5600 \| 0.2270 \| 0.9619 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
mrizalf7/xlm-r-qa-squad-retrained	mrizalf7	2023-06-25T09:57:29Z	102	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "question-answering", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-06-13T19:17:39Z	--- license: mit tags: - generated_from_trainer model-index: - name: xlm-roberta-finetuned-small-squad-indonesian-rizal-4-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-finetuned-small-squad-indonesian-rizal-4-2 This model is a fine-tuned version of [mrizalf7/xlm-roberta-finetuned-small-squad-indonesian-rizal-4](https://huggingface.co/mrizalf7/xlm-roberta-finetuned-small-squad-indonesian-rizal-4) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.4152 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 1 \| 6.1326 \| \| No log \| 2.0 \| 2 \| 6.1326 \| \| No log \| 3.0 \| 3 \| 5.4152 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
lucasbertola/ppo-LunarLander-v2	lucasbertola	2023-06-25T09:29:20Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-19T11:40:03Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 295.14 +/- 14.94 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
sd-concepts-library/pokemon-raichu-sd-model	sd-concepts-library	2023-06-25T09:26:29Z	0	0	null	[ "base_model:stabilityai/stable-diffusion-2", "base_model:finetune:stabilityai/stable-diffusion-2", "license:mit", "region:us" ]	null	2023-06-25T09:26:28Z	--- license: mit base_model: stabilityai/stable-diffusion-2 --- ### Pokemon Raichu - SD model on Stable Diffusion This is the `<cat-toy>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<cat-toy> 0](https://huggingface.co/sd-concepts-library/pokemon-raichu-sd-model/resolve/main/concept_images/0.jpeg) ![<cat-toy> 1](https://huggingface.co/sd-concepts-library/pokemon-raichu-sd-model/resolve/main/concept_images/1.jpeg) ![<cat-toy> 2](https://huggingface.co/sd-concepts-library/pokemon-raichu-sd-model/resolve/main/concept_images/2.jpeg) ![<cat-toy> 3](https://huggingface.co/sd-concepts-library/pokemon-raichu-sd-model/resolve/main/concept_images/3.jpeg)
ahishamm/vit-base-HAM-10000-sharpened	ahishamm	2023-06-25T09:17:26Z	190	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-06-25T08:42:48Z	--- license: apache-2.0 tags: - image-classification - generated_from_trainer metrics: - accuracy - recall - f1 - precision model-index: - name: vit-base-HAM-10000-sharpened results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-HAM-10000-sharpened This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the ahishamm/HAM_db_sharpened dataset. It achieves the following results on the evaluation set: - Loss: 0.4392 - Accuracy: 0.8529 - Recall: 0.8529 - F1: 0.8529 - Precision: 0.8529 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Recall \| F1 \| Precision \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:------:\|:---------:\| \| 0.7303 \| 0.2 \| 100 \| 0.7828 \| 0.7197 \| 0.7197 \| 0.7197 \| 0.7197 \| \| 0.7198 \| 0.4 \| 200 \| 0.7519 \| 0.7377 \| 0.7377 \| 0.7377 \| 0.7377 \| \| 0.7519 \| 0.6 \| 300 \| 0.7125 \| 0.7541 \| 0.7541 \| 0.7541 \| 0.7541 \| \| 0.6657 \| 0.8 \| 400 \| 0.6623 \| 0.7571 \| 0.7571 \| 0.7571 \| 0.7571 \| \| 0.5896 \| 1.0 \| 500 \| 0.5964 \| 0.7835 \| 0.7835 \| 0.7835 \| 0.7835 \| \| 0.515 \| 1.2 \| 600 \| 0.5745 \| 0.8015 \| 0.8015 \| 0.8015 \| 0.8015 \| \| 0.4318 \| 1.4 \| 700 \| 0.5061 \| 0.8200 \| 0.8200 \| 0.8200 \| 0.8200 \| \| 0.4299 \| 1.6 \| 800 \| 0.5239 \| 0.8075 \| 0.8075 \| 0.8075 \| 0.8075 \| \| 0.4793 \| 1.8 \| 900 \| 0.5366 \| 0.8125 \| 0.8125 \| 0.8125 \| 0.8125 \| \| 0.4202 \| 2.0 \| 1000 \| 0.4882 \| 0.8244 \| 0.8244 \| 0.8244 \| 0.8244 \| \| 0.2105 \| 2.2 \| 1100 \| 0.5330 \| 0.8234 \| 0.8234 \| 0.8234 \| 0.8234 \| \| 0.2597 \| 2.4 \| 1200 \| 0.4604 \| 0.8369 \| 0.8369 \| 0.8369 \| 0.8369 \| \| 0.2261 \| 2.59 \| 1300 \| 0.4893 \| 0.8409 \| 0.8409 \| 0.8409 \| 0.8409 \| \| 0.1853 \| 2.79 \| 1400 \| 0.4793 \| 0.8494 \| 0.8494 \| 0.8494 \| 0.8494 \| \| 0.1739 \| 2.99 \| 1500 \| 0.4392 \| 0.8529 \| 0.8529 \| 0.8529 \| 0.8529 \| \| 0.0629 \| 3.19 \| 1600 \| 0.4941 \| 0.8584 \| 0.8584 \| 0.8584 \| 0.8584 \| \| 0.0802 \| 3.39 \| 1700 \| 0.4974 \| 0.8613 \| 0.8613 \| 0.8613 \| 0.8613 \| \| 0.0712 \| 3.59 \| 1800 \| 0.5416 \| 0.8594 \| 0.8594 \| 0.8594 \| 0.8594 \| \| 0.0365 \| 3.79 \| 1900 \| 0.5318 \| 0.8574 \| 0.8574 \| 0.8574 \| 0.8574 \| \| 0.0591 \| 3.99 \| 2000 \| 0.5344 \| 0.8574 \| 0.8574 \| 0.8574 \| 0.8574 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
kchen621/ppo-LunarLander-v2	kchen621	2023-06-25T09:13:58Z	1	0	transformers	[ "transformers", "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "endpoints_compatible", "region:us" ]	reinforcement-learning	2023-06-19T19:22:36Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -169.25 +/- 80.22 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo_lun' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'kchen621/ppo-LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```
Ellbendls/Pixelcopter-PLE-v0	Ellbendls	2023-06-25T09:09:39Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-06-23T12:37:16Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 62.70 +/- 42.68 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
anas21/keras-dummy-functional-demo	anas21	2023-06-25T09:07:24Z	0	0	keras	[ "keras", "tf-keras", "region:us" ]	null	2023-06-24T23:19:07Z	--- library_name: keras --- ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: \| Hyperparameters \| Value \| \| :-- \| :-- \| \| name \| Adam \| \| weight_decay \| None \| \| clipnorm \| None \| \| global_clipnorm \| None \| \| clipvalue \| None \| \| use_ema \| False \| \| ema_momentum \| 0.99 \| \| ema_overwrite_frequency \| None \| \| jit_compile \| False \| \| is_legacy_optimizer \| False \| \| learning_rate \| 0.0010000000474974513 \| \| beta_1 \| 0.9 \| \| beta_2 \| 0.999 \| \| epsilon \| 1e-07 \| \| amsgrad \| False \| \| training_precision \| float32 \| ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
Shridipta-06/LunarLander-v2_unit8part1	Shridipta-06	2023-06-25T08:50:28Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-06-25T08:46:05Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -128.49 +/- 35.10 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'Shridipta-06/LunarLander-v2_unit8part1' 'batch_size': 512 'minibatch_size': 128} ```
RoundtTble/dinov2_vits14_onnx	RoundtTble	2023-06-25T08:20:24Z	0	0	null	[ "onnx", "region:us" ]	null	2023-06-24T07:10:50Z	# dinov2_vits14 ## ONNX Model Check this [PR](https://github.com/facebookresearch/dinov2/pull/129). ## Run Run triton container. ``` make triton ``` ``` docker logs dinov2_vits14_triton ============================= == Triton Inference Server == ============================= NVIDIA Release 23.04 (build 58408265) Triton Server Version 2.33.0 Copyright (c) 2018-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. Various files include modifications (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. This container image and its contents are governed by the NVIDIA Deep Learning Container License. By pulling and using the container, you accept the terms and conditions of this license: https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license WARNING: CUDA Minor Version Compatibility mode ENABLED. Using driver version 525.105.17 which has support for CUDA 12.0. This container was built with CUDA 12.1 and will be run in Minor Version Compatibility mode. CUDA Forward Compatibility is preferred over Minor Version Compatibility for use with this container but was unavailable: [[Forward compatibility was attempted on non supported HW (CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE) cuInit()=804]] See https://docs.nvidia.com/deploy/cuda-compatibility/ for details. I0625 08:05:36.712010 1 pinned_memory_manager.cc:240] Pinned memory pool is created at '0x7f6c46000000' with size 268435456 I0625 08:05:36.712625 1 cuda_memory_manager.cc:105] CUDA memory pool is created on device 0 with size 67108864 I0625 08:05:36.717785 1 model_lifecycle.cc:459] loading: dinov2_vits14:1 I0625 08:05:36.723707 1 onnxruntime.cc:2504] TRITONBACKEND_Initialize: onnxruntime I0625 08:05:36.723725 1 onnxruntime.cc:2514] Triton TRITONBACKEND API version: 1.12 I0625 08:05:36.723731 1 onnxruntime.cc:2520] 'onnxruntime' TRITONBACKEND API version: 1.12 I0625 08:05:36.723735 1 onnxruntime.cc:2550] backend configuration: {"cmdline":{"auto-complete-config":"true","backend-directory":"/opt/tritonserver/backends","min-compute-capability":"6.000000","default-max-batch-size":"4"}} I0625 08:05:36.770311 1 onnxruntime.cc:2608] TRITONBACKEND_ModelInitialize: dinov2_vits14 (version 1) I0625 08:05:36.770781 1 onnxruntime.cc:666] skipping model configuration auto-complete for 'dinov2_vits14': inputs and outputs already specified I0625 08:05:36.771205 1 onnxruntime.cc:2651] TRITONBACKEND_ModelInstanceInitialize: dinov2_vits14_0 (GPU device 0) 2023-06-25 08:05:37.157976034 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 465, index: 122, mask: {125, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.158142138 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 466, index: 123, mask: {62, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.158159030 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 467, index: 124, mask: {126, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.158174259 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 468, index: 125, mask: {63, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.165944431 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 344, index: 1, mask: {1, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.158230084 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 469, index: 126, mask: {127, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.169979079 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 347, index: 4, mask: {66, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.169927531 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 345, index: 2, mask: {65, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.169954703 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 346, index: 3, mask: {2, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.173982388 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 350, index: 7, mask: {4, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.173929448 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 348, index: 5, mask: {3, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.173954065 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 349, index: 6, mask: {67, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.181926759 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 351, index: 8, mask: {68, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.185932583 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 352, index: 9, mask: {5, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.189924821 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 353, index: 10, mask: {69, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.193940975 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 464, index: 121, mask: {61, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.194020786 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 357, index: 14, mask: {71, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.193940915 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 354, index: 11, mask: {6, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.193968147 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 355, index: 12, mask: {70, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.193992072 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 356, index: 13, mask: {7, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.197974211 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 360, index: 17, mask: {9, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.197928554 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 358, index: 15, mask: {8, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.197950686 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 359, index: 16, mask: {72, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.201924259 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 361, index: 18, mask: {73, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.205931957 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 362, index: 19, mask: {10, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.209926179 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 363, index: 20, mask: {74, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.213927705 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 364, index: 21, mask: {11, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.217799496 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 365, index: 22, mask: {75, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.217849460 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 366, index: 23, mask: {12, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.221966294 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 367, index: 24, mask: {76, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.221966304 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 463, index: 120, mask: {124, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.225931100 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 462, index: 119, mask: {60, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.225933645 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 368, index: 25, mask: {13, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.229929350 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 369, index: 26, mask: {77, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.233930445 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 370, index: 27, mask: {14, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.233930525 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 461, index: 118, mask: {123, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.237930518 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 371, index: 28, mask: {78, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.241927085 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 372, index: 29, mask: {15, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.245926977 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 373, index: 30, mask: {79, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.249931199 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 374, index: 31, mask: {16, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.253927515 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 375, index: 32, mask: {80, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.257925694 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 376, index: 33, mask: {17, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.261929715 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 377, index: 34, mask: {81, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.265966397 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 378, index: 35, mask: {18, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.269926725 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 379, index: 36, mask: {82, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.273931337 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 380, index: 37, mask: {19, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.281941021 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 381, index: 38, mask: {83, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.282017776 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 398, index: 55, mask: {28, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.282038465 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 382, index: 39, mask: {20, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.282090914 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 383, index: 40, mask: {84, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.286235010 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 385, index: 42, mask: {85, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.285955121 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 401, index: 58, mask: {93, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.282070957 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 399, index: 56, mask: {92, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.286082321 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 384, index: 41, mask: {21, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.285929422 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 400, index: 57, mask: {29, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.293926803 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 405, index: 62, mask: {95, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.289931018 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 402, index: 59, mask: {30, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.289956767 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 403, index: 60, mask: {94, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.301929004 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 388, index: 45, mask: {23, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.289975973 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 404, index: 61, mask: {31, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.294054945 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 406, index: 63, mask: {32, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.294078880 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 407, index: 64, mask: {96, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.314023441 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 409, index: 66, mask: {97, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.289931068 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 386, index: 43, mask: {22, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.318030297 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 411, index: 68, mask: {98, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.289956797 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 387, index: 44, mask: {86, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.301929014 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 408, index: 65, mask: {33, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.314096058 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 410, index: 67, mask: {34, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.334030890 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 414, index: 71, mask: {36, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.305931271 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 389, index: 46, mask: {87, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.321929038 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 390, index: 47, mask: {24, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.321948134 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 391, index: 48, mask: {88, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.321965006 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 392, index: 49, mask: {25, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.321981437 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 393, index: 50, mask: {89, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.321996396 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 394, index: 51, mask: {26, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322012065 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 395, index: 52, mask: {90, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322026713 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 396, index: 53, mask: {27, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322049907 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 397, index: 54, mask: {91, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322065276 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 460, index: 117, mask: {59, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322080735 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 425, index: 82, mask: {105, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322096315 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 426, index: 83, mask: {42, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322112155 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 427, index: 84, mask: {106, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322127053 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 428, index: 85, mask: {43, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322143324 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 429, index: 86, mask: {107, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322157170 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 430, index: 87, mask: {44, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322173340 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 431, index: 88, mask: {108, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322188569 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 432, index: 89, mask: {45, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322205311 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 433, index: 90, mask: {109, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322219938 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 434, index: 91, mask: {46, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322235177 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 435, index: 92, mask: {110, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322249955 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 436, index: 93, mask: {47, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322267158 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 437, index: 94, mask: {111, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322281345 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 438, index: 95, mask: {48, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322296904 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 439, index: 96, mask: {112, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322312113 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 440, index: 97, mask: {49, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322329005 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 441, index: 98, mask: {113, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322343652 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 442, index: 99, mask: {50, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322359492 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 443, index: 100, mask: {114, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322377907 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 444, index: 101, mask: {51, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322393366 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 445, index: 102, mask: {115, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322408725 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 446, index: 103, mask: {52, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322423233 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 447, index: 104, mask: {116, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322437289 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 448, index: 105, mask: {53, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322453440 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 449, index: 106, mask: {117, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322467697 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 450, index: 107, mask: {54, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322483076 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 451, index: 108, mask: {118, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322496812 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 452, index: 109, mask: {55, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.445929743 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 417, index: 74, mask: {101, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322511880 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 453, index: 110, mask: {119, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322525526 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 454, index: 111, mask: {56, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322541977 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 455, index: 112, mask: {120, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.454013818 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 422, index: 79, mask: {40, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322555663 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 456, index: 113, mask: {57, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.457932126 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 423, index: 80, mask: {104, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322571683 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 457, index: 114, mask: {121, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322585920 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 458, index: 115, mask: {58, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.318158029 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 412, index: 69, mask: {35, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.334163851 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 415, index: 72, mask: {100, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.341919085 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 416, index: 73, mask: {37, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.323408365 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 413, index: 70, mask: {99, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.453923387 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 418, index: 75, mask: {38, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.453947493 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 419, index: 76, mask: {102, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.453965727 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 420, index: 77, mask: {39, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.453991656 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 421, index: 78, mask: {103, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.458087059 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 424, index: 81, mask: {41, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.585007204 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 459, index: 116, mask: {122, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:38.570069572 [W:onnxruntime:, session_state.cc:1136 VerifyEachNodeIsAssignedToAnEp] Some nodes were not assigned to the preferred execution providers which may or may not have an negative impact on performance. e.g. ORT explicitly assigns shape related ops to CPU to improve perf. 2023-06-25 08:05:38.570088387 [W:onnxruntime:, session_state.cc:1138 VerifyEachNodeIsAssignedToAnEp] Rerunning with verbose output on a non-minimal build will show node assignments. I0625 08:05:39.975559 1 model_lifecycle.cc:694] successfully loaded 'dinov2_vits14' version 1 I0625 08:05:39.975625 1 server.cc:583] +------------------+------+ \| Repository Agent \| Path \| +------------------+------+ +------------------+------+ I0625 08:05:39.975662 1 server.cc:610] +-------------+-----------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------+ \| Backend \| Path \| Config \| +-------------+-----------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------+ \| onnxruntime \| /opt/tritonserver/backends/onnxruntime/libtriton_onnxruntime.so \| {"cmdline":{"auto-complete-config":"true","backend-directory":"/opt/tritonserver/backends","min-compute-capability":"6.000000","default-max-batch-size":"4"}} \| +-------------+-----------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------+ I0625 08:05:39.975683 1 server.cc:653] +---------------+---------+--------+ \| Model \| Version \| Status \| +---------------+---------+--------+ \| dinov2_vits14 \| 1 \| READY \| +---------------+---------+--------+ I0625 08:05:39.991510 1 metrics.cc:808] Collecting metrics for GPU 0: NVIDIA GeForce RTX 3090 I0625 08:05:39.992145 1 metrics.cc:701] Collecting CPU metrics I0625 08:05:39.992360 1 tritonserver.cc:2387] +----------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ \| Option \| Value \| +----------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ \| server_id \| triton \| \| server_version \| 2.33.0 \| \| server_extensions \| classification sequence model_repository model_repository(unload_dependents) schedule_policy model_configuration system_shared_memory cuda_shared_memory binary_tensor_data parameters statistics trace logging \| \| model_repository_path[0] \| /models \| \| model_control_mode \| MODE_NONE \| \| strict_model_config \| 0 \| \| rate_limit \| OFF \| \| pinned_memory_pool_byte_size \| 268435456 \| \| cuda_memory_pool_byte_size{0} \| 67108864 \| \| min_supported_compute_capability \| 6.0 \| \| strict_readiness \| 1 \| \| exit_timeout \| 30 \| \| cache_enabled \| 0 \| +----------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ I0625 08:05:39.993603 1 grpc_server.cc:2450] Started GRPCInferenceService at 0.0.0.0:8001 I0625 08:05:39.993771 1 http_server.cc:3555] Started HTTPService at 0.0.0.0:8000 I0625 08:05:40.034678 1 http_server.cc:185] Started Metrics Service at 0.0.0.0:8002 ``` Perf analyzer `dinov2_vits14` ``` make perf ``` ``` docker run --gpus all --rm -it --net host nvcr.io/nvidia/tritonserver:23.04-py3-sdk perf_analyzer -m dinov2_vits14 --percentile=95 -i grpc -u 0.0.0.0:8001 --concurrency-range 16:16 --shape input:3,280,280 ================================= == Triton Inference Server SDK == ================================= NVIDIA Release 23.04 (build 58408269) Copyright (c) 2018-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. Various files include modifications (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. This container image and its contents are governed by the NVIDIA Deep Learning Container License. By pulling and using the container, you accept the terms and conditions of this license: https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license WARNING: CUDA Minor Version Compatibility mode ENABLED. Using driver version 525.105.17 which has support for CUDA 12.0. This container was built with CUDA 12.1 and will be run in Minor Version Compatibility mode. CUDA Forward Compatibility is preferred over Minor Version Compatibility for use with this container but was unavailable: [[Forward compatibility was attempted on non supported HW (CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE) cuInit()=804]] See https://docs.nvidia.com/deploy/cuda-compatibility/ for details. * Measurement Settings * Batch size: 1 Service Kind: Triton Using "time_windows" mode for stabilization Measurement window: 5000 msec Latency limit: 0 msec Concurrency limit: 16 concurrent requests Using synchronous calls for inference Stabilizing using p95 latency Request concurrency: 16 Client: Request count: 9403 Throughput: 522.33 infer/sec p50 latency: 30482 usec p90 latency: 32100 usec p95 latency: 32564 usec p99 latency: 34203 usec Avg gRPC time: 30589 usec ((un)marshal request/response 93 usec + response wait 30496 usec) Server: Inference count: 9403 Execution count: 1177 Successful request count: 9403 Avg request latency: 24295 usec (overhead 220 usec + queue 9042 usec + compute input 1511 usec + compute infer 13485 usec + compute output 37 usec) Inferences/Second vs. Client p95 Batch Latency Concurrency: 16, throughput: 522.33 infer/sec, latency 32564 usec ```
Neupane9Sujal/Text_Summarization	Neupane9Sujal	2023-06-25T07:51:58Z	108	0	transformers	[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "code", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-05-25T10:28:20Z	--- language: - en metrics: - rouge tags: - code ---
Lajonbot/Amazon-LightGPT-pl-qlora	Lajonbot	2023-06-25T07:40:56Z	0	0	null	[ "tensorboard", "pl", "dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish", "license:openrail", "region:us" ]	null	2023-05-29T06:22:37Z	--- license: openrail datasets: - Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish language: - pl ---
Lajonbot/lamini-instruct-tuned-3b-pl-lora	Lajonbot	2023-06-25T07:37:46Z	0	0	null	[ "pl", "dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish", "license:openrail", "region:us" ]	null	2023-06-15T06:08:17Z	--- license: openrail datasets: - Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish language: - pl ---
Lajonbot/stablelm-base-alpha-3b-instruct-pl-lora	Lajonbot	2023-06-25T07:37:23Z	0	0	null	[ "tensorboard", "pl", "dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish", "license:openrail", "region:us" ]	null	2023-06-15T06:13:44Z	--- license: openrail datasets: - Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish language: - pl ---
Lajonbot/polish-gpt2-small-instruct	Lajonbot	2023-06-25T07:36:40Z	114	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "pl", "dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish", "license:openrail", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-20T19:33:30Z	--- license: openrail datasets: - Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish language: - pl ---
Davlan/bert-base-multilingual-cased-finetuned-swahili	Davlan	2023-06-25T07:32:51Z	568	3	transformers	[ "transformers", "pytorch", "tf", "safetensors", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	Hugging Face's logo --- language: ha datasets: --- # bert-base-multilingual-cased-finetuned-swahili ## Model description bert-base-multilingual-cased-finetuned-swahili is a Swahili BERT model obtained by fine-tuning bert-base-multilingual-cased model on Swahili language texts. It provides better performance than the multilingual BERT on text classification and named entity recognition datasets. Specifically, this model is a bert-base-multilingual-cased model that was fine-tuned on Swahili corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers pipeline for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-swahili') >>> unmasker("Jumatatu, Bwana Kagame alielezea shirika la France24 huko [MASK] kwamba "hakuna uhalifu ulitendwa") [{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Paris kwamba hakuna uhalifu ulitendwa', 'score': 0.31642526388168335, 'token': 10728, 'token_str': 'Paris'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Rwanda kwamba hakuna uhalifu ulitendwa', 'score': 0.15753623843193054, 'token': 57557, 'token_str': 'Rwanda'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Burundi kwamba hakuna uhalifu ulitendwa', 'score': 0.07211585342884064, 'token': 57824, 'token_str': 'Burundi'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko France kwamba hakuna uhalifu ulitendwa', 'score': 0.029844321310520172, 'token': 10688, 'token_str': 'France'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Senegal kwamba hakuna uhalifu ulitendwa', 'score': 0.0265930388122797, 'token': 38052, 'token_str': 'Senegal'}] ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on [Swahili CC-100](http://data.statmt.org/cc-100/) ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset\| mBERT F1 \| sw_bert F1 -\|-\|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) \| 86.80 \| 89.36 ### BibTeX entry and citation info By David Adelani ``` ```
Davlan/xlm-roberta-base-finetuned-swahili	Davlan	2023-06-25T07:31:57Z	119	3	transformers	[ "transformers", "pytorch", "safetensors", "xlm-roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	Hugging Face's logo --- language: sw datasets: --- # xlm-roberta-base-finetuned-swahili ## Model description xlm-roberta-base-finetuned-swahili is a Swahili RoBERTa model obtained by fine-tuning xlm-roberta-base model on Swahili language texts. It provides better performance than the XLM-RoBERTa on text classification and named entity recognition datasets. Specifically, this model is a xlm-roberta-base model that was fine-tuned on Swahili corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers pipeline for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-swahili') >>> unmasker("Jumatatu, Bwana Kagame alielezea shirika la France24 huko <mask> kwamba hakuna uhalifu ulitendwa") [{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Ufaransa kwamba hakuna uhalifu ulitendwa', 'score': 0.5077782273292542, 'token': 190096, 'token_str': 'Ufaransa'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Paris kwamba hakuna uhalifu ulitendwa', 'score': 0.3657738268375397, 'token': 7270, 'token_str': 'Paris'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Gabon kwamba hakuna uhalifu ulitendwa', 'score': 0.01592041552066803, 'token': 176392, 'token_str': 'Gabon'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko France kwamba hakuna uhalifu ulitendwa', 'score': 0.010881908237934113, 'token': 9942, 'token_str': 'France'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Marseille kwamba hakuna uhalifu ulitendwa', 'score': 0.009554869495332241, 'token': 185918, 'token_str': 'Marseille'}] ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on [Swahili CC-100](http://data.statmt.org/cc-100/) ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset\| XLM-R F1 \| sw_roberta F1 -\|-\|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) \| 87.55 \| 89.46 ### BibTeX entry and citation info By David Adelani ``` ```
AdShenoy/Bart-samsum-fastai	AdShenoy	2023-06-25T07:20:59Z	0	0	fastai	[ "fastai", "region:us" ]	null	2023-06-24T06:53:20Z	--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
Davlan/xlm-roberta-base-finetuned-xhosa	Davlan	2023-06-25T07:14:21Z	171	1	transformers	[ "transformers", "pytorch", "safetensors", "xlm-roberta", "fill-mask", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- license: apache-2.0 ---
Davlan/byt5-base-eng-yor-mt	Davlan	2023-06-25T07:13:35Z	147	2	transformers	[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "arxiv:2103.08647", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	Hugging Face's logo --- language: - yo - en datasets: - JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) --- # byt5-base-eng-yor-mt ## Model description byt5-base-eng-yor-mt is a machine translation model from English language to Yorùbá language based on a fine-tuned byt5-base model. It establishes a strong baseline for automatically translating texts from English to Yorùbá. Specifically, this model is a byt5-base model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) #### Limitations and bias This model is limited by its training dataset. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset ## Training procedure This model was trained on NVIDIA V100 GPU ## Eval results on Test set (BLEU score) Fine-tuning byt5-base achieves 12.23 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 9.82 ### BibTeX entry and citation info By David Adelani ``` ```
masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0	masakhane	2023-06-25T07:13:23Z	325	8	transformers	[ "transformers", "pytorch", "safetensors", "xlm-roberta", "token-classification", "am", "bm", "obj", "ee", "fon", "ha", "ig", "rw", "lg", "luo", "mos", "ny", "pcm", "sn", "sw", "tn", "tw", "wo", "xh", "yo", "zu", "multilingual", "dataset:masakhaner2", "arxiv:2103.11811", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-12-15T12:52:06Z	--- license: afl-3.0 language: - am - bm - obj - ee - fon - ha - ig - rw - lg - luo - mos - ny - pcm - sn - sw - tn - tw - wo - xh - yo - zu - multilingual datasets: - masakhaner2 --- # masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0 ## Model description masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0 is a Named Entity Recognition (NER) model for 21 African languages. Specifically, this model is a Davlan/afro-xlmr-large model that was fine-tuned on an aggregation of African language datasets obtained from two versions of MasakhaNER dataset i.e. [MasakhaNER 1.0](https://huggingface.co/datasets/masakhaner) and [MasakhaNER 2.0](https://huggingface.co/datasets/masakhane/masakhaner2). The languages covered are: - Amharic (Amharic) - Bambara (bam) - Ghomala (bbj) - Ewe (ewe) - Fon (fon) - Hausa (hau) - Igbo (ibo) - Kinyarwanda (kin) - Luganda (lug) - Dholuo (luo) -Mossi (mos) - Chichewa (nya) - Nigerian Pidgin - chShona (sna) - Kiswahili (swą) - Setswana (tsn) - Twi (twi) - Wolof (wol) - isiXhosa (xho) - Yorùbá (yor) - isiZulu (zul) It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organization (ORG), and person (PER). ## Intended uses & limitations #### How to use You can use this model with Transformers pipeline for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0") model = AutoModelForTokenClassification.from_pretrained("masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria" ner_results = nlp(example) print(ner_results) ``` ## Eval results on MasakhaNER (F-score) Model evaluated on MasakhaNER 1.0 and MasakhaNER 2.0 test sets language\| MasakhaNER 1.0 \| MasakhaNER 2.0 -\|-\|- amh \|80.5\| bam \|\| 83.1 bbj \|\| 76.6 ewe \|\| 89.6 fon \|\| 83.8 hau \|90.3\| 87.5 ibo \|89.5\| 93.5 kin \|82.0\| 87.6 lug \|87.1\| 89.7 luo \|80.8\| 82.5 mos \|\| 75.5 nya \|\| 92.7 pcm \|91.1\| 90.9 sna \|\| 96.5 swa \|88.5\| 93.4 tsn \|\| 90.3 twi \|\| 81.3 wol \|72.7\| 87.3 xho \|\| 90.0 yor \|88.1\| 90.5 zul \|\| 91.3 avg \|85.1\| 87.7 #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on the aggregation of [MasakhaNER 1.0](https://huggingface.co/datasets/masakhaner) and [MasakhaNER 2.0](https://huggingface.co/datasets/masakhane/masakhaner2) datasets The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Abbreviation\|Description -\|- O\|Outside of a named entity B-DATE \|Beginning of a DATE entity right after another DATE entity I-DATE \|DATE entity B-PER \|Beginning of a person’s name right after another person’s name I-PER \|Person’s name B-ORG \|Beginning of an organisation right after another organisation I-ORG \|Organisation B-LOC \|Beginning of a location right after another location I-LOC \|Location ## Training procedure This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the [original MasakhaNER paper](https://arxiv.org/abs/2103.11811) which trained & evaluated the model on MasakhaNER corpus. ### BibTeX entry and citation info ``` @article{Adelani2022MasakhaNER2A, title={MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition}, author={David Ifeoluwa Adelani and Graham Neubig and Sebastian Ruder and Shruti Rijhwani and Michael Beukman and Chester Palen-Michel and Constantine Lignos and Jesujoba Oluwadara Alabi and Shamsuddeen Hassan Muhammad and Peter Nabende and Cheikh M. Bamba Dione and Andiswa Bukula and Rooweither Mabuya and Bonaventure F. P. Dossou and Blessing K. Sibanda and Happy Buzaaba and Jonathan Mukiibi and Godson Kalipe and Derguene Mbaye and Amelia Taylor and Fatoumata Kabore and Chris C. Emezue and Anuoluwapo Aremu and Perez Ogayo and Catherine W. Gitau and Edwin Munkoh-Buabeng and Victoire Memdjokam Koagne and Allahsera Auguste Tapo and Tebogo Macucwa and Vukosi Marivate and Elvis Mboning and Tajuddeen R. Gwadabe and Tosin P. Adewumi and Orevaoghene Ahia and Joyce Nakatumba-Nabende and Neo L. Mokono and Ignatius M Ezeani and Chiamaka Ijeoma Chukwuneke and Mofetoluwa Adeyemi and Gilles Hacheme and Idris Abdulmumin and Odunayo Ogundepo and Oreen Yousuf and Tatiana Moteu Ngoli and Dietrich Klakow}, journal={ArXiv}, year={2022}, volume={abs/2210.12391} } ```
NasimB/gpt2-dp-mod-aochild-10chars	NasimB	2023-06-25T06:53:44Z	6	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-25T03:14:38Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-dp-mod-aochild-10chars results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-dp-mod-aochild-10chars This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.4173 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.7077 \| 0.27 \| 500 \| 5.6423 \| \| 5.3468 \| 0.54 \| 1000 \| 5.2154 \| \| 5.0042 \| 0.8 \| 1500 \| 4.9608 \| \| 4.7637 \| 1.07 \| 2000 \| 4.7969 \| \| 4.5583 \| 1.34 \| 2500 \| 4.6931 \| \| 4.4721 \| 1.61 \| 3000 \| 4.5939 \| \| 4.3855 \| 1.88 \| 3500 \| 4.5049 \| \| 4.218 \| 2.15 \| 4000 \| 4.4679 \| \| 4.1202 \| 2.41 \| 4500 \| 4.4175 \| \| 4.105 \| 2.68 \| 5000 \| 4.3697 \| \| 4.0733 \| 2.95 \| 5500 \| 4.3257 \| \| 3.8601 \| 3.22 \| 6000 \| 4.3344 \| \| 3.8504 \| 3.49 \| 6500 \| 4.3033 \| \| 3.8507 \| 3.76 \| 7000 \| 4.2759 \| \| 3.8215 \| 4.02 \| 7500 \| 4.2709 \| \| 3.5828 \| 4.29 \| 8000 \| 4.2887 \| \| 3.6183 \| 4.56 \| 8500 \| 4.2711 \| \| 3.6264 \| 4.83 \| 9000 \| 4.2489 \| \| 3.5136 \| 5.1 \| 9500 \| 4.2794 \| \| 3.3547 \| 5.36 \| 10000 \| 4.2895 \| \| 3.383 \| 5.63 \| 10500 \| 4.2727 \| \| 3.3982 \| 5.9 \| 11000 \| 4.2594 \| \| 3.2002 \| 6.17 \| 11500 \| 4.3133 \| \| 3.1199 \| 6.44 \| 12000 \| 4.3184 \| \| 3.1483 \| 6.71 \| 12500 \| 4.3123 \| \| 3.1516 \| 6.97 \| 13000 \| 4.3013 \| \| 2.9083 \| 7.24 \| 13500 \| 4.3587 \| \| 2.9076 \| 7.51 \| 14000 \| 4.3641 \| \| 2.9176 \| 7.78 \| 14500 \| 4.3616 \| \| 2.8855 \| 8.05 \| 15000 \| 4.3806 \| \| 2.7292 \| 8.32 \| 15500 \| 4.3978 \| \| 2.7443 \| 8.58 \| 16000 \| 4.4023 \| \| 2.7445 \| 8.85 \| 16500 \| 4.4046 \| \| 2.702 \| 9.12 \| 17000 \| 4.4125 \| \| 2.6515 \| 9.39 \| 17500 \| 4.4159 \| \| 2.6552 \| 9.66 \| 18000 \| 4.4170 \| \| 2.6529 \| 9.92 \| 18500 \| 4.4173 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
teoha/openai-whisper-medium-PeftType.LORA-colab	teoha	2023-06-25T06:51:18Z	2	0	peft	[ "peft", "region:us" ]	null	2023-06-25T06:51:11Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
zanafi/sentiment_model	zanafi	2023-06-25T06:31:04Z	16	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "dataset:indonlu", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-23T06:53:10Z	--- license: mit tags: - generated_from_trainer datasets: - indonlu metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment_model results: - task: name: Text Classification type: text-classification dataset: name: indonlu type: indonlu config: emot split: validation args: emot metrics: - name: Accuracy type: accuracy value: 0.7363636363636363 - name: Precision type: precision value: 0.7397155596092384 - name: Recall type: recall value: 0.7459489407651173 - name: F1 type: f1 value: 0.741920437379511 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sentiment_model This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the indonlu dataset. It achieves the following results on the evaluation set: - Loss: 0.7788 - Accuracy: 0.7364 - Precision: 0.7397 - Recall: 0.7459 - F1: 0.7419 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 1.1939 \| 1.0 \| 221 \| 0.8261 \| 0.6932 \| 0.7203 \| 0.7034 \| 0.7056 \| \| 0.6866 \| 2.0 \| 442 \| 0.7925 \| 0.725 \| 0.7378 \| 0.7377 \| 0.7346 \| \| 0.4791 \| 3.0 \| 663 \| 0.7788 \| 0.7364 \| 0.7397 \| 0.7459 \| 0.7419 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
sukantan/all-mpnet-base-v2-ftlegal-v3	sukantan	2023-06-25T06:20:52Z	5	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "dataset:sukantan/nyaya-st-training", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-06-25T06:20:46Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity datasets: - sukantan/nyaya-st-training --- # sukantan/all-mpnet-base-v2-ftlegal-v3 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sukantan/all-mpnet-base-v2-ftlegal-v3') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=sukantan/all-mpnet-base-v2-ftlegal-v3) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 391 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MegaBatchMarginLoss.MegaBatchMarginLoss` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 391, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
Raizel123/pamelasafitrilora	Raizel123	2023-06-25T04:37:40Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-25T04:34:50Z	--- license: creativeml-openrail-m ---
ardhies/CuteAsianFace	ardhies	2023-06-25T04:10:18Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-25T04:06:53Z	--- license: creativeml-openrail-m ---
Laurie/baichuan-7b-qlora-moss	Laurie	2023-06-25T04:06:12Z	5	0	peft	[ "peft", "text-generation", "zh", "en", "dataset:fnlp/moss-003-sft-data", "license:apache-2.0", "region:us" ]	text-generation	2023-06-25T03:38:18Z	--- library_name: peft license: apache-2.0 datasets: - fnlp/moss-003-sft-data language: - zh - en pipeline_tag: text-generation --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0.dev0 ## 使用方法 git clone https://huggingface.co/Laurie/baichuan-7b-qlora-moss cd baichuan-7b-qlora-moss python src/web_demo.py \ --model_name_or_path baichuan-inc/baichuan-7B \ --checkpoint_dir .
razaali/swin-tiny-patch4-window7-224-finetuned-eurosat	razaali	2023-06-25T04:00:02Z	211	0	transformers	[ "transformers", "pytorch", "tensorboard", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-06-25T03:25:51Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.977037037037037 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0662 - Accuracy: 0.9770 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2501 \| 1.0 \| 190 \| 0.1077 \| 0.9626 \| \| 0.1375 \| 2.0 \| 380 \| 0.0892 \| 0.9707 \| \| 0.1324 \| 3.0 \| 570 \| 0.0662 \| 0.9770 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
CJacobnriia/spatnzRVC	CJacobnriia	2023-06-25T03:56:17Z	0	0	null	[ "en", "region:us" ]	null	2023-06-25T01:52:32Z	--- language: - en --- This is an RVC model of spatnz (https://www.youtube.com/channel/UCcNPbOeFo-qM0wpis8Lwdig) ![spatnz.png](https://huggingface.co/CJacobnriia/spatnzRVC/resolve/main/spatnz.png)
FutureMiracle/CGEC-BART-Model	FutureMiracle	2023-06-25T03:55:55Z	0	3	fairseq	[ "fairseq", "BART", "pytorch", "CGEC", "translation", "zh", "license:apache-2.0", "region:us" ]	translation	2023-06-21T08:28:46Z	--- license: apache-2.0 language: - zh library_name: fairseq tags: - BART - pytorch - CGEC metrics: - bleu pipeline_tag: translation --- # 中文语法纠错任务介绍 Task:中文语法纠错任务(Chinese Grammatical Error Correction,CGEC) CGEC任务输入一句中文文本，文本纠错技术对句子中存在拼写、语法、语义等错误进行自动纠正，输出纠正后的文本。 # 中文语法纠错方法主流的方法为seq2seq和seq2edits，常用的中文纠错数据集包括Lang8、NLPCC18和CGED等。 # 模型描述我们采用基于transformer的seq2seq方法建模文本纠错任务。模型选择上，我们使用中文BART作为预训练模型，然后在Lang8和CGED训练数据上进行finetune。在不引入额外资源的情况下，本模型在LANG8测试集上达到了SOTA。 # 模型训练模型训练是基于fairseq库进行训练的。 # 如何使用 step1: 下载fairseq库，并进行安装 step2: 使用interactive.py方法进行推理 python -u ${FAIRSEQ_DIR}/interactive.py $PROCESSED_DIR \ --task syntax-enhanced-translation \ --path ${MODEL_PATH} \ --beam ${BEAM} \ --nbest ${N_BEST} \ -s src \ -t tgt \ --buffer-size 1000 \ --batch-size 32 \ --num-workers 12 \ --log-format tqdm \ --remove-bpe \ --fp16 \ --output_file $OUTPUT_DIR/output.nbest \ <$OUTPUT_DIR/lang8_test.char
blackmount8/open-llama-7b-open-instruct-ct2-float16	blackmount8	2023-06-25T03:49:04Z	9	0	transformers	[ "transformers", "text-generation", "en", "dataset:VMware/open-instruct-v1-oasst-dolly-hhrlhf", "license:cc", "region:us" ]	text-generation	2023-06-24T15:05:27Z	--- inference: false license: cc datasets: - VMware/open-instruct-v1-oasst-dolly-hhrlhf language: - en library_name: transformers pipeline_tag: text-generation --- # blackmount8/open-llama-7B-open-instruct-ct2-float16 Float16 version of [VMware/open-llama-7b-open-instruct](https://huggingface.co/VMware/open-llama-7b-open-instruct), quantized using CTranslate2. ## VMware/open-llama-7B-open-instruct Instruction-tuned version of the fully trained Open LLama 7B model. The model is open for `<b>`COMMERCIAL USE `</b>`. `<br>` `<b>` NOTE `</b>` : The model was trained using the Alpaca prompt template `<b>` NOTE `</b>` : Fast tokenizer results in incorrect encoding, set the ``use_fast = False`` parameter, when instantiating the tokenizer ## License - `<b>`Commercially Viable `</b>` - Instruction dataset, [VMware/open-instruct-v1-oasst-dolly-hhrlhf](https://huggingface.co/datasets/VMware/open-instruct-v1-oasst-dolly-hhrlhf) is under cc-by-sa-3.0 - Language Model, ([openlm-research/open_llama_7b](https://huggingface.co/openlm-research/open_llama_7b)) is under apache-2.0 ## Nomenclature - Model : Open-llama - Model Size: 7B parameters - Dataset: Open-instruct-v1 (oasst, dolly, hhrlhf) ## Use in CTranslate2 ``` import ctranslate2 from transformers import AutoTokenizer model_name = "blackmount8/open-llama-7b-open-instruct-ct2-float16" tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, padding_side="left", truncation_side="left") model = ctranslate2.Generator(model_name, device="auto", compute_type="float16") input_text = ["What is the meaning of stonehenge?", "Hello mate!"] input_ids = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True).input_ids input_tokens = [tokenizer.convert_ids_to_tokens(ele) for ele in input_ids] outputs = model.generate_batch(input_tokens, max_length=128) output_tokens = [ ele.sequences_ids[0] for ele in outputs ] output = tokenizer.batch_decode(output_tokens) print(output) ```
blackmount8/open-llama-13b-open-instruct-ct2-float16	blackmount8	2023-06-25T03:48:21Z	4	0	transformers	[ "transformers", "text-generation", "en", "dataset:VMware/open-instruct-v1-oasst-dolly-hhrlhf", "license:cc", "region:us" ]	text-generation	2023-06-24T16:44:56Z	--- inference: false license: cc datasets: - VMware/open-instruct-v1-oasst-dolly-hhrlhf language: - en library_name: transformers pipeline_tag: text-generation --- # blackmount8/open-llama-13B-open-instruct-ct2-float16 Float16 version of [VMware/open-llama-13b-open-instruct](https://huggingface.co/VMware/open-llama-13b-open-instruct), quantized using CTranslate2. ## VMware/open-llama-13B-open-instruct Instruction-tuned version of the fully trained Open LLama 13B model. The model is open for `<b>`COMMERCIAL USE `</b>`. `<br>` `<b>` NOTE `</b>` : The model was trained using the Alpaca prompt template `<b>` NOTE `</b>` : Fast tokenizer results in incorrect encoding, set the ``use_fast = False`` parameter, when instantiating the tokenizer ## License - `<b>`Commercially Viable `</b>` - Instruction dataset, [VMware/open-instruct-v1-oasst-dolly-hhrlhf](https://huggingface.co/datasets/VMware/open-instruct-v1-oasst-dolly-hhrlhf) is under cc-by-sa-3.0 - Language Model, ([openlm-research/open_llama_13b](https://huggingface.co/openlm-research/open_llama_13b)) is under apache-2.0 ## Nomenclature - Model : Open-llama - Model Size: 13B parameters - Dataset: Open-instruct-v1 (oasst, dolly, hhrlhf) ## Use in CTranslate2 ``` import ctranslate2 from transformers import AutoTokenizer model_name = "blackmount8/open-llama-13b-open-instruct-ct2-float16" tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, padding_side="left", truncation_side="left") model = ctranslate2.Generator(model_name, device="auto", compute_type="float16") input_text = ["What is the meaning of stonehenge?", "Hello mate!"] input_ids = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True).input_ids input_tokens = [tokenizer.convert_ids_to_tokens(ele) for ele in input_ids] outputs = model.generate_batch(input_tokens, max_length=128) output_tokens = [ ele.sequences_ids[0] for ele in outputs ] output = tokenizer.batch_decode(output_tokens) print(output) ```
duyhngoc/Wave2Vec2_OV_Vie	duyhngoc	2023-06-25T03:47:48Z	77	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "vivos", "generated_from_trainer", "dataset:vivos", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-06-21T10:58:36Z	--- license: apache-2.0 tags: - automatic-speech-recognition - vivos - generated_from_trainer datasets: - vivos metrics: - wer model-index: - name: Wave2Vec2_OV_Vie results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Wave2Vec2_OV_Vie This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the VIVOS - NA dataset. It achieves the following results on the evaluation set: - Loss: 3.5894 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---:\| \| No log \| 0.27 \| 100 \| 3.9210 \| 1.0 \| \| No log \| 0.55 \| 200 \| 3.4375 \| 1.0 \| \| No log \| 0.82 \| 300 \| 3.4356 \| 1.0 \| \| No log \| 1.1 \| 400 \| 3.4045 \| 1.0 \| \| 4.1866 \| 1.37 \| 500 \| 3.4694 \| 1.0 \| \| 4.1866 \| 1.65 \| 600 \| 3.6266 \| 1.0 \| \| 4.1866 \| 1.92 \| 700 \| 3.5694 \| 1.0 \| \| 4.1866 \| 2.19 \| 800 \| 3.5733 \| 1.0 \| \| 4.1866 \| 2.47 \| 900 \| 3.6381 \| 1.0 \| \| 3.4376 \| 2.74 \| 1000 \| 3.6604 \| 1.0 \| \| 3.4376 \| 3.02 \| 1100 \| 3.5868 \| 1.0 \| \| 3.4376 \| 3.29 \| 1200 \| 3.4988 \| 1.0 \| \| 3.4376 \| 3.57 \| 1300 \| 3.5409 \| 1.0 \| \| 3.4376 \| 3.84 \| 1400 \| 3.4883 \| 1.0 \| \| 3.4365 \| 4.12 \| 1500 \| 3.6125 \| 1.0 \| \| 3.4365 \| 4.39 \| 1600 \| 3.6123 \| 1.0 \| \| 3.4365 \| 4.66 \| 1700 \| 3.5978 \| 1.0 \| \| 3.4365 \| 4.94 \| 1800 \| 3.5693 \| 1.0 \| \| 3.4365 \| 5.21 \| 1900 \| 3.5659 \| 1.0 \| \| 3.4339 \| 5.49 \| 2000 \| 3.6234 \| 1.0 \| \| 3.4339 \| 5.76 \| 2100 \| 3.5997 \| 1.0 \| \| 3.4339 \| 6.04 \| 2200 \| 3.6529 \| 1.0 \| \| 3.4339 \| 6.31 \| 2300 \| 3.5780 \| 1.0 \| \| 3.4339 \| 6.58 \| 2400 \| 3.5844 \| 1.0 \| \| 3.4333 \| 6.86 \| 2500 \| 3.5792 \| 1.0 \| \| 3.4333 \| 7.13 \| 2600 \| 3.5468 \| 1.0 \| \| 3.4333 \| 7.41 \| 2700 \| 3.5691 \| 1.0 \| \| 3.4333 \| 7.68 \| 2800 \| 3.5408 \| 1.0 \| \| 3.4333 \| 7.96 \| 2900 \| 3.5482 \| 1.0 \| \| 3.4294 \| 8.23 \| 3000 \| 3.6070 \| 1.0 \| \| 3.4294 \| 8.5 \| 3100 \| 3.5905 \| 1.0 \| \| 3.4294 \| 8.78 \| 3200 \| 3.6018 \| 1.0 \| \| 3.4294 \| 9.05 \| 3300 \| 3.6326 \| 1.0 \| \| 3.4294 \| 9.33 \| 3400 \| 3.6214 \| 1.0 \| \| 3.4293 \| 9.6 \| 3500 \| 3.6372 \| 1.0 \| \| 3.4293 \| 9.88 \| 3600 \| 3.6215 \| 1.0 \| \| 3.4293 \| 10.15 \| 3700 \| 3.5106 \| 1.0 \| \| 3.4293 \| 10.43 \| 3800 \| 3.5066 \| 1.0 \| \| 3.4293 \| 10.7 \| 3900 \| 3.5352 \| 1.0 \| \| 3.4295 \| 10.97 \| 4000 \| 3.5129 \| 1.0 \| \| 3.4295 \| 11.25 \| 4100 \| 3.6384 \| 1.0 \| \| 3.4295 \| 11.52 \| 4200 \| 3.6019 \| 1.0 \| \| 3.4295 \| 11.8 \| 4300 \| 3.5876 \| 1.0 \| \| 3.4295 \| 12.07 \| 4400 \| 3.6207 \| 1.0 \| \| 3.4252 \| 12.35 \| 4500 \| 3.5998 \| 1.0 \| \| 3.4252 \| 12.62 \| 4600 \| 3.6216 \| 1.0 \| \| 3.4252 \| 12.89 \| 4700 \| 3.6073 \| 1.0 \| \| 3.4252 \| 13.17 \| 4800 \| 3.5567 \| 1.0 \| \| 3.4252 \| 13.44 \| 4900 \| 3.5745 \| 1.0 \| \| 3.4274 \| 13.72 \| 5000 \| 3.5738 \| 1.0 \| \| 3.4274 \| 13.99 \| 5100 \| 3.5914 \| 1.0 \| \| 3.4274 \| 14.27 \| 5200 \| 3.6004 \| 1.0 \| \| 3.4274 \| 14.54 \| 5300 \| 3.5968 \| 1.0 \| \| 3.4274 \| 14.81 \| 5400 \| 3.5908 \| 1.0 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.0 - Tokenizers 0.13.3
tyavika/pytorch	tyavika	2023-06-25T03:32:35Z	77	0	transformers	[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]	question-answering	2023-06-25T03:00:51Z	--- tags: - generated_from_trainer model-index: - name: pytorch results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pytorch This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cpu - Datasets 2.12.0 - Tokenizers 0.13.2
jiuzhan/YoLoV7-dog	jiuzhan	2023-06-25T03:27:58Z	0	0	null	[ "region:us" ]	null	2023-06-25T03:11:07Z	# 狗的种类识别用了500张图片的数据集，有点少。分了38个种类，每种张数有点少。效果一般。
MazVer/queenbee	MazVer	2023-06-25T02:55:12Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-25T02:52:19Z	--- license: creativeml-openrail-m ---
gaiamolinaro/ppo-SnowballTarget	gaiamolinaro	2023-06-25T02:36:18Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-06-25T02:36:11Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: gaiamolinaro/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
klaylouis1932/xlm-roberta-base-finetuned-panx-de	klaylouis1932	2023-06-25T02:22:45Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-06-24T07:47:55Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8653353814644136 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1339 - F1: 0.8653 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2583 \| 1.0 \| 525 \| 0.1596 \| 0.8231 \| \| 0.1262 \| 2.0 \| 1050 \| 0.1395 \| 0.8468 \| \| 0.0824 \| 3.0 \| 1575 \| 0.1339 \| 0.8653 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
nbiish/learning-taxi-v3	nbiish	2023-06-25T02:14:07Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-25T02:14:05Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: learning-taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.73 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="nbiish/learning-taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
mort666/faster-whisper-large-v2-th	mort666	2023-06-25T02:13:04Z	644	8	ctranslate2	[ "ctranslate2", "audio", "automatic-speech-recognition", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "license:mit", "region:us" ]	automatic-speech-recognition	2023-06-18T15:28:40Z	--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - 'no' - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su tags: - audio - automatic-speech-recognition license: mit library_name: ctranslate2 --- # Whisper large-v2 (Thai Finetune) model for CTranslate2 This repository contains the conversion of the [biodatlab/whisper-th-large-combined](https://huggingface.co/biodatlab/whisper-th-large-combined) which is finetune of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) for the Thai language to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("large-v2") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model biodatlab/whisper-th-large-combined --output_dir faster-whisper-large-v2-th \ --copy_files tokenizer.json --quantization float16 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information For more information about the original model, see its [model card](https://huggingface.co/biodatlab/whisper-th-large-combined).
cczhong/chinese-alpaca-plus-lora-7b-merged-ggml-4b	cczhong	2023-06-25T01:30:50Z	0	3	null	[ "region:us" ]	null	2023-06-24T17:53:57Z	need ggml-v3 to run. llama-cpp-python > 0.1.57 from https://github.com/ymcui/Chinese-LLaMA-Alpaca
yashgharat/HFTaxi-v3	yashgharat	2023-06-25T01:18:43Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-25T01:18:41Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: HFTaxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="yashgharat/HFTaxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
NasimB/gpt2-dp-mod_aochild	NasimB	2023-06-25T00:27:44Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-24T20:59:20Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-dp-mod_aochild results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-dp-mod_aochild This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.4146 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.706 \| 0.27 \| 500 \| 5.6466 \| \| 5.3616 \| 0.54 \| 1000 \| 5.2058 \| \| 5.0148 \| 0.81 \| 1500 \| 4.9571 \| \| 4.7595 \| 1.08 \| 2000 \| 4.8100 \| \| 4.5716 \| 1.35 \| 2500 \| 4.6947 \| \| 4.4792 \| 1.62 \| 3000 \| 4.5951 \| \| 4.3985 \| 1.89 \| 3500 \| 4.5126 \| \| 4.2203 \| 2.16 \| 4000 \| 4.4747 \| \| 4.1373 \| 2.42 \| 4500 \| 4.4206 \| \| 4.1109 \| 2.69 \| 5000 \| 4.3695 \| \| 4.0827 \| 2.96 \| 5500 \| 4.3285 \| \| 3.8662 \| 3.23 \| 6000 \| 4.3409 \| \| 3.863 \| 3.5 \| 6500 \| 4.3058 \| \| 3.8585 \| 3.77 \| 7000 \| 4.2777 \| \| 3.8073 \| 4.04 \| 7500 \| 4.2766 \| \| 3.594 \| 4.31 \| 8000 \| 4.2886 \| \| 3.6275 \| 4.58 \| 8500 \| 4.2700 \| \| 3.6373 \| 4.85 \| 9000 \| 4.2436 \| \| 3.488 \| 5.12 \| 9500 \| 4.2800 \| \| 3.3669 \| 5.39 \| 10000 \| 4.2884 \| \| 3.3981 \| 5.66 \| 10500 \| 4.2764 \| \| 3.3991 \| 5.93 \| 11000 \| 4.2533 \| \| 3.177 \| 6.2 \| 11500 \| 4.3110 \| \| 3.1321 \| 6.47 \| 12000 \| 4.3137 \| \| 3.1491 \| 6.73 \| 12500 \| 4.3083 \| \| 3.1544 \| 7.0 \| 13000 \| 4.3112 \| \| 2.8924 \| 7.27 \| 13500 \| 4.3587 \| \| 2.9109 \| 7.54 \| 14000 \| 4.3634 \| \| 2.9185 \| 7.81 \| 14500 \| 4.3600 \| \| 2.8619 \| 8.08 \| 15000 \| 4.3819 \| \| 2.7347 \| 8.35 \| 15500 \| 4.3980 \| \| 2.7435 \| 8.62 \| 16000 \| 4.4007 \| \| 2.752 \| 8.89 \| 16500 \| 4.4012 \| \| 2.6887 \| 9.16 \| 17000 \| 4.4116 \| \| 2.6506 \| 9.43 \| 17500 \| 4.4137 \| \| 2.6588 \| 9.7 \| 18000 \| 4.4144 \| \| 2.66 \| 9.97 \| 18500 \| 4.4146 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
alantao912/models	alantao912	2023-06-25T00:07:35Z	13	0	transformers	[ "transformers", "pytorch", "blip", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "license:bsd-3-clause", "endpoints_compatible", "region:us" ]	image-text-to-text	2023-06-24T20:19:09Z	--- license: bsd-3-clause tags: - generated_from_trainer datasets: - imagefolder model-index: - name: models results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # models This model is a fine-tuned version of [Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.4107 - Wer Score: 0.5495 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\| \| 9.4536 \| 0.05 \| 10 \| 7.8217 \| 41.7753 \| \| 7.3267 \| 0.11 \| 20 \| 6.6585 \| 0.7753 \| \| 6.2358 \| 0.16 \| 30 \| 5.7758 \| 0.5667 \| \| 5.2862 \| 0.22 \| 40 \| 4.7628 \| 0.5419 \| \| 4.3786 \| 0.27 \| 50 \| 3.9203 \| 0.6398 \| \| 3.5554 \| 0.33 \| 60 \| 3.1482 \| 0.5613 \| \| 2.849 \| 0.38 \| 70 \| 2.5209 \| 0.5548 \| \| 2.3041 \| 0.44 \| 80 \| 2.0561 \| 0.5645 \| \| 1.8999 \| 0.49 \| 90 \| 1.7474 \| 0.5645 \| \| 1.658 \| 0.55 \| 100 \| 1.5722 \| 0.5548 \| \| 1.5238 \| 0.6 \| 110 \| 1.4836 \| 0.5591 \| \| 1.4726 \| 0.66 \| 120 \| 1.4461 \| 0.5538 \| \| 1.4328 \| 0.71 \| 130 \| 1.4285 \| 0.5473 \| \| 1.4211 \| 0.77 \| 140 \| 1.4205 \| 0.5559 \| \| 1.4202 \| 0.82 \| 150 \| 1.4156 \| 0.5548 \| \| 1.4098 \| 0.88 \| 160 \| 1.4129 \| 0.5505 \| \| 1.4124 \| 0.93 \| 170 \| 1.4113 \| 0.5548 \| \| 1.4075 \| 0.99 \| 180 \| 1.4107 \| 0.5495 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cpu - Datasets 2.13.1 - Tokenizers 0.13.3
benbav97/ppo-Huggy	benbav97	2023-06-24T23:46:50Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-06-24T22:49:23Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: benbav97/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
mohameddhiab/rate-jokes-bert	mohameddhiab	2023-06-24T23:45:08Z	121	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-24T23:21:00Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: rate-jokes-bert results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # rate-jokes-bert This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0871 - F1: 0.0444 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 64 \| 2.4209 \| 0.0028 \| \| No log \| 2.0 \| 128 \| 2.3785 \| 0.0130 \| \| No log \| 3.0 \| 192 \| 2.3215 \| 0.0729 \| \| No log \| 4.0 \| 256 \| 2.1787 \| 0.0444 \| \| No log \| 5.0 \| 320 \| 2.1038 \| 0.0444 \| \| No log \| 6.0 \| 384 \| 2.0944 \| 0.0444 \| \| No log \| 7.0 \| 448 \| 2.0911 \| 0.0444 \| \| 2.2915 \| 8.0 \| 512 \| 2.0901 \| 0.0444 \| \| 2.2915 \| 9.0 \| 576 \| 2.0892 \| 0.0444 \| \| 2.2915 \| 10.0 \| 640 \| 2.0871 \| 0.0444 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
97jmlr/pyramids	97jmlr	2023-06-24T23:32:30Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-06-24T23:32:23Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: 97jmlr/pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Monk666/my_awesome_eli5_clm-model	Monk666	2023-06-24T23:28:23Z	63	0	transformers	[ "transformers", "tf", "gpt2", "text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-06-24T23:19:53Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Monk666/my_awesome_eli5_clm-model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Monk666/my_awesome_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.7288 - Validation Loss: 3.7309 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 3.9096 \| 3.7608 \| 0 \| \| 3.7906 \| 3.7412 \| 1 \| \| 3.7288 \| 3.7309 \| 2 \| ### Framework versions - Transformers 4.28.1 - TensorFlow 2.11.0 - Datasets 2.1.0 - Tokenizers 0.13.3
MindNetML/dqn-SpaceInvadersNoFrameskip-v4	MindNetML	2023-06-24T23:09:09Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-24T23:08:32Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 572.50 +/- 179.80 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga MindNetML -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga MindNetML -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga MindNetML ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 3), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
mohalm/videomae-base-finetuned-ucf101-subset	mohalm	2023-06-24T23:03:09Z	61	0	transformers	[ "transformers", "pytorch", "tensorboard", "videomae", "video-classification", "generated_from_trainer", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2023-06-24T20:21:51Z	--- license: cc-by-nc-4.0 tags: - generated_from_trainer model-index: - name: videomae-base-finetuned-ucf101-subset results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # videomae-base-finetuned-ucf101-subset This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0008 - eval_accuracy: 1.0 - eval_runtime: 223.6754 - eval_samples_per_second: 0.443 - eval_steps_per_second: 0.076 - epoch: 1.01 - step: 43 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 164 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
TheBloke/orca_mini_7B-GGML	TheBloke	2023-06-24T22:49:41Z	0	21	transformers	[ "transformers", "en", "dataset:psmathur/alpaca_orca", "dataset:psmathur/dolly-v2_orca", "dataset:psmathur/WizardLM_Orca", "arxiv:2306.02707", "license:mit", "region:us" ]	null	2023-06-24T22:07:15Z	--- inference: false license: mit language: - en library_name: transformers datasets: - psmathur/alpaca_orca - psmathur/dolly-v2_orca - psmathur/WizardLM_Orca --- <!-- header start --> <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <!-- header end --> # Pankaj Mathur's Orca Mini 7B GGML These files are GGML format model files for [Pankaj Mathur's Orca Mini 7B](https://huggingface.co/psmathur/orca_mini_7b). GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as: * [text-generation-webui](https://github.com/oobabooga/text-generation-webui) * [KoboldCpp](https://github.com/LostRuins/koboldcpp) * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) * [ctransformers](https://github.com/marella/ctransformers) ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/orca_mini_7B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/orca_mini_7B-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/psmathur/orca_mini_7b) ## Prompt template: ``` ### System: You are an AI assistant that follows instruction extremely well. Help as much as you can. ### User: prompt ### Response: ``` or ``` ### System: You are an AI assistant that follows instruction extremely well. Help as much as you can. ### User: prompt ### Input: input ### Response: ``` <!-- compatibility_ggml start --> ## Compatibility ### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0` I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit `2d5db48`. These are guaranteed to be compatbile with any UIs, tools and libraries released since late May. ### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K` These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`. They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python and ctransformers. Other tools and libraries may or may not be compatible - check their documentation if in doubt. ## Explanation of the new k-quant methods The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw * GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type. Refer to the Provided Files table below to see what files use which methods, and how. <!-- compatibility_ggml end --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| orca-mini-7b.ggmlv3.q2_K.bin \| q2_K \| 2 \| 2.87 GB \| 5.37 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. \| \| orca-mini-7b.ggmlv3.q3_K_L.bin \| q3_K_L \| 3 \| 3.60 GB \| 6.10 GB \| New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K \| \| orca-mini-7b.ggmlv3.q3_K_M.bin \| q3_K_M \| 3 \| 3.28 GB \| 5.78 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K \| \| orca-mini-7b.ggmlv3.q3_K_S.bin \| q3_K_S \| 3 \| 2.95 GB \| 5.45 GB \| New k-quant method. Uses GGML_TYPE_Q3_K for all tensors \| \| orca-mini-7b.ggmlv3.q4_0.bin \| q4_0 \| 4 \| 3.79 GB \| 6.29 GB \| Original llama.cpp quant method, 4-bit. \| \| orca-mini-7b.ggmlv3.q4_1.bin \| q4_1 \| 4 \| 4.21 GB \| 6.71 GB \| Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. \| \| orca-mini-7b.ggmlv3.q4_K_M.bin \| q4_K_M \| 4 \| 4.08 GB \| 6.58 GB \| New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K \| \| orca-mini-7b.ggmlv3.q4_K_S.bin \| q4_K_S \| 4 \| 3.83 GB \| 6.33 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for all tensors \| \| orca-mini-7b.ggmlv3.q5_0.bin \| q5_0 \| 5 \| 4.63 GB \| 7.13 GB \| Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. \| \| orca-mini-7b.ggmlv3.q5_1.bin \| q5_1 \| 5 \| 5.06 GB \| 7.56 GB \| Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. \| \| orca-mini-7b.ggmlv3.q5_K_M.bin \| q5_K_M \| 5 \| 4.78 GB \| 7.28 GB \| New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K \| \| orca-mini-7b.ggmlv3.q5_K_S.bin \| q5_K_S \| 5 \| 4.65 GB \| 7.15 GB \| New k-quant method. Uses GGML_TYPE_Q5_K for all tensors \| \| orca-mini-7b.ggmlv3.q6_K.bin \| q6_K \| 6 \| 5.53 GB \| 8.03 GB \| New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors \| \| orca-mini-7b.ggmlv3.q8_0.bin \| q8_0 \| 8 \| 7.16 GB \| 9.66 GB \| Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `llama.cpp` I use the following command line; adjust for your tastes and needs: ``` ./main -t 10 -ngl 32 -m orca-mini-7b.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### System:\nYou are an story writing assistant who writes very long, detailed and interesting stories\n\n### User:\nWrite a story about llamas\n\n### Input:\n{input}\n\n### Response:\n" ``` If you're able to use full GPU offloading, you should use `-t 1` to get best performance. If not able to fully offload to GPU, you should use more cores. Change `-t 10` to the number of physical CPU cores you have, or a lower number depending on what gives best performance. Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md). <!-- footer start --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov. Patreon special mentions: Pyrater, WelcomeToTheClub, Kalila, Mano Prime, Trenton Dambrowitz, Spiking Neurons AB, Pierre Kircher, Fen Risland, Kevin Schuppel, Luke, Rainer Wilmers, vamX, Gabriel Puliatti, Alex , Karl Bernard, Ajan Kanaga, Talal Aujan, Space Cruiser, ya boyyy, biorpg, Johann-Peter Hartmann, Asp the Wyvern, Ai Maven, Ghost , Preetika Verma, Nikolai Manek, trip7s trip, John Detwiler, Fred von Graf, Artur Olbinski, subjectnull, John Villwock, Junyu Yang, Rod A, Lone Striker, Chris McCloskey, Iucharbius , Matthew Berman, Illia Dulskyi, Khalefa Al-Ahmad, Imad Khwaja, chris gileta, Willem Michiel, Greatston Gnanesh, Derek Yates, K, Alps Aficionado, Oscar Rangel, David Flickinger, Luke Pendergrass, Deep Realms, Eugene Pentland, Cory Kujawski, terasurfer , Jonathan Leane, senxiiz, Joseph William Delisle, Sean Connelly, webtim, zynix , Nathan LeClaire. Thank you to all my generous patrons and donaters! <!-- footer end --> # Original model card: Pankaj Mathur's Orca Mini 7B # orca_mini_7b An [OpenLLaMa-7B model](https://github.com/openlm-research/open_llama) model trained on explain tuned datasets, created using Instructions and Input from WizardLM, Alpaca & Dolly-V2 datasets and applying Orca Research Paper dataset construction approaches. # Dataset We build explain tuned [WizardLM dataset ~70K](https://github.com/nlpxucan/WizardLM), [Alpaca dataset ~52K](https://crfm.stanford.edu/2023/03/13/alpaca.html) & [Dolly-V2 dataset ~15K](https://github.com/databrickslabs/dolly) created using approaches from [Orca Research Paper](https://arxiv.org/abs/2306.02707). We leverage all of the 15 system instructions provided in Orca Research Paper. to generate custom datasets, in contrast to vanilla instruction tuning approaches used by original datasets. This helps student model aka this model to learn *thought* process from teacher model, which is ChatGPT (gpt-3.5-turbo-0301 version). Please see below example usage how the System prompt is added before each instruction. # Training The training configurations are provided in the table below. The training takes on 8x A100(80G) GPUs and lasts for around 7 Hours for cost of $84 using [Lambda Labs](https://lambdalabs.com) We used DeepSpeed with fully sharded data parallelism, also know as [ZeRO stage 3](https://engineering.fb.com/2021/07/15/open-source/fsdp/) by writing our own fine tunning scripts plus leveraging some of the model training code provided by amazing [OpenAlpaca repo](https://github.com/yxuansu/OpenAlpaca) Here are some of params used during training: \|\|\| \|:-------------:\|:-------------:\| \|batch_size\|32\| \|train_micro_batch_size_per_gpu\|2\| \|gradient_accumulation_steps\|2\| \|Learning rate\|2e-5\| \|Max length\|1024\| \|Epochs\|3\| \|Optimizer\|AdamW\| # Example Usage Below shows an example on how to use this model ```python import torch from transformers import LlamaForCausalLM, LlamaTokenizer # Hugging Face model_path model_path = 'psmathur/orca_mini_7b' tokenizer = LlamaTokenizer.from_pretrained(model_path) model = LlamaForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map='auto', ) #generate text function def generate_text(system, instruction, input=None): if input: prompt = f"### System:\n{system}\n\n### User:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n" else: prompt = f"### System:\n{system}\n\n### User:\n{instruction}\n\n### Response:\n" tokens = tokenizer.encode(prompt) tokens = torch.LongTensor(tokens).unsqueeze(0) tokens = tokens.to('cuda') instance = {'input_ids': tokens,'top_p': 1.0, 'temperature':0.7, 'generate_len': 1024, 'top_k': 50} length = len(tokens[0]) with torch.no_grad(): rest = model.generate( input_ids=tokens, max_length=length+instance['generate_len'], use_cache=True, do_sample=True, top_p=instance['top_p'], temperature=instance['temperature'], top_k=instance['top_k'] ) output = rest[0][length:] string = tokenizer.decode(output, skip_special_tokens=True) return f'[!] Response: {string}' # Sample Test Instruction Used by Youtuber Sam Witteveen https://www.youtube.com/@samwitteveenai system = 'You are an AI assistant that follows instruction extremely well. Help as much as you can.' instruction = 'Write a letter to Sam Altman, CEO of OpenAI, requesting him to convert GPT4 a private model by OpenAI to an open source project' print(generate_text(system, instruction)) ``` ``` [!] Response: Dear Sam Altman, I am writing to request that you convert the GPT4 private model developed by OpenAI to an open source project. As a user of OpenAI, I have been waiting for the day when I can use the advanced natural language processing capabilities of GPT4 in a more open and accessible way. While OpenAI has made significant progress in developing AI applications, it has primarily focused on building private models that are not accessible to the general public. However, with the recent release of GPT-3, there is a growing demand for more open and accessible AI tools. Converting GPT4 to an open source project would allow for greater transparency, collaboration, and innovation. It would also help to build trust in the technology and ensure that it is used ethically and responsibly. I urge you to consider converting GPT4 to an open source project. This would be a significant contribution to the AI community and would help to create a more open and accessible future. Thank you for your consideration. Sincerely, [Your Name] ``` P.S. I am #opentowork and #collaboration, if you can help, please reach out to me at psmathur.public@gmail.com Next Goals: 1) Try more data like actually using FLAN-v2, just like Orka Research Paper (I am open for suggestions) 2) Provide more options for Text generation UI. (may be https://github.com/oobabooga/text-generation-webui) 3) Provide 4bit GGML/GPTQ quantized model (may be [TheBloke](https://huggingface.co/TheBloke) can help here) Limitations & Biases: This model can produce factually incorrect output, and should not be relied on to produce factually accurate information. This model was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Disclaimer: The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes. Citiation: If you found wizardlm_alpaca_dolly_orca_open_llama_7b useful in your research or applications, please kindly cite using the following BibTeX: ``` @misc{wizardlm_alpaca_dolly_orca_open_llama_7b, author = {Pankaj Mathur}, title = {wizardlm_alpaca_dolly_orca_open_llama_7b: An explain tuned OpenLLaMA-7b model on custom wizardlm, alpaca, & dolly datasets}, year = {2023}, publisher = {GitHub, HuggingFace}, journal = {GitHub repository, HuggingFace repository}, howpublished = {\url{https://github.com/pankajarm/wizardlm_alpaca_dolly_orca_open_llama_7b}, \url{https://https://huggingface.co/psmathur/wizardlm_alpaca_dolly_orca_open_llama_7b}}, } ``` ``` @software{openlm2023openllama, author = {Xinyang Geng and Hao Liu}, title = {OpenLLaMA: An Open Reproduction of LLaMA}, month = May, year = 2023, url = {https://github.com/openlm-research/open_llama} } ``` ``` @misc{openalpaca, author = {Yixuan Su and Tian Lan and Deng Cai}, title = {OpenAlpaca: A Fully Open-Source Instruction-Following Model Based On OpenLLaMA}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/yxuansu/OpenAlpaca}}, } ``` ``` @misc{alpaca, author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto }, title = {Stanford Alpaca: An Instruction-following LLaMA model}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}}, } ```
97jmlr/ppo-SnowballTarget	97jmlr	2023-06-24T22:43:03Z	3	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-06-24T22:42:57Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: 97jmlr/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
digiplay/illustro1stEdition_illustroV1	digiplay	2023-06-24T22:33:19Z	384	2	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-24T20:27:59Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info: https://civitai.com/models/96101/illustro-1st-edition-photoreal-fantasy-model Original Author's DEMO image : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/10af4e3b-b554-4fe9-9d4a-5f7a834f884d/width=2304/03905-377024016-1%20beautiful%20female,%20%20((Jeremy%20Lipking))_intricate,%20detailed,%20knight,%20fantasy,%20(engraved%20filigree%20armor),%20perfect%20lighting,%20perfe.jpeg) image detail link : https://civitai.com/images/1266239
EIStakovskii/french_toxicity_classifier_plus	EIStakovskii	2023-06-24T22:31:55Z	108	2	transformers	[ "transformers", "pytorch", "safetensors", "camembert", "text-classification", "fr", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-21T07:48:14Z	--- language: fr # <-- my language widget: - text: "J'aime ta coiffure" example_title: "NOT TOXIC 1" - text: "Va te faire foutre" example_title: "TOXIC 1" - text: "Quel mauvais temps, n'est-ce pas ?" example_title: "NOT TOXIC 2" - text: "J'espère que tu vas mourir, connard !" example_title: "TOXIC 2" - text: "j'aime beaucoup ta veste" example_title: "NOT TOXIC 3" license: other --- This model was trained for toxicity labeling. Label_1 means TOXIC, Label_0 means NOT TOXIC The model was fine-tuned based off [the CamemBERT language model](https://huggingface.co/camembert-base). The accuracy is 93% on the test split during training and 79% on a manually picked (and thus harder) sample of 200 sentences (100 label 1, 100 label 0) at the end of the training. The model was finetuned on 32k sentences. The train data was the translations of the English data (around 30k sentences) from [the multilingual_detox dataset](https://github.com/s-nlp/multilingual_detox) by [Skolkovo Institute](https://huggingface.co/SkolkovoInstitute) using [the opus-mt-en-fr translation model](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) by [Helsinki-NLP](https://huggingface.co/Helsinki-NLP) and the data from [the jigsaw dataset](https://www.kaggle.com/competitions/jigsaw-multilingual-toxic-comment-classification/data) on kaggle.
dar-tau/ppo-LunarLander-v2	dar-tau	2023-06-24T22:12:59Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-24T22:06:50Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 256.44 +/- 18.33 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
pongjin/en_with_korean_model_large_960h	pongjin	2023-06-24T21:56:41Z	79	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "en", "dataset:pongjin/en_corpora_parliament_processed", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-06-16T16:55:59Z	--- license: apache-2.0 datasets: - pongjin/en_corpora_parliament_processed language: - en pipeline_tag: automatic-speech-recognition metrics: - wer --- This model has been referred to the following links 1) https://huggingface.co/blog/wav2vec2-with-ngram 2) https://huggingface.co/blog/fine-tune-wav2vec2-english Thanks to [patrickvonplaten Patrick von Platen](https://huggingface.co/patrickvonplaten) 해당 모델은 한국인의 영어 발화 인식 성능 개선을 위해 facebook/wav2vec2-large-960h 로 파인튜닝한 모델에 KenLM 5-gram 을 붙인 ASR + LM 모델입니다. If you want to use LM, you must have kenlm installed https://github.com/kpu/kenlm ```python pip install https://github.com/kpu/kenlm/archive/master.zip ``` 학습 데이터 출처 : https://aiopen.etri.re.kr/voiceModel >transformers==4.24.0 >huggingface_hub==0.13.2 \| wer \| epoch \| batch \| lr \| weight_decay\| warmup_steps\| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 0.17 \| 10 \| 16 \| 1e-4 \| 0.005 \| 1000 \|
SandeepKanao/HL7-FHIR-Model-V1	SandeepKanao	2023-06-24T21:45:41Z	105	1	transformers	[ "transformers", "pytorch", "distilbert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-06-22T12:44:31Z	--- license: apache-2.0 language: - en tags: - Token Classification co2_eq_emissions: 0.0279399890043426 widget: - text: ""MSH\|^~&\|SendingAPP\|MYTEST\|\|\|20230621090000\|\|ORU^R01\|1\|P\|2.5.1\|\|\|\|\|\|UNICODE PID\|1\|\|13579246^^^TEST\|\|Taylor^Michael\|\|19830520\|M\|\|\|987 Pine St^^Anytown^NY^23456\|\|555-456-7890 PV1\|1\|\|bc^^004 OBR\|1\|\|13579246\|BCD^LEFT Breast Cancer Diagnosis^99MRC\|\|20230621090000\|\|\|Taylor^Sarah\|\|20230620090000\|\|\|N OBX\|1\|ST\|FINDINGS^Findings^99MRC\|\|Lab report shows asymmetric density in the right breast.\|F\|\|\|R OBX\|2\|ST\|IMPRESSION^Impression^99MRC\|\|BIRADS category: 4 - Probably left side as issues.\|F\|\|\|R OBX\|3\|ST\|RECOMMENDATION^Recommendation^99MRC\|\|Follow-up specialit visit in six months.\|F\|\|\|R"" example_title: "example 1" - text: "MSH\|^~&\|SendingAPP\|MYTEST\|\|\|20230621090000\|\|ORU^R01\|1\|P\|2.5.1\|\|\|\|\|\|UNICODE PID\|1\|\|13579246^^^TEST\|\|Taylor^Michael\|\|19830520\|M\|\|\|987 Pine St^^Anytown^NY^23456\|\|555-456-7890 PV1\|1\|\|bc^^004 OBR\|1\|\|13579246\|BCD^LEFT Breast Cancer Diagnosis^99MRC\|\|20230621090000\|\|\|Taylor^Sarah\|\|20230620090000\|\|\|N OBX\|1\|ST\|FINDINGS^Findings^99MRC\|\|Lab report shows asymmetric density in the right breast.\|F\|\|\|R OBX\|2\|ST\|IMPRESSION^Impression^99MRC\|\|BIRADS category: 4 - Probably left side as issues.\|F\|\|\|R OBX\|3\|ST\|RECOMMENDATION^Recommendation^99MRC\|\|Follow-up specialit visit in six months.\|F\|\|\|R" ## About the Model An English Named Entity Recognition model, trained on Maccrobat to recognize the bio-medical entities (107 entities) from a given text corpus (case reports etc.). This model was built on top of distilbert-base-uncased - Dataset: Maccrobat https://figshare.com/articles/dataset/MACCROBAT2018/9764942 - Carbon emission: 0.0279399890043426 Kg - Training time: 30.16527 minutes - GPU used : 1 x GeForce RTX 3060 Laptop GPU Checkout the tutorial video for explanation of this model and corresponding python library: https://youtu.be/xpiDPdBpS18 ## Usage The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library. ```python from transformers import pipeline from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all") model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all") pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") # pass device=0 if using gpu pipe("""The patient reported no recurrence of palpitations at follow-up 6 months after the ablation.""") ``` ## Author
digiplay/ChillyMix_v1	digiplay	2023-06-24T21:19:54Z	291	2	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-23T16:14:13Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/58772?modelVersionId=63220 Original Author's DEMO image : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/096853c2-2dfe-48c4-b0dc-eea621d02727/width=512/00629-3254057508-chillyMixV1Fp16.jpeg) image detailed link: https://civitai.com/images/701538
conrevo/Segment-Anything-A1111	conrevo	2023-06-24T21:18:24Z	0	1	null	[ "region:us" ]	null	2023-06-16T20:01:12Z	This repository contains - The extracted plugin model from https://huggingface.co/shi-labs/Matting-Anything - The ultralytics model from https://github.com/CASIA-IVA-Lab/FastSAM These models should only be accompanied with https://github.com/continue-revolution/sd-webui-segment-anything

PhongLe1311/my_awesome_billsum_model

PhongLe1311

2023-06-25T15:30:09Z

99

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:billsum", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-06-25T15:20:04Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - billsum metrics: - rouge model-index: - name: my_awesome_billsum_model results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: billsum type: billsum config: default split: ca_test args: default metrics: - name: Rouge1 type: rouge value: 0.1408 ---  # my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5181 - Rouge1: 0.1408 - Rouge2: 0.0514 - Rougel: 0.1173 - Rougelsum: 0.1173 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 62 | 2.8150 | 0.1264 | 0.0373 | 0.1061 | 0.1061 | 19.0 | | No log | 2.0 | 124 | 2.5989 | 0.1379 | 0.0501 | 0.1164 | 0.1165 | 19.0 | | No log | 3.0 | 186 | 2.5349 | 0.1396 | 0.0525 | 0.1179 | 0.1181 | 19.0 | | No log | 4.0 | 248 | 2.5181 | 0.1408 | 0.0514 | 0.1173 | 0.1173 | 19.0 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

ahessamb/bertopic-test

ahessamb

2023-06-25T15:29:15Z

3

0

bertopic

[ "bertopic", "text-classification", "region:us" ]

text-classification

2023-06-25T15:29:09Z

--- tags: - bertopic library_name: bertopic pipeline_tag: text-classification --- # bertopic-test This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model. BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets. ## Usage To use this model, please install BERTopic: ``` pip install -U bertopic ``` You can use the model as follows: ```python from bertopic import BERTopic topic_model = BERTopic.load("ahessamb/bertopic-test") topic_model.get_topic_info() ``` ## Topic overview * Number of topics: 50 * Number of training documents: 1570 <details> <summary>Click here for an overview of all topics.</summary> | Topic ID | Topic Keywords | Topic Frequency | Label | |----------|----------------|-----------------|-------| | 0 | liquidations - forcefully - betting - liquidation - contracts | 8 | 0_liquidations_forcefully_betting_liquidation | | 1 | litecoin - wsm - presale - 77 - near | 94 | 1_litecoin_wsm_presale_77 | | 2 | sec - court - terraform - dismiss - lawyers | 49 | 2_sec_court_terraform_dismiss | | 3 | huobi - hkvac - bsl - web3 - code | 12 | 3_huobi_hkvac_bsl_web3 | | 4 | lucie - shiba - susbarium - puppynet - portals | 3 | 4_lucie_shiba_susbarium_puppynet | | 5 | 000006819 - shiba - accuracy - finbold - estimates | 27 | 5_000006819_shiba_accuracy_finbold | | 6 | tokens - sec - binance - securities - coinbase | 45 | 6_tokens_sec_binance_securities | | 7 | mckinsey - ai - nanjing - productivity - diffusion | 43 | 7_mckinsey_ai_nanjing_productivity | | 8 | resistance - swing - fib - zone - ltc | 32 | 8_resistance_swing_fib_zone | | 9 | brinkman - tategpt - bitcoin - artists - wealth | 26 | 9_brinkman_tategpt_bitcoin_artists | | 10 | stablecoin - stablecoins - decline - redemptions - tusd | 2 | 10_stablecoin_stablecoins_decline_redemptions | | 11 | mutant - mayc - bayc - club - mcmullen | 64 | 11_mutant_mayc_bayc_club | | 12 | xrp - ema - ripple - bullish - cryptocurrencies | 43 | 12_xrp_ema_ripple_bullish | | 13 | tether - cbdcs - loans - federal - nafcu | 27 | 13_tether_cbdcs_loans_federal | | 14 | rate - tradingview - bnb - breakout - coinmarketcap | 85 | 14_rate_tradingview_bnb_breakout | | 15 | 26 - bulls - rsi - ceiling - 300 | 2 | 15_26_bulls_rsi_ceiling | | 16 | lowest - jump - week - wallet - staggering | 3 | 16_lowest_jump_week_wallet | | 17 | xrp - ripple - mekras - sbi - institutions | 56 | 17_xrp_ripple_mekras_sbi | | 18 | debt - mortgages - trillion - government - suspends | 3 | 18_debt_mortgages_trillion_government | | 19 | longitude - chronometer - bitcoin - ships - graffiti | 2 | 19_longitude_chronometer_bitcoin_ships | | 20 | volumes - piggy - aud - xrp - usdt | 15 | 20_volumes_piggy_aud_xrp | | 21 | root - ledger - stakers - sidechains - compatibility | 4 | 21_root_ledger_stakers_sidechains | | 22 | astra - letter - concerns - investors - bitwise | 4 | 22_astra_letter_concerns_investors | | 23 | gold - governments - manipulated - stocks - mined | 10 | 23_gold_governments_manipulated_stocks | | 24 | tether - sygnum - documents - bank - coindesk | 9 | 24_tether_sygnum_documents_bank | | 25 | rewards - governance - lido - proposal - june | 45 | 25_rewards_governance_lido_proposal | | 26 | listings - coin - fairerc20 - bittrex - withdrawals | 68 | 26_listings_coin_fairerc20_bittrex | | 27 | peaq - ordibots - cosmos - fetch - machine | 81 | 27_peaq_ordibots_cosmos_fetch | | 28 | uniswap - v4 - orders - hooks - differing | 23 | 28_uniswap_v4_orders_hooks | | 29 | price - neo - matic - rise - altcoin | 92 | 29_price_neo_matic_rise | | 30 | emptydoc - staff - policy - binance - workspaces | 2 | 30_emptydoc_staff_policy_binance | | 31 | lunc - synthetix - terra - perps - staking | 33 | 31_lunc_synthetix_terra_perps | | 32 | tweet - dogecoin - chart - meme - negative | 3 | 32_tweet_dogecoin_chart_meme | | 33 | binance - securities - exchange - cz - regulators | 63 | 33_binance_securities_exchange_cz | | 34 | bitmart - sale - xrp - discount - event | 4 | 34_bitmart_sale_xrp_discount | | 35 | yuan - event - olympics - canadians - organizers | 49 | 35_yuan_event_olympics_canadians | | 36 | gusd - fidelity - bitcoin - proposal - blackrock | 52 | 36_gusd_fidelity_bitcoin_proposal | | 37 | bills - mcglone - markets - stablecoins - liquidity | 56 | 37_bills_mcglone_markets_stablecoins | | 38 | asset - gain - drop - trading - hours | 2 | 38_asset_gain_drop_trading | | 39 | epstein - hamsterwheel - vulnerability - bounty - certick | 28 | 39_epstein_hamsterwheel_vulnerability_bounty | | 40 | pyth - transparency - data - terra - oracle | 19 | 40_pyth_transparency_data_terra | | 41 | shiba - inu - weighted - collapse - recovery | 2 | 41_shiba_inu_weighted_collapse | | 42 | neo - opensea - carey - security - impersonators | 24 | 42_neo_opensea_carey_security | | 43 | balancer - zkevm - liquidity - defi - 8020 | 3 | 43_balancer_zkevm_liquidity_defi | | 44 | reed - battle - platform - argument - trading | 22 | 44_reed_battle_platform_argument | | 45 | ada - cardano - whale - sell - investors | 4 | 45_ada_cardano_whale_sell | | 46 | uk - coinbase - hong - crypto - regulatory | 65 | 46_uk_coinbase_hong_crypto | | 47 | ethereum - tvl - defi - arbitrum - airdrop | 54 | 47_ethereum_tvl_defi_arbitrum | | 48 | swyftx - shibarium - token - shibaswap - shiba | 54 | 48_swyftx_shibarium_token_shibaswap | | 49 | bitcoin - mining - gain - miners - difficulty | 54 | 49_bitcoin_mining_gain_miners | </details> ## Training hyperparameters * calculate_probabilities: False * language: None * low_memory: False * min_topic_size: 10 * n_gram_range: (1, 1) * nr_topics: None * seed_topic_list: None * top_n_words: 10 * verbose: False ## Framework versions * Numpy: 1.22.4 * HDBSCAN: 0.8.29 * UMAP: 0.5.3 * Pandas: 1.5.3 * Scikit-Learn: 1.2.2 * Sentence-transformers: 2.2.2 * Transformers: 4.30.2 * Numba: 0.56.4 * Plotly: 5.13.1 * Python: 3.10.12

SwampMan/ppo-Huggy

SwampMan

2023-06-25T15:20:32Z

1

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2023-06-25T15:20:22Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: SwampMan/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

cagmfr/q-FrozenLake-v1-4x4-noSlippery

cagmfr

2023-06-25T15:20:16Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-06-25T15:20:14Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="cagmfr/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

NasimB/gpt2-2-dp-mod-aochild-cut

NasimB

2023-06-25T15:09:04Z

22

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-06-25T07:34:36Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-2-dp-mod-aochild-cut results: [] ---  # gpt2-2-dp-mod-aochild-cut This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.4109 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.7147 | 0.27 | 500 | 5.6451 | | 5.3609 | 0.54 | 1000 | 5.2108 | | 5.0162 | 0.81 | 1500 | 4.9585 | | 4.7627 | 1.08 | 2000 | 4.8126 | | 4.5775 | 1.35 | 2500 | 4.7013 | | 4.4856 | 1.62 | 3000 | 4.6034 | | 4.4038 | 1.89 | 3500 | 4.5175 | | 4.2252 | 2.16 | 4000 | 4.4775 | | 4.1408 | 2.42 | 4500 | 4.4236 | | 4.1136 | 2.69 | 5000 | 4.3721 | | 4.0852 | 2.96 | 5500 | 4.3281 | | 3.87 | 3.23 | 6000 | 4.3418 | | 3.8651 | 3.5 | 6500 | 4.3062 | | 3.8601 | 3.77 | 7000 | 4.2781 | | 3.8091 | 4.04 | 7500 | 4.2785 | | 3.5972 | 4.31 | 8000 | 4.2888 | | 3.6301 | 4.58 | 8500 | 4.2678 | | 3.6398 | 4.85 | 9000 | 4.2396 | | 3.4906 | 5.12 | 9500 | 4.2803 | | 3.3704 | 5.39 | 10000 | 4.2849 | | 3.4008 | 5.66 | 10500 | 4.2718 | | 3.4029 | 5.93 | 11000 | 4.2491 | | 3.1804 | 6.2 | 11500 | 4.3116 | | 3.1361 | 6.47 | 12000 | 4.3119 | | 3.1532 | 6.73 | 12500 | 4.3067 | | 3.1591 | 7.0 | 13000 | 4.3072 | | 2.8974 | 7.27 | 13500 | 4.3563 | | 2.9167 | 7.54 | 14000 | 4.3589 | | 2.9248 | 7.81 | 14500 | 4.3580 | | 2.8683 | 8.08 | 15000 | 4.3791 | | 2.741 | 8.35 | 15500 | 4.3939 | | 2.7503 | 8.62 | 16000 | 4.3968 | | 2.7573 | 8.89 | 16500 | 4.3983 | | 2.6961 | 9.16 | 17000 | 4.4075 | | 2.6562 | 9.43 | 17500 | 4.4101 | | 2.6653 | 9.7 | 18000 | 4.4107 | | 2.667 | 9.97 | 18500 | 4.4109 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

Smaraa/t5-text-simplification_1e4_adafactor_biendata

Smaraa

2023-06-25T15:07:10Z

6

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-06-25T12:37:38Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-text-simplification_1e4_adafactor_biendata results: [] ---  # t5-text-simplification_1e4_adafactor_biendata This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7562 - Rouge1: 10.4603 - Rouge2: 2.642 - Rougel: 9.6362 - Rougelsum: 9.6589 - Gen Len: 13.2838 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 464 | 0.5489 | 29.7693 | 11.1997 | 25.6091 | 25.5979 | 14.7281 | | 0.9314 | 2.0 | 928 | 0.5392 | 29.9099 | 10.9645 | 25.334 | 25.3259 | 14.7188 | | 0.5594 | 3.0 | 1392 | 0.5342 | 30.3194 | 11.4204 | 25.8248 | 25.8255 | 14.7666 | | 0.5333 | 4.0 | 1856 | 0.5376 | 30.8368 | 11.6152 | 26.3172 | 26.3583 | 14.1578 | | 0.5192 | 5.0 | 2320 | 0.8890 | 7.5517 | 1.4313 | 7.0971 | 7.1064 | 9.9191 | | 0.8897 | 6.0 | 2784 | 0.8252 | 6.9283 | 1.3484 | 6.5916 | 6.5877 | 10.9894 | | 0.9385 | 7.0 | 3248 | 0.7971 | 8.2401 | 1.9957 | 7.7693 | 7.7675 | 10.7732 | | 0.9089 | 8.0 | 3712 | 0.7725 | 9.7559 | 2.2249 | 9.0272 | 9.0098 | 10.7175 | | 0.8824 | 9.0 | 4176 | 0.7552 | 12.006 | 2.8041 | 11.0115 | 10.992 | 10.7838 | | 0.8658 | 10.0 | 4640 | 0.7490 | 13.311 | 3.4159 | 12.1933 | 12.1551 | 10.6499 | | 0.864 | 11.0 | 5104 | 0.7448 | 13.9983 | 3.6176 | 12.7712 | 12.7347 | 10.752 | | 0.868 | 12.0 | 5568 | 0.7495 | 12.318 | 3.2975 | 11.3451 | 11.3218 | 12.0252 | | 0.8844 | 13.0 | 6032 | 0.7552 | 10.6154 | 2.7347 | 9.8228 | 9.8116 | 13.191 | | 0.8844 | 14.0 | 6496 | 0.7562 | 10.4603 | 2.642 | 9.6362 | 9.6589 | 13.2838 | | 0.8971 | 15.0 | 6960 | 0.7562 | 10.4603 | 2.642 | 9.6362 | 9.6589 | 13.2838 | | 0.8981 | 16.0 | 7424 | 0.7562 | 10.4603 | 2.642 | 9.6362 | 9.6589 | 13.2838 | | 0.8956 | 17.0 | 7888 | 0.7562 | 10.4603 | 2.642 | 9.6362 | 9.6589 | 13.2838 | | 0.8984 | 18.0 | 8352 | 0.7562 | 10.4603 | 2.642 | 9.6362 | 9.6589 | 13.2838 | | 0.8959 | 19.0 | 8816 | 0.7562 | 10.4603 | 2.642 | 9.6362 | 9.6589 | 13.2838 | | 0.8977 | 20.0 | 9280 | 0.7562 | 10.4603 | 2.642 | 9.6362 | 9.6589 | 13.2838 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Smaraa/gpt2-text-simplification_1e4_adafactor_biendata

Smaraa

2023-06-25T14:56:13Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-06-25T12:42:47Z

--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-text-simplification_1e4_adafactor_biendata results: [] ---  # gpt2-text-simplification_1e4_adafactor_biendata This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9089 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 464 | 0.7729 | | 1.0489 | 2.0 | 928 | 0.7546 | | 0.754 | 3.0 | 1392 | 0.7497 | | 0.7034 | 4.0 | 1856 | 0.7530 | | 0.6619 | 5.0 | 2320 | 0.7560 | | 0.6265 | 6.0 | 2784 | 0.7639 | | 0.5921 | 7.0 | 3248 | 0.7747 | | 0.5621 | 8.0 | 3712 | 0.7848 | | 0.5359 | 9.0 | 4176 | 0.7969 | | 0.5115 | 10.0 | 4640 | 0.8113 | | 0.4879 | 11.0 | 5104 | 0.8256 | | 0.4683 | 12.0 | 5568 | 0.8373 | | 0.4491 | 13.0 | 6032 | 0.8519 | | 0.4491 | 14.0 | 6496 | 0.8642 | | 0.4324 | 15.0 | 6960 | 0.8741 | | 0.4176 | 16.0 | 7424 | 0.8841 | | 0.4054 | 17.0 | 7888 | 0.8924 | | 0.3946 | 18.0 | 8352 | 0.8994 | | 0.3868 | 19.0 | 8816 | 0.9043 | | 0.3813 | 20.0 | 9280 | 0.9089 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

sukantan/all-MiniLM-L6-v2-ftlegal-v1

sukantan

2023-06-25T14:44:18Z

8

0

sentence-transformers

[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "dataset:sukantan/nyaya-st-training", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2023-06-25T14:44:14Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity datasets: - sukantan/nyaya-st-training --- # sukantan/all-MiniLM-L6-v2-ftlegal-v1 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sukantan/all-MiniLM-L6-v2-ftlegal-v1') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sukantan/all-MiniLM-L6-v2-ftlegal-v1) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 391 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MegaBatchMarginLoss.MegaBatchMarginLoss` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 391, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors

wza/llama-65b-qlora-fin-1epoch

wza

2023-06-25T14:09:07Z

2

0

peft

[ "peft", "region:us" ]

null

2023-06-23T01:52:13Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0

wza/llama-65b-qlora-fin-2epoch

wza

2023-06-25T14:04:59Z

1

0

peft

[ "peft", "region:us" ]

null

2023-06-25T12:56:22Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0

Smaraa/bart-text-simplification_1e4_adafactor_biendata

Smaraa

2023-06-25T14:04:43Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-06-25T12:33:30Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-text-simplification_1e4_adafactor_biendata results: [] ---  # bart-text-simplification_1e4_adafactor_biendata This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7599 - Rouge1: 29.7176 - Rouge2: 10.9512 - Rougel: 25.5101 - Rougelsum: 25.526 - Gen Len: 15.2029 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 232 | 0.5813 | 30.604 | 12.4253 | 26.5172 | 26.4807 | 15.2241 | | No log | 2.0 | 464 | 0.5739 | 31.9076 | 12.798 | 27.4728 | 27.4929 | 15.2241 | | 1.0176 | 3.0 | 696 | 0.5700 | 31.3776 | 12.2852 | 27.1116 | 27.0878 | 15.6459 | | 1.0176 | 4.0 | 928 | 0.5762 | 30.8731 | 12.3014 | 26.9196 | 26.8301 | 14.6353 | | 0.4798 | 5.0 | 1160 | 0.5863 | 29.927 | 11.7166 | 25.9447 | 25.921 | 14.4297 | | 0.4798 | 6.0 | 1392 | 0.6003 | 29.9528 | 11.2098 | 25.6908 | 25.7209 | 14.7414 | | 0.3855 | 7.0 | 1624 | 0.6179 | 30.1161 | 11.2863 | 26.1433 | 26.1519 | 15.1698 | | 0.3855 | 8.0 | 1856 | 0.6290 | 29.5566 | 11.1229 | 25.6003 | 25.5754 | 14.87 | | 0.3092 | 9.0 | 2088 | 0.6538 | 29.7844 | 11.2434 | 25.8222 | 25.8067 | 14.9708 | | 0.3092 | 10.0 | 2320 | 0.6698 | 28.9941 | 10.6603 | 25.0054 | 25.0198 | 15.0239 | | 0.247 | 11.0 | 2552 | 0.6906 | 28.732 | 10.4525 | 24.8897 | 24.8953 | 14.9721 | | 0.247 | 12.0 | 2784 | 0.7023 | 29.0609 | 10.4762 | 24.9678 | 24.9893 | 15.317 | | 0.198 | 13.0 | 3016 | 0.7200 | 29.9516 | 11.2397 | 25.7347 | 25.7489 | 15.1485 | | 0.198 | 14.0 | 3248 | 0.7263 | 29.1565 | 10.7363 | 25.2238 | 25.203 | 14.9761 | | 0.198 | 15.0 | 3480 | 0.7376 | 30.0068 | 11.2078 | 26.0012 | 26.0235 | 14.9589 | | 0.1602 | 16.0 | 3712 | 0.7489 | 29.8747 | 11.0555 | 25.7321 | 25.7543 | 15.2931 | | 0.1602 | 17.0 | 3944 | 0.7487 | 29.6901 | 10.8692 | 25.5467 | 25.5808 | 15.2798 | | 0.1342 | 18.0 | 4176 | 0.7553 | 29.5496 | 10.8611 | 25.2895 | 25.3218 | 15.3156 | | 0.1342 | 19.0 | 4408 | 0.7590 | 29.7733 | 11.1577 | 25.671 | 25.6883 | 15.1313 | | 0.1184 | 20.0 | 4640 | 0.7599 | 29.7176 | 10.9512 | 25.5101 | 25.526 | 15.2029 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

rdenadai/BR_BERTo

rdenadai

2023-06-25T14:02:18Z

180

3

transformers

[ "transformers", "pytorch", "jax", "safetensors", "roberta", "fill-mask", "portuguese", "brazil", "pt_BR", "pt", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-02T23:29:05Z

--- language: pt tags: - portuguese - brazil - pt_BR widget: - text: gostei muito dessa <mask> --- # BR_BERTo Portuguese (Brazil) model for text inference. ## Params Trained on a corpus of 6_993_330 sentences. - Vocab size: 150_000 - RobertaForMaskedLM size : 512 - Num train epochs: 3 - Time to train: ~10days (on GCP with a Nvidia T4) I follow the great tutorial from HuggingFace team: [How to train a new language model from scratch using Transformers and Tokenizers](https://huggingface.co/blog/how-to-train) More infor here: [BR_BERTo](https://github.com/rdenadai/BR-BERTo)

anas21/English1SpeechToTextModel

anas21

2023-06-25T13:48:32Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-06-25T13:34:09Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: English1SpeechToTextModel results: [] ---  # English1SpeechToTextModel This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 10 - eval_batch_size: 8 - seed: 10 - gradient_accumulation_steps: 10 - total_train_batch_size: 100 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

findnitai/FaceGen

findnitai

2023-06-25T13:25:03Z

138

3

diffusers

[ "diffusers", "safetensors", "text-to-image", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-06-24T03:47:05Z

--- license: apache-2.0 pipeline_tag: text-to-image --- Few examples of unique faces generated by the model. Trained on FFHQ dataset. ![7qfdf0.gif](https://s3.amazonaws.com/moonup/production/uploads/6430e44437ee6d9b76cb8388/fqmUfSW6C9vB-YDIZyTfm.gif)

lucasbertola/q-FrozenLake-v1-8x8-noSlipper

lucasbertola

2023-06-25T13:23:29Z

0

null

[ "FrozenLake-v1-8x8-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "Lucas_is_the_best", "model-index", "region:us" ]

reinforcement-learning

2023-06-25T13:18:21Z

--- tags: - FrozenLake-v1-8x8-no_slippery - q-learning - reinforcement-learning - custom-implementation - Lucas_is_the_best model-index: - name: q-FrozenLake-v1-8x8-noSlipper results: - metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-8x8-no_slippery type: FrozenLake-v1-8x8-no_slippery --- # **Q-Learning** Agent playing1 This is a trained model of a **Q-Learning** agent playing ## Usage ```python model = load_from_hub(repo_id="lucasbertola/q-FrozenLake-v1-4x4-noSlipper", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

PaulineJamin/q-FrozenLake-v1-4x4-noSlippery

PaulineJamin

2023-06-25T13:16:52Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-06-25T12:25:16Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="PaulineJamin/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

S3S3/q-Taxi-v3

S3S3

2023-06-25T13:05:40Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-06-25T13:05:36Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.44 +/- 2.75 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="S3S3/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

paramrah/shoesv2

paramrah

2023-06-25T13:00:03Z

2

0

tf-keras

[ "tf-keras", "mobilenet", "image-classification", "region:us" ]

image-classification

2023-06-25T12:59:39Z

--- pipeline_tag: image-classification ---

bogdancazan/bart-base-newsela-biendata-with-domain-adaptation

bogdancazan

2023-06-25T12:57:32Z

105

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-06-19T14:35:21Z

training_args = TrainingArguments( output_dir='bart-base-newsela-biendata-with-domain-adaptation', num_train_epochs=20, warmup_steps=250, per_device_train_batch_size=BATCH_SIZE, weight_decay=0.01, learning_rate=2e-4, fp16=True, optim="adafactor", ) Step Training Loss 500 5.677000 1000 2.361900 1500 1.826000 2000 1.672900 2500 1.597900 3000 1.555700 3500 1.520600 4000 1.496300 4500 1.476800 TrainOutput(global_step=4640, training_loss=2.1116079396214977, metrics={'train_runtime': 1059.6025, 'train_samples_per_second': 279.992, 'train_steps_per_second': 4.379, 'total_flos': 0.0, 'train_loss': 2.1116079396214977, 'epoch': 20.0})

S3S3/q-FrozenLake-v1-4x4-noSlippery

S3S3

2023-06-25T12:53:11Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-06-25T12:53:07Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="S3S3/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

VilohitT/question_answering_majorproject_2nd

VilohitT

2023-06-25T12:47:34Z

0

peft

[ "peft", "region:us" ]

null

2023-06-25T12:47:29Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

AtomGradient/Adjust_ChatGLM_6B

AtomGradient

2023-06-25T12:45:31Z

104

0

transformers

[ "transformers", "pytorch", "chatglm", "feature-extraction", "custom_code", "license:other", "region:us" ]

feature-extraction

2023-06-25T12:04:00Z

--- license: other --- ``` from transformers import AutoConfig, AutoModel, AutoTokenizer import os import torch # 载入Tokenizer tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) config = AutoConfig.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True, pre_seq_len=128) model = AutoModel.from_pretrained("THUDM/chatglm-6b", config=config, trust_remote_code=True) prefix_state_dict = torch.load(os.path.join("./Adjust_ChatGLM_6B/", "pytorch_model.bin")) new_prefix_state_dict = {} for k, v in prefix_state_dict.items(): if k.startswith("transformer.prefix_encoder."): new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict) model = model.quantize(4) model = model.half().cuda() model.transformer.prefix_encoder.float() model = model.eval() response, history = model.chat(tokenizer, "生成衬衣的广告词", history=[]) print(response) ```

TheBloke/vicuna-13b-v1.3.0-GGML

TheBloke

2023-06-25T12:41:16Z

0

16

null

[ "arxiv:2302.13971", "arxiv:2306.05685", "license:other", "region:us" ]

null

2023-06-25T10:52:15Z

--- inference: false license: other ---  <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div>  # LmSys' Vicuna 13B v1.3 GGML These files are GGML format model files for [LmSys' Vicuna 13B v1.3](https://huggingface.co/lmsys/vicuna-13b-v1.3). **NOTE**: This model was recently updated by the LmSys Team. If you already downloaded Vicuna 13B v1.3 GPTQ or GGML, you may want to re-download it from this repo, as the weights were updated. The original model I uploaded has been renamed to v1.3-preview. GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as: * [text-generation-webui](https://github.com/oobabooga/text-generation-webui) * [KoboldCpp](https://github.com/LostRuins/koboldcpp) * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) * [ctransformers](https://github.com/marella/ctransformers) ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/vicuna-13b-v1.3.0-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/vicuna-13b-v1.3.0-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/lmsys/vicuna-13b-v1.3) ## Prompt template ``` A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: prompt ASSISTANT: ```  ## Compatibility ### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0` I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit `2d5db48`. These are guaranteed to be compatbile with any UIs, tools and libraries released since late May. ### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K` These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`. They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python and ctransformers. Other tools and libraries may or may not be compatible - check their documentation if in doubt. ## Explanation of the new k-quant methods The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw * GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type. Refer to the Provided Files table below to see what files use which methods, and how.  ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | vicuna-13b-v1.3.0.ggmlv3.q2_K.bin | q2_K | 2 | 5.51 GB | 8.01 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. | | vicuna-13b-v1.3.0.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 6.93 GB | 9.43 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K | | vicuna-13b-v1.3.0.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 6.31 GB | 8.81 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K | | vicuna-13b-v1.3.0.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 5.66 GB | 8.16 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors | | vicuna-13b-v1.3.0.ggmlv3.q4_0.bin | q4_0 | 4 | 7.32 GB | 9.82 GB | Original llama.cpp quant method, 4-bit. | | vicuna-13b-v1.3.0.ggmlv3.q4_1.bin | q4_1 | 4 | 8.14 GB | 10.64 GB | Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. | | vicuna-13b-v1.3.0.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 7.87 GB | 10.37 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K | | vicuna-13b-v1.3.0.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 7.37 GB | 9.87 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors | | vicuna-13b-v1.3.0.ggmlv3.q5_0.bin | q5_0 | 5 | 8.95 GB | 11.45 GB | Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. | | vicuna-13b-v1.3.0.ggmlv3.q5_1.bin | q5_1 | 5 | 9.76 GB | 12.26 GB | Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. | | vicuna-13b-v1.3.0.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 9.23 GB | 11.73 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K | | vicuna-13b-v1.3.0.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 8.97 GB | 11.47 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors | | vicuna-13b-v1.3.0.ggmlv3.q6_K.bin | q6_K | 6 | 10.68 GB | 13.18 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors | | vicuna-13b-v1.3.0.ggmlv3.q8_0.bin | q8_0 | 8 | 13.83 GB | 16.33 GB | Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `llama.cpp` I use the following command line; adjust for your tastes and needs: ``` ./main -t 10 -ngl 32 -m vicuna-13b-v1.3.0.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "USER: Write a story about llamas\nASSISTANT:" ``` If you're able to use full GPU offloading, you should use `-t 1` to get best performance. If not able to fully offload to GPU, you should use more cores. Change `-t 10` to the number of physical CPU cores you have, or a lower number depending on what gives best performance. Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).  ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov. **Patreon special mentions**: Pyrater, WelcomeToTheClub, Kalila, Mano Prime, Trenton Dambrowitz, Spiking Neurons AB, Pierre Kircher, Fen Risland, Kevin Schuppel, Luke, Rainer Wilmers, vamX, Gabriel Puliatti, Alex , Karl Bernard, Ajan Kanaga, Talal Aujan, Space Cruiser, ya boyyy, biorpg, Johann-Peter Hartmann, Asp the Wyvern, Ai Maven, Ghost , Preetika Verma, Nikolai Manek, trip7s trip, John Detwiler, Fred von Graf, Artur Olbinski, subjectnull, John Villwock, Junyu Yang, Rod A, Lone Striker, Chris McCloskey, Iucharbius , Matthew Berman, Illia Dulskyi, Khalefa Al-Ahmad, Imad Khwaja, chris gileta, Willem Michiel, Greatston Gnanesh, Derek Yates, K, Alps Aficionado, Oscar Rangel, David Flickinger, Luke Pendergrass, Deep Realms, Eugene Pentland, Cory Kujawski, terasurfer , Jonathan Leane, senxiiz, Joseph William Delisle, Sean Connelly, webtim, zynix , Nathan LeClaire. Thank you to all my generous patrons and donaters!  # Original model card: LmSys' Vicuna 13B v1.3 # Vicuna Model Card ## Model Details Vicuna is a chat assistant trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. - **Developed by:** [LMSYS](https://lmsys.org/) - **Model type:** An auto-regressive language model based on the transformer architecture. - **License:** Non-commercial license - **Finetuned from model:** [LLaMA](https://arxiv.org/abs/2302.13971). ### Model Sources - **Repository:** https://github.com/lm-sys/FastChat - **Blog:** https://lmsys.org/blog/2023-03-30-vicuna/ - **Paper:** https://arxiv.org/abs/2306.05685 - **Demo:** https://chat.lmsys.org/ ## Uses The primary use of Vicuna is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence. ## How to Get Started with the Model Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights. APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api. ## Training Details Vicuna v1.3 is fine-tuned from LLaMA with supervised instruction fine-tuning. The training data is around 140K conversations collected from ShareGPT.com. See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf). ## Evaluation Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf). ## Difference between different versions of Vicuna See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)

emilianJR/HRA_hyperrealism_art

emilianJR

2023-06-25T12:30:23Z

52

2

diffusers

[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-06-25T12:20:01Z

--- language: - en license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Diffuser model for this SD checkpoint: https://civitai.com/models/80515/hrahyperrealism-art **emilianJR/HRA_hyperrealism_art** is the HuggingFace diffuser that you can use with **diffusers.StableDiffusionPipeline()**. Examples | Examples | Examples ---- | ---- | ---- ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/57a93272-f14e-4252-b6c4-485264e07a9d/width=450/194806-2023-06-03-20230603034324-640-896.jpeg) | ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/07069db6-0ec5-4d96-a9ac-cc3c8501a1d8/width=450/194800-2023-06-03-20230603034105-640-896.jpeg) | ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/a3682d16-7fd3-4e7d-8698-e9c51dcdcbb6/width=450/194705-2023-06-03-20230603030320-640-896.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/23d67259-0a4f-4168-af5d-feeeffcf8101/width=450/194799-2023-06-03-20230603034104-640-896.jpeg) | ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/1dff82b9-748e-477f-b0f3-f2b9860aa093/width=450/194790-2023-06-03-20230603033734-640-896.jpeg) | ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/43d23920-55db-4c6b-abd6-33cdacb6d4eb/width=450/194690-2023-06-03-20230603025728-640-896.jpeg) ------- ## 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). ```python from diffusers import StableDiffusionPipeline import torch model_id = "emilianJR/HRA_hyperrealism_art" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "YOUR PROMPT" image = pipe(prompt).images[0] image.save("image.png") ``` ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)

Luke537/image_classification_food_model

Luke537

2023-06-25T12:30:18Z

189

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:food101", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-06-24T19:15:08Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - food101 metrics: - accuracy model-index: - name: image_classification_food_model results: - task: name: Image Classification type: image-classification dataset: name: food101 type: food101 config: default split: train[:5000] args: default metrics: - name: Accuracy type: accuracy value: 0.893 ---  # image_classification_food_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 1.6474 - Accuracy: 0.893 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7587 | 0.99 | 62 | 2.5481 | 0.844 | | 1.8903 | 2.0 | 125 | 1.8096 | 0.874 | | 1.6502 | 2.98 | 186 | 1.6474 | 0.893 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cpu - Datasets 2.13.0 - Tokenizers 0.13.3

emilianJR/majicMIX_realistic_v6

emilianJR

2023-06-25T12:26:15Z

69

14

diffusers

[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-06-18T12:42:51Z

--- language: - en license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Diffuser model for this SD checkpoint: https://civitai.com/models/43331/majicmix-realistic **emilianJR/majicMIX_realistic_v6** is the HuggingFace diffuser that you can use with **diffusers.StableDiffusionPipeline()**. Examples | Examples | Examples ---- | ---- | ---- ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/55e308aa-aec9-4816-b76e-523d9235a6e1/width=450/00005-321001525.jpeg) | ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/f1bb9271-3628-45c6-8b2c-05ee3b19af0f/width=450/00027-1961413425.jpeg) | ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/18150a9d-1e07-494f-8498-cd0c033907c5/width=450/00042-2448422190.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d748fcfd-29f1-4fe1-95e2-d34765bccca9/width=450/00058-3698311310.jpeg) | ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/a5b348c9-7b5b-4235-a943-834eec84a17a/width=450/00003-703532927.jpeg) | ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/66820fd3-98f2-4fbb-b904-0507de39c36a/width=450/00002-140050360.jpeg) ------- ## 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). ```python from diffusers import StableDiffusionPipeline import torch model_id = "emilianJR/majicMIX_realistic_v6" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "YOUR PROMPT" image = pipe(prompt).images[0] image.save("image.png") ``` ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)

bogdancazan/t5-base-newsela-biendata-with-domain-adaptation

bogdancazan

2023-06-25T12:24:30Z

105

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-06-19T13:46:06Z

training_args = TrainingArguments( output_dir='t5-base-wikilarge-newsela-with-domain-adaptation', num_train_epochs=20, warmup_steps=250, per_device_train_batch_size=BATCH_SIZE, weight_decay=0.01, learning_rate=2e-4, # fp16=True, optim="adafactor", ) Step Training Loss 500 4.184500 1000 2.470900 1500 2.128900 2000 1.951600 2500 1.834400 3000 1.755800 3500 1.701800 4000 1.656300 4500 1.628800 TrainOutput(global_step=4640, training_loss=2.1286644540984057, metrics={'train_runtime': 4090.6694, 'train_samples_per_second': 72.526, 'train_steps_per_second': 1.134, 'total_flos': 0.0, 'train_loss': 2.1286644540984057, 'epoch': 20.0})

Smaraa/bart-text-simplification_1e4_adafactor

Smaraa

2023-06-25T11:45:02Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-06-24T11:26:12Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-text-simplification_1e4_adafactor results: [] ---  # bart-text-simplification_1e4_adafactor This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8377 - Rouge1: 60.5348 - Rouge2: 41.6762 - Rougel: 55.5994 - Rougelsum: 55.5841 - Gen Len: 18.7487 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.1741 | 1.0 | 1163 | 0.6416 | 62.4 | 44.1316 | 57.9029 | 57.8644 | 18.8482 | | 0.1553 | 2.0 | 2326 | 0.6504 | 62.2879 | 43.9281 | 57.4714 | 57.461 | 18.8063 | | 0.1369 | 3.0 | 3489 | 0.6656 | 61.2481 | 42.605 | 56.5118 | 56.4636 | 18.733 | | 0.1286 | 4.0 | 4652 | 0.6906 | 61.3015 | 42.1608 | 56.2688 | 56.1707 | 18.7487 | | 0.1141 | 5.0 | 5815 | 0.7082 | 62.1771 | 43.1481 | 57.0231 | 57.0673 | 18.911 | | 0.1016 | 6.0 | 6978 | 0.7188 | 61.408 | 42.2759 | 56.1699 | 56.1779 | 18.8377 | | 0.0961 | 7.0 | 8141 | 0.7334 | 60.802 | 41.9149 | 56.0171 | 56.0279 | 18.8168 | | 0.0869 | 8.0 | 9304 | 0.7509 | 60.6564 | 41.3587 | 55.4436 | 55.468 | 18.7382 | | 0.0783 | 9.0 | 10467 | 0.7713 | 60.3551 | 41.8074 | 55.6856 | 55.679 | 18.7173 | | 0.0751 | 10.0 | 11630 | 0.7785 | 60.378 | 41.6134 | 55.5217 | 55.505 | 18.8325 | | 0.0679 | 11.0 | 12793 | 0.7835 | 60.5835 | 41.6735 | 55.5469 | 55.5791 | 18.7435 | | 0.0619 | 12.0 | 13956 | 0.8012 | 60.8152 | 41.2014 | 55.7186 | 55.7233 | 18.9424 | | 0.0611 | 13.0 | 15119 | 0.8091 | 60.8188 | 41.8074 | 55.6684 | 55.8026 | 18.7958 | | 0.0568 | 14.0 | 16282 | 0.8175 | 60.9209 | 41.5689 | 55.8838 | 55.8642 | 18.7277 | | 0.0527 | 15.0 | 17445 | 0.8250 | 61.0215 | 41.9079 | 55.9018 | 55.8709 | 18.9162 | | 0.0524 | 16.0 | 18608 | 0.8317 | 60.8214 | 41.6554 | 55.8053 | 55.7947 | 18.7277 | | 0.0504 | 17.0 | 19771 | 0.8310 | 60.6533 | 41.6507 | 55.9289 | 55.9426 | 18.7958 | | 0.0486 | 18.0 | 20934 | 0.8345 | 60.4722 | 41.5319 | 55.3384 | 55.3655 | 18.6859 | | 0.0491 | 19.0 | 22097 | 0.8379 | 60.4012 | 41.2452 | 55.5059 | 55.5553 | 18.8115 | | 0.0489 | 20.0 | 23260 | 0.8377 | 60.5348 | 41.6762 | 55.5994 | 55.5841 | 18.7487 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

alfredplpl/unlimited-1-0

alfredplpl

2023-06-25T11:44:51Z

34

0

diffusers

[ "diffusers", "safetensors", "stable-diffusion", "text-to-image", "arxiv:2112.10752", "arxiv:2212.03860", "license:other", "autotrain_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-06-25T11:21:59Z

--- license: other tags: - stable-diffusion - text-to-image inference: false --- # Unlimited 1.0 Model Card ![eyecatch.jpg](eyecatch.jpg) Title: Unleash your limit. English version is [here](README_en.md). # はじめに Unlimitedは、情報漏洩したNovel AI Diffusionの代わりとなるように開発した、AIアートに特化した画像生成AIです。 # ライセンスについてライセンスについては、もとのライセンス CreativeML Open RAIL++-M License に例外を除き商用利用禁止を追加しただけです。例外を除き商用利用禁止を追加した理由は創作業界に悪影響を及ぼしかねないという懸念からです。営利企業にいる方は法務部にいる人と相談してください。趣味で利用する方はあまり気にしなくても一般常識を守り、お使いください。 **なお、商用利用したい方は別途こちら (ozaki.yasunori@outlook.com) にご相談ください。** # 法律について本モデルは日本にて作成されました。したがって、日本の法律が適用されます。本モデルの学習は、著作権法第30条の4に基づき、合法であると主張します。また、本モデルの配布については、著作権法や刑法175条に照らしてみても、正犯や幇助犯にも該当しないと主張します。詳しくは柿沼弁護士の[見解](https://twitter.com/tka0120/status/1601483633436393473?s=20&t=yvM9EX0Em-_7lh8NJln3IQ)を御覧ください。ただし、ライセンスにもある通り、本モデルの生成物は各種法令に従って取り扱って下さい。 # 使い方モデルは[safetensors形式](unlimited_1_0.safetensors)からダウンロードできます。以下、一般的なモデルカードの日本語訳です。 ## モデル詳細 - **モデルタイプ:** 拡散モデルベースの text-to-image 生成モデル - **言語:** 日本語 - **ライセンス:** CreativeML Open RAIL++-M-NC License - **モデルの説明:** このモデルはプロンプトに応じて適切な画像を生成することができます。アルゴリズムは [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) と [OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip) です。 - **補足:** - **参考文献:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ## モデルの使用例 Stable Diffusion v2と同じ使い方です。たくさんの方法がありますが、２つのパターンを提供します。 - Web UI - Diffusers ### Web UIの場合 Stable Diffusion v2 の使い方と同じく、safetensor形式のモデルファイルをモデルフォルダに入れてください。詳しいインストール方法は、[こちらの記事](https://note.com/it_navi/n/n6ffb66513769)を参照してください。なお、xformersをインストールし、--xformers --disable-nan-checkオプションをオンにすることをおすすめします。そうでない場合は--no-halfオプションをオンにしてください。 ### Diffusersの場合 [🤗's Diffusers library](https://github.com/huggingface/diffusers) を使ってください。まずは、以下のスクリプトを実行し、ライブラリをいれてください。 ```bash pip install --upgrade git+https://github.com/huggingface/diffusers.git transformers accelerate scipy ``` 次のスクリプトを実行し、画像を生成してください。 ```python from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler import torch model_id = "alfredplpl/unlimited-1-0" scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "masterpiece, anime, close up, white short hair, red eyes, 1girl, solo, red roses" negative_prompt="lowres , kanji, monochrome, ((bad anatomy)), ((bad hands)), text, missing finger, extra digits, fewer digits, blurry, ((mutated hands and fingers)), (poorly drawn face), ((mutation)), ((deformed face)), (ugly), ((bad proportions)), ((extra limbs)), extra face, (double head), (extra head), ((extra feet)), monster, logo, cropped, jpeg, humpbacked, long body, long neck, ((jpeg artifacts)), ((censored)), ((bad aesthetic))" images = pipe(prompt,negative_prompt=negative_prompt, num_inference_steps=30).images images[0].save("girl.png") ``` **注意**: - [xformers](https://github.com/facebookresearch/xformers) を使うと早くなります。 - GPUを使う際にGPUのメモリが少ない人は `pipe.enable_attention_slicing()` を使ってください。 #### 想定される用途 - 自己表現 - このAIを使い、「あなた」らしさを発信すること - 画像生成AIに関する報道 - 公共放送だけでなく、営利企業でも可能 - 画像合成AIに関する情報を「知る権利」は創作業界に悪影響を及ぼさないと判断したためです。また、報道の自由などを尊重しました。 - 研究開発 - Discord上でのモデルの利用 - プロンプトエンジニアリング - ファインチューニング（追加学習とも） - DreamBooth など - 他のモデルとのマージ - 本モデルの性能をFIDなどで調べること - 本モデルがStable Diffusion以外のモデルとは独立であることをチェックサムやハッシュ関数などで調べること - 教育 - 美大生や専門学校生の卒業制作 - 大学生の卒業論文や課題制作 - 先生が画像生成AIの現状を伝えること - Hugging Face の Community にかいてある用途 - 日本語か英語で質問してください #### 想定されない用途 - 物事を事実として表現するようなこと - 収益化されているYouTubeなどのコンテンツへの使用 - 商用のサービスとして直接提供すること - 先生を困らせるようなこと - その他、創作業界に悪影響を及ぼすこと # 使用してはいけない用途や悪意のある用途 - デジタル贋作 ([Digital Forgery](https://arxiv.org/abs/2212.03860)) は公開しないでください（著作権法に違反するおそれ） - 他人の作品を無断でImage-to-Imageしないでください（著作権法に違反するおそれ） - わいせつ物を頒布しないでください (刑法175条に違反するおそれ） - いわゆる業界のマナーを守らないようなこと - 事実に基づかないことを事実のように語らないようにしてください（威力業務妨害罪が適用されるおそれ） - フェイクニュース ## モデルの限界やバイアス ### モデルの限界 - 拡散モデルや大規模言語モデルは、いまだに未知の部分が多く、その限界は判明していない。 ### バイアス - 拡散モデルや大規模言語モデルは、いまだに未知の部分が多く、バイアスは判明していない。 ## 学習 **学習データ** 国内法に準拠したデータとモデル。 **学習プロセス** - **ハードウェア:** A6000x2 ## 評価結果第三者による評価を求めています。 ## 環境への影響 - **ハードウェアタイプ:** A6000x2 - **使用時間（単位は時間）:** 1000 - **学習した場所:** 日本 ## 参考文献 @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } *このモデルカードは [Stable Diffusion v2](https://huggingface.co/stabilityai/stable-diffusion-2/raw/main/README.md) に基づいて書かれました。

PraveenJesu/openai-whisper-medium-peft-lora-colab

PraveenJesu

2023-06-25T11:43:36Z

0

peft

[ "peft", "region:us" ]

null

2023-06-25T11:43:33Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0

Erfan2001/distilbert_NoTokenized

Erfan2001

2023-06-25T11:43:35Z

109

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-24T22:00:23Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: xxx results: [] ---  # xxx This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6856 - Accuracy: 0.7758 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7996 | 1.0 | 4284 | 0.7921 | 0.7287 | | 0.5539 | 2.0 | 8568 | 0.6856 | 0.7758 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

edfryo/bangkelser

edfryo

2023-06-25T11:39:27Z

0

null

[ "license:bigscience-openrail-m", "region:us" ]

null

2023-05-09T11:58:00Z

--- license: bigscience-openrail-m ---

jondurbin/airoboros-13b-gpt4-1.4-fp16

jondurbin

2023-06-25T11:39:17Z

1,423

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "dataset:jondurbin/airoboros-gpt4-1.4", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-06-22T10:46:42Z

--- license: other datasets: - jondurbin/airoboros-gpt4-1.4 --- float16 version of https://huggingface.co/jondurbin/airoboros-13b-gpt4-1.4

Ryukijano/DialoGPT_med_model

Ryukijano

2023-06-25T11:38:19Z

118

1

transformers

[ "transformers", "pytorch", "safetensors", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-24T12:37:08Z

Hello there , this bot is trained on DialoGTP for an epoch of 45

jiyuanq/falcon-40b-instruct-gptq-128g-act

jiyuanq

2023-06-25T10:35:13Z

14

0

transformers

[ "transformers", "safetensors", "RefinedWeb", "text-generation", "custom_code", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-06-25T08:31:32Z

--- library_name: transformers pipeline_tag: text-generation --- falcon-40b-instruct quantized with GPTQ using the script in https://github.com/huggingface/text-generation-inference/pull/438 - group size: 128 - act order: true - nsamples: 128 - dataset: wikitext2

abhishek-kumar/dreambooth_test

abhishek-kumar

2023-06-25T10:34:42Z

30

0

diffusers

[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-06-24T16:02:54Z

--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - abhishek-kumar/output This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.

Omogo/xlm-roberta-base-finetuned-panx-de

Omogo

2023-06-25T10:27:58Z

124

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-06-25T07:39:34Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8602627537962806 ---  # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1355 - F1: 0.8603 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2574 | 1.0 | 525 | 0.1627 | 0.8221 | | 0.1295 | 2.0 | 1050 | 0.1435 | 0.8467 | | 0.0815 | 3.0 | 1575 | 0.1355 | 0.8603 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

TheBloke/orca_mini_3B-GGML

TheBloke

2023-06-25T10:25:04Z

0

59

transformers

[ "transformers", "en", "dataset:psmathur/alpaca_orca", "dataset:psmathur/dolly-v2_orca", "dataset:psmathur/WizardLM_Orca", "arxiv:2306.02707", "license:mit", "region:us" ]

null

2023-06-24T22:33:56Z

--- inference: false license: mit language: - en library_name: transformers datasets: - psmathur/alpaca_orca - psmathur/dolly-v2_orca - psmathur/WizardLM_Orca ---  <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div>  # Pankaj Mathur's Orca Mini 3B GGML These files are GGML format model files for [Pankaj Mathur's Orca Mini 3B](https://huggingface.co/psmathur/orca_mini_3b). GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as: * [text-generation-webui](https://github.com/oobabooga/text-generation-webui) * [KoboldCpp](https://github.com/LostRuins/koboldcpp) * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) * [ctransformers](https://github.com/marella/ctransformers) ## Repositories available * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/orca_mini_3B-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/psmathur/orca_mini_3b) ## Prompt template: ``` ### System: You are an AI assistant that follows instruction extremely well. Help as much as you can. ### User: prompt ### Response: ``` or ``` ### System: You are an AI assistant that follows instruction extremely well. Help as much as you can. ### User: prompt ### Input: input ### Response: ```  ## Compatibility ### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0` I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit `2d5db48`. These are guaranteed to be compatbile with any UIs, tools and libraries released since late May. ### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K` These cannot be provided with Open Llama 3B models at this time, due to an issue in llama.cpp. This is being worked on in the llama.cpp repo. More issues here: https://github.com/ggerganov/llama.cpp/issues/1919 Refer to the Provided Files table below to see what files use which methods, and how.  ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | orca-mini-3b.ggmlv3.q4_0.bin | q4_0 | 4 | 1.93 GB | 4.43 GB | Original llama.cpp quant method, 4-bit. | | orca-mini-3b.ggmlv3.q4_1.bin | q4_1 | 4 | 2.14 GB | 4.64 GB | Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. | | orca-mini-3b.ggmlv3.q5_0.bin | q5_0 | 5 | 2.36 GB | 4.86 GB | Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. | | orca-mini-3b.ggmlv3.q5_1.bin | q5_1 | 5 | 2.57 GB | 5.07 GB | Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. | | orca-mini-3b.ggmlv3.q8_0.bin | q8_0 | 8 | 3.64 GB | 6.14 GB | Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `llama.cpp` I use the following command line; adjust for your tastes and needs: ``` ./main -t 10 -ngl 32 -m orca-mini-3b.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### System:\nYou are an story writing assistant who writes very long, detailed and interesting stories\n\n### User:\nWrite a story about llamas\n\n### Input:\n{input}\n\n### Response:\n" ``` If you're able to use full GPU offloading, you should use `-t 1` to get best performance. If not able to fully offload to GPU, you should use more cores. Change `-t 10` to the number of physical CPU cores you have, or a lower number depending on what gives best performance. Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).  ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov. **Patreon special mentions**: Pyrater, WelcomeToTheClub, Kalila, Mano Prime, Trenton Dambrowitz, Spiking Neurons AB, Pierre Kircher, Fen Risland, Kevin Schuppel, Luke, Rainer Wilmers, vamX, Gabriel Puliatti, Alex , Karl Bernard, Ajan Kanaga, Talal Aujan, Space Cruiser, ya boyyy, biorpg, Johann-Peter Hartmann, Asp the Wyvern, Ai Maven, Ghost , Preetika Verma, Nikolai Manek, trip7s trip, John Detwiler, Fred von Graf, Artur Olbinski, subjectnull, John Villwock, Junyu Yang, Rod A, Lone Striker, Chris McCloskey, Iucharbius , Matthew Berman, Illia Dulskyi, Khalefa Al-Ahmad, Imad Khwaja, chris gileta, Willem Michiel, Greatston Gnanesh, Derek Yates, K, Alps Aficionado, Oscar Rangel, David Flickinger, Luke Pendergrass, Deep Realms, Eugene Pentland, Cory Kujawski, terasurfer , Jonathan Leane, senxiiz, Joseph William Delisle, Sean Connelly, webtim, zynix , Nathan LeClaire. Thank you to all my generous patrons and donaters!  # Original model card: Pankaj Mathur's Orca Mini 3B # orca_mini_3b An [OpenLLaMa-3B model](https://github.com/openlm-research/open_llama) model trained on explain tuned datasets, created using Instructions and Input from WizardLM, Alpaca & Dolly-V2 datasets and applying Orca Research Paper dataset construction approaches. # Dataset We build explain tuned [WizardLM dataset ~70K](https://github.com/nlpxucan/WizardLM), [Alpaca dataset ~52K](https://crfm.stanford.edu/2023/03/13/alpaca.html) & [Dolly-V2 dataset ~15K](https://github.com/databrickslabs/dolly) created using approaches from [Orca Research Paper](https://arxiv.org/abs/2306.02707). We leverage all of the 15 system instructions provided in Orca Research Paper. to generate custom datasets, in contrast to vanilla instruction tuning approaches used by original datasets. This helps student model aka this model to learn ***thought*** process from teacher model, which is ChatGPT (gpt-3.5-turbo-0301 version). Please see below example usage how the **System** prompt is added before each **instruction**. # Training The training configurations are provided in the table below. The training takes on 8x A100(80G) GPUs and lasts for around 4 Hours for cost of $48 using [Lambda Labs](https://lambdalabs.com) We used DeepSpeed with fully sharded data parallelism, also know as [ZeRO stage 3](https://engineering.fb.com/2021/07/15/open-source/fsdp/) by writing our own fine tunning scripts plus leveraging some of the model training code provided by amazing [OpenAlpaca repo](https://github.com/yxuansu/OpenAlpaca) Here are some of params used during training: ||| |:-------------:|:-------------:| |*batch_size*|64| |*train_micro_batch_size_per_gpu*|4| |*gradient_accumulation_steps*|2| |*Learning rate*|2e-5| |*Max length*|1024| |*Epochs*|3| |*Optimizer*|AdamW| # Example Usage Below shows an example on how to use this model ```python import torch from transformers import LlamaForCausalLM, LlamaTokenizer # Hugging Face model_path model_path = 'psmathur/orca_mini_3b' tokenizer = LlamaTokenizer.from_pretrained(model_path) model = LlamaForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map='auto', ) #generate text function def generate_text(system, instruction, input=None): if input: prompt = f"### System:\n{system}\n\n### User:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n" else: prompt = f"### System:\n{system}\n\n### User:\n{instruction}\n\n### Response:\n" tokens = tokenizer.encode(prompt) tokens = torch.LongTensor(tokens).unsqueeze(0) tokens = tokens.to('cuda') instance = {'input_ids': tokens,'top_p': 1.0, 'temperature':0.7, 'generate_len': 1024, 'top_k': 50} length = len(tokens[0]) with torch.no_grad(): rest = model.generate( input_ids=tokens, max_length=length+instance['generate_len'], use_cache=True, do_sample=True, top_p=instance['top_p'], temperature=instance['temperature'], top_k=instance['top_k'] ) output = rest[0][length:] string = tokenizer.decode(output, skip_special_tokens=True) return f'[!] Response: {string}' # Sample Test Instruction Used by Youtuber Sam Witteveen https://www.youtube.com/@samwitteveenai system = 'You are an AI assistant that follows instruction extremely well. Help as much as you can.' instruction = 'Write a letter to Sam Altman, CEO of OpenAI, requesting him to convert GPT4 a private model by OpenAI to an open source project' print(generate_text(system, instruction)) ``` ``` [!] Response: Dear Sam Altman, I am writing to request that you convert the GPT4 private model developed by OpenAI to an open source project. As a user of OpenAI, I have been waiting for the day when I can use the advanced natural language processing capabilities of GPT4 in a more open and accessible way. While OpenAI has made significant progress in developing AI applications, it has primarily focused on building private models that are not accessible to the general public. However, with the recent release of GPT-3, there is a growing demand for more open and accessible AI tools. Converting GPT4 to an open source project would allow for greater transparency, collaboration, and innovation. It would also help to build trust in the technology and ensure that it is used ethically and responsibly. I urge you to consider converting GPT4 to an open source project. This would be a significant contribution to the AI community and would help to create a more open and accessible future. Thank you for your consideration. Sincerely, [Your Name] ``` **P.S. I am #opentowork and #collaboration, if you can help, please reach out to me at psmathur.public@gmail.com** Next Goals: 1) Try more data like actually using FLAN-v2, just like Orka Research Paper (I am open for suggestions) 2) Provide more options for Text generation UI. (may be https://github.com/oobabooga/text-generation-webui) 3) Provide 4bit GGML/GPTQ quantized model (may be [TheBloke](https://huggingface.co/TheBloke) can help here) Limitations & Biases: This model can produce factually incorrect output, and should not be relied on to produce factually accurate information. This model was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Disclaimer: The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes. Citiation: If you found wizardlm_alpaca_dolly_orca_open_llama_3b useful in your research or applications, please kindly cite using the following BibTeX: ``` @misc{wizardlm_alpaca_dolly_orca_open_llama_3b, author = {Pankaj Mathur}, title = {wizardlm_alpaca_dolly_orca_open_llama_3b: An explain tuned OpenLLaMA-3b model on custom wizardlm, alpaca, & dolly datasets}, year = {2023}, publisher = {GitHub, HuggingFace}, journal = {GitHub repository, HuggingFace repository}, howpublished = {\url{https://github.com/pankajarm/wizardlm_alpaca_dolly_orca_open_llama_3b}, \url{https://https://huggingface.co/psmathur/wizardlm_alpaca_dolly_orca_open_llama_3b}}, } ``` ``` @software{openlm2023openllama, author = {Xinyang Geng and Hao Liu}, title = {OpenLLaMA: An Open Reproduction of LLaMA}, month = May, year = 2023, url = {https://github.com/openlm-research/open_llama} } ``` ``` @misc{openalpaca, author = {Yixuan Su and Tian Lan and Deng Cai}, title = {OpenAlpaca: A Fully Open-Source Instruction-Following Model Based On OpenLLaMA}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/yxuansu/OpenAlpaca}}, } ``` ``` @misc{alpaca, author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto }, title = {Stanford Alpaca: An Instruction-following LLaMA model}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}}, } ```

Sp1786/mutliclass-sentiment-analysis-bert

Sp1786

2023-06-25T10:22:55Z

4

0

transformers

[ "transformers", "bert", "code", "text-classification", "en", "dataset:Sp1786/multiclass-sentiment-analysis-dataset", "license:apache-2.0", "endpoints_compatible", "region:us" ]

text-classification

2023-06-21T11:23:59Z

--- license: apache-2.0 datasets: - Sp1786/multiclass-sentiment-analysis-dataset language: - en metrics: - bleu - sacrebleu library_name: transformers pipeline_tag: text-classification tags: - code ---

kbondar17/test-trainer

kbondar17

2023-06-25T10:12:41Z

103

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-25T10:06:32Z

--- license: mit tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: test-trainer results: [] ---  # test-trainer This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4009 - F1: 0.6363 - Roc Auc: 0.7682 - Accuracy: 0.6079 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| | No log | 1.0 | 125 | 0.2975 | 0.5710 | 0.7129 | 0.4693 | | No log | 2.0 | 250 | 0.3742 | 0.6226 | 0.7621 | 0.6013 | | No log | 3.0 | 375 | 0.4009 | 0.6363 | 0.7682 | 0.6079 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

dhruvil237/userutterance_classification_verplus

dhruvil237

2023-06-25T10:05:26Z

13

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "doi:10.57967/hf/0811", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-05T12:20:52Z

--- license: mit tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: userutterance_classification_verplus results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos config: plus split: validation args: plus metrics: - name: Accuracy type: accuracy value: 0.9619354838709677 ---  # userutterance_classification_verplus This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.2270 - Accuracy: 0.9619 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 5.0219 | 0.21 | 200 | 4.9813 | 0.0077 | | 4.8915 | 0.42 | 400 | 4.5741 | 0.1155 | | 4.2736 | 0.63 | 600 | 3.5359 | 0.4719 | | 3.2701 | 0.84 | 800 | 2.4291 | 0.7429 | | 2.3578 | 1.05 | 1000 | 1.5793 | 0.8413 | | 1.5695 | 1.26 | 1200 | 1.0029 | 0.8994 | | 1.0412 | 1.47 | 1400 | 0.6475 | 0.9187 | | 0.7034 | 1.68 | 1600 | 0.4439 | 0.9303 | | 0.501 | 1.89 | 1800 | 0.3400 | 0.9381 | | 0.3187 | 2.1 | 2000 | 0.2793 | 0.9439 | | 0.2185 | 2.31 | 2200 | 0.2538 | 0.9490 | | 0.1669 | 2.52 | 2400 | 0.2210 | 0.9523 | | 0.1081 | 2.73 | 2600 | 0.2225 | 0.9519 | | 0.1004 | 2.94 | 2800 | 0.2136 | 0.9555 | | 0.0665 | 3.14 | 3000 | 0.2078 | 0.9561 | | 0.0509 | 3.35 | 3200 | 0.2155 | 0.9568 | | 0.05 | 3.56 | 3400 | 0.2107 | 0.9581 | | 0.0527 | 3.77 | 3600 | 0.2171 | 0.9568 | | 0.0447 | 3.98 | 3800 | 0.2128 | 0.9590 | | 0.0259 | 4.19 | 4000 | 0.2099 | 0.9587 | | 0.0279 | 4.4 | 4200 | 0.2179 | 0.9577 | | 0.0176 | 4.61 | 4400 | 0.2191 | 0.9574 | | 0.0288 | 4.82 | 4600 | 0.2216 | 0.9590 | | 0.0328 | 5.03 | 4800 | 0.2237 | 0.9606 | | 0.0154 | 5.24 | 5000 | 0.2241 | 0.9616 | | 0.0157 | 5.45 | 5200 | 0.2265 | 0.9603 | | 0.023 | 5.66 | 5400 | 0.2276 | 0.9613 | | 0.0178 | 5.87 | 5600 | 0.2270 | 0.9619 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

mrizalf7/xlm-r-qa-squad-retrained

mrizalf7

2023-06-25T09:57:29Z

102

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "question-answering", "generated_from_trainer", "license:mit", "endpoints_compatible", "region:us" ]

question-answering

2023-06-13T19:17:39Z

--- license: mit tags: - generated_from_trainer model-index: - name: xlm-roberta-finetuned-small-squad-indonesian-rizal-4-2 results: [] ---  # xlm-roberta-finetuned-small-squad-indonesian-rizal-4-2 This model is a fine-tuned version of [mrizalf7/xlm-roberta-finetuned-small-squad-indonesian-rizal-4](https://huggingface.co/mrizalf7/xlm-roberta-finetuned-small-squad-indonesian-rizal-4) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.4152 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 1 | 6.1326 | | No log | 2.0 | 2 | 6.1326 | | No log | 3.0 | 3 | 5.4152 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

lucasbertola/ppo-LunarLander-v2

lucasbertola

2023-06-25T09:29:20Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-06-19T11:40:03Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 295.14 +/- 14.94 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

sd-concepts-library/pokemon-raichu-sd-model

sd-concepts-library

2023-06-25T09:26:29Z

0

null

[ "base_model:stabilityai/stable-diffusion-2", "base_model:finetune:stabilityai/stable-diffusion-2", "license:mit", "region:us" ]

null

2023-06-25T09:26:28Z

--- license: mit base_model: stabilityai/stable-diffusion-2 --- ### Pokemon Raichu - SD model on Stable Diffusion This is the `<cat-toy>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<cat-toy> 0](https://huggingface.co/sd-concepts-library/pokemon-raichu-sd-model/resolve/main/concept_images/0.jpeg) ![<cat-toy> 1](https://huggingface.co/sd-concepts-library/pokemon-raichu-sd-model/resolve/main/concept_images/1.jpeg) ![<cat-toy> 2](https://huggingface.co/sd-concepts-library/pokemon-raichu-sd-model/resolve/main/concept_images/2.jpeg) ![<cat-toy> 3](https://huggingface.co/sd-concepts-library/pokemon-raichu-sd-model/resolve/main/concept_images/3.jpeg)

ahishamm/vit-base-HAM-10000-sharpened

ahishamm

2023-06-25T09:17:26Z

190

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-06-25T08:42:48Z

--- license: apache-2.0 tags: - image-classification - generated_from_trainer metrics: - accuracy - recall - f1 - precision model-index: - name: vit-base-HAM-10000-sharpened results: [] ---  # vit-base-HAM-10000-sharpened This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the ahishamm/HAM_db_sharpened dataset. It achieves the following results on the evaluation set: - Loss: 0.4392 - Accuracy: 0.8529 - Recall: 0.8529 - F1: 0.8529 - Precision: 0.8529 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| | 0.7303 | 0.2 | 100 | 0.7828 | 0.7197 | 0.7197 | 0.7197 | 0.7197 | | 0.7198 | 0.4 | 200 | 0.7519 | 0.7377 | 0.7377 | 0.7377 | 0.7377 | | 0.7519 | 0.6 | 300 | 0.7125 | 0.7541 | 0.7541 | 0.7541 | 0.7541 | | 0.6657 | 0.8 | 400 | 0.6623 | 0.7571 | 0.7571 | 0.7571 | 0.7571 | | 0.5896 | 1.0 | 500 | 0.5964 | 0.7835 | 0.7835 | 0.7835 | 0.7835 | | 0.515 | 1.2 | 600 | 0.5745 | 0.8015 | 0.8015 | 0.8015 | 0.8015 | | 0.4318 | 1.4 | 700 | 0.5061 | 0.8200 | 0.8200 | 0.8200 | 0.8200 | | 0.4299 | 1.6 | 800 | 0.5239 | 0.8075 | 0.8075 | 0.8075 | 0.8075 | | 0.4793 | 1.8 | 900 | 0.5366 | 0.8125 | 0.8125 | 0.8125 | 0.8125 | | 0.4202 | 2.0 | 1000 | 0.4882 | 0.8244 | 0.8244 | 0.8244 | 0.8244 | | 0.2105 | 2.2 | 1100 | 0.5330 | 0.8234 | 0.8234 | 0.8234 | 0.8234 | | 0.2597 | 2.4 | 1200 | 0.4604 | 0.8369 | 0.8369 | 0.8369 | 0.8369 | | 0.2261 | 2.59 | 1300 | 0.4893 | 0.8409 | 0.8409 | 0.8409 | 0.8409 | | 0.1853 | 2.79 | 1400 | 0.4793 | 0.8494 | 0.8494 | 0.8494 | 0.8494 | | 0.1739 | 2.99 | 1500 | 0.4392 | 0.8529 | 0.8529 | 0.8529 | 0.8529 | | 0.0629 | 3.19 | 1600 | 0.4941 | 0.8584 | 0.8584 | 0.8584 | 0.8584 | | 0.0802 | 3.39 | 1700 | 0.4974 | 0.8613 | 0.8613 | 0.8613 | 0.8613 | | 0.0712 | 3.59 | 1800 | 0.5416 | 0.8594 | 0.8594 | 0.8594 | 0.8594 | | 0.0365 | 3.79 | 1900 | 0.5318 | 0.8574 | 0.8574 | 0.8574 | 0.8574 | | 0.0591 | 3.99 | 2000 | 0.5344 | 0.8574 | 0.8574 | 0.8574 | 0.8574 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

kchen621/ppo-LunarLander-v2

kchen621

2023-06-25T09:13:58Z

1

0

transformers

[ "transformers", "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "endpoints_compatible", "region:us" ]

reinforcement-learning

2023-06-19T19:22:36Z

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -169.25 +/- 80.22 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo_lun' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'kchen621/ppo-LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```

Ellbendls/Pixelcopter-PLE-v0

Ellbendls

2023-06-25T09:09:39Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-06-23T12:37:16Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 62.70 +/- 42.68 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

anas21/keras-dummy-functional-demo

anas21

2023-06-25T09:07:24Z

0

keras

[ "keras", "tf-keras", "region:us" ]

null

2023-06-24T23:19:07Z

Shridipta-06/LunarLander-v2_unit8part1

Shridipta-06

2023-06-25T08:50:28Z

0

null

[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]

reinforcement-learning

2023-06-25T08:46:05Z

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -128.49 +/- 35.10 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'Shridipta-06/LunarLander-v2_unit8part1' 'batch_size': 512 'minibatch_size': 128} ```

RoundtTble/dinov2_vits14_onnx

RoundtTble

2023-06-25T08:20:24Z

0

null

[ "onnx", "region:us" ]

null

2023-06-24T07:10:50Z

# dinov2_vits14 ## ONNX Model Check this [PR](https://github.com/facebookresearch/dinov2/pull/129). ## Run Run triton container. ``` make triton ``` ``` docker logs dinov2_vits14_triton ============================= == Triton Inference Server == ============================= NVIDIA Release 23.04 (build 58408265) Triton Server Version 2.33.0 Copyright (c) 2018-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. Various files include modifications (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. This container image and its contents are governed by the NVIDIA Deep Learning Container License. By pulling and using the container, you accept the terms and conditions of this license: https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license WARNING: CUDA Minor Version Compatibility mode ENABLED. Using driver version 525.105.17 which has support for CUDA 12.0. This container was built with CUDA 12.1 and will be run in Minor Version Compatibility mode. CUDA Forward Compatibility is preferred over Minor Version Compatibility for use with this container but was unavailable: [[Forward compatibility was attempted on non supported HW (CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE) cuInit()=804]] See https://docs.nvidia.com/deploy/cuda-compatibility/ for details. I0625 08:05:36.712010 1 pinned_memory_manager.cc:240] Pinned memory pool is created at '0x7f6c46000000' with size 268435456 I0625 08:05:36.712625 1 cuda_memory_manager.cc:105] CUDA memory pool is created on device 0 with size 67108864 I0625 08:05:36.717785 1 model_lifecycle.cc:459] loading: dinov2_vits14:1 I0625 08:05:36.723707 1 onnxruntime.cc:2504] TRITONBACKEND_Initialize: onnxruntime I0625 08:05:36.723725 1 onnxruntime.cc:2514] Triton TRITONBACKEND API version: 1.12 I0625 08:05:36.723731 1 onnxruntime.cc:2520] 'onnxruntime' TRITONBACKEND API version: 1.12 I0625 08:05:36.723735 1 onnxruntime.cc:2550] backend configuration: {"cmdline":{"auto-complete-config":"true","backend-directory":"/opt/tritonserver/backends","min-compute-capability":"6.000000","default-max-batch-size":"4"}} I0625 08:05:36.770311 1 onnxruntime.cc:2608] TRITONBACKEND_ModelInitialize: dinov2_vits14 (version 1) I0625 08:05:36.770781 1 onnxruntime.cc:666] skipping model configuration auto-complete for 'dinov2_vits14': inputs and outputs already specified I0625 08:05:36.771205 1 onnxruntime.cc:2651] TRITONBACKEND_ModelInstanceInitialize: dinov2_vits14_0 (GPU device 0) 2023-06-25 08:05:37.157976034 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 465, index: 122, mask: {125, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.158142138 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 466, index: 123, mask: {62, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.158159030 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 467, index: 124, mask: {126, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.158174259 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 468, index: 125, mask: {63, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.165944431 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 344, index: 1, mask: {1, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.158230084 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 469, index: 126, mask: {127, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.169979079 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 347, index: 4, mask: {66, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.169927531 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 345, index: 2, mask: {65, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.169954703 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 346, index: 3, mask: {2, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.173982388 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 350, index: 7, mask: {4, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.173929448 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 348, index: 5, mask: {3, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.173954065 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 349, index: 6, mask: {67, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.181926759 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 351, index: 8, mask: {68, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.185932583 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 352, index: 9, mask: {5, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.189924821 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 353, index: 10, mask: {69, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.193940975 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 464, index: 121, mask: {61, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.194020786 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 357, index: 14, mask: {71, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.193940915 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 354, index: 11, mask: {6, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.193968147 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 355, index: 12, mask: {70, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.193992072 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 356, index: 13, mask: {7, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.197974211 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 360, index: 17, mask: {9, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.197928554 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 358, index: 15, mask: {8, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.197950686 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 359, index: 16, mask: {72, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.201924259 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 361, index: 18, mask: {73, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.205931957 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 362, index: 19, mask: {10, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.209926179 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 363, index: 20, mask: {74, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.213927705 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 364, index: 21, mask: {11, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.217799496 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 365, index: 22, mask: {75, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.217849460 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 366, index: 23, mask: {12, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.221966294 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 367, index: 24, mask: {76, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.221966304 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 463, index: 120, mask: {124, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.225931100 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 462, index: 119, mask: {60, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.225933645 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 368, index: 25, mask: {13, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.229929350 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 369, index: 26, mask: {77, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.233930445 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 370, index: 27, mask: {14, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.233930525 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 461, index: 118, mask: {123, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.237930518 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 371, index: 28, mask: {78, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.241927085 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 372, index: 29, mask: {15, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.245926977 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 373, index: 30, mask: {79, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.249931199 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 374, index: 31, mask: {16, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.253927515 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 375, index: 32, mask: {80, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.257925694 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 376, index: 33, mask: {17, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.261929715 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 377, index: 34, mask: {81, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.265966397 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 378, index: 35, mask: {18, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.269926725 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 379, index: 36, mask: {82, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.273931337 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 380, index: 37, mask: {19, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.281941021 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 381, index: 38, mask: {83, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.282017776 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 398, index: 55, mask: {28, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.282038465 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 382, index: 39, mask: {20, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.282090914 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 383, index: 40, mask: {84, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.286235010 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 385, index: 42, mask: {85, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.285955121 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 401, index: 58, mask: {93, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.282070957 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 399, index: 56, mask: {92, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.286082321 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 384, index: 41, mask: {21, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.285929422 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 400, index: 57, mask: {29, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.293926803 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 405, index: 62, mask: {95, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.289931018 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 402, index: 59, mask: {30, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.289956767 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 403, index: 60, mask: {94, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.301929004 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 388, index: 45, mask: {23, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.289975973 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 404, index: 61, mask: {31, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.294054945 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 406, index: 63, mask: {32, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.294078880 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 407, index: 64, mask: {96, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.314023441 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 409, index: 66, mask: {97, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.289931068 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 386, index: 43, mask: {22, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.318030297 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 411, index: 68, mask: {98, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.289956797 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 387, index: 44, mask: {86, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.301929014 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 408, index: 65, mask: {33, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.314096058 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 410, index: 67, mask: {34, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.334030890 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 414, index: 71, mask: {36, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.305931271 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 389, index: 46, mask: {87, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.321929038 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 390, index: 47, mask: {24, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.321948134 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 391, index: 48, mask: {88, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.321965006 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 392, index: 49, mask: {25, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.321981437 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 393, index: 50, mask: {89, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.321996396 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 394, index: 51, mask: {26, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322012065 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 395, index: 52, mask: {90, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322026713 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 396, index: 53, mask: {27, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322049907 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 397, index: 54, mask: {91, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322065276 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 460, index: 117, mask: {59, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322080735 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 425, index: 82, mask: {105, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322096315 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 426, index: 83, mask: {42, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322112155 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 427, index: 84, mask: {106, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322127053 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 428, index: 85, mask: {43, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322143324 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 429, index: 86, mask: {107, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322157170 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 430, index: 87, mask: {44, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322173340 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 431, index: 88, mask: {108, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322188569 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 432, index: 89, mask: {45, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322205311 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 433, index: 90, mask: {109, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322219938 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 434, index: 91, mask: {46, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322235177 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 435, index: 92, mask: {110, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322249955 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 436, index: 93, mask: {47, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322267158 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 437, index: 94, mask: {111, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322281345 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 438, index: 95, mask: {48, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322296904 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 439, index: 96, mask: {112, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322312113 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 440, index: 97, mask: {49, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322329005 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 441, index: 98, mask: {113, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322343652 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 442, index: 99, mask: {50, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322359492 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 443, index: 100, mask: {114, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322377907 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 444, index: 101, mask: {51, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322393366 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 445, index: 102, mask: {115, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322408725 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 446, index: 103, mask: {52, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322423233 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 447, index: 104, mask: {116, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322437289 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 448, index: 105, mask: {53, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322453440 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 449, index: 106, mask: {117, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322467697 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 450, index: 107, mask: {54, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322483076 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 451, index: 108, mask: {118, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322496812 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 452, index: 109, mask: {55, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.445929743 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 417, index: 74, mask: {101, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322511880 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 453, index: 110, mask: {119, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322525526 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 454, index: 111, mask: {56, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322541977 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 455, index: 112, mask: {120, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.454013818 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 422, index: 79, mask: {40, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322555663 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 456, index: 113, mask: {57, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.457932126 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 423, index: 80, mask: {104, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322571683 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 457, index: 114, mask: {121, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.322585920 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 458, index: 115, mask: {58, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.318158029 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 412, index: 69, mask: {35, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.334163851 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 415, index: 72, mask: {100, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.341919085 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 416, index: 73, mask: {37, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.323408365 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 413, index: 70, mask: {99, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.453923387 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 418, index: 75, mask: {38, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.453947493 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 419, index: 76, mask: {102, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.453965727 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 420, index: 77, mask: {39, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.453991656 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 421, index: 78, mask: {103, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.458087059 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 424, index: 81, mask: {41, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:37.585007204 [E:onnxruntime:log, env.cc:251 ThreadMain] pthread_setaffinity_np failed for thread: 459, index: 116, mask: {122, }, error code: 22 error msg: Invalid argument. Specify the number of threads explicitly so the affinity is not set. 2023-06-25 08:05:38.570069572 [W:onnxruntime:, session_state.cc:1136 VerifyEachNodeIsAssignedToAnEp] Some nodes were not assigned to the preferred execution providers which may or may not have an negative impact on performance. e.g. ORT explicitly assigns shape related ops to CPU to improve perf. 2023-06-25 08:05:38.570088387 [W:onnxruntime:, session_state.cc:1138 VerifyEachNodeIsAssignedToAnEp] Rerunning with verbose output on a non-minimal build will show node assignments. I0625 08:05:39.975559 1 model_lifecycle.cc:694] successfully loaded 'dinov2_vits14' version 1 I0625 08:05:39.975625 1 server.cc:583] +------------------+------+ | Repository Agent | Path | +------------------+------+ +------------------+------+ I0625 08:05:39.975662 1 server.cc:610] +-------------+-----------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Backend | Path | Config | +-------------+-----------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------+ | onnxruntime | /opt/tritonserver/backends/onnxruntime/libtriton_onnxruntime.so | {"cmdline":{"auto-complete-config":"true","backend-directory":"/opt/tritonserver/backends","min-compute-capability":"6.000000","default-max-batch-size":"4"}} | +-------------+-----------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------+ I0625 08:05:39.975683 1 server.cc:653] +---------------+---------+--------+ | Model | Version | Status | +---------------+---------+--------+ | dinov2_vits14 | 1 | READY | +---------------+---------+--------+ I0625 08:05:39.991510 1 metrics.cc:808] Collecting metrics for GPU 0: NVIDIA GeForce RTX 3090 I0625 08:05:39.992145 1 metrics.cc:701] Collecting CPU metrics I0625 08:05:39.992360 1 tritonserver.cc:2387] +----------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Option | Value | +----------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | server_id | triton | | server_version | 2.33.0 | | server_extensions | classification sequence model_repository model_repository(unload_dependents) schedule_policy model_configuration system_shared_memory cuda_shared_memory binary_tensor_data parameters statistics trace logging | | model_repository_path[0] | /models | | model_control_mode | MODE_NONE | | strict_model_config | 0 | | rate_limit | OFF | | pinned_memory_pool_byte_size | 268435456 | | cuda_memory_pool_byte_size{0} | 67108864 | | min_supported_compute_capability | 6.0 | | strict_readiness | 1 | | exit_timeout | 30 | | cache_enabled | 0 | +----------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ I0625 08:05:39.993603 1 grpc_server.cc:2450] Started GRPCInferenceService at 0.0.0.0:8001 I0625 08:05:39.993771 1 http_server.cc:3555] Started HTTPService at 0.0.0.0:8000 I0625 08:05:40.034678 1 http_server.cc:185] Started Metrics Service at 0.0.0.0:8002 ``` Perf analyzer `dinov2_vits14` ``` make perf ``` ``` docker run --gpus all --rm -it --net host nvcr.io/nvidia/tritonserver:23.04-py3-sdk perf_analyzer -m dinov2_vits14 --percentile=95 -i grpc -u 0.0.0.0:8001 --concurrency-range 16:16 --shape input:3,280,280 ================================= == Triton Inference Server SDK == ================================= NVIDIA Release 23.04 (build 58408269) Copyright (c) 2018-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. Various files include modifications (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved. This container image and its contents are governed by the NVIDIA Deep Learning Container License. By pulling and using the container, you accept the terms and conditions of this license: https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license WARNING: CUDA Minor Version Compatibility mode ENABLED. Using driver version 525.105.17 which has support for CUDA 12.0. This container was built with CUDA 12.1 and will be run in Minor Version Compatibility mode. CUDA Forward Compatibility is preferred over Minor Version Compatibility for use with this container but was unavailable: [[Forward compatibility was attempted on non supported HW (CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE) cuInit()=804]] See https://docs.nvidia.com/deploy/cuda-compatibility/ for details. *** Measurement Settings *** Batch size: 1 Service Kind: Triton Using "time_windows" mode for stabilization Measurement window: 5000 msec Latency limit: 0 msec Concurrency limit: 16 concurrent requests Using synchronous calls for inference Stabilizing using p95 latency Request concurrency: 16 Client: Request count: 9403 Throughput: 522.33 infer/sec p50 latency: 30482 usec p90 latency: 32100 usec p95 latency: 32564 usec p99 latency: 34203 usec Avg gRPC time: 30589 usec ((un)marshal request/response 93 usec + response wait 30496 usec) Server: Inference count: 9403 Execution count: 1177 Successful request count: 9403 Avg request latency: 24295 usec (overhead 220 usec + queue 9042 usec + compute input 1511 usec + compute infer 13485 usec + compute output 37 usec) Inferences/Second vs. Client p95 Batch Latency Concurrency: 16, throughput: 522.33 infer/sec, latency 32564 usec ```

Neupane9Sujal/Text_Summarization

Neupane9Sujal

2023-06-25T07:51:58Z

108

0

transformers

[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "code", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-05-25T10:28:20Z

--- language: - en metrics: - rouge tags: - code ---

Lajonbot/Amazon-LightGPT-pl-qlora

Lajonbot

2023-06-25T07:40:56Z

0

null

[ "tensorboard", "pl", "dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish", "license:openrail", "region:us" ]

null

2023-05-29T06:22:37Z

--- license: openrail datasets: - Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish language: - pl ---

Lajonbot/lamini-instruct-tuned-3b-pl-lora

Lajonbot

2023-06-25T07:37:46Z

0

null

[ "pl", "dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish", "license:openrail", "region:us" ]

null

2023-06-15T06:08:17Z

--- license: openrail datasets: - Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish language: - pl ---

Lajonbot/stablelm-base-alpha-3b-instruct-pl-lora

Lajonbot

2023-06-25T07:37:23Z

0

null

[ "tensorboard", "pl", "dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish", "license:openrail", "region:us" ]

null

2023-06-15T06:13:44Z

--- license: openrail datasets: - Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish language: - pl ---

Lajonbot/polish-gpt2-small-instruct

Lajonbot

2023-06-25T07:36:40Z

114

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "pl", "dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish", "license:openrail", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-06-20T19:33:30Z

--- license: openrail datasets: - Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish language: - pl ---

Davlan/bert-base-multilingual-cased-finetuned-swahili

Davlan

2023-06-25T07:32:51Z

568

3

transformers

[ "transformers", "pytorch", "tf", "safetensors", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-02T23:29:04Z

Hugging Face's logo --- language: ha datasets: --- # bert-base-multilingual-cased-finetuned-swahili ## Model description **bert-base-multilingual-cased-finetuned-swahili** is a **Swahili BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Swahili language texts. It provides **better performance** than the multilingual BERT on text classification and named entity recognition datasets. Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Swahili corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-swahili') >>> unmasker("Jumatatu, Bwana Kagame alielezea shirika la France24 huko [MASK] kwamba "hakuna uhalifu ulitendwa") [{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Paris kwamba hakuna uhalifu ulitendwa', 'score': 0.31642526388168335, 'token': 10728, 'token_str': 'Paris'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Rwanda kwamba hakuna uhalifu ulitendwa', 'score': 0.15753623843193054, 'token': 57557, 'token_str': 'Rwanda'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Burundi kwamba hakuna uhalifu ulitendwa', 'score': 0.07211585342884064, 'token': 57824, 'token_str': 'Burundi'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko France kwamba hakuna uhalifu ulitendwa', 'score': 0.029844321310520172, 'token': 10688, 'token_str': 'France'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Senegal kwamba hakuna uhalifu ulitendwa', 'score': 0.0265930388122797, 'token': 38052, 'token_str': 'Senegal'}] ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on [Swahili CC-100](http://data.statmt.org/cc-100/) ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| mBERT F1 | sw_bert F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 86.80 | 89.36 ### BibTeX entry and citation info By David Adelani ``` ```

Davlan/xlm-roberta-base-finetuned-swahili

Davlan

2023-06-25T07:31:57Z

119

3

transformers

[ "transformers", "pytorch", "safetensors", "xlm-roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-02T23:29:04Z

Hugging Face's logo --- language: sw datasets: --- # xlm-roberta-base-finetuned-swahili ## Model description **xlm-roberta-base-finetuned-swahili** is a **Swahili RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Swahili language texts. It provides **better performance** than the XLM-RoBERTa on text classification and named entity recognition datasets. Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Swahili corpus. ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for masked token prediction. ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-swahili') >>> unmasker("Jumatatu, Bwana Kagame alielezea shirika la France24 huko <mask> kwamba hakuna uhalifu ulitendwa") [{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Ufaransa kwamba hakuna uhalifu ulitendwa', 'score': 0.5077782273292542, 'token': 190096, 'token_str': 'Ufaransa'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Paris kwamba hakuna uhalifu ulitendwa', 'score': 0.3657738268375397, 'token': 7270, 'token_str': 'Paris'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Gabon kwamba hakuna uhalifu ulitendwa', 'score': 0.01592041552066803, 'token': 176392, 'token_str': 'Gabon'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko France kwamba hakuna uhalifu ulitendwa', 'score': 0.010881908237934113, 'token': 9942, 'token_str': 'France'}, {'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Marseille kwamba hakuna uhalifu ulitendwa', 'score': 0.009554869495332241, 'token': 185918, 'token_str': 'Marseille'}] ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on [Swahili CC-100](http://data.statmt.org/cc-100/) ## Training procedure This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (F-score, average over 5 runs) Dataset| XLM-R F1 | sw_roberta F1 -|-|- [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 87.55 | 89.46 ### BibTeX entry and citation info By David Adelani ``` ```

AdShenoy/Bart-samsum-fastai

AdShenoy

2023-06-25T07:20:59Z

0

fastai

[ "fastai", "region:us" ]

null

2023-06-24T06:53:20Z

--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed

Davlan/xlm-roberta-base-finetuned-xhosa

Davlan

2023-06-25T07:14:21Z

171

1

transformers

[ "transformers", "pytorch", "safetensors", "xlm-roberta", "fill-mask", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-02T23:29:04Z

--- license: apache-2.0 ---

Davlan/byt5-base-eng-yor-mt

Davlan

2023-06-25T07:13:35Z

147

2

transformers

[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "arxiv:2103.08647", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-02T23:29:04Z

Hugging Face's logo --- language: - yo - en datasets: - JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) --- # byt5-base-eng-yor-mt ## Model description **byt5-base-eng-yor-mt** is a **machine translation** model from English language to Yorùbá language based on a fine-tuned byt5-base model. It establishes a **strong baseline** for automatically translating texts from English to Yorùbá. Specifically, this model is a *byt5-base* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) #### Limitations and bias This model is limited by its training dataset. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset ## Training procedure This model was trained on NVIDIA V100 GPU ## Eval results on Test set (BLEU score) Fine-tuning byt5-base achieves **12.23 BLEU** on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 9.82 ### BibTeX entry and citation info By David Adelani ``` ```

masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0

masakhane

2023-06-25T07:13:23Z

325

8

transformers

[ "transformers", "pytorch", "safetensors", "xlm-roberta", "token-classification", "am", "bm", "obj", "ee", "fon", "ha", "ig", "rw", "lg", "luo", "mos", "ny", "pcm", "sn", "sw", "tn", "tw", "wo", "xh", "yo", "zu", "multilingual", "dataset:masakhaner2", "arxiv:2103.11811", "license:afl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-12-15T12:52:06Z

--- license: afl-3.0 language: - am - bm - obj - ee - fon - ha - ig - rw - lg - luo - mos - ny - pcm - sn - sw - tn - tw - wo - xh - yo - zu - multilingual datasets: - masakhaner2 --- # masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0 ## Model description **masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0** is a **Named Entity Recognition (NER)** model for 21 African languages. Specifically, this model is a *Davlan/afro-xlmr-large* model that was fine-tuned on an aggregation of African language datasets obtained from two versions of MasakhaNER dataset i.e. [MasakhaNER 1.0](https://huggingface.co/datasets/masakhaner) and [MasakhaNER 2.0](https://huggingface.co/datasets/masakhane/masakhaner2). The languages covered are: - Amharic (Amharic) - Bambara (bam) - Ghomala (bbj) - Ewe (ewe) - Fon (fon) - Hausa (hau) - Igbo (ibo) - Kinyarwanda (kin) - Luganda (lug) - Dholuo (luo) -Mossi (mos) - Chichewa (nya) - Nigerian Pidgin - chShona (sna) - Kiswahili (swą) - Setswana (tsn) - Twi (twi) - Wolof (wol) - isiXhosa (xho) - Yorùbá (yor) - isiZulu (zul) It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organization (ORG), and person (PER). ## Intended uses & limitations #### How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0") model = AutoModelForTokenClassification.from_pretrained("masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria" ner_results = nlp(example) print(ner_results) ``` ## Eval results on MasakhaNER (F-score) Model evaluated on MasakhaNER 1.0 and MasakhaNER 2.0 test sets language| MasakhaNER 1.0 | MasakhaNER 2.0 -|-|- amh |80.5| bam || 83.1 bbj || 76.6 ewe || 89.6 fon || 83.8 hau |90.3| 87.5 ibo |89.5| 93.5 kin |82.0| 87.6 lug |87.1| 89.7 luo |80.8| 82.5 mos || 75.5 nya || 92.7 pcm |91.1| 90.9 sna || 96.5 swa |88.5| 93.4 tsn || 90.3 twi || 81.3 wol |72.7| 87.3 xho || 90.0 yor |88.1| 90.5 zul || 91.3 avg |**85.1**| **87.7** #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data This model was fine-tuned on the aggregation of [MasakhaNER 1.0](https://huggingface.co/datasets/masakhaner) and [MasakhaNER 2.0](https://huggingface.co/datasets/masakhane/masakhaner2) datasets The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Abbreviation|Description -|- O|Outside of a named entity B-DATE |Beginning of a DATE entity right after another DATE entity I-DATE |DATE entity B-PER |Beginning of a person’s name right after another person’s name I-PER |Person’s name B-ORG |Beginning of an organisation right after another organisation I-ORG |Organisation B-LOC |Beginning of a location right after another location I-LOC |Location ## Training procedure This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the [original MasakhaNER paper](https://arxiv.org/abs/2103.11811) which trained & evaluated the model on MasakhaNER corpus. ### BibTeX entry and citation info ``` @article{Adelani2022MasakhaNER2A, title={MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition}, author={David Ifeoluwa Adelani and Graham Neubig and Sebastian Ruder and Shruti Rijhwani and Michael Beukman and Chester Palen-Michel and Constantine Lignos and Jesujoba Oluwadara Alabi and Shamsuddeen Hassan Muhammad and Peter Nabende and Cheikh M. Bamba Dione and Andiswa Bukula and Rooweither Mabuya and Bonaventure F. P. Dossou and Blessing K. Sibanda and Happy Buzaaba and Jonathan Mukiibi and Godson Kalipe and Derguene Mbaye and Amelia Taylor and Fatoumata Kabore and Chris C. Emezue and Anuoluwapo Aremu and Perez Ogayo and Catherine W. Gitau and Edwin Munkoh-Buabeng and Victoire Memdjokam Koagne and Allahsera Auguste Tapo and Tebogo Macucwa and Vukosi Marivate and Elvis Mboning and Tajuddeen R. Gwadabe and Tosin P. Adewumi and Orevaoghene Ahia and Joyce Nakatumba-Nabende and Neo L. Mokono and Ignatius M Ezeani and Chiamaka Ijeoma Chukwuneke and Mofetoluwa Adeyemi and Gilles Hacheme and Idris Abdulmumin and Odunayo Ogundepo and Oreen Yousuf and Tatiana Moteu Ngoli and Dietrich Klakow}, journal={ArXiv}, year={2022}, volume={abs/2210.12391} } ```

NasimB/gpt2-dp-mod-aochild-10chars

NasimB

2023-06-25T06:53:44Z

6

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-06-25T03:14:38Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-dp-mod-aochild-10chars results: [] ---  # gpt2-dp-mod-aochild-10chars This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.4173 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.7077 | 0.27 | 500 | 5.6423 | | 5.3468 | 0.54 | 1000 | 5.2154 | | 5.0042 | 0.8 | 1500 | 4.9608 | | 4.7637 | 1.07 | 2000 | 4.7969 | | 4.5583 | 1.34 | 2500 | 4.6931 | | 4.4721 | 1.61 | 3000 | 4.5939 | | 4.3855 | 1.88 | 3500 | 4.5049 | | 4.218 | 2.15 | 4000 | 4.4679 | | 4.1202 | 2.41 | 4500 | 4.4175 | | 4.105 | 2.68 | 5000 | 4.3697 | | 4.0733 | 2.95 | 5500 | 4.3257 | | 3.8601 | 3.22 | 6000 | 4.3344 | | 3.8504 | 3.49 | 6500 | 4.3033 | | 3.8507 | 3.76 | 7000 | 4.2759 | | 3.8215 | 4.02 | 7500 | 4.2709 | | 3.5828 | 4.29 | 8000 | 4.2887 | | 3.6183 | 4.56 | 8500 | 4.2711 | | 3.6264 | 4.83 | 9000 | 4.2489 | | 3.5136 | 5.1 | 9500 | 4.2794 | | 3.3547 | 5.36 | 10000 | 4.2895 | | 3.383 | 5.63 | 10500 | 4.2727 | | 3.3982 | 5.9 | 11000 | 4.2594 | | 3.2002 | 6.17 | 11500 | 4.3133 | | 3.1199 | 6.44 | 12000 | 4.3184 | | 3.1483 | 6.71 | 12500 | 4.3123 | | 3.1516 | 6.97 | 13000 | 4.3013 | | 2.9083 | 7.24 | 13500 | 4.3587 | | 2.9076 | 7.51 | 14000 | 4.3641 | | 2.9176 | 7.78 | 14500 | 4.3616 | | 2.8855 | 8.05 | 15000 | 4.3806 | | 2.7292 | 8.32 | 15500 | 4.3978 | | 2.7443 | 8.58 | 16000 | 4.4023 | | 2.7445 | 8.85 | 16500 | 4.4046 | | 2.702 | 9.12 | 17000 | 4.4125 | | 2.6515 | 9.39 | 17500 | 4.4159 | | 2.6552 | 9.66 | 18000 | 4.4170 | | 2.6529 | 9.92 | 18500 | 4.4173 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

teoha/openai-whisper-medium-PeftType.LORA-colab

teoha

2023-06-25T06:51:18Z

2

0

peft

[ "peft", "region:us" ]

null

2023-06-25T06:51:11Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0

zanafi/sentiment_model

zanafi

2023-06-25T06:31:04Z

16

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "dataset:indonlu", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-23T06:53:10Z

--- license: mit tags: - generated_from_trainer datasets: - indonlu metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment_model results: - task: name: Text Classification type: text-classification dataset: name: indonlu type: indonlu config: emot split: validation args: emot metrics: - name: Accuracy type: accuracy value: 0.7363636363636363 - name: Precision type: precision value: 0.7397155596092384 - name: Recall type: recall value: 0.7459489407651173 - name: F1 type: f1 value: 0.741920437379511 ---  # sentiment_model This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the indonlu dataset. It achieves the following results on the evaluation set: - Loss: 0.7788 - Accuracy: 0.7364 - Precision: 0.7397 - Recall: 0.7459 - F1: 0.7419 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.1939 | 1.0 | 221 | 0.8261 | 0.6932 | 0.7203 | 0.7034 | 0.7056 | | 0.6866 | 2.0 | 442 | 0.7925 | 0.725 | 0.7378 | 0.7377 | 0.7346 | | 0.4791 | 3.0 | 663 | 0.7788 | 0.7364 | 0.7397 | 0.7459 | 0.7419 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

sukantan/all-mpnet-base-v2-ftlegal-v3

sukantan

2023-06-25T06:20:52Z

5

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "dataset:sukantan/nyaya-st-training", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2023-06-25T06:20:46Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity datasets: - sukantan/nyaya-st-training --- # sukantan/all-mpnet-base-v2-ftlegal-v3 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sukantan/all-mpnet-base-v2-ftlegal-v3') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sukantan/all-mpnet-base-v2-ftlegal-v3) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 391 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MegaBatchMarginLoss.MegaBatchMarginLoss` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 391, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors

Raizel123/pamelasafitrilora

Raizel123

2023-06-25T04:37:40Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-06-25T04:34:50Z

--- license: creativeml-openrail-m ---

ardhies/CuteAsianFace

ardhies

2023-06-25T04:10:18Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-06-25T04:06:53Z

--- license: creativeml-openrail-m ---

Laurie/baichuan-7b-qlora-moss

Laurie

2023-06-25T04:06:12Z

5

0

peft

[ "peft", "text-generation", "zh", "en", "dataset:fnlp/moss-003-sft-data", "license:apache-2.0", "region:us" ]

text-generation

2023-06-25T03:38:18Z

--- library_name: peft license: apache-2.0 datasets: - fnlp/moss-003-sft-data language: - zh - en pipeline_tag: text-generation --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0.dev0 ## 使用方法 git clone https://huggingface.co/Laurie/baichuan-7b-qlora-moss cd baichuan-7b-qlora-moss python src/web_demo.py \ --model_name_or_path baichuan-inc/baichuan-7B \ --checkpoint_dir .

razaali/swin-tiny-patch4-window7-224-finetuned-eurosat

razaali

2023-06-25T04:00:02Z

211

0

transformers

[ "transformers", "pytorch", "tensorboard", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-06-25T03:25:51Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.977037037037037 ---  # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0662 - Accuracy: 0.9770 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2501 | 1.0 | 190 | 0.1077 | 0.9626 | | 0.1375 | 2.0 | 380 | 0.0892 | 0.9707 | | 0.1324 | 3.0 | 570 | 0.0662 | 0.9770 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

CJacobnriia/spatnzRVC

CJacobnriia

2023-06-25T03:56:17Z

0

null

[ "en", "region:us" ]

null

2023-06-25T01:52:32Z

--- language: - en --- This is an RVC model of spatnz (https://www.youtube.com/channel/UCcNPbOeFo-qM0wpis8Lwdig) ![spatnz.png](https://huggingface.co/CJacobnriia/spatnzRVC/resolve/main/spatnz.png)

FutureMiracle/CGEC-BART-Model

FutureMiracle

2023-06-25T03:55:55Z

0

3

fairseq

[ "fairseq", "BART", "pytorch", "CGEC", "translation", "zh", "license:apache-2.0", "region:us" ]

translation

2023-06-21T08:28:46Z

--- license: apache-2.0 language: - zh library_name: fairseq tags: - BART - pytorch - CGEC metrics: - bleu pipeline_tag: translation --- # 中文语法纠错任务介绍 Task:中文语法纠错任务(Chinese Grammatical Error Correction,CGEC) CGEC任务输入一句中文文本，文本纠错技术对句子中存在拼写、语法、语义等错误进行自动纠正，输出纠正后的文本。 # 中文语法纠错方法主流的方法为seq2seq和seq2edits，常用的中文纠错数据集包括Lang8、NLPCC18和CGED等。 # 模型描述我们采用基于transformer的seq2seq方法建模文本纠错任务。模型选择上，我们使用中文BART作为预训练模型，然后在Lang8和CGED训练数据上进行finetune。在不引入额外资源的情况下，本模型在LANG8测试集上达到了SOTA。 # 模型训练模型训练是基于fairseq库进行训练的。 # 如何使用 step1: 下载fairseq库，并进行安装 step2: 使用interactive.py方法进行推理 python -u ${FAIRSEQ_DIR}/interactive.py $PROCESSED_DIR \ --task syntax-enhanced-translation \ --path ${MODEL_PATH} \ --beam ${BEAM} \ --nbest ${N_BEST} \ -s src \ -t tgt \ --buffer-size 1000 \ --batch-size 32 \ --num-workers 12 \ --log-format tqdm \ --remove-bpe \ --fp16 \ --output_file $OUTPUT_DIR/output.nbest \ <$OUTPUT_DIR/lang8_test.char

blackmount8/open-llama-7b-open-instruct-ct2-float16

blackmount8

2023-06-25T03:49:04Z

9

0

transformers

[ "transformers", "text-generation", "en", "dataset:VMware/open-instruct-v1-oasst-dolly-hhrlhf", "license:cc", "region:us" ]

text-generation

2023-06-24T15:05:27Z

--- inference: false license: cc datasets: - VMware/open-instruct-v1-oasst-dolly-hhrlhf language: - en library_name: transformers pipeline_tag: text-generation --- # blackmount8/open-llama-7B-open-instruct-ct2-float16 Float16 version of [VMware/open-llama-7b-open-instruct](https://huggingface.co/VMware/open-llama-7b-open-instruct), quantized using CTranslate2. ## VMware/open-llama-7B-open-instruct Instruction-tuned version of the fully trained Open LLama 7B model. The model is open for `<b>`COMMERCIAL USE `</b>`. `<br>` `<b>` NOTE `</b>` : The model was trained using the Alpaca prompt template `<b>` NOTE `</b>` : Fast tokenizer results in incorrect encoding, set the ``use_fast = False`` parameter, when instantiating the tokenizer ## License - `<b>`Commercially Viable `</b>` - Instruction dataset, [VMware/open-instruct-v1-oasst-dolly-hhrlhf](https://huggingface.co/datasets/VMware/open-instruct-v1-oasst-dolly-hhrlhf) is under cc-by-sa-3.0 - Language Model, ([openlm-research/open_llama_7b](https://huggingface.co/openlm-research/open_llama_7b)) is under apache-2.0 ## Nomenclature - Model : Open-llama - Model Size: 7B parameters - Dataset: Open-instruct-v1 (oasst, dolly, hhrlhf) ## Use in CTranslate2 ``` import ctranslate2 from transformers import AutoTokenizer model_name = "blackmount8/open-llama-7b-open-instruct-ct2-float16" tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, padding_side="left", truncation_side="left") model = ctranslate2.Generator(model_name, device="auto", compute_type="float16") input_text = ["What is the meaning of stonehenge?", "Hello mate!"] input_ids = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True).input_ids input_tokens = [tokenizer.convert_ids_to_tokens(ele) for ele in input_ids] outputs = model.generate_batch(input_tokens, max_length=128) output_tokens = [ ele.sequences_ids[0] for ele in outputs ] output = tokenizer.batch_decode(output_tokens) print(output) ```

blackmount8/open-llama-13b-open-instruct-ct2-float16

blackmount8

2023-06-25T03:48:21Z

4

0

transformers

[ "transformers", "text-generation", "en", "dataset:VMware/open-instruct-v1-oasst-dolly-hhrlhf", "license:cc", "region:us" ]

text-generation

2023-06-24T16:44:56Z

--- inference: false license: cc datasets: - VMware/open-instruct-v1-oasst-dolly-hhrlhf language: - en library_name: transformers pipeline_tag: text-generation --- # blackmount8/open-llama-13B-open-instruct-ct2-float16 Float16 version of [VMware/open-llama-13b-open-instruct](https://huggingface.co/VMware/open-llama-13b-open-instruct), quantized using CTranslate2. ## VMware/open-llama-13B-open-instruct Instruction-tuned version of the fully trained Open LLama 13B model. The model is open for `<b>`COMMERCIAL USE `</b>`. `<br>` `<b>` NOTE `</b>` : The model was trained using the Alpaca prompt template `<b>` NOTE `</b>` : Fast tokenizer results in incorrect encoding, set the ``use_fast = False`` parameter, when instantiating the tokenizer ## License - `<b>`Commercially Viable `</b>` - Instruction dataset, [VMware/open-instruct-v1-oasst-dolly-hhrlhf](https://huggingface.co/datasets/VMware/open-instruct-v1-oasst-dolly-hhrlhf) is under cc-by-sa-3.0 - Language Model, ([openlm-research/open_llama_13b](https://huggingface.co/openlm-research/open_llama_13b)) is under apache-2.0 ## Nomenclature - Model : Open-llama - Model Size: 13B parameters - Dataset: Open-instruct-v1 (oasst, dolly, hhrlhf) ## Use in CTranslate2 ``` import ctranslate2 from transformers import AutoTokenizer model_name = "blackmount8/open-llama-13b-open-instruct-ct2-float16" tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, padding_side="left", truncation_side="left") model = ctranslate2.Generator(model_name, device="auto", compute_type="float16") input_text = ["What is the meaning of stonehenge?", "Hello mate!"] input_ids = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True).input_ids input_tokens = [tokenizer.convert_ids_to_tokens(ele) for ele in input_ids] outputs = model.generate_batch(input_tokens, max_length=128) output_tokens = [ ele.sequences_ids[0] for ele in outputs ] output = tokenizer.batch_decode(output_tokens) print(output) ```

duyhngoc/Wave2Vec2_OV_Vie

duyhngoc

2023-06-25T03:47:48Z

77

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "vivos", "generated_from_trainer", "dataset:vivos", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-06-21T10:58:36Z

--- license: apache-2.0 tags: - automatic-speech-recognition - vivos - generated_from_trainer datasets: - vivos metrics: - wer model-index: - name: Wave2Vec2_OV_Vie results: [] ---  # Wave2Vec2_OV_Vie This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the VIVOS - NA dataset. It achieves the following results on the evaluation set: - Loss: 3.5894 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | No log | 0.27 | 100 | 3.9210 | 1.0 | | No log | 0.55 | 200 | 3.4375 | 1.0 | | No log | 0.82 | 300 | 3.4356 | 1.0 | | No log | 1.1 | 400 | 3.4045 | 1.0 | | 4.1866 | 1.37 | 500 | 3.4694 | 1.0 | | 4.1866 | 1.65 | 600 | 3.6266 | 1.0 | | 4.1866 | 1.92 | 700 | 3.5694 | 1.0 | | 4.1866 | 2.19 | 800 | 3.5733 | 1.0 | | 4.1866 | 2.47 | 900 | 3.6381 | 1.0 | | 3.4376 | 2.74 | 1000 | 3.6604 | 1.0 | | 3.4376 | 3.02 | 1100 | 3.5868 | 1.0 | | 3.4376 | 3.29 | 1200 | 3.4988 | 1.0 | | 3.4376 | 3.57 | 1300 | 3.5409 | 1.0 | | 3.4376 | 3.84 | 1400 | 3.4883 | 1.0 | | 3.4365 | 4.12 | 1500 | 3.6125 | 1.0 | | 3.4365 | 4.39 | 1600 | 3.6123 | 1.0 | | 3.4365 | 4.66 | 1700 | 3.5978 | 1.0 | | 3.4365 | 4.94 | 1800 | 3.5693 | 1.0 | | 3.4365 | 5.21 | 1900 | 3.5659 | 1.0 | | 3.4339 | 5.49 | 2000 | 3.6234 | 1.0 | | 3.4339 | 5.76 | 2100 | 3.5997 | 1.0 | | 3.4339 | 6.04 | 2200 | 3.6529 | 1.0 | | 3.4339 | 6.31 | 2300 | 3.5780 | 1.0 | | 3.4339 | 6.58 | 2400 | 3.5844 | 1.0 | | 3.4333 | 6.86 | 2500 | 3.5792 | 1.0 | | 3.4333 | 7.13 | 2600 | 3.5468 | 1.0 | | 3.4333 | 7.41 | 2700 | 3.5691 | 1.0 | | 3.4333 | 7.68 | 2800 | 3.5408 | 1.0 | | 3.4333 | 7.96 | 2900 | 3.5482 | 1.0 | | 3.4294 | 8.23 | 3000 | 3.6070 | 1.0 | | 3.4294 | 8.5 | 3100 | 3.5905 | 1.0 | | 3.4294 | 8.78 | 3200 | 3.6018 | 1.0 | | 3.4294 | 9.05 | 3300 | 3.6326 | 1.0 | | 3.4294 | 9.33 | 3400 | 3.6214 | 1.0 | | 3.4293 | 9.6 | 3500 | 3.6372 | 1.0 | | 3.4293 | 9.88 | 3600 | 3.6215 | 1.0 | | 3.4293 | 10.15 | 3700 | 3.5106 | 1.0 | | 3.4293 | 10.43 | 3800 | 3.5066 | 1.0 | | 3.4293 | 10.7 | 3900 | 3.5352 | 1.0 | | 3.4295 | 10.97 | 4000 | 3.5129 | 1.0 | | 3.4295 | 11.25 | 4100 | 3.6384 | 1.0 | | 3.4295 | 11.52 | 4200 | 3.6019 | 1.0 | | 3.4295 | 11.8 | 4300 | 3.5876 | 1.0 | | 3.4295 | 12.07 | 4400 | 3.6207 | 1.0 | | 3.4252 | 12.35 | 4500 | 3.5998 | 1.0 | | 3.4252 | 12.62 | 4600 | 3.6216 | 1.0 | | 3.4252 | 12.89 | 4700 | 3.6073 | 1.0 | | 3.4252 | 13.17 | 4800 | 3.5567 | 1.0 | | 3.4252 | 13.44 | 4900 | 3.5745 | 1.0 | | 3.4274 | 13.72 | 5000 | 3.5738 | 1.0 | | 3.4274 | 13.99 | 5100 | 3.5914 | 1.0 | | 3.4274 | 14.27 | 5200 | 3.6004 | 1.0 | | 3.4274 | 14.54 | 5300 | 3.5968 | 1.0 | | 3.4274 | 14.81 | 5400 | 3.5908 | 1.0 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.0 - Tokenizers 0.13.3

tyavika/pytorch

tyavika

2023-06-25T03:32:35Z

77

0

transformers

[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]

question-answering

2023-06-25T03:00:51Z

--- tags: - generated_from_trainer model-index: - name: pytorch results: [] ---  # pytorch This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cpu - Datasets 2.12.0 - Tokenizers 0.13.2

jiuzhan/YoLoV7-dog

jiuzhan

2023-06-25T03:27:58Z

0

null

[ "region:us" ]

null

2023-06-25T03:11:07Z

# 狗的种类识别用了500张图片的数据集，有点少。分了38个种类，每种张数有点少。效果一般。

MazVer/queenbee

MazVer

2023-06-25T02:55:12Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-06-25T02:52:19Z

--- license: creativeml-openrail-m ---

gaiamolinaro/ppo-SnowballTarget

gaiamolinaro

2023-06-25T02:36:18Z

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]

reinforcement-learning

2023-06-25T02:36:11Z

--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: gaiamolinaro/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

klaylouis1932/xlm-roberta-base-finetuned-panx-de

klaylouis1932

2023-06-25T02:22:45Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-06-24T07:47:55Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8653353814644136 ---  # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1339 - F1: 0.8653 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2583 | 1.0 | 525 | 0.1596 | 0.8231 | | 0.1262 | 2.0 | 1050 | 0.1395 | 0.8468 | | 0.0824 | 3.0 | 1575 | 0.1339 | 0.8653 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

nbiish/learning-taxi-v3

nbiish

2023-06-25T02:14:07Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-06-25T02:14:05Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: learning-taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.73 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="nbiish/learning-taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

mort666/faster-whisper-large-v2-th

mort666

2023-06-25T02:13:04Z

644

8

ctranslate2

[ "ctranslate2", "audio", "automatic-speech-recognition", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "license:mit", "region:us" ]

automatic-speech-recognition

2023-06-18T15:28:40Z

--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - 'no' - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su tags: - audio - automatic-speech-recognition license: mit library_name: ctranslate2 --- # Whisper large-v2 (Thai Finetune) model for CTranslate2 This repository contains the conversion of the [biodatlab/whisper-th-large-combined](https://huggingface.co/biodatlab/whisper-th-large-combined) which is finetune of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) for the Thai language to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("large-v2") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model biodatlab/whisper-th-large-combined --output_dir faster-whisper-large-v2-th \ --copy_files tokenizer.json --quantization float16 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information **For more information about the original model, see its [model card](https://huggingface.co/biodatlab/whisper-th-large-combined).**

cczhong/chinese-alpaca-plus-lora-7b-merged-ggml-4b

cczhong

2023-06-25T01:30:50Z

0

3

null

[ "region:us" ]

null

2023-06-24T17:53:57Z

need ggml-v3 to run. llama-cpp-python > 0.1.57 from https://github.com/ymcui/Chinese-LLaMA-Alpaca

yashgharat/HFTaxi-v3

yashgharat

2023-06-25T01:18:43Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-06-25T01:18:41Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: HFTaxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="yashgharat/HFTaxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

NasimB/gpt2-dp-mod_aochild

NasimB

2023-06-25T00:27:44Z

3

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-06-24T20:59:20Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-dp-mod_aochild results: [] ---  # gpt2-dp-mod_aochild This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.4146 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.706 | 0.27 | 500 | 5.6466 | | 5.3616 | 0.54 | 1000 | 5.2058 | | 5.0148 | 0.81 | 1500 | 4.9571 | | 4.7595 | 1.08 | 2000 | 4.8100 | | 4.5716 | 1.35 | 2500 | 4.6947 | | 4.4792 | 1.62 | 3000 | 4.5951 | | 4.3985 | 1.89 | 3500 | 4.5126 | | 4.2203 | 2.16 | 4000 | 4.4747 | | 4.1373 | 2.42 | 4500 | 4.4206 | | 4.1109 | 2.69 | 5000 | 4.3695 | | 4.0827 | 2.96 | 5500 | 4.3285 | | 3.8662 | 3.23 | 6000 | 4.3409 | | 3.863 | 3.5 | 6500 | 4.3058 | | 3.8585 | 3.77 | 7000 | 4.2777 | | 3.8073 | 4.04 | 7500 | 4.2766 | | 3.594 | 4.31 | 8000 | 4.2886 | | 3.6275 | 4.58 | 8500 | 4.2700 | | 3.6373 | 4.85 | 9000 | 4.2436 | | 3.488 | 5.12 | 9500 | 4.2800 | | 3.3669 | 5.39 | 10000 | 4.2884 | | 3.3981 | 5.66 | 10500 | 4.2764 | | 3.3991 | 5.93 | 11000 | 4.2533 | | 3.177 | 6.2 | 11500 | 4.3110 | | 3.1321 | 6.47 | 12000 | 4.3137 | | 3.1491 | 6.73 | 12500 | 4.3083 | | 3.1544 | 7.0 | 13000 | 4.3112 | | 2.8924 | 7.27 | 13500 | 4.3587 | | 2.9109 | 7.54 | 14000 | 4.3634 | | 2.9185 | 7.81 | 14500 | 4.3600 | | 2.8619 | 8.08 | 15000 | 4.3819 | | 2.7347 | 8.35 | 15500 | 4.3980 | | 2.7435 | 8.62 | 16000 | 4.4007 | | 2.752 | 8.89 | 16500 | 4.4012 | | 2.6887 | 9.16 | 17000 | 4.4116 | | 2.6506 | 9.43 | 17500 | 4.4137 | | 2.6588 | 9.7 | 18000 | 4.4144 | | 2.66 | 9.97 | 18500 | 4.4146 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

alantao912/models

alantao912

2023-06-25T00:07:35Z

13

0

transformers

[ "transformers", "pytorch", "blip", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "license:bsd-3-clause", "endpoints_compatible", "region:us" ]

image-text-to-text

2023-06-24T20:19:09Z

--- license: bsd-3-clause tags: - generated_from_trainer datasets: - imagefolder model-index: - name: models results: [] ---  # models This model is a fine-tuned version of [Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.4107 - Wer Score: 0.5495 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Score | |:-------------:|:-----:|:----:|:---------------:|:---------:| | 9.4536 | 0.05 | 10 | 7.8217 | 41.7753 | | 7.3267 | 0.11 | 20 | 6.6585 | 0.7753 | | 6.2358 | 0.16 | 30 | 5.7758 | 0.5667 | | 5.2862 | 0.22 | 40 | 4.7628 | 0.5419 | | 4.3786 | 0.27 | 50 | 3.9203 | 0.6398 | | 3.5554 | 0.33 | 60 | 3.1482 | 0.5613 | | 2.849 | 0.38 | 70 | 2.5209 | 0.5548 | | 2.3041 | 0.44 | 80 | 2.0561 | 0.5645 | | 1.8999 | 0.49 | 90 | 1.7474 | 0.5645 | | 1.658 | 0.55 | 100 | 1.5722 | 0.5548 | | 1.5238 | 0.6 | 110 | 1.4836 | 0.5591 | | 1.4726 | 0.66 | 120 | 1.4461 | 0.5538 | | 1.4328 | 0.71 | 130 | 1.4285 | 0.5473 | | 1.4211 | 0.77 | 140 | 1.4205 | 0.5559 | | 1.4202 | 0.82 | 150 | 1.4156 | 0.5548 | | 1.4098 | 0.88 | 160 | 1.4129 | 0.5505 | | 1.4124 | 0.93 | 170 | 1.4113 | 0.5548 | | 1.4075 | 0.99 | 180 | 1.4107 | 0.5495 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cpu - Datasets 2.13.1 - Tokenizers 0.13.3

benbav97/ppo-Huggy

benbav97

2023-06-24T23:46:50Z

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2023-06-24T22:49:23Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: benbav97/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

mohameddhiab/rate-jokes-bert

mohameddhiab

2023-06-24T23:45:08Z

121

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-24T23:21:00Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: rate-jokes-bert results: [] ---  # rate-jokes-bert This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0871 - F1: 0.0444 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 64 | 2.4209 | 0.0028 | | No log | 2.0 | 128 | 2.3785 | 0.0130 | | No log | 3.0 | 192 | 2.3215 | 0.0729 | | No log | 4.0 | 256 | 2.1787 | 0.0444 | | No log | 5.0 | 320 | 2.1038 | 0.0444 | | No log | 6.0 | 384 | 2.0944 | 0.0444 | | No log | 7.0 | 448 | 2.0911 | 0.0444 | | 2.2915 | 8.0 | 512 | 2.0901 | 0.0444 | | 2.2915 | 9.0 | 576 | 2.0892 | 0.0444 | | 2.2915 | 10.0 | 640 | 2.0871 | 0.0444 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

97jmlr/pyramids

97jmlr

2023-06-24T23:32:30Z

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]

reinforcement-learning

2023-06-24T23:32:23Z

--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: 97jmlr/pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Monk666/my_awesome_eli5_clm-model

Monk666

2023-06-24T23:28:23Z

63

0

transformers

[ "transformers", "tf", "gpt2", "text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-06-24T23:19:53Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Monk666/my_awesome_eli5_clm-model results: [] ---  # Monk666/my_awesome_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.7288 - Validation Loss: 3.7309 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.9096 | 3.7608 | 0 | | 3.7906 | 3.7412 | 1 | | 3.7288 | 3.7309 | 2 | ### Framework versions - Transformers 4.28.1 - TensorFlow 2.11.0 - Datasets 2.1.0 - Tokenizers 0.13.3

MindNetML/dqn-SpaceInvadersNoFrameskip-v4

MindNetML

2023-06-24T23:09:09Z

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-06-24T23:08:32Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 572.50 +/- 179.80 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga MindNetML -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga MindNetML -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga MindNetML ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 3), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

mohalm/videomae-base-finetuned-ucf101-subset

mohalm

2023-06-24T23:03:09Z

61

0

transformers

[ "transformers", "pytorch", "tensorboard", "videomae", "video-classification", "generated_from_trainer", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

video-classification

2023-06-24T20:21:51Z

--- license: cc-by-nc-4.0 tags: - generated_from_trainer model-index: - name: videomae-base-finetuned-ucf101-subset results: [] ---  # videomae-base-finetuned-ucf101-subset This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0008 - eval_accuracy: 1.0 - eval_runtime: 223.6754 - eval_samples_per_second: 0.443 - eval_steps_per_second: 0.076 - epoch: 1.01 - step: 43 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 164 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

TheBloke/orca_mini_7B-GGML

TheBloke

2023-06-24T22:49:41Z

0

21

transformers

[ "transformers", "en", "dataset:psmathur/alpaca_orca", "dataset:psmathur/dolly-v2_orca", "dataset:psmathur/WizardLM_Orca", "arxiv:2306.02707", "license:mit", "region:us" ]

null

2023-06-24T22:07:15Z

--- inference: false license: mit language: - en library_name: transformers datasets: - psmathur/alpaca_orca - psmathur/dolly-v2_orca - psmathur/WizardLM_Orca ---  <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div>  # Pankaj Mathur's Orca Mini 7B GGML These files are GGML format model files for [Pankaj Mathur's Orca Mini 7B](https://huggingface.co/psmathur/orca_mini_7b). GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as: * [text-generation-webui](https://github.com/oobabooga/text-generation-webui) * [KoboldCpp](https://github.com/LostRuins/koboldcpp) * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) * [ctransformers](https://github.com/marella/ctransformers) ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/orca_mini_7B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/orca_mini_7B-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/psmathur/orca_mini_7b) ## Prompt template: ``` ### System: You are an AI assistant that follows instruction extremely well. Help as much as you can. ### User: prompt ### Response: ``` or ``` ### System: You are an AI assistant that follows instruction extremely well. Help as much as you can. ### User: prompt ### Input: input ### Response: ```  ## Compatibility ### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0` I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit `2d5db48`. These are guaranteed to be compatbile with any UIs, tools and libraries released since late May. ### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K` These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`. They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python and ctransformers. Other tools and libraries may or may not be compatible - check their documentation if in doubt. ## Explanation of the new k-quant methods The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw * GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type. Refer to the Provided Files table below to see what files use which methods, and how.  ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | orca-mini-7b.ggmlv3.q2_K.bin | q2_K | 2 | 2.87 GB | 5.37 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. | | orca-mini-7b.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 3.60 GB | 6.10 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K | | orca-mini-7b.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 3.28 GB | 5.78 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K | | orca-mini-7b.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 2.95 GB | 5.45 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors | | orca-mini-7b.ggmlv3.q4_0.bin | q4_0 | 4 | 3.79 GB | 6.29 GB | Original llama.cpp quant method, 4-bit. | | orca-mini-7b.ggmlv3.q4_1.bin | q4_1 | 4 | 4.21 GB | 6.71 GB | Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. | | orca-mini-7b.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 4.08 GB | 6.58 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K | | orca-mini-7b.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 3.83 GB | 6.33 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors | | orca-mini-7b.ggmlv3.q5_0.bin | q5_0 | 5 | 4.63 GB | 7.13 GB | Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. | | orca-mini-7b.ggmlv3.q5_1.bin | q5_1 | 5 | 5.06 GB | 7.56 GB | Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. | | orca-mini-7b.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 4.78 GB | 7.28 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K | | orca-mini-7b.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 4.65 GB | 7.15 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors | | orca-mini-7b.ggmlv3.q6_K.bin | q6_K | 6 | 5.53 GB | 8.03 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors | | orca-mini-7b.ggmlv3.q8_0.bin | q8_0 | 8 | 7.16 GB | 9.66 GB | Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `llama.cpp` I use the following command line; adjust for your tastes and needs: ``` ./main -t 10 -ngl 32 -m orca-mini-7b.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### System:\nYou are an story writing assistant who writes very long, detailed and interesting stories\n\n### User:\nWrite a story about llamas\n\n### Input:\n{input}\n\n### Response:\n" ``` If you're able to use full GPU offloading, you should use `-t 1` to get best performance. If not able to fully offload to GPU, you should use more cores. Change `-t 10` to the number of physical CPU cores you have, or a lower number depending on what gives best performance. Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).  ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov. **Patreon special mentions**: Pyrater, WelcomeToTheClub, Kalila, Mano Prime, Trenton Dambrowitz, Spiking Neurons AB, Pierre Kircher, Fen Risland, Kevin Schuppel, Luke, Rainer Wilmers, vamX, Gabriel Puliatti, Alex , Karl Bernard, Ajan Kanaga, Talal Aujan, Space Cruiser, ya boyyy, biorpg, Johann-Peter Hartmann, Asp the Wyvern, Ai Maven, Ghost , Preetika Verma, Nikolai Manek, trip7s trip, John Detwiler, Fred von Graf, Artur Olbinski, subjectnull, John Villwock, Junyu Yang, Rod A, Lone Striker, Chris McCloskey, Iucharbius , Matthew Berman, Illia Dulskyi, Khalefa Al-Ahmad, Imad Khwaja, chris gileta, Willem Michiel, Greatston Gnanesh, Derek Yates, K, Alps Aficionado, Oscar Rangel, David Flickinger, Luke Pendergrass, Deep Realms, Eugene Pentland, Cory Kujawski, terasurfer , Jonathan Leane, senxiiz, Joseph William Delisle, Sean Connelly, webtim, zynix , Nathan LeClaire. Thank you to all my generous patrons and donaters!  # Original model card: Pankaj Mathur's Orca Mini 7B # orca_mini_7b An [OpenLLaMa-7B model](https://github.com/openlm-research/open_llama) model trained on explain tuned datasets, created using Instructions and Input from WizardLM, Alpaca & Dolly-V2 datasets and applying Orca Research Paper dataset construction approaches. # Dataset We build explain tuned [WizardLM dataset ~70K](https://github.com/nlpxucan/WizardLM), [Alpaca dataset ~52K](https://crfm.stanford.edu/2023/03/13/alpaca.html) & [Dolly-V2 dataset ~15K](https://github.com/databrickslabs/dolly) created using approaches from [Orca Research Paper](https://arxiv.org/abs/2306.02707). We leverage all of the 15 system instructions provided in Orca Research Paper. to generate custom datasets, in contrast to vanilla instruction tuning approaches used by original datasets. This helps student model aka this model to learn ***thought*** process from teacher model, which is ChatGPT (gpt-3.5-turbo-0301 version). Please see below example usage how the **System** prompt is added before each **instruction**. # Training The training configurations are provided in the table below. The training takes on 8x A100(80G) GPUs and lasts for around 7 Hours for cost of $84 using [Lambda Labs](https://lambdalabs.com) We used DeepSpeed with fully sharded data parallelism, also know as [ZeRO stage 3](https://engineering.fb.com/2021/07/15/open-source/fsdp/) by writing our own fine tunning scripts plus leveraging some of the model training code provided by amazing [OpenAlpaca repo](https://github.com/yxuansu/OpenAlpaca) Here are some of params used during training: ||| |:-------------:|:-------------:| |*batch_size*|32| |*train_micro_batch_size_per_gpu*|2| |*gradient_accumulation_steps*|2| |*Learning rate*|2e-5| |*Max length*|1024| |*Epochs*|3| |*Optimizer*|AdamW| # Example Usage Below shows an example on how to use this model ```python import torch from transformers import LlamaForCausalLM, LlamaTokenizer # Hugging Face model_path model_path = 'psmathur/orca_mini_7b' tokenizer = LlamaTokenizer.from_pretrained(model_path) model = LlamaForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map='auto', ) #generate text function def generate_text(system, instruction, input=None): if input: prompt = f"### System:\n{system}\n\n### User:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n" else: prompt = f"### System:\n{system}\n\n### User:\n{instruction}\n\n### Response:\n" tokens = tokenizer.encode(prompt) tokens = torch.LongTensor(tokens).unsqueeze(0) tokens = tokens.to('cuda') instance = {'input_ids': tokens,'top_p': 1.0, 'temperature':0.7, 'generate_len': 1024, 'top_k': 50} length = len(tokens[0]) with torch.no_grad(): rest = model.generate( input_ids=tokens, max_length=length+instance['generate_len'], use_cache=True, do_sample=True, top_p=instance['top_p'], temperature=instance['temperature'], top_k=instance['top_k'] ) output = rest[0][length:] string = tokenizer.decode(output, skip_special_tokens=True) return f'[!] Response: {string}' # Sample Test Instruction Used by Youtuber Sam Witteveen https://www.youtube.com/@samwitteveenai system = 'You are an AI assistant that follows instruction extremely well. Help as much as you can.' instruction = 'Write a letter to Sam Altman, CEO of OpenAI, requesting him to convert GPT4 a private model by OpenAI to an open source project' print(generate_text(system, instruction)) ``` ``` [!] Response: Dear Sam Altman, I am writing to request that you convert the GPT4 private model developed by OpenAI to an open source project. As a user of OpenAI, I have been waiting for the day when I can use the advanced natural language processing capabilities of GPT4 in a more open and accessible way. While OpenAI has made significant progress in developing AI applications, it has primarily focused on building private models that are not accessible to the general public. However, with the recent release of GPT-3, there is a growing demand for more open and accessible AI tools. Converting GPT4 to an open source project would allow for greater transparency, collaboration, and innovation. It would also help to build trust in the technology and ensure that it is used ethically and responsibly. I urge you to consider converting GPT4 to an open source project. This would be a significant contribution to the AI community and would help to create a more open and accessible future. Thank you for your consideration. Sincerely, [Your Name] ``` **P.S. I am #opentowork and #collaboration, if you can help, please reach out to me at psmathur.public@gmail.com** Next Goals: 1) Try more data like actually using FLAN-v2, just like Orka Research Paper (I am open for suggestions) 2) Provide more options for Text generation UI. (may be https://github.com/oobabooga/text-generation-webui) 3) Provide 4bit GGML/GPTQ quantized model (may be [TheBloke](https://huggingface.co/TheBloke) can help here) Limitations & Biases: This model can produce factually incorrect output, and should not be relied on to produce factually accurate information. This model was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Disclaimer: The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes. Citiation: If you found wizardlm_alpaca_dolly_orca_open_llama_7b useful in your research or applications, please kindly cite using the following BibTeX: ``` @misc{wizardlm_alpaca_dolly_orca_open_llama_7b, author = {Pankaj Mathur}, title = {wizardlm_alpaca_dolly_orca_open_llama_7b: An explain tuned OpenLLaMA-7b model on custom wizardlm, alpaca, & dolly datasets}, year = {2023}, publisher = {GitHub, HuggingFace}, journal = {GitHub repository, HuggingFace repository}, howpublished = {\url{https://github.com/pankajarm/wizardlm_alpaca_dolly_orca_open_llama_7b}, \url{https://https://huggingface.co/psmathur/wizardlm_alpaca_dolly_orca_open_llama_7b}}, } ``` ``` @software{openlm2023openllama, author = {Xinyang Geng and Hao Liu}, title = {OpenLLaMA: An Open Reproduction of LLaMA}, month = May, year = 2023, url = {https://github.com/openlm-research/open_llama} } ``` ``` @misc{openalpaca, author = {Yixuan Su and Tian Lan and Deng Cai}, title = {OpenAlpaca: A Fully Open-Source Instruction-Following Model Based On OpenLLaMA}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/yxuansu/OpenAlpaca}}, } ``` ``` @misc{alpaca, author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto }, title = {Stanford Alpaca: An Instruction-following LLaMA model}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}}, } ```

97jmlr/ppo-SnowballTarget

97jmlr

2023-06-24T22:43:03Z

3

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]

reinforcement-learning

2023-06-24T22:42:57Z

--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: 97jmlr/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

digiplay/illustro1stEdition_illustroV1

digiplay

2023-06-24T22:33:19Z

384

2

diffusers

[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-06-24T20:27:59Z

--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info: https://civitai.com/models/96101/illustro-1st-edition-photoreal-fantasy-model Original Author's DEMO image : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/10af4e3b-b554-4fe9-9d4a-5f7a834f884d/width=2304/03905-377024016-1%20beautiful%20female,%20%20((Jeremy%20Lipking))_intricate,%20detailed,%20knight,%20fantasy,%20(engraved%20filigree%20armor),%20perfect%20lighting,%20perfe.jpeg) image detail link : https://civitai.com/images/1266239

EIStakovskii/french_toxicity_classifier_plus

EIStakovskii

2023-06-24T22:31:55Z

108

2

transformers

[ "transformers", "pytorch", "safetensors", "camembert", "text-classification", "fr", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-10-21T07:48:14Z

--- language: fr # <-- my language widget: - text: "J'aime ta coiffure" example_title: "NOT TOXIC 1" - text: "Va te faire foutre" example_title: "TOXIC 1" - text: "Quel mauvais temps, n'est-ce pas ?" example_title: "NOT TOXIC 2" - text: "J'espère que tu vas mourir, connard !" example_title: "TOXIC 2" - text: "j'aime beaucoup ta veste" example_title: "NOT TOXIC 3" license: other --- This model was trained for toxicity labeling. Label_1 means TOXIC, Label_0 means NOT TOXIC The model was fine-tuned based off [the CamemBERT language model](https://huggingface.co/camembert-base). The accuracy is 93% on the test split during training and 79% on a manually picked (and thus harder) sample of 200 sentences (100 label 1, 100 label 0) at the end of the training. The model was finetuned on 32k sentences. The train data was the translations of the English data (around 30k sentences) from [the multilingual_detox dataset](https://github.com/s-nlp/multilingual_detox) by [Skolkovo Institute](https://huggingface.co/SkolkovoInstitute) using [the opus-mt-en-fr translation model](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) by [Helsinki-NLP](https://huggingface.co/Helsinki-NLP) and the data from [the jigsaw dataset](https://www.kaggle.com/competitions/jigsaw-multilingual-toxic-comment-classification/data) on kaggle.

dar-tau/ppo-LunarLander-v2

dar-tau

2023-06-24T22:12:59Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-06-24T22:06:50Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 256.44 +/- 18.33 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

pongjin/en_with_korean_model_large_960h

pongjin

2023-06-24T21:56:41Z

79

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "en", "dataset:pongjin/en_corpora_parliament_processed", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-06-16T16:55:59Z

--- license: apache-2.0 datasets: - pongjin/en_corpora_parliament_processed language: - en pipeline_tag: automatic-speech-recognition metrics: - wer --- **This model has been referred to the following links** 1) https://huggingface.co/blog/wav2vec2-with-ngram 2) https://huggingface.co/blog/fine-tune-wav2vec2-english Thanks to [patrickvonplaten Patrick von Platen](https://huggingface.co/patrickvonplaten) 해당 모델은 한국인의 영어 발화 인식 성능 개선을 위해 facebook/wav2vec2-large-960h 로 파인튜닝한 모델에 KenLM 5-gram 을 붙인 ASR + LM 모델입니다. If you want to use LM, you must have kenlm installed https://github.com/kpu/kenlm ```python pip install https://github.com/kpu/kenlm/archive/master.zip ``` 학습 데이터 출처 : https://aiopen.etri.re.kr/voiceModel >transformers==4.24.0 >huggingface_hub==0.13.2 | wer | epoch | batch | lr | weight_decay| warmup_steps| | --- | --- | --- | --- | --- | --- | | 0.17 | 10 | 16 | 1e-4 | 0.005 | 1000 |

SandeepKanao/HL7-FHIR-Model-V1

SandeepKanao

2023-06-24T21:45:41Z

105

1

transformers

[ "transformers", "pytorch", "distilbert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-06-22T12:44:31Z

--- license: apache-2.0 language: - en tags: - Token Classification co2_eq_emissions: 0.0279399890043426 widget: - text: ""MSH|^~&|SendingAPP|MYTEST|||20230621090000||ORU^R01|1|P|2.5.1||||||UNICODE PID|1||13579246^^^TEST||Taylor^Michael||19830520|M|||987 Pine St^^Anytown^NY^23456||555-456-7890 PV1|1||bc^^004 OBR|1||13579246|BCD^LEFT Breast Cancer Diagnosis^99MRC||20230621090000|||Taylor^Sarah||20230620090000|||N OBX|1|ST|FINDINGS^Findings^99MRC||Lab report shows asymmetric density in the right breast.|F|||R OBX|2|ST|IMPRESSION^Impression^99MRC||BIRADS category: 4 - Probably left side as issues.|F|||R OBX|3|ST|RECOMMENDATION^Recommendation^99MRC||Follow-up specialit visit in six months.|F|||R"" example_title: "example 1" - text: "MSH|^~&|SendingAPP|MYTEST|||20230621090000||ORU^R01|1|P|2.5.1||||||UNICODE PID|1||13579246^^^TEST||Taylor^Michael||19830520|M|||987 Pine St^^Anytown^NY^23456||555-456-7890 PV1|1||bc^^004 OBR|1||13579246|BCD^LEFT Breast Cancer Diagnosis^99MRC||20230621090000|||Taylor^Sarah||20230620090000|||N OBX|1|ST|FINDINGS^Findings^99MRC||Lab report shows asymmetric density in the right breast.|F|||R OBX|2|ST|IMPRESSION^Impression^99MRC||BIRADS category: 4 - Probably left side as issues.|F|||R OBX|3|ST|RECOMMENDATION^Recommendation^99MRC||Follow-up specialit visit in six months.|F|||R" ## About the Model An English Named Entity Recognition model, trained on Maccrobat to recognize the bio-medical entities (107 entities) from a given text corpus (case reports etc.). This model was built on top of distilbert-base-uncased - Dataset: Maccrobat https://figshare.com/articles/dataset/MACCROBAT2018/9764942 - Carbon emission: 0.0279399890043426 Kg - Training time: 30.16527 minutes - GPU used : 1 x GeForce RTX 3060 Laptop GPU Checkout the tutorial video for explanation of this model and corresponding python library: https://youtu.be/xpiDPdBpS18 ## Usage The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library. ```python from transformers import pipeline from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all") model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all") pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") # pass device=0 if using gpu pipe("""The patient reported no recurrence of palpitations at follow-up 6 months after the ablation.""") ``` ## Author

digiplay/ChillyMix_v1

digiplay

2023-06-24T21:19:54Z

291

2

diffusers

[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-06-23T16:14:13Z

--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/58772?modelVersionId=63220 Original Author's DEMO image : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/096853c2-2dfe-48c4-b0dc-eea621d02727/width=512/00629-3254057508-chillyMixV1Fp16.jpeg) image detailed link: https://civitai.com/images/701538

conrevo/Segment-Anything-A1111

conrevo

2023-06-24T21:18:24Z

0

1

null

[ "region:us" ]

null

2023-06-16T20:01:12Z

This repository contains - The extracted plugin model from https://huggingface.co/shi-labs/Matting-Anything - The ultralytics model from https://github.com/CASIA-IVA-Lab/FastSAM These models should only be accompanied with https://github.com/continue-revolution/sd-webui-segment-anything