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 2025-08-31 12:31:28	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 530 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-08-31 12:30:56	card stringlengths 11 1.01M
Christiansg/finetuning-sentiment-amazon-group23	Christiansg	2023-06-17T22:41:36Z	103	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-17T21:25:16Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: finetuning-sentiment-amazon-group23 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. --> # finetuning-sentiment-amazon-group23 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5877 - Accuracy: 0.8733 ## 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: 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: 2 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
jvilaseca/Reinforce-Cartpole2	jvilaseca	2023-06-17T22:21:52Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T22:17:34Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Cartpole2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . 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
Tyrranen/ppo-LunarLander-v2	Tyrranen	2023-06-17T19:07:45Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T19:06:38Z	--- 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: 257.55 +/- 19.71 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 ... ```
CreatorPhan/ViQA-small	CreatorPhan	2023-06-17T18:40:13Z	107	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "vi", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-06-17T16:58:15Z	--- language: - vi pipeline_tag: text2text-generation # inference: # parameters: # function_to_apply: "none" widget: - text: >- Trả lời câu hỏi: Công dụng của paracetamol? Trong nội dung: PARACETAMOL DẠNG UỐNG – HƯỚNG DẪN SỬ DỤNG AN TOÀN, HỢP LÝ Trong tình hình diễn biến phức tạp của dịch COVID-19, các thuốc giảm đau hạ sốt thông dụng như Paracetamol được người dân mua về dự trữ trong hộp thuốc gia đình với mục đích phòng dịch. Tuy nhiên, việc sử dụng thuốc hợp lý và đúng cách đôi khi chưa được chú ý, vì vậy việc hiểu và sử dụng thuốc Paracetamol an toàn là rất cần thiết. I. Tổng quan thuốc Paracetamol - Paracetamol dạng uống là thuốc thuộc nhóm giảm đau, hạ sốt và nằm trong danh mục thuốc không kê đơn của Bộ Y tế. Chính vì vậy Paracetamol rất phổ biến trên thị trường với nhiều chế phẩm có dạng bào chế và hàm lượng từ thấp đến cao. - Tác dụng chính của Paracetamol là giảm đau, hạ sốt nên thuốc được sử dụng rộng rãi trong điều trị các chứng đau và sốt từ nhẹ đến vừa như: cảm cúm, nhức đầu, đau bụng, đau nhức… - Thuốc không nên sử dụng cho những người dị ứng với Paracetamol, người suy gan nặng. II. Nguy cơ khi sử dụng Paracetamol - Việc Paracetamol được sử dụng rộng rãi cùng với tâm lý chủ quan, thiếu nhận thức dẫn đến việc quá liều thuốc gây nên các tác dụng phụ không mong muốn, trong đó nguy hiểm nhất là tình trạng hoại tử gan, có thể dẫn đến tử vong nếu không được xử trí kịp thời. - Nguyên nhân gây ngộ độc gan khi sử dụng Paracetamol quá liều là nồng độ NAPQI (sinh ra do Paracetamol chuyển hóa qua gan) không thể chuyển hóa hết và tích luỹ gây độc cho gan. - Các biểu hiện ngộ độc gan do Paracetamol có thể là: ban đầu là buồn nôn, nôn, đau bụng, sau đó nguy kich hơn có thể kích động, hôn mê, mạch huyết áp không ổn định… có thể nguy cơ tử vong. - text: >- Trả lời câu hỏi: Tòa nhà cao nhất Việt Nam? Trong nội dung: The Landmark 81 là một toà nhà chọc trời trong tổ hợp dự án Vinhomes Tân Cảng , một dự án có tổng mức đầu tư 40.000 tỷ đồng , do Công ty Cổ phần Đầu tư xây dựng Tân Liên Phát thuộc Vingroup làm chủ đầu tư . Toà tháp cao 81 tầng , hiện tại là toà nhà cao nhất Việt Nam và là toà nhà cao nhất Đông Nam Á từ tháng 3 năm 2018 . Toà tháp cao 81 tầng , hiện tại là toà nhà cao nhất Việt Nam và là toà nhà cao nhất Đông Nam Á từ tháng 3 năm 2018 . Dự án được xây dựng ở Tân Cảng , quận Bình Thạnh , ven sông Sài Gòn . Dự án được khởi công ngày 26/07/2014 . --- Mô hình này được tuning từ pretrained ViFlanT5-small model với 77M tham số với 2 epochs trên 87GB text của bộ CC100. Mô hình được huấn luyện cho tác vụ đọc hiểu tiếng Việt. Cung cấp cho mô hình câu hỏi và ngữ cảnh (không quá 400 từ) và mô hình sẽ trích xuất ra câu trả lời trong ngữ cảnh đó. ``` from transformers import AutoTokenizer, T5ForConditionalGeneration device = 'cpu' model_path = "CreatorPhan/ViQA-small" model = T5ForConditionalGeneration.from_pretrained(model_path).to(device) tokenizer = AutoTokenizer.from_pretrained(model_path) context = """ PARACETAMOL DẠNG UỐNG – HƯỚNG DẪN SỬ DỤNG AN TOÀN, HỢP LÝ Trong tình hình diễn biến phức tạp của dịch COVID-19, các thuốc giảm đau hạ sốt thông dụng như Paracetamol được người dân mua về dự trữ trong hộp thuốc gia đình với mục đích phòng dịch. Tuy nhiên, việc sử dụng thuốc hợp lý và đúng cách đôi khi chưa được chú ý, vì vậy việc hiểu và sử dụng thuốc Paracetamol an toàn là rất cần thiết. I. Tổng quan thuốc Paracetamol - Paracetamol dạng uống là thuốc thuộc nhóm giảm đau, hạ sốt và nằm trong danh mục thuốc không kê đơn của Bộ Y tế. Chính vì vậy Paracetamol rất phổ biến trên thị trường với nhiều chế phẩm có dạng bào chế và hàm lượng từ thấp đến cao. - Tác dụng chính của Paracetamol là giảm đau, hạ sốt nên thuốc được sử dụng rộng rãi trong điều trị các chứng đau và sốt từ nhẹ đến vừa như: cảm cúm, nhức đầu, đau bụng, đau nhức… - Thuốc không nên sử dụng cho những người dị ứng với Paracetamol, người suy gan nặng. II. Nguy cơ khi sử dụng Paracetamol - Việc Paracetamol được sử dụng rộng rãi cùng với tâm lý chủ quan, thiếu nhận thức dẫn đến việc quá liều thuốc gây nên các tác dụng phụ không mong muốn, trong đó nguy hiểm nhất là tình trạng hoại tử gan, có thể dẫn đến tử vong nếu không được xử trí kịp thời. - Nguyên nhân gây ngộ độc gan khi sử dụng Paracetamol quá liều là nồng độ NAPQI (sinh ra do Paracetamol chuyển hóa qua gan) không thể chuyển hóa hết và tích luỹ gây độc cho gan. - Các biểu hiện ngộ độc gan do Paracetamol có thể là: ban đầu là buồn nôn, nôn, đau bụng, sau đó nguy kich hơn có thể kích động, hôn mê, mạch huyết áp không ổn định… có thể nguy cơ tử vong. """ question = "Công dụng của paracetamol?" prompt = f"Trả lời câu hỏi: {question} Trong nội dung: {context}" tokens = tokenizer(prompt, return_tensors='pt').input_ids output = model.generate(tokens.to(device), max_new_tokens=170)[0] predict = tokenizer.decode(output, skip_special_tokens=True) print(len(predict.split())) print(predict) ```
mrm8488/distilgpt2-finetuned-jhegarty-books	mrm8488	2023-06-17T17:56:10Z	152	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-10T11:31:15Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-jhegarty-books 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. --> # distilgpt2-finetuned-jhegarty-books This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6008 ## 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: 2 - eval_batch_size: 2 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 49 \| 3.6667 \| \| No log \| 2.0 \| 98 \| 3.6202 \| \| No log \| 3.0 \| 147 \| 3.6019 \| \| No log \| 4.0 \| 196 \| 3.6008 \| ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
mmirmahdi/Taxi-v3	mmirmahdi	2023-06-17T17:31:14Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T17:30:59Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 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="mmirmahdi/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"]) ```
0xghagevaibhav/meMyself	0xghagevaibhav	2023-06-17T17:11:20Z	0	0	null	[ "hi", "license:unknown", "region:us" ]	null	2023-06-17T17:09:19Z	--- license: unknown language: - hi ---
erens/mikasalast	erens	2023-06-17T17:01:46Z	30	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-17T16:46:29Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### mikasaLAST Dreambooth model trained by erens with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
pszemraj/long-t5-tglobal-xl-16384-booksci-summary-v1	pszemraj	2023-06-17T16:51:52Z	5	1	transformers	[ "transformers", "pytorch", "longt5", "text2text-generation", "generated_from_trainer", "en", "dataset:pszemraj/scientific_lay_summarisation-elife-norm", "license:bsd-3-clause", "license:apache-2.0", "model-index", "autotrain_compatible", "region:us" ]	text2text-generation	2023-06-17T12:23:26Z	--- license: - bsd-3-clause - apache-2.0 tags: - generated_from_trainer datasets: - pszemraj/scientific_lay_summarisation-elife-norm metrics: - rouge model-index: - name: >- long-t5-tglobal-xl-16384-book-summary-scientific_lay_summarisation-elife-norm-16384-summ-v1 results: - task: name: Summarization type: summarization dataset: name: pszemraj/scientific_lay_summarisation-elife-norm type: pszemraj/scientific_lay_summarisation-elife-norm split: validation metrics: - name: Rouge1 type: rouge value: 47.4591 language: - en library_name: transformers inference: False --- # long-t5-tglobal-xl-16384-booksci-summary-v1 This model is a fine-tuned version of [pszemraj/long-t5-tglobal-xl-16384-book-summary](https://huggingface.co/pszemraj/long-t5-tglobal-xl-16384-book-summary) on the pszemraj/scientific_lay_summarisation-elife-norm dataset. It achieves the following results on the evaluation set: - Loss: 1.7518 - Rouge1: 47.4591 - Rouge2: 12.7287 - Rougel: 21.5549 - Rougelsum: 44.8709 - Gen Len: 384.39 ## Model description An experiment of further fine-tuning a booksum model on a different dataset. Compare to either the starting checkpoint (_linked above_) or to the [variant only fine-tuned on the scientific lay summaries](https://huggingface.co/pszemraj/long-t5-tglobal-xl-sci-simplify-elife). ## Intended uses & limitations More information needed ## Training and evaluation data the pszemraj/scientific_lay_summarisation-elife-norm dataset, input 16384 tokens then truncate, output 1024 tokens then truncate. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 878 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.02 - num_epochs: 2.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 1.9629 \| 1.0 \| 543 \| 1.7637 \| 46.6926 \| 12.4769 \| 21.4364 \| 44.4329 \| 381.23 \| \| 1.8555 \| 2.0 \| 1086 \| 1.7518 \| 47.4591 \| 12.7287 \| 21.5549 \| 44.8709 \| 384.39 \|
vlkn/flan-t5-small-taboo-for-llms	vlkn	2023-06-17T16:20:59Z	15	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	2023-06-03T13:32:22Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-small-taboo-for-llms 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. --> # flan-t5-small-taboo-for-llms This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4825 - Rouge1: 27.3897 - Rouge2: 9.9232 - Rougel: 24.2026 - Rougelsum: 24.6485 - Gen Len: 18.5172 ## 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: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 137 \| 2.5897 \| 26.6789 \| 9.9538 \| 23.6637 \| 24.2407 \| 18.3621 \| \| No log \| 2.0 \| 274 \| 2.5560 \| 25.4162 \| 9.6277 \| 22.7084 \| 23.0883 \| 18.3966 \| \| No log \| 3.0 \| 411 \| 2.5377 \| 26.0239 \| 9.7748 \| 23.4425 \| 23.7935 \| 18.6034 \| \| 2.8204 \| 4.0 \| 548 \| 2.5241 \| 26.6294 \| 9.9168 \| 23.8023 \| 24.2756 \| 18.7241 \| \| 2.8204 \| 5.0 \| 685 \| 2.5120 \| 25.8274 \| 9.9333 \| 23.8865 \| 24.0724 \| 18.7586 \| \| 2.8204 \| 6.0 \| 822 \| 2.5031 \| 26.7774 \| 9.9651 \| 24.3654 \| 24.6102 \| 18.6034 \| \| 2.8204 \| 7.0 \| 959 \| 2.4985 \| 26.5058 \| 10.0422 \| 24.0403 \| 24.635 \| 18.4655 \| \| 2.6101 \| 8.0 \| 1096 \| 2.4934 \| 26.6953 \| 9.9536 \| 24.0293 \| 24.6809 \| 18.4655 \| \| 2.6101 \| 9.0 \| 1233 \| 2.4907 \| 26.7978 \| 9.6249 \| 23.714 \| 23.9992 \| 18.6034 \| \| 2.6101 \| 10.0 \| 1370 \| 2.4847 \| 27.2135 \| 9.878 \| 23.8398 \| 24.2389 \| 18.5 \| \| 2.4726 \| 11.0 \| 1507 \| 2.4856 \| 27.1799 \| 9.9337 \| 23.9393 \| 24.4067 \| 18.5172 \| \| 2.4726 \| 12.0 \| 1644 \| 2.4835 \| 27.4491 \| 10.1828 \| 24.0926 \| 24.4819 \| 18.5 \| \| 2.4726 \| 13.0 \| 1781 \| 2.4825 \| 27.3897 \| 9.9232 \| 24.2026 \| 24.6485 \| 18.5172 \| \| 2.4726 \| 14.0 \| 1918 \| 2.4836 \| 27.5567 \| 10.7405 \| 24.2497 \| 24.6566 \| 18.5345 \| \| 2.3731 \| 15.0 \| 2055 \| 2.4872 \| 27.7517 \| 11.0182 \| 24.1007 \| 24.7218 \| 18.4828 \| \| 2.3731 \| 16.0 \| 2192 \| 2.4852 \| 27.3461 \| 11.3381 \| 24.084 \| 24.5125 \| 18.4655 \| \| 2.3731 \| 17.0 \| 2329 \| 2.4872 \| 27.3558 \| 11.1005 \| 24.047 \| 24.4973 \| 18.4655 \| \| 2.3731 \| 18.0 \| 2466 \| 2.4841 \| 26.9427 \| 10.9288 \| 23.7324 \| 24.4298 \| 18.5345 \| \| 2.2967 \| 19.0 \| 2603 \| 2.4881 \| 27.5 \| 10.8437 \| 24.1593 \| 24.6028 \| 18.4483 \| \| 2.2967 \| 20.0 \| 2740 \| 2.4908 \| 27.517 \| 11.0039 \| 24.1049 \| 24.7111 \| 18.5 \| \| 2.2967 \| 21.0 \| 2877 \| 2.4917 \| 27.7333 \| 10.935 \| 24.4076 \| 24.9887 \| 18.4138 \| \| 2.2553 \| 22.0 \| 3014 \| 2.4926 \| 27.6275 \| 10.7562 \| 24.2295 \| 24.7476 \| 18.4138 \| \| 2.2553 \| 23.0 \| 3151 \| 2.4945 \| 27.9085 \| 10.943 \| 24.6135 \| 25.2373 \| 18.4138 \| \| 2.2553 \| 24.0 \| 3288 \| 2.4948 \| 27.5261 \| 10.7141 \| 24.2429 \| 24.816 \| 18.4138 \| \| 2.2553 \| 25.0 \| 3425 \| 2.4931 \| 27.5522 \| 10.8702 \| 24.5576 \| 25.0714 \| 18.4655 \| \| 2.213 \| 26.0 \| 3562 \| 2.4942 \| 27.4758 \| 11.0064 \| 24.5062 \| 25.05 \| 18.4655 \| \| 2.213 \| 27.0 \| 3699 \| 2.4954 \| 27.6967 \| 11.1744 \| 24.7646 \| 25.3172 \| 18.4655 \| \| 2.213 \| 28.0 \| 3836 \| 2.4951 \| 27.7428 \| 10.9365 \| 24.6427 \| 25.2432 \| 18.5172 \| \| 2.213 \| 29.0 \| 3973 \| 2.4949 \| 27.6877 \| 10.9522 \| 24.6101 \| 25.2471 \| 18.4655 \| \| 2.1865 \| 30.0 \| 4110 \| 2.4952 \| 27.7295 \| 11.0173 \| 24.6556 \| 25.2397 \| 18.4655 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
aphi/q-FrozenLake-v1-4x4-noSlippery	aphi	2023-06-17T15:42:48Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T15:42:37Z	--- 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="aphi/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"]) ```
l3cube-pune/assamese-bert	l3cube-pune	2023-06-17T15:38:14Z	195	0	transformers	[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "as", "arxiv:2211.11418", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-11-20T08:12:25Z	--- license: cc-by-4.0 language: as --- ## AssameseBERT AssameseBERT is an Assamese BERT model trained on publicly available Assamese monolingual datasets. Preliminary details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] . Citing: ``` @article{joshi2022l3cubehind, title={L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages}, author={Joshi, Raviraj}, journal={arXiv preprint arXiv:2211.11418}, year={2022} } ``` Other Monolingual Indic BERT models are listed below: <br> <a href='https://huggingface.co/l3cube-pune/marathi-bert-v2'> Marathi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-roberta'> Marathi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-albert'> Marathi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-bert-v2'> Hindi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-roberta'> Hindi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-albert'> Hindi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-bert'> Dev BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-roberta'> Dev RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-albert'> Dev AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/kannada-bert'> Kannada BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/telugu-bert'> Telugu BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/malayalam-bert'> Malayalam BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/tamil-bert'> Tamil BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/gujarati-bert'> Gujarati BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/odia-bert'> Oriya BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/bengali-bert'> Bengali BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/punjabi-bert'> Punjabi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/assamese-bert'> Assamese BERT </a> <br>
l3cube-pune/bengali-bert	l3cube-pune	2023-06-17T15:37:35Z	163	2	transformers	[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "bn", "arxiv:2211.11418", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-11-20T08:45:01Z	--- license: cc-by-4.0 language: bn --- ## BengaliBERT BengaliBERT is a Bengali BERT model trained on publicly available Bengali monolingual datasets. Preliminary details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] . Citing: ``` @article{joshi2022l3cubehind, title={L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages}, author={Joshi, Raviraj}, journal={arXiv preprint arXiv:2211.11418}, year={2022} } ``` Other Monolingual Indic BERT models are listed below: <br> <a href='https://huggingface.co/l3cube-pune/marathi-bert-v2'> Marathi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-roberta'> Marathi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-albert'> Marathi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-bert-v2'> Hindi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-roberta'> Hindi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-albert'> Hindi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-bert'> Dev BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-roberta'> Dev RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-albert'> Dev AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/kannada-bert'> Kannada BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/telugu-bert'> Telugu BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/malayalam-bert'> Malayalam BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/tamil-bert'> Tamil BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/gujarati-bert'> Gujarati BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/odia-bert'> Oriya BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/bengali-bert'> Bengali BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/punjabi-bert'> Punjabi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/assamese-bert'> Assamese BERT </a> <br>
paulowoicho/t5-podcast-summarisation	paulowoicho	2023-06-17T15:36:56Z	154	8	transformers	[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "summarisation", "lm-head", "en", "arxiv:2004.04270", "arxiv:1910.10683", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- language: - en datasets: - Spotify Podcasts Dataset tags: - t5 - summarisation - pytorch - lm-head metrics: - ROUGE pipeline: - summarisation --- # T5 for Automatic Podcast Summarisation This model is the result of fine-tuning [t5-base](https://huggingface.co/t5-base) on the [Spotify Podcast Dataset](https://arxiv.org/abs/2004.04270). It is based on [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) which was pretrained on the [C4 dataset](https://huggingface.co/datasets/c4). Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) Authors: Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu ## Intended uses & limitations This model is intended to be used for automatic podcast summarisation. As creator provided descriptions were used for training, the model also learned to generate promotional material (links, hashtags, etc) in its summaries, as such some post processing may be required on the model's outputs. If using on Colab, the instance will crash if the number of tokens in the transcript exceeds 7000. I discovered that the model generated reasonable summaries even when the podcast transcript was truncated to reduce the number of tokens. #### How to use The model can be used with the summarisation as follows: ```python from transformers import pipeline summarizer = pipeline("summarization", model="paulowoicho/t5-podcast-summarisation", tokenizer="paulowoicho/t5-podcast-summarisation") summary = summarizer(podcast_transcript, min_length=5, max_length=20) print(summary[0]['summary_text']) ``` ## Training data This model is the result of fine-tuning [t5-base](https://huggingface.co/t5-base) on the [Spotify Podcast Dataset](https://arxiv.org/abs/2004.04270). [Pre-processing](https://github.com/paulowoicho/msc_project/blob/master/reformat.py) was done on the original data before fine-tuning. ## Training procedure Training was largely based on [Fine-tune T5 for Summarization](https://github.com/abhimishra91/transformers-tutorials/blob/master/transformers_summarization_wandb.ipynb) by [Abhishek Kumar Mishra](https://github.com/abhimishra91)
l3cube-pune/odia-bert	l3cube-pune	2023-06-17T15:36:40Z	457	0	transformers	[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "or", "arxiv:2211.11418", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-11-20T08:26:35Z	--- license: cc-by-4.0 language: or --- ## OdiaBERT OdiaBERT is an Odia BERT model trained on publicly available Odia monolingual datasets. Preliminary details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] . Citing: ``` @article{joshi2022l3cubehind, title={L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages}, author={Joshi, Raviraj}, journal={arXiv preprint arXiv:2211.11418}, year={2022} } ``` Other Monolingual Indic BERT models are listed below: <br> <a href='https://huggingface.co/l3cube-pune/marathi-bert-v2'> Marathi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-roberta'> Marathi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-albert'> Marathi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-bert-v2'> Hindi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-roberta'> Hindi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-albert'> Hindi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-bert'> Dev BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-roberta'> Dev RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-albert'> Dev AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/kannada-bert'> Kannada BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/telugu-bert'> Telugu BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/malayalam-bert'> Malayalam BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/tamil-bert'> Tamil BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/gujarati-bert'> Gujarati BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/odia-bert'> Oriya BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/bengali-bert'> Bengali BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/punjabi-bert'> Punjabi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/assamese-bert'> Assamese BERT </a> <br>
l3cube-pune/tamil-bert	l3cube-pune	2023-06-17T15:36:00Z	554	3	transformers	[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "ta", "arxiv:2211.11418", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-11-20T07:45:13Z	--- license: cc-by-4.0 language: ta --- ## TamilBERT TamilBERT is a Tamil BERT model trained on publicly available Tamil monolingual datasets. Preliminary details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] . Citing: ``` @article{joshi2022l3cubehind, title={L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages}, author={Joshi, Raviraj}, journal={arXiv preprint arXiv:2211.11418}, year={2022} } ``` Other Monolingual Indic BERT models are listed below: <br> <a href='https://huggingface.co/l3cube-pune/marathi-bert-v2'> Marathi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-roberta'> Marathi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-albert'> Marathi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-bert-v2'> Hindi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-roberta'> Hindi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-albert'> Hindi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-bert'> Dev BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-roberta'> Dev RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-albert'> Dev AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/kannada-bert'> Kannada BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/telugu-bert'> Telugu BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/malayalam-bert'> Malayalam BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/tamil-bert'> Tamil BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/gujarati-bert'> Gujarati BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/odia-bert'> Oriya BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/bengali-bert'> Bengali BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/punjabi-bert'> Punjabi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/assamese-bert'> Assamese BERT </a> <br>
l3cube-pune/malayalam-bert	l3cube-pune	2023-06-17T15:35:26Z	417	5	transformers	[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "ml", "arxiv:2211.11418", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-11-20T07:31:42Z	--- license: cc-by-4.0 language: ml --- ## MalayalamBERT MalayalamBERT is a Malayalam BERT model trained on publicly available Malayalam monolingual datasets. Preliminary details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] . Citing: ``` @article{joshi2022l3cubehind, title={L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages}, author={Joshi, Raviraj}, journal={arXiv preprint arXiv:2211.11418}, year={2022} } ``` Other Monolingual Indic BERT models are listed below: <br> <a href='https://huggingface.co/l3cube-pune/marathi-bert-v2'> Marathi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-roberta'> Marathi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-albert'> Marathi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-bert-v2'> Hindi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-roberta'> Hindi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-albert'> Hindi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-bert'> Dev BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-roberta'> Dev RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-albert'> Dev AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/kannada-bert'> Kannada BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/telugu-bert'> Telugu BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/malayalam-bert'> Malayalam BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/tamil-bert'> Tamil BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/gujarati-bert'> Gujarati BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/odia-bert'> Oriya BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/bengali-bert'> Bengali BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/punjabi-bert'> Punjabi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/assamese-bert'> Assamese BERT </a> <br>
l3cube-pune/hindi-roberta	l3cube-pune	2023-06-17T15:31:32Z	117	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "fill-mask", "hi", "arxiv:2211.11418", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-08-19T18:21:53Z	--- license: cc-by-4.0 language: hi --- ## HindRoBERTa HindRoBERTa is a Hindi RoBERTa model. It is a multilingual RoBERTa (xlm-roberta-base) model fine-tuned on publicly available Hindi monolingual datasets. [project link] (https://github.com/l3cube-pune/MarathiNLP) More details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] . Citing: ``` @article{joshi2022l3cubehind, title={L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages}, author={Joshi, Raviraj}, journal={arXiv preprint arXiv:2211.11418}, year={2022} } ``` Other Monolingual Indic BERT models are listed below: <br> <a href='https://huggingface.co/l3cube-pune/marathi-bert-v2'> Marathi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-roberta'> Marathi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-albert'> Marathi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-bert-v2'> Hindi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-roberta'> Hindi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-albert'> Hindi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-bert'> Dev BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-roberta'> Dev RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-albert'> Dev AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/kannada-bert'> Kannada BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/telugu-bert'> Telugu BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/malayalam-bert'> Malayalam BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/tamil-bert'> Tamil BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/gujarati-bert'> Gujarati BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/odia-bert'> Oriya BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/bengali-bert'> Bengali BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/punjabi-bert'> Punjabi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/assamese-bert'> Assamese BERT </a> <br>
l3cube-pune/marathi-bert-v2	l3cube-pune	2023-06-17T15:30:14Z	391	0	transformers	[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "mr", "dataset:L3Cube-MahaCorpus", "arxiv:2202.01159", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-08-16T17:52:15Z	--- license: cc-by-4.0 language: mr datasets: - L3Cube-MahaCorpus --- ## MahaBERT MahaBERT is a Marathi BERT model. It is a multilingual BERT (google/muril-base-cased) model fine-tuned on L3Cube-MahaCorpus and other publicly available Marathi monolingual datasets. [dataset link] (https://github.com/l3cube-pune/MarathiNLP) More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2202.01159) ``` @inproceedings{joshi-2022-l3cube, title = "{L}3{C}ube-{M}aha{C}orpus and {M}aha{BERT}: {M}arathi Monolingual Corpus, {M}arathi {BERT} Language Models, and Resources", author = "Joshi, Raviraj", booktitle = "Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.wildre-1.17", pages = "97--101", } ``` Other Monolingual Indic BERT models are listed below: <br> <a href='https://huggingface.co/l3cube-pune/marathi-bert-v2'> Marathi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-roberta'> Marathi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-albert'> Marathi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-bert-v2'> Hindi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-roberta'> Hindi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-albert'> Hindi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-bert'> Dev BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-roberta'> Dev RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-albert'> Dev AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/kannada-bert'> Kannada BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/telugu-bert'> Telugu BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/malayalam-bert'> Malayalam BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/tamil-bert'> Tamil BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/gujarati-bert'> Gujarati BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/odia-bert'> Oriya BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/bengali-bert'> Bengali BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/punjabi-bert'> Punjabi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/assamese-bert'> Assamese BERT </a> <br>
xqs/ppo-LunarLander-v2	xqs	2023-06-17T15:27:14Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T15:26:34Z	--- 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: 256.17 +/- 23.91 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 ... ```
Benned/TestLyco	Benned	2023-06-17T15:24:39Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-17T15:22:06Z	--- license: creativeml-openrail-m ---
atrytone/scibert_uncased_claim_id	atrytone	2023-06-17T15:16:47Z	112	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "text-classification", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-18T05:07:15Z	--- license: apache-2.0 language: - en --- Fine-tuned SciBERT uncased model [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) for claim detection from abstracts.
biodatlab/MIReAD-Neuro	biodatlab	2023-06-17T15:16:26Z	122	2	transformers	[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-09T05:23:59Z	--- language: - en pipeline_tag: text-classification metrics: - f1 - accuracy - recall - precision library_name: transformers widget: - text: The past 25 years have seen a strong increase in the number of publications related to criticality in different areas of neuroscience. The potential of criticality to explain various brain properties, including optimal information processing, has made it an increasingly exciting area of investigation for neuroscientists. Recent reviews on this topic, sometimes termed brain criticality, make brief mention of clinical applications of these findings to several neurological disorders such as epilepsy, neurodegenerative disease, and neonatal hypoxia. Other clinicallyrelevant domains - including anesthesia, sleep medicine, developmental-behavioral pediatrics, and psychiatry - are seldom discussed in review papers of brain criticality. Thorough assessments of these application areas and their relevance for clinicians have also yet to be published. In this scoping review, studies of brain criticality involving human data of all ages are evaluated for their current and future clinical relevance. To make the results of these studies understandable to a more clinical audience, a review of the key concepts behind criticality (e.g., phase transitions, long-range temporal correlation, self-organized criticality, power laws, branching processes) precedes the discussion of human clinical studies. Open questions and forthcoming areas of investigation are also considered. --- # MIReAD Neuro This model is a fine-tuned version of [arazd/MIReAD](https://huggingface.co/arazd/MIReAD) on a dataset of Neuroscience papers from 200 journals collected from various sources for a journal classification task. It achieves the following results on the evaluation set: - Loss: 2.7117 - Accuracy: 0.4011 - F1: 0.3962 - Precision: 0.4066 - Recall: 0.3999 ## Model description This model was trained on a journal classification task. ## Intended uses & limitations The intended use of this model is to create abstract embeddings for semantic similarity search for neuroscience-related articles. ## Model Usage To load the model: ```py from transformers import BertForSequenceClassification, AutoTokenizer model_path = "biodatlab/MIReAD-Neuro" model = BertForSequenceClassification.from_pretrained(model_path) tokenizer = AutoTokenizer.from_pretrained(model_path) ``` To create embeddings and for classification: ```py # sample abstract & title text title = "Why Brain Criticality Is Clinically Relevant: A Scoping Review." abstract = "The past 25 years have seen a strong increase in the number of publications related to criticality in different areas of neuroscience..." text = title + tokenizer.sep_token + abstract tokens = tokenizer( text, max_length=512, padding=True, truncation=True, return_tensors="pt" ) # to generate an embedding from a given title and abstract with torch.no_grad(): output = model.bert(tokens) embedding = output.last_hidden_state[:, 0, :] # to classify (200 journals) a given title and abstract output = model(tokens) class = output.logits ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - num_epochs: 6
sttteephen/ppo-LunarLander-v2	sttteephen	2023-06-17T14:47:52Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T14:47:13Z	--- 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: 259.73 +/- 23.16 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 ... ```
JCTN/controlnet_qrcode-control_v11p_sd21	JCTN	2023-06-17T14:45:13Z	14	2	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "controlnet", "image-to-image", "en", "license:openrail++", "region:us" ]	image-to-image	2023-06-16T21:21:29Z	--- tags: - stable-diffusion - controlnet - image-to-image license: openrail++ language: - en pipeline_tag: image-to-image --- # QR Code Conditioned ControlNet Models for Stable Diffusion 2.1 ![1](https://www.dropbox.com/s/c1kx64v1cpsh2mp/1.png?raw=1) ## Model Description This repo holds the safetensors & diffusers versions of the QR code conditioned ControlNet for Stable Diffusion v2.1. The Stable Diffusion 2.1 version is marginally more effective, as it was developed to address my specific needs. However, a 1.5 version model was also trained on the same dataset for those who are using the older version. ## How to use with diffusers ```bash pip -q install diffusers transformers accelerate torch xformers ``` ```python import torch from PIL import Image from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, DDIMScheduler from diffusers.utils import load_image controlnet = ControlNetModel.from_pretrained("DionTimmer/controlnet_qrcode-control_v11p_sd21", torch_dtype=torch.float16) pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( "stabilityai/stable-diffusion-2-1", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 ) pipe.enable_xformers_memory_efficient_attention() pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) pipe.enable_model_cpu_offload() def resize_for_condition_image(input_image: Image, resolution: int): input_image = input_image.convert("RGB") W, H = input_image.size k = float(resolution) / min(H, W) H = k W = k H = int(round(H / 64.0)) * 64 W = int(round(W / 64.0)) * 64 img = input_image.resize((W, H), resample=Image.LANCZOS) return img # play with guidance_scale, controlnet_conditioning_scale and strength to make a valid QR Code Image # qr code image source_image = load_image("https://s3.amazonaws.com/moonup/production/uploads/6064e095abd8d3692e3e2ed6/A_RqHaAM6YHBodPLwqtjn.png") # initial image, anything init_image = load_image("https://s3.amazonaws.com/moonup/production/uploads/noauth/KfMBABpOwIuNolv1pe3qX.jpeg") condition_image = resize_for_condition_image(source_image, 768) init_image = resize_for_condition_image(init_image, 768) generator = torch.manual_seed(123121231) image = pipe(prompt="a bilboard in NYC with a qrcode", negative_prompt="ugly, disfigured, low quality, blurry, nsfw", image=init_image, control_image=condition_image, width=768, height=768, guidance_scale=20, controlnet_conditioning_scale=1.5, generator=generator, strength=0.9, num_inference_steps=150, ) image.images[0] ``` ## Performance and Limitations These models perform quite well in most cases, but please note that they are not 100% accurate. In some instances, the QR code shape might not come through as expected. You can increase the ControlNet weight to emphasize the QR code shape. However, be cautious as this might negatively impact the style of your output.To optimize for scanning, please generate your QR codes with correction mode 'H' (30%). To balance between style and shape, a gentle fine-tuning of the control weight might be required based on the individual input and the desired output, aswell as the correct prompt. Some prompts do not work until you increase the weight by a lot. The process of finding the right balance between these factors is part art and part science. For the best results, it is recommended to generate your artwork at a resolution of 768. This allows for a higher level of detail in the final product, enhancing the quality and effectiveness of the QR code-based artwork. ## Installation The simplest way to use this is to place the .safetensors model and its .yaml config file in the folder where your other controlnet models are installed, which varies per application. For usage in auto1111 they can be placed in the webui/models/ControlNet folder. They can be loaded using the controlnet webui extension which you can install through the extensions tab in the webui (https://github.com/Mikubill/sd-webui-controlnet). Make sure to enable your controlnet unit and set your input image as the QR code. Set the model to either the SD2.1 or 1.5 version depending on your base stable diffusion model, or it will error. No pre-processor is needed, though you can use the invert pre-processor for a different variation of results. 768 is the preferred resolution for generation since it allows for more detail. Make sure to look up additional info on how to use controlnet if you get stuck, once you have the webui up and running its really easy to install the controlnet extension aswell.
Bala-A87/Huggy-DRL	Bala-A87	2023-06-17T14:35:21Z	2	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-06-17T14:34:51Z	--- 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: Bala-A87/Huggy-DRL 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
VTSTech/Desktop-GPT-111m	VTSTech	2023-06-17T14:30:24Z	146	2	transformers	[ "transformers", "pytorch", "safetensors", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-04-17T16:29:06Z	This is my first attempt at training a model. Based on Cerebras-GPT-111m - Trained on a Conversational Dataset with some Q/A Trained using code from https://github.com/Dampish0/ModelTrainingLocal My homepage: https://www.vts-tech.org My Github: https://github.com/Veritas83 --- license: cc-by-nc-4.0 --- tags: - CasualLM - AutoModel - AutoTokenizer - text-generation - question-answer ---
mtebad/classification_model	mtebad	2023-06-17T13:51:57Z	111	1	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-17T09:59:38Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: classification_model results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.937 --- <!-- 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. --> # classification_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1669 - Accuracy: 0.937 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2461 \| 1.0 \| 1000 \| 0.1964 \| 0.9265 \| \| 0.1464 \| 2.0 \| 2000 \| 0.1669 \| 0.937 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
thiendio/rl_course_vizdoom_health_gathering_supreme	thiendio	2023-06-17T13:49:02Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T13:47:55Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 7.95 +/- 1.58 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r thiendio/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
catrabbitbear/lunar-lander	catrabbitbear	2023-06-17T13:43:33Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T13:43:04Z	--- 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: 256.65 +/- 38.49 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 ... ```
2tle/kobart-std-to-jeju	2tle	2023-06-17T13:41:43Z	104	1	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "ko", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-06-17T13:31:26Z	--- license: mit language: - ko metrics: - bleu --- # Korean Standard To Jejueo(Jeju Dialect) Translator BART Model ## Dataset - [AI Hub Korean Jejueo(Jeju Dialect) Voice data](https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=121) ## Model Score - BLEU: 40%
tux/Reinforce-copter2	tux	2023-06-17T13:38:46Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T13:38:33Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-copter2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 22.20 +/- 15.59 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
SikongSphere/sikong-alpaca-7b-chinese	SikongSphere	2023-06-17T13:30:51Z	7	1	transformers	[ "transformers", "pytorch", "llama", "text-generation", "generated_from_trainer", "dataset:customized", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-17T12:19:19Z	--- tags: - generated_from_trainer datasets: - customized model-index: - name: finetune 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. --> # finetune This model is a fine-tuned version of [/root/autodl-tmp/sikong/repo/LMFlow/output_models/chinese-alpaca-7b-merged](https://huggingface.co//root/autodl-tmp/sikong/repo/LMFlow/output_models/chinese-alpaca-7b-merged) on the customized 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: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 8 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50.0 ### Training results ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.10.1 - Tokenizers 0.13.3
sd-dreambooth-library/baysafinal	sd-dreambooth-library	2023-06-17T13:30:08Z	31	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-17T13:28:10Z	--- license: creativeml-openrail-m tags: - text-to-image widget: - text: Baysafinal1 --- ### Baysafinal Dreambooth model trained by LabanAsmar with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of: Baysafinal1 (use that on your prompt) ![Baysafinal1 0](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%281%29.jpg)![Baysafinal1 1](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%282%29.jpg)![Baysafinal1 2](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%283%29.jpg)![Baysafinal1 3](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%284%29.jpg)![Baysafinal1 4](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%285%29.jpg)![Baysafinal1 5](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%286%29.jpg)![Baysafinal1 6](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%287%29.jpg)![Baysafinal1 7](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%288%29.jpg)![Baysafinal1 8](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%289%29.jpg)![Baysafinal1 9](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%2810%29.jpg)![Baysafinal1 10](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%2811%29.jpg)![Baysafinal1 11](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%2812%29.jpg)
antphb/DS-Chatbox-bigscience-bloom-560m	antphb	2023-06-17T13:15:50Z	151	0	transformers	[ "transformers", "pytorch", "tensorboard", "bloom", "text-generation", "generated_from_trainer", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-17T11:27:42Z	--- license: bigscience-bloom-rail-1.0 tags: - generated_from_trainer model-index: - name: DS-Chatbox-bigscience-bloom-560m 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. --> # DS-Chatbox-bigscience-bloom-560m This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 4.8320 - eval_runtime: 175.7948 - eval_samples_per_second: 37.402 - eval_steps_per_second: 4.676 - epoch: 0.03 - step: 500 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 3.0 ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
Vas123/my_awesome_mind_model	Vas123	2023-06-17T12:49:22Z	159	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:minds14", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-06-14T14:38:36Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - minds14 metrics: - accuracy model-index: - name: my_awesome_mind_model 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. --> # my_awesome_mind_model This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. It achieves the following results on the evaluation set: - Loss: 2.6448 - Accuracy: 0.0531 ## 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: 3e-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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.8 \| 3 \| 2.6343 \| 0.1150 \| \| No log \| 1.87 \| 7 \| 2.6413 \| 0.0973 \| \| 2.636 \| 2.93 \| 11 \| 2.6433 \| 0.0796 \| \| 2.636 \| 4.0 \| 15 \| 2.6424 \| 0.0708 \| \| 2.636 \| 4.8 \| 18 \| 2.6433 \| 0.0619 \| \| 2.6231 \| 5.87 \| 22 \| 2.6456 \| 0.0354 \| \| 2.6231 \| 6.93 \| 26 \| 2.6451 \| 0.0619 \| \| 2.6184 \| 8.0 \| 30 \| 2.6448 \| 0.0531 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
thiendio/ppo-from-scratch-lunar	thiendio	2023-06-17T12:26:39Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T12:26:16Z	--- 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: -158.29 +/- 101.80 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters
alexanderjoossens/w2v2-libri-10min	alexanderjoossens	2023-06-17T12:16:40Z	105	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-05-22T09:09:45Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: w2v2-libri-10min 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. --> # w2v2-libri-10min This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2500 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 1.18.3 - Tokenizers 0.13.3
SikongSphere/sikong-llama-7b-chinese	SikongSphere	2023-06-17T12:01:59Z	7	2	transformers	[ "transformers", "pytorch", "llama", "text-generation", "generated_from_trainer", "dataset:customized", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-17T09:09:19Z	--- tags: - generated_from_trainer datasets: - customized model-index: - name: finetune 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. --> # finetune This model is a fine-tuned version of [/root/autodl-tmp/sikong/repo/LMFlow/output_models/Linly-Chinese-LLaMA-7b-hf](https://huggingface.co//root/autodl-tmp/sikong/repo/LMFlow/output_models/Linly-Chinese-LLaMA-7b-hf) on the customized 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: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 8 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50.0 ### Training results ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.10.1 - Tokenizers 0.13.3
jalFaizy/ppo-lunar	jalFaizy	2023-06-17T11:42:59Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T11:42:28Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: trial1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 270.71 +/- 14.10 name: mean_reward verified: false --- # trial1 Agent playing LunarLander-v2 This is a trained model of a trial1 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 ... ```
Xavia0012/bert-tomi	Xavia0012	2023-06-17T11:02:13Z	119	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-06-12T19:49:11Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-tomi 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. --> # bert-tomi 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: 0.4198 ## 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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 20 \| 1.5815 \| \| No log \| 2.0 \| 40 \| 0.7518 \| \| No log \| 3.0 \| 60 \| 0.7153 \| \| No log \| 4.0 \| 80 \| 0.6354 \| \| No log \| 5.0 \| 100 \| 0.5895 \| \| No log \| 6.0 \| 120 \| 0.4882 \| \| No log \| 7.0 \| 140 \| 0.4590 \| \| No log \| 8.0 \| 160 \| 0.4303 \| \| No log \| 9.0 \| 180 \| 0.4644 \| \| No log \| 10.0 \| 200 \| 0.4416 \| \| No log \| 11.0 \| 220 \| 0.4348 \| \| No log \| 12.0 \| 240 \| 0.5306 \| \| No log \| 13.0 \| 260 \| 0.4412 \| \| No log \| 14.0 \| 280 \| 0.4053 \| \| No log \| 15.0 \| 300 \| 0.4185 \| \| No log \| 16.0 \| 320 \| 0.3982 \| \| No log \| 17.0 \| 340 \| 0.4291 \| \| No log \| 18.0 \| 360 \| 0.4316 \| \| No log \| 19.0 \| 380 \| 0.4328 \| \| No log \| 20.0 \| 400 \| 0.4198 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.10.1 - Tokenizers 0.12.1
mattladewig/distilbert-base-uncased-finetuned-ner	mattladewig	2023-06-17T10:34:27Z	61	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "token-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-06-17T08:37:53Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: mattladewig/distilbert-base-uncased-finetuned-ner 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. --> # mattladewig/distilbert-base-uncased-finetuned-ner 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: - Train Loss: 0.0342 - Validation Loss: 0.0614 - Train Precision: 0.9248 - Train Recall: 0.9365 - Train F1: 0.9306 - Train Accuracy: 0.9833 - 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Precision \| Train Recall \| Train F1 \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:---------------:\|:------------:\|:--------:\|:--------------:\|:-----:\| \| 0.1951 \| 0.0694 \| 0.9087 \| 0.9181 \| 0.9134 \| 0.9799 \| 0 \| \| 0.0530 \| 0.0621 \| 0.9246 \| 0.9301 \| 0.9273 \| 0.9823 \| 1 \| \| 0.0342 \| 0.0614 \| 0.9248 \| 0.9365 \| 0.9306 \| 0.9833 \| 2 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.0 - Tokenizers 0.13.3
Chetna19/distilbert-base-uncased-distilled-squad_qa_model	Chetna19	2023-06-17T10:13:01Z	123	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:subjqa", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-06-11T13:02:02Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - subjqa model-index: - name: distilbert-base-uncased-distilled-squad_qa_model 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. --> # distilbert-base-uncased-distilled-squad_qa_model This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on the subjqa dataset. It achieves the following results on the evaluation set: - Loss: 2.9380 ## 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: 1e-07 - 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: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 4.1556 \| 1.0 \| 32 \| 4.1242 \| \| 4.0411 \| 2.0 \| 64 \| 4.0582 \| \| 3.9828 \| 3.0 \| 96 \| 3.9948 \| \| 3.9068 \| 4.0 \| 128 \| 3.9378 \| \| 3.8152 \| 5.0 \| 160 \| 3.8835 \| \| 3.7906 \| 6.0 \| 192 \| 3.8329 \| \| 3.7543 \| 7.0 \| 224 \| 3.7842 \| \| 3.7173 \| 8.0 \| 256 \| 3.7377 \| \| 3.6717 \| 9.0 \| 288 \| 3.6958 \| \| 3.6219 \| 10.0 \| 320 \| 3.6559 \| \| 3.587 \| 11.0 \| 352 \| 3.6185 \| \| 3.6111 \| 12.0 \| 384 \| 3.5808 \| \| 3.5374 \| 13.0 \| 416 \| 3.5483 \| \| 3.4506 \| 14.0 \| 448 \| 3.5175 \| \| 3.4286 \| 15.0 \| 480 \| 3.4873 \| \| 3.4021 \| 16.0 \| 512 \| 3.4596 \| \| 3.432 \| 17.0 \| 544 \| 3.4328 \| \| 3.3235 \| 18.0 \| 576 \| 3.4079 \| \| 3.3627 \| 19.0 \| 608 \| 3.3841 \| \| 3.323 \| 20.0 \| 640 \| 3.3615 \| \| 3.3127 \| 21.0 \| 672 \| 3.3389 \| \| 3.2635 \| 22.0 \| 704 \| 3.3199 \| \| 3.2542 \| 23.0 \| 736 \| 3.3013 \| \| 3.2302 \| 24.0 \| 768 \| 3.2846 \| \| 3.1699 \| 25.0 \| 800 \| 3.2676 \| \| 3.2333 \| 26.0 \| 832 \| 3.2516 \| \| 3.2204 \| 27.0 \| 864 \| 3.2364 \| \| 3.1809 \| 28.0 \| 896 \| 3.2218 \| \| 3.1739 \| 29.0 \| 928 \| 3.2082 \| \| 3.1966 \| 30.0 \| 960 \| 3.1950 \| \| 3.1513 \| 31.0 \| 992 \| 3.1826 \| \| 3.135 \| 32.0 \| 1024 \| 3.1713 \| \| 3.1253 \| 33.0 \| 1056 \| 3.1599 \| \| 3.0768 \| 34.0 \| 1088 \| 3.1498 \| \| 3.1031 \| 35.0 \| 1120 \| 3.1394 \| \| 3.064 \| 36.0 \| 1152 \| 3.1293 \| \| 3.0391 \| 37.0 \| 1184 \| 3.1200 \| \| 3.0701 \| 38.0 \| 1216 \| 3.1117 \| \| 3.0787 \| 39.0 \| 1248 \| 3.1032 \| \| 3.0423 \| 40.0 \| 1280 \| 3.0956 \| \| 3.0214 \| 41.0 \| 1312 \| 3.0875 \| \| 3.0289 \| 42.0 \| 1344 \| 3.0804 \| \| 2.9667 \| 43.0 \| 1376 \| 3.0736 \| \| 3.0341 \| 44.0 \| 1408 \| 3.0671 \| \| 3.0098 \| 45.0 \| 1440 \| 3.0606 \| \| 3.0202 \| 46.0 \| 1472 \| 3.0544 \| \| 2.9598 \| 47.0 \| 1504 \| 3.0490 \| \| 2.9734 \| 48.0 \| 1536 \| 3.0430 \| \| 2.9381 \| 49.0 \| 1568 \| 3.0375 \| \| 2.9444 \| 50.0 \| 1600 \| 3.0328 \| \| 2.9357 \| 51.0 \| 1632 \| 3.0280 \| \| 2.9453 \| 52.0 \| 1664 \| 3.0237 \| \| 2.9906 \| 53.0 \| 1696 \| 3.0191 \| \| 2.934 \| 54.0 \| 1728 \| 3.0148 \| \| 2.9076 \| 55.0 \| 1760 \| 3.0110 \| \| 2.9874 \| 56.0 \| 1792 \| 3.0070 \| \| 2.9682 \| 57.0 \| 1824 \| 3.0032 \| \| 2.9287 \| 58.0 \| 1856 \| 2.9994 \| \| 2.9575 \| 59.0 \| 1888 \| 2.9956 \| \| 2.8618 \| 60.0 \| 1920 \| 2.9926 \| \| 2.9614 \| 61.0 \| 1952 \| 2.9893 \| \| 2.9463 \| 62.0 \| 1984 \| 2.9861 \| \| 2.8927 \| 63.0 \| 2016 \| 2.9834 \| \| 2.9048 \| 64.0 \| 2048 \| 2.9805 \| \| 2.9161 \| 65.0 \| 2080 \| 2.9777 \| \| 2.9117 \| 66.0 \| 2112 \| 2.9753 \| \| 2.932 \| 67.0 \| 2144 \| 2.9729 \| \| 2.9148 \| 68.0 \| 2176 \| 2.9706 \| \| 2.8919 \| 69.0 \| 2208 \| 2.9683 \| \| 2.9278 \| 70.0 \| 2240 \| 2.9662 \| \| 2.869 \| 71.0 \| 2272 \| 2.9643 \| \| 2.8844 \| 72.0 \| 2304 \| 2.9622 \| \| 2.8636 \| 73.0 \| 2336 \| 2.9603 \| \| 2.8734 \| 74.0 \| 2368 \| 2.9585 \| \| 2.8934 \| 75.0 \| 2400 \| 2.9569 \| \| 2.86 \| 76.0 \| 2432 \| 2.9551 \| \| 2.8366 \| 77.0 \| 2464 \| 2.9539 \| \| 2.8887 \| 78.0 \| 2496 \| 2.9522 \| \| 2.8632 \| 79.0 \| 2528 \| 2.9511 \| \| 2.8691 \| 80.0 \| 2560 \| 2.9496 \| \| 2.8597 \| 81.0 \| 2592 \| 2.9484 \| \| 2.8775 \| 82.0 \| 2624 \| 2.9473 \| \| 2.8491 \| 83.0 \| 2656 \| 2.9461 \| \| 2.8639 \| 84.0 \| 2688 \| 2.9450 \| \| 2.8659 \| 85.0 \| 2720 \| 2.9443 \| \| 2.8557 \| 86.0 \| 2752 \| 2.9433 \| \| 2.8188 \| 87.0 \| 2784 \| 2.9423 \| \| 2.8896 \| 88.0 \| 2816 \| 2.9416 \| \| 2.8102 \| 89.0 \| 2848 \| 2.9409 \| \| 2.8452 \| 90.0 \| 2880 \| 2.9403 \| \| 2.8437 \| 91.0 \| 2912 \| 2.9399 \| \| 2.8193 \| 92.0 \| 2944 \| 2.9397 \| \| 2.8645 \| 93.0 \| 2976 \| 2.9391 \| \| 2.8745 \| 94.0 \| 3008 \| 2.9388 \| \| 2.8568 \| 95.0 \| 3040 \| 2.9385 \| \| 2.8832 \| 96.0 \| 3072 \| 2.9382 \| \| 2.8801 \| 97.0 \| 3104 \| 2.9382 \| \| 2.8488 \| 98.0 \| 3136 \| 2.9383 \| \| 2.8233 \| 99.0 \| 3168 \| 2.9380 \| \| 2.8505 \| 100.0 \| 3200 \| 2.9380 \| ### Framework versions - Transformers 4.28.0 - Pytorch 1.13.0a0+d321be6 - Datasets 2.12.0 - Tokenizers 0.13.3
kevinng77/unsup_bert_L3	kevinng77	2023-06-17T10:00:06Z	107	0	transformers	[ "transformers", "pytorch", "onnx", "bert", "text-classification", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-17T08:47:52Z	--- license: apache-2.0 language: - en metrics: - accuracy - f1 pipeline_tag: text-classification --- ```python # transformers==4.29.1 from transformers import AutoTokenizer, pipeline from optimum.onnxruntime import ORTModelForSequenceClassification onnx_model_path = "kevinng77/unsup_bert_L3" tokenizer = AutoTokenizer.from_pretrained(onnx_model_path) onnx_model = ORTModelForSequenceClassification.from_pretrained(onnx_model_path) onnx_pipe = pipeline(task="text-classification", model=onnx_model, tokenizer=tokenizer) onnx_pipe("How many rows are there in the table?") ```
hts98/whisper-tiny-paper	hts98	2023-06-17T09:45:10Z	77	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-06-16T15:36:23Z	--- tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-tiny-paper 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. --> # whisper-tiny-paper This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6807 - Wer: 50.8558 ## 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: 48 - eval_batch_size: 48 - 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| No log \| 1.0 \| 72 \| 0.6515 \| 50.3886 \| \| No log \| 2.0 \| 144 \| 0.6566 \| 50.8012 \| \| No log \| 3.0 \| 216 \| 0.6624 \| 50.3713 \| \| No log \| 4.0 \| 288 \| 0.6684 \| 50.8026 \| \| No log \| 5.0 \| 360 \| 0.6807 \| 50.8558 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.7.0 - Tokenizers 0.13.3
ganghe74/distilbert-base-uncased-finetuned-emotion	ganghe74	2023-06-17T09:34:40Z	105	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-17T09:13:51Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9225 - name: F1 type: f1 value: 0.922469380812715 --- <!-- 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. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2170 - Accuracy: 0.9225 - F1: 0.9225 ## 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: 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8057 \| 1.0 \| 250 \| 0.3170 \| 0.905 \| 0.9023 \| \| 0.242 \| 2.0 \| 500 \| 0.2170 \| 0.9225 \| 0.9225 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.13.0 - Tokenizers 0.13.3
edu-linguistic/opt-1.3b-edu-sft	edu-linguistic	2023-06-17T09:28:57Z	0	0	null	[ "en", "dataset:yahma/alpaca-cleaned", "dataset:Nebulous/gpt4all_pruned", "region:us" ]	null	2023-06-15T14:16:11Z	--- datasets: - yahma/alpaca-cleaned - Nebulous/gpt4all_pruned language: - en --- ## Inference Example: ```python from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer peft_model_id = "edu-linguistic/opt-1.3b-edu-sft" model_name = 'facebook/opt-1.3b' config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(model_name) model = PeftModel.from_pretrained(model, peft_model_id) tokenizer = AutoTokenizer.from_pretrained(model_name) question = "<\|prompter\|> Consider the following function: f(x1, x2) = ln(x1). This function is…" question = tokenizer.encode(question, return_tensors='pt') generation_kwargs = { "do_sample": True, "top_k": 0, "top_p": 0.9, "bos_token_id": tokenizer.bos_token_id, "pad_token_id": tokenizer.pad_token_id, "eos_token_id": tokenizer.eos_token_id, "num_return_sequences": 1, "min_new_tokens": 10, "max_new_tokens": 512, } response = model.generate(input_ids=question, **generation_kwargs) response = tokenizer.decode(response[0], skip_special_tokens=False, clean_up_tokenization_spaces=False ) print(response) ```
coyude/Nous-Hermes-13b-Chinese-GGML	coyude	2023-06-17T09:28:23Z	0	22	transformers	[ "transformers", "text-generation", "zh", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2023-06-11T03:42:04Z	--- license: apache-2.0 language: - zh - en library_name: transformers pipeline_tag: text-generation --- 原始模型：https://huggingface.co/NousResearch/Nous-Hermes-13b lora：https://huggingface.co/ziqingyang/chinese-alpaca-lora-13b 将Nous-Hermes-13b与chinese-alpaca-lora-13b进行合并，增强模型的中文能力，~~不过存在翻译腔~~ 使用项目： https://github.com/ymcui/Chinese-LLaMA-Alpaca https://github.com/ggerganov/llama.cpp 推荐q5_k_m或q4_k_m 该仓库模型均为ggmlv3模型 Text-generation-webui懒人包： https://www.bilibili.com/read/cv23495183 --------------------------------------------------------------------------------------------- Original model: https://huggingface.co/NousResearch/Nous-Hermes-13b Lora: https://huggingface.co/ziqingyang/chinese-alpaca-lora-13b The Nous-Hermes-13b model is merged with the chinese-alpaca-lora-13b model to enhance the Chinese language capability of the model, ~~although it may exhibit a translation style.~~ Usage projects: https://github.com/ymcui/Chinese-LLaMA-Alpaca https://github.com/ggerganov/llama.cpp q5_k_m or q4_k_m is recommended. All models in this repository are ggmlv3 models.
parkyunmin/my_awesome_eli5_clm-model	parkyunmin	2023-06-17T09:09:15Z	211	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-17T05:54:26Z	--- license: mit tags: - generated_from_trainer model-index: - name: my_awesome_eli5_clm-model 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. --> # my_awesome_eli5_clm-model This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5380 ## 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: 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 49 \| 1.6679 \| \| No log \| 2.0 \| 98 \| 1.5629 \| \| No log \| 3.0 \| 147 \| 1.5380 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
PabloQuant29/ppo-LunarLander-v2	PabloQuant29	2023-06-17T08:36:13Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T08:35:40Z	--- 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: 244.46 +/- 18.98 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 ... ```
AriesChen/GeoLLM	AriesChen	2023-06-17T08:32:06Z	195	3	transformers	[ "transformers", "pytorch", "chatglm", "feature-extraction", "custom_code", "region:us" ]	feature-extraction	2023-06-17T08:30:04Z	# GeoLLM Large Language Model for Geology Large language models are used to organize geology-related knowledge (geology, geophysics, geophysical logging, etc.). This version uses the [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B) base model and fine-tunes it using P-tuning. --- ### Sedimentology Sedimentology, the study of sedimentary rocks and the processes by which they are formed, includes and is related to a large number of phenomena. Sedimentology includes the five fundamental processes defined by the term sediaentation --weathering, erosion, transportation, deposition and diagenesis. Datasets：《沉积岩石学（第四版）》朱筱敏 Model： ChatGLM-6B Fine-tuning： P-Tuning v2 Before fine-tuning ``` response, history = model.chat(tokenizer, "什么是沉积岩石学？", history=[]) response 沉积岩石学是一门研究沉积岩的形成、结构和成分的学科，主要关注地球表面上不同条件下的沉积过程和岩石形成机制，包括岩浆沉积、冰川沉积、洪水沉积、海洋沉积等。沉积岩石学在地质学、地球物理学、地球化学、材料科学等领域都有广泛应用，因为沉积岩是许多自然和人工地质工程的基础，如地质勘探、矿产资源开发、土木工程、环境科学等。沉积岩石学的研究对象包括沉积岩的地质特征、成分和构造特征，以及沉积岩与地壳、岩浆和变质岩的关系。研究方法包括沉积岩分析、岩相学分析、岩浆动力学分析等。 ``` After fine-tuning ``` response, history = model.chat(tokenizer, "什么是沉积岩石学？", history=[]) response 沉积岩石学是研究沉积岩的物质成分、结构构造、岩石类型、沉积物沉积作用和沉积物质形成环境以及沉积岩分布规律的一门科学。 ``` Error Analysis: We meticulously refined the model by approximately 500 entries from academic textbooks, subsequently applying P-Tuning v2 for optimization. Detailed control of parameters was not conducted for the time being. Given the scarcity of the training data and the fine-tuning parameters, the outcomes might exhibit some irregularities. Results Analysis: It is evident that the fine-tuned model shows enhanced reliability(more precise and concise) when providing answers within specialized knowledge domains. Moving forward, we will persist in enriching our training data and optimizing our fine-tuning methodologies in order to yield superior results. --- ### TODO 1. Geophysical Exploration 2. Geophysical logging 3. Petroleum Geology etc... --- ### Related Resources 1. [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B): ChatGLM-6B is an open bilingual language model based on General Language Model (GLM) framework, with 6.2 billion parameters.
okazaki-lab/ss_wsd	okazaki-lab	2023-06-17T08:21:08Z	0	0	transformers	[ "transformers", "word_sense_disambiguation", "en", "dataset:SemCor", "dataset:WordNet", "dataset:WSD_Evaluation_Framework", "arxiv:2304.11340", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2023-06-17T07:20:37Z	--- license: apache-2.0 language: - en tags: - word_sense_disambiguation library_name: transformers datasets: - SemCor - WordNet - WSD_Evaluation_Framework metrics: - f1 --- # Semantic Specialization for Knowledge-based Word Sense Disambiguation * This repository contains the trained model (projection heads) and sense/context embeddings used for training and evaluating the model. * If you want to learn how to use these files, please refer to the [semantic_specialization_for_wsd](https://github.com/s-mizuki-nlp/semantic_specialization_for_wsd) repository. ## Trained Model (Projection Heads) * File: checkpoints/baseline/last.ckpt * This is one of the trained models used for reporting the main results (Table 2 in [Mizuki and Okazaki, EACL2023]). NOTE: Five runs were performed in total. * The main hyperparameters used for training are as follows: \| Argument name \| Value \| Description \| \|----------------------------------------------------------------\|----------------------------\|------------------------------------------------------------------------------------\| \| max_epochs \| 15 \| Maximum number of training epochs \| \| cfg_similarity_class.temperature ($\beta^{-1}$) \| 0.015625 (=1/64) \| Temperature parameter for the contrastive loss \| \| batch_size ($N_B$) \| 256 \| Number of samples in each batch for the attract-repel and self-training objectives \| \| coef_max_pool_margin_loss ($\alpha$) \| 0.2 \| Coefficient for the self-training loss \| \| cfg_gloss_projection_head.n_layer \| 2 \| Number of FFNN layers for the projection heads \| \| cfg_gloss_projection_head.max_l2_norm_ratio ($\epsilon$) \| 0.015 \| Hyperparameter for the distance constraint integrated in the projection heads \| ## Sense/context embeddings * Directory: `data/bert_embeddings/` * Sense embeddings: `bert-large-cased_WordNet_Gloss_Corpus.hdf5` * Context embeddings for the self-training objective: `bert-large-cased_SemCor.hdf5` * Context embeddings for evaluating the WSD task: `bert-large-cased_WSDEval-ALL.hdf5` # Reference ``` @inproceedings{Mizuki:EACL2023, title = "Semantic Specialization for Knowledge-based Word Sense Disambiguation", author = "Mizuki, Sakae and Okazaki, Naoaki", booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume", series = {EACL}, month = may, year = "2023", address = "Dubrovnik, Croatia", publisher = "Association for Computational Linguistics", pages = "3449--3462", } ``` * [arXiv version](https://arxiv.org/abs/2304.11340) is also available.
SM16/TreeClassifier	SM16	2023-06-17T08:15:11Z	218	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-06-17T07:27:25Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: TreeClassifier results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 1.0 --- # TreeClassifier Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### Pepper Tree ![Pepper Tree](images/Pepper_Tree.jpg) #### Weeping Willow ![Weeping Willow](images/Weeping_Willow.jpg)
musabg/mt5-xl-tr-summarization	musabg	2023-06-17T07:25:20Z	5	1	transformers	[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "tr", "dataset:musabg/wikipedia-tr-summarization", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-06-08T16:24:59Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - musabg/wikipedia-tr-summarization metrics: - rouge model-index: - name: mt5-xl-tr-summarization results: - task: name: Summarization type: summarization dataset: name: musabg/wikipedia-tr-summarization type: musabg/wikipedia-tr-summarization split: validation metrics: - name: Rouge1 type: rouge value: 56.4468 language: - tr --- # mT5-Xl Turkish Summarization This model is a fine-tuned version of [google/mt5-xl](https://huggingface.co/google/mt5-xl) on the musabg/wikipedia-tr-summarization dataset. This can be used with HF summarization pipeline. ## 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: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Eval results It achieves the following results on the evaluation set: - Loss: 0.5676 - Rouge1: 56.4468 - Rouge2: 41.3258 - Rougel: 48.1909 - Rougelsum: 48.4284 - Gen Len: 75.9265 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 1.13.1 - Datasets 2.12.0 - Tokenizers 0.13.3
Irgendsoeine/FaceTheVote3	Irgendsoeine	2023-06-17T07:10:58Z	4	0	tf-keras	[ "tf-keras", "mobilenet", "image-classification", "region:us" ]	image-classification	2023-06-17T06:56:45Z	--- pipeline_tag: image-classification ---
tux/dqn-SpaceInvadersNoFrameskip-v4	tux	2023-06-17T07:09:15Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-16T09:52:12Z	--- 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: 606.00 +/- 186.22 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 tux -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 tux -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 tux ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('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'} ```
Csuarezg/SBERTA-finetuned	Csuarezg	2023-06-17T07:04:30Z	104	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "feature-extraction", "es", "dataset:xnli", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2023-06-12T21:04:08Z	--- datasets: - xnli language: - es library_name: transformers ---
kjiwon1222/my_awesome_eli5_clm-model	kjiwon1222	2023-06-17T06:54:34Z	217	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-17T06:32:49Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: my_awesome_eli5_clm-model 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. --> # my_awesome_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7506 ## 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: 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.8621 \| 1.0 \| 1137 \| 3.7690 \| \| 3.7782 \| 2.0 \| 2274 \| 3.7533 \| \| 3.7245 \| 3.0 \| 3411 \| 3.7506 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
Arindam75/Reinforce-pixelcopter-v1	Arindam75	2023-06-17T06:22:04Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T06:21:05Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-pixelcopter-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 18.90 +/- 13.60 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
sunflowermarshmallows/dqn-SpaceInvadersNoFrameskip-v4	sunflowermarshmallows	2023-06-17T05:25:16Z	1	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T05:24:36Z	--- 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: 629.00 +/- 184.89 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 sunflowermarshmallows -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 sunflowermarshmallows -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 sunflowermarshmallows ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('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'} ```
nolanaatama/skrmkhllvjprvc500pchsmgzb	nolanaatama	2023-06-17T05:02:20Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-17T04:59:21Z	--- license: creativeml-openrail-m ---
eason0203/Reinforce-cartpole	eason0203	2023-06-17T04:34:33Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T04:34:18Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cartpole results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 418.80 +/- 129.36 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . 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
nolanaatama/rngrndrvcv800pchsrthysttylrsvrsn	nolanaatama	2023-06-17T04:33:50Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-05-31T04:13:59Z	--- license: creativeml-openrail-m ---
ALPHONSE28/EQUIPO06SEMANA09	ALPHONSE28	2023-06-17T04:33:00Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-16T06:38:04Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: EQUIPO06SEMANA09 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. --> # EQUIPO06SEMANA09 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2161 - Accuracy: 0.9233 - F1: 0.9514 ## 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 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
AustinCarthy/OnlyPhishGPT2_subdomain_100KP_BFall_fromB_200K_topP_0.75_ratio5	AustinCarthy	2023-06-17T04:00:39Z	0	0	null	[ "tensorboard", "generated_from_trainer", "license:apache-2.0", "region:us" ]	null	2023-06-16T22:42:13Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: OnlyPhishGPT2_subdomain_100KP_BFall_fromB_200K_topP_0.75_ratio5 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. --> # OnlyPhishGPT2_subdomain_100KP_BFall_fromB_200K_topP_0.75_ratio5 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the Train benign: Fall,Test Benign: Fall, Train phish: Fall, Test phish: Fall, generated url dataset: generated_phish_OnlyPhishGPT2_using_benigh_200K_top_p_0.75 dataset. It achieves the following results on the evaluation set: - Loss: 0.0194 - Accuracy: 0.9979 - F1: 0.9778 - Precision: 0.9987 - Recall: 0.9578 - Roc Auc Score: 0.9789 - Tpr At Fpr 0.01: 0.9642 ## 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: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| Roc Auc Score \| Tpr At Fpr 0.01 \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|:-------------:\|:---------------:\| \| 0.0035 \| 1.0 \| 56250 \| 0.0126 \| 0.9975 \| 0.9736 \| 0.9917 \| 0.9562 \| 0.9779 \| 0.9052 \| \| 0.002 \| 2.0 \| 112500 \| 0.0159 \| 0.9977 \| 0.9755 \| 0.9975 \| 0.9544 \| 0.9771 \| 0.9466 \| \| 0.0008 \| 3.0 \| 168750 \| 0.0136 \| 0.9981 \| 0.9793 \| 0.9977 \| 0.9616 \| 0.9807 \| 0.958 \| \| 0.0 \| 4.0 \| 225000 \| 0.0235 \| 0.9973 \| 0.9708 \| 0.9992 \| 0.944 \| 0.9720 \| 0.9574 \| \| 0.0004 \| 5.0 \| 281250 \| 0.0194 \| 0.9979 \| 0.9778 \| 0.9987 \| 0.9578 \| 0.9789 \| 0.9642 \| ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
digiplay/ShowmakerMix_v1	digiplay	2023-06-17T03:05:06Z	310	3	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-13T01:35:32Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info: https://civitai.com/models/16032/showmakermix Original Author's DEMO image: ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/06e7a817-ddbb-4088-5fa0-fb2dd6bfd700/width=450/197406.jpeg)
DreamerGPT/D7b-5-1	DreamerGPT	2023-06-17T01:38:49Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2023-06-17T01:20:31Z	--- license: apache-2.0 --- # D7b-5-1 [https://github.com/DreamerGPT/DreamerGPT](https://github.com/DreamerGPT/DreamerGPT)
mskani/controlnet-hands	mskani	2023-06-17T01:35:09Z	0	5	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-16T14:10:39Z	--- license: creativeml-openrail-m ---
zhangjian94cn/Taxi-v3	zhangjian94cn	2023-06-17T01:33:35Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-17T01:33:26Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-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="zhangjian94cn/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"]) ```
darshan7/Model_xlnet_results	darshan7	2023-06-17T01:22:18Z	59	0	transformers	[ "transformers", "tf", "xlnet", "question-answering", "generated_from_keras_callback", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-06-14T19:04:11Z	--- license: mit tags: - generated_from_keras_callback model-index: - name: darshan7/Model_xlnet_results 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. --> # darshan7/Model_xlnet_results This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0058 - Validation Loss: 0.0110 - Epoch: 9 ## 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 181655, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.0392 \| 0.0262 \| 0 \| \| 0.0211 \| 0.0185 \| 1 \| \| 0.0151 \| 0.0161 \| 2 \| \| 0.0110 \| 0.0127 \| 3 \| \| 0.0074 \| 0.0110 \| 4 \| \| 0.0058 \| 0.0110 \| 5 \| \| 0.0058 \| 0.0110 \| 6 \| \| 0.0058 \| 0.0110 \| 7 \| \| 0.0059 \| 0.0110 \| 8 \| \| 0.0058 \| 0.0110 \| 9 \| ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
DreamerGPT/D13b-3-3	DreamerGPT	2023-06-17T01:21:55Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2023-06-17T00:58:23Z	--- license: apache-2.0 --- # D13b-3-3 [https://github.com/DreamerGPT/DreamerGPT](https://github.com/DreamerGPT/DreamerGPT)
sheshenin/vvshsh	sheshenin	2023-06-17T00:40:05Z	32	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-17T00:35:21Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### VikaSH Dreambooth model trained by sheshenin with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
Ioanaaaaaaa/bert-base-uncased-with-preprocess-finetuned-emotion-5-epochs-5e-05-lr-0.1-weight_decay	Ioanaaaaaaa	2023-06-16T23:47:54Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-16T23:30:14Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: bert-base-uncased-with-preprocess-finetuned-emotion-5-epochs-5e-05-lr-0.1-weight_decay results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.941 - name: F1 type: f1 value: 0.9411169346964399 --- <!-- 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. --> # bert-base-uncased-with-preprocess-finetuned-emotion-5-epochs-5e-05-lr-0.1-weight_decay This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2591 - Accuracy: 0.941 - F1: 0.9411 ## 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: 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.0799 \| 1.0 \| 250 \| 0.1898 \| 0.9375 \| 0.9377 \| \| 0.0516 \| 2.0 \| 500 \| 0.2290 \| 0.938 \| 0.9383 \| \| 0.0386 \| 3.0 \| 750 \| 0.2107 \| 0.9415 \| 0.9419 \| \| 0.0195 \| 4.0 \| 1000 \| 0.2607 \| 0.9435 \| 0.9433 \| \| 0.0149 \| 5.0 \| 1250 \| 0.2591 \| 0.941 \| 0.9411 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
TheBloke/robin-33B-v2-GGML	TheBloke	2023-06-16T23:31:16Z	0	5	null	[ "license:other", "region:us" ]	null	2023-06-16T18:09:39Z	--- 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/Jq4vkcDakD">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 --> # OptimalScale's Robin 33B v2 GGML These files are GGML format model files for [OptimalScale's Robin 33B v2](https://huggingface.co/OptimalScale/robin-33b-v2-delta). 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) * [ParisNeo/GPT4All-UI](https://github.com/ParisNeo/gpt4all-ui) * [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/robin-33B-v2-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/robin-33B-v2-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/robin-33B-v2-fp16) ## Prompt template ``` A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions ###Human: 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 \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| robin-33b.ggmlv3.q2_K.bin \| q2_K \| 2 \| 13.71 GB \| 16.21 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. \| \| robin-33b.ggmlv3.q3_K_L.bin \| q3_K_L \| 3 \| 17.28 GB \| 19.78 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 \| \| robin-33b.ggmlv3.q3_K_M.bin \| q3_K_M \| 3 \| 15.72 GB \| 18.22 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 \| \| robin-33b.ggmlv3.q3_K_S.bin \| q3_K_S \| 3 \| 14.06 GB \| 16.56 GB \| New k-quant method. Uses GGML_TYPE_Q3_K for all tensors \| \| robin-33b.ggmlv3.q4_0.bin \| q4_0 \| 4 \| 18.30 GB \| 20.80 GB \| Original llama.cpp quant method, 4-bit. \| \| robin-33b.ggmlv3.q4_1.bin \| q4_1 \| 4 \| 20.33 GB \| 22.83 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. \| \| robin-33b.ggmlv3.q4_K_M.bin \| q4_K_M \| 4 \| 19.62 GB \| 22.12 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 \| \| robin-33b.ggmlv3.q4_K_S.bin \| q4_K_S \| 4 \| 18.36 GB \| 20.86 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for all tensors \| \| robin-33b.ggmlv3.q5_0.bin \| q5_0 \| 5 \| 22.37 GB \| 24.87 GB \| Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. \| \| robin-33b.ggmlv3.q5_1.bin \| q5_1 \| 5 \| 24.40 GB \| 26.90 GB \| Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. \| \| robin-33b.ggmlv3.q5_K_M.bin \| q5_K_M \| 5 \| 23.05 GB \| 25.55 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 \| \| robin-33b.ggmlv3.q5_K_S.bin \| q5_K_S \| 5 \| 22.40 GB \| 24.90 GB \| New k-quant method. Uses GGML_TYPE_Q5_K for all tensors \| \| robin-33b.ggmlv3.q6_K.bin \| q6_K \| 6 \| 26.69 GB \| 29.19 GB \| New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors \| \| robin-33b.ggmlv3.q8_0.bin \| q8_0 \| 8 \| 34.56 GB \| 37.06 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 robin-33b.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n###Human: write a story about llamas\n###Assistant:" ``` Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. 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/Jq4vkcDakD) ## 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: vamX, K, Jonathan Leane, Lone Striker, Sean Connelly, Chris McCloskey, WelcomeToTheClub, Nikolai Manek, John Detwiler, Kalila, David Flickinger, Fen Risland, subjectnull, Johann-Peter Hartmann, Talal Aujan, John Villwock, senxiiz, Khalefa Al-Ahmad, Kevin Schuppel, Alps Aficionado, Derek Yates, Mano Prime, Nathan LeClaire, biorpg, trip7s trip, Asp the Wyvern, chris gileta, Iucharbius , Artur Olbinski, Ai Maven, Joseph William Delisle, Luke Pendergrass, Illia Dulskyi, Eugene Pentland, Ajan Kanaga, Willem Michiel, Space Cruiser, Pyrater, Preetika Verma, Junyu Yang, Oscar Rangel, Spiking Neurons AB, Pierre Kircher, webtim, Cory Kujawski, terasurfer , Trenton Dambrowitz, Gabriel Puliatti, Imad Khwaja, Luke. Thank you to all my generous patrons and donaters! <!-- footer end --> # Original model card: OptimalScale's Robin 33B v2 No model card provided in source repository.
ghze/Taxi_v3	ghze	2023-06-16T23:00:53Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-16T23:00:48Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi_v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 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="ghze/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"]) ```
ghze/Taxi	ghze	2023-06-16T22:59:16Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-16T22:59:09Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 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="ghze/Taxi", 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"]) ```
sam34738/indicbert	sam34738	2023-06-16T22:03:57Z	167	0	transformers	[ "transformers", "pytorch", "albert", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-16T21:56:33Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: indicbert 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. --> # indicbert This model is a fine-tuned version of [ai4bharat/indic-bert](https://huggingface.co/ai4bharat/indic-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9751 - Accuracy: 0.6689 - F1: 0.6899 ## 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: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-05 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.7041 \| 1.0 \| 2100 \| 0.7416 \| 0.6589 \| 0.6710 \| \| 0.8083 \| 2.0 \| 4200 \| 0.9751 \| 0.6689 \| 0.6899 \| ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
Enterprize1/ppo-LunarLander-v2	Enterprize1	2023-06-16T21:45:24Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-16T21:45:00Z	--- 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: 242.78 +/- 66.66 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 ... ```
yinxiaoz/bert-finetuned-ner	yinxiaoz	2023-06-16T21:37:53Z	109	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-06-15T05:15:04Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.9326065411298315 - name: Recall type: recall value: 0.9501851228542578 - name: F1 type: f1 value: 0.9413137712570858 - name: Accuracy type: accuracy value: 0.9867104256195914 --- <!-- 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. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0600 - Precision: 0.9326 - Recall: 0.9502 - F1: 0.9413 - Accuracy: 0.9867 ## 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: 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 \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0884 \| 1.0 \| 1756 \| 0.0675 \| 0.9186 \| 0.9339 \| 0.9261 \| 0.9822 \| \| 0.0345 \| 2.0 \| 3512 \| 0.0611 \| 0.9291 \| 0.9485 \| 0.9387 \| 0.9862 \| \| 0.0182 \| 3.0 \| 5268 \| 0.0600 \| 0.9326 \| 0.9502 \| 0.9413 \| 0.9867 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
stanford-crfm/music-small-ar-800k	stanford-crfm	2023-06-16T21:28:12Z	183	1	transformers	[ "transformers", "pytorch", "gpt2", "arxiv:2306.08620", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2023-06-05T00:01:12Z	--- license: apache-2.0 --- This is a Small (128M parameter) Transformer trained for 800k steps on arrival-time encoded music from the [Lakh MIDI dataset](https://colinraffel.com/projects/lmd/). # References for the Anticipatory Music Transformer The Anticipatory Music Transformer paper is available on [ArXiv](http://arxiv.org/abs/2306.08620). The full model card is available [here](https://johnthickstun.com/assets/pdf/music-modelcard.pdf). Code for using this model is available on [GitHub](https://github.com/jthickstun/anticipation/). See the accompanying [blog post](https://crfm.stanford.edu/2023/06/16/anticipatory-music-transformer.html) for additional discussion of this model.
stanford-crfm/music-small-800k	stanford-crfm	2023-06-16T21:27:08Z	664	1	transformers	[ "transformers", "pytorch", "gpt2", "arxiv:2306.08620", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2023-06-04T23:54:35Z	--- license: apache-2.0 --- This is a Small (128M parameter) Transformer trained for 800k steps on arrival-time encoded music from the [Lakh MIDI dataset](https://colinraffel.com/projects/lmd/). This model was trained with anticipation. # References for the Anticipatory Music Transformer The Anticipatory Music Transformer paper is available on [ArXiv](http://arxiv.org/abs/2306.08620). The full model card is available [here](https://johnthickstun.com/assets/pdf/music-modelcard.pdf). Code for using this model is available on [GitHub](https://github.com/jthickstun/anticipation/). See the accompanying [blog post](https://crfm.stanford.edu/2023/06/16/anticipatory-music-transformer.html) for additional discussion of this model.
stanford-crfm/music-medium-800k	stanford-crfm	2023-06-16T21:25:52Z	572	4	transformers	[ "transformers", "pytorch", "gpt2", "arxiv:2306.08620", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2023-06-05T00:17:20Z	--- license: apache-2.0 --- This is a Medium (360M parameter) Transformer trained for 800k steps on arrival-time encoded music from the [Lakh MIDI dataset](https://colinraffel.com/projects/lmd/). This model was trained with anticipation. # References for the Anticipatory Music Transformer The Anticipatory Music Transformer paper is available on [ArXiv](http://arxiv.org/abs/2306.08620). The full model card is available [here](https://johnthickstun.com/assets/pdf/music-modelcard.pdf). Code for using this model is available on [GitHub](https://github.com/jthickstun/anticipation/). See the accompanying [blog post](https://crfm.stanford.edu/2023/06/16/anticipatory-music-transformer.html) for additional discussion of this model.
stanford-crfm/music-medium-100k	stanford-crfm	2023-06-16T21:24:54Z	176	0	transformers	[ "transformers", "pytorch", "gpt2", "arxiv:2306.08620", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2023-06-05T00:08:04Z	--- license: apache-2.0 --- This is a Medium (360M parameter) Transformer trained for 100k steps on arrival-time encoded music from the [Lakh MIDI dataset](https://colinraffel.com/projects/lmd/). This model was trained with anticipation. # References for the Anticipatory Music Transformer The Anticipatory Music Transformer paper is available on [ArXiv](http://arxiv.org/abs/2306.08620). The full model card is available [here](https://johnthickstun.com/assets/pdf/music-modelcard.pdf). Code for using this model is available on [GitHub](https://github.com/jthickstun/anticipation/). See the accompanying [blog post](https://crfm.stanford.edu/2023/06/16/anticipatory-music-transformer.html) for additional discussion of this model.
stanford-crfm/music-large-100k	stanford-crfm	2023-06-16T21:24:11Z	189	0	transformers	[ "transformers", "pytorch", "gpt2", "arxiv:2306.08620", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2023-06-05T00:22:37Z	--- license: apache-2.0 --- This is a Large (780M parameter) Transformer trained for 100k steps on arrival-time encoded music from the [Lakh MIDI dataset](https://colinraffel.com/projects/lmd/). This model was trained with anticipation. # References for the Anticipatory Music Transformer The Anticipatory Music Transformer paper is available on [ArXiv](http://arxiv.org/abs/2306.08620). The full model card is available [here](https://johnthickstun.com/assets/pdf/music-modelcard.pdf). Code for using this model is available on [GitHub](https://github.com/jthickstun/anticipation/). See the accompanying [blog post](https://crfm.stanford.edu/2023/06/16/anticipatory-music-transformer.html) for additional discussion of this model.
Schnitzl/detr-resnet-50_finetuned_cppe5	Schnitzl	2023-06-16T20:54:42Z	191	0	transformers	[ "transformers", "pytorch", "tensorboard", "detr", "object-detection", "generated_from_trainer", "dataset:cppe-5", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2023-06-16T17:17:04Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - cppe-5 model-index: - name: detr-resnet-50_finetuned_cppe5 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. --> # detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 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: 1e-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: 100 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.13.0 - Tokenizers 0.13.3
crlandsc/bsrnn-vocals	crlandsc	2023-06-16T20:25:39Z	0	2	null	[ "audio source separation", "music demixing", "band-split recurrent neural network", "bsrnn", "spectrogram", "vocals", "region:us" ]	null	2023-06-16T20:18:04Z	--- tags: - audio source separation - music demixing - band-split recurrent neural network - bsrnn - spectrogram - vocals --- # Model Card for bsrnn-vocals Vocals model for [Music-Demixing-with-Band-Split-RNN](https://github.com/crlandsc/Music-Demixing-with-Band-Split-RNN).
GEMCorp/q-Taxi-v3	GEMCorp	2023-06-16T20:19:33Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-16T20:08:42Z	--- 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: 7.56 +/- 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="GEMCorp/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"]) ```
sngsfydy/resnet-50-finetuned-eurosat	sngsfydy	2023-06-16T20:17:05Z	209	0	transformers	[ "transformers", "pytorch", "tensorboard", "resnet", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-06-16T19:14:44Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: resnet-50-finetuned-eurosat 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. --> # resnet-50-finetuned-eurosat This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0706 - Accuracy: 0.5152 ## 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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.6069 \| 0.99 \| 20 \| 1.5839 \| 0.3879 \| \| 1.5395 \| 1.98 \| 40 \| 1.4860 \| 0.5485 \| \| 1.4321 \| 2.96 \| 60 \| 1.3500 \| 0.5364 \| \| 1.3292 \| 4.0 \| 81 \| 1.1826 \| 0.5212 \| \| 1.233 \| 4.99 \| 101 \| 1.0706 \| 0.5152 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
TheBloke/robin-13B-v2-GGML	TheBloke	2023-06-16T20:13:21Z	0	6	null	[ "license:other", "region:us" ]	null	2023-06-16T18:59:47Z	--- 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/Jq4vkcDakD">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 --> # OptimalScale's Robin 13B v2 GGML These files are GGML format model files for [OptimalScale's Robin 13B v2](https://huggingface.co/OptimalScale/robin-13b-v2-delta). 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) * [ParisNeo/GPT4All-UI](https://github.com/ParisNeo/gpt4all-ui) * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) * [ctransformers](https://github.com/marella/ctransformers) ## Prompt template ``` A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions ###Human: prompt ###Assistant: ``` ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/robin-13B-v2-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/robin-13B-v2-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/robin-13B-v2-fp16) <!-- 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 \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| robin-13b.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. \| \| robin-13b.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 \| \| robin-13b.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 \| \| robin-13b.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 \| \| robin-13b.ggmlv3.q4_0.bin \| q4_0 \| 4 \| 7.32 GB \| 9.82 GB \| Original llama.cpp quant method, 4-bit. \| \| robin-13b.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. \| \| robin-13b.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 \| \| robin-13b.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 \| \| robin-13b.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. \| \| robin-13b.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. \| \| robin-13b.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 \| \| robin-13b.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 \| \| robin-13b.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 \| \| robin-13b.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 robin-13b.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n###Human: write a story about llamas\n###Assistant:" ``` Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. 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/Jq4vkcDakD) ## 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: vamX, K, Jonathan Leane, Lone Striker, Sean Connelly, Chris McCloskey, WelcomeToTheClub, Nikolai Manek, John Detwiler, Kalila, David Flickinger, Fen Risland, subjectnull, Johann-Peter Hartmann, Talal Aujan, John Villwock, senxiiz, Khalefa Al-Ahmad, Kevin Schuppel, Alps Aficionado, Derek Yates, Mano Prime, Nathan LeClaire, biorpg, trip7s trip, Asp the Wyvern, chris gileta, Iucharbius , Artur Olbinski, Ai Maven, Joseph William Delisle, Luke Pendergrass, Illia Dulskyi, Eugene Pentland, Ajan Kanaga, Willem Michiel, Space Cruiser, Pyrater, Preetika Verma, Junyu Yang, Oscar Rangel, Spiking Neurons AB, Pierre Kircher, webtim, Cory Kujawski, terasurfer , Trenton Dambrowitz, Gabriel Puliatti, Imad Khwaja, Luke. Thank you to all my generous patrons and donaters! <!-- footer end --> # Original model card: OptimalScale's Robin 13B v2 No model card provided in source repository.
FALLENSTAR/MitsubishiChariotLoRa	FALLENSTAR	2023-06-16T20:10:47Z	0	0	null	[ "region:us" ]	null	2023-06-09T22:52:05Z	### Model Description That LoRa based on Mitsubishi Chariot/Chariot grandis 1997-2003. It's also a test model that poorly configured, so you have to play with the settings... The best images I was able to get with this LoRa were at these settings: Steps: 25 Sampler: DPM++ SDE Karras, CFG scale: 6.5 and with LoRa strength 0.8-1 ![00078-3304362297.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/2zwn_ybCAMQij0uC5C2xO.png) ![00080-176898055.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/3B-I5jhupVxoOCFgQVWln.png) ![00144-12952343.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/0Sqxm4quAS9P12NVxCVYG.png) ![00146-3385434376.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/jzItGhLhj3STdfdqHNgDv.png) ![00150-980315653.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/DYer7-yboQtaRymzhTZVe.png) ![00162-751177051.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/GYISC31YZ10YhLs8KHg8O.png) ![00096-2832495824.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/51Jc3IAV6rkNsUu-3NnQN.png) ![00002-3097255927.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/6m13zDtJSdK4syfujs2bu.png) ![00044-1201350781.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/11wdBPSt2rZljp2LrPFzV.png) ![00036-481276871.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/vf3A8yRQURMkXQw6beyBE.png) ![00035-307154204.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/Pfe5dCwI9t_qXZzuI08t3.png) ![00034-2484733566.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/4OmkzRvSUCRRO91Gz-nh4.png) ![00033-417478899.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/q8S6-lxV2uY3BwZ5WkcL5.png) ![00029-3305906991.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/DSZHwQN1jhA1leW2jcvG8.png) ![00024-2340357030.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/6-LTPowo0YXLtIpGpByc7.png) ![00018-4030886737.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/ybEN_cmAo6xkha5W8EVuF.png) ![00005-3932370153.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/JBxIq6dzqXgIyWPAawOQW.png)
FALLENSTAR/CedricGloriaLoRa	FALLENSTAR	2023-06-16T20:10:23Z	0	0	null	[ "region:us" ]	null	2023-06-09T20:58:56Z	### Model Description First of all, it's LoRa. It is based on my favorite Nissan Cedric/Gloria Y31 Hardtop from the years '87-91. It is a test model, so it has defects. I don't remember how many samples and epochs were used in it... But, with some of the checkpoints it turns out very similar and funny. The best images I was able to get with this LoRa were at these settings: Steps: 25 Sampler: DPM++ SDE Karras, CFG scale: 6.5 and with LoRa strength 0.8-1 ### Results ![00056-2998859893.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/Hqc-19MXbw-tOHcytmCgO.png) ![00037-1095574331.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/6d8uCXt5U2KvnqtZdl1d2.png) ![00039-89618184.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/tmzZoZQT6fK4_UPCCA5sk.png) ![00041-562450270.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/tp0QNuodfbsEkgLh8Yhht.png) ![00066-2959432492.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/FO5BR5uRtvAwm7jMCzUTa.png) ![00058-1357569836.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/yC3gSTgtQnnBTg3ibDtXw.png) ![00065-3713693640.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/xRNkGnsBb8kiW15_v7nws.png) ![00013-3313807285.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/1r1CFhjES8GncHPFudrGZ.png) ![00067-1452084223.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/8a86GW52YDweESQKp5fRR.png) ![00004-873995140.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/ZCu2ffZkEGW_DlLWYu6QC.png) ![00084-419571825.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/4PXZdvNBX-kWwMyyZ1bRm.png) ![00021-3472542211.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/_2fwDF2KUlXq7bS-P65Hi.png) ![00048-2623366528.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/m-XuUdhEXhT7-Vaa_D0pa.png)
FALLENSTAR/TurbofansLoRa	FALLENSTAR	2023-06-16T20:09:36Z	0	0	null	[ "region:us" ]	null	2023-06-11T01:32:23Z	### Model Description This LoRa is based on Turbofan or Aero Covers, an invention from Japan. Turbofan were created to effectively cool the brake discs. Originally they were used in motorsports, and were made out of aluminum. Now, thanks to new brake technology, Turbofans are not used for their original purpose. And they are not popular in professional motorsports. But, to me, they add a futuristic style to car tuning. The best images I was able to get with this LoRa were at these settings: Steps: 25 Sampler: DPM++ SDE Karras, CFG scale: 6.5 and with LoRa strength 0.8-1 ![00094-3727343714.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/t4pEcqkV-1UZzNmVS8G5s.png) ![00096-3846199597.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/D0Wh8K3xII6uhsdaMpm_T.png) ![00112-2164319160.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/GUKuqj2iMjHGrh1ByuNkG.png) ![00117-4155383315.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/1E5xBqoBmnB8YZfr32mtU.png)
TheBloke/robin-33B-v2-fp16	TheBloke	2023-06-16T20:07:31Z	1,566	3	transformers	[ "transformers", "pytorch", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-06-16T18:09:39Z	--- 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/Jq4vkcDakD">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 --> # OptimalScale's Robin 33B v2 fp16 These files are pytorch format fp16 model files for [OptimalScale's Robin 33B v2](https://huggingface.co/OptimalScale/robin-33b-v2-delta). It is the result of merging and/or converting the source repository to float16. ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/robin-33B-v2-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/robin-33B-v2-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/robin-33B-v2-fp16) ## Prompt template ``` A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions ###Human: prompt ###Assistant: ``` <!-- 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/Jq4vkcDakD) ## 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: vamX, K, Jonathan Leane, Lone Striker, Sean Connelly, Chris McCloskey, WelcomeToTheClub, Nikolai Manek, John Detwiler, Kalila, David Flickinger, Fen Risland, subjectnull, Johann-Peter Hartmann, Talal Aujan, John Villwock, senxiiz, Khalefa Al-Ahmad, Kevin Schuppel, Alps Aficionado, Derek Yates, Mano Prime, Nathan LeClaire, biorpg, trip7s trip, Asp the Wyvern, chris gileta, Iucharbius , Artur Olbinski, Ai Maven, Joseph William Delisle, Luke Pendergrass, Illia Dulskyi, Eugene Pentland, Ajan Kanaga, Willem Michiel, Space Cruiser, Pyrater, Preetika Verma, Junyu Yang, Oscar Rangel, Spiking Neurons AB, Pierre Kircher, webtim, Cory Kujawski, terasurfer , Trenton Dambrowitz, Gabriel Puliatti, Imad Khwaja, Luke. Thank you to all my generous patrons and donaters! <!-- footer end --> # Original model card: OptimalScale's Robin 33B v2 No model card provided in source repository.
TheBloke/robin-7B-v2-GGML	TheBloke	2023-06-16T20:04:09Z	0	8	null	[ "license:other", "region:us" ]	null	2023-06-16T18:28:00Z	--- 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/Jq4vkcDakD">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 --> # OptimalScale's Robin 7B v2 GGML These files are GGML format model files for [OptimalScale's Robin 7B v2](https://huggingface.co/OptimalScale/robin-7b-v2-delta). 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) * [ParisNeo/GPT4All-UI](https://github.com/ParisNeo/gpt4all-ui) * [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/robin-7B-v2-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/robin-7B-v2-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/robin-7B-v2-fp16) ## Prompt template ``` A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions ###Human: 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 \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| robin-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. \| \| robin-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 \| \| robin-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 \| \| robin-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 \| \| robin-7b.ggmlv3.q4_0.bin \| q4_0 \| 4 \| 3.79 GB \| 6.29 GB \| Original llama.cpp quant method, 4-bit. \| \| robin-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. \| \| robin-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 \| \| robin-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 \| \| robin-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. \| \| robin-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. \| \| robin-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 \| \| robin-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 \| \| robin-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 \| \| robin-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 robin-7b.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n###Human: write a story about llamas\n###Assistant:" ``` Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. 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/Jq4vkcDakD) ## 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: vamX, K, Jonathan Leane, Lone Striker, Sean Connelly, Chris McCloskey, WelcomeToTheClub, Nikolai Manek, John Detwiler, Kalila, David Flickinger, Fen Risland, subjectnull, Johann-Peter Hartmann, Talal Aujan, John Villwock, senxiiz, Khalefa Al-Ahmad, Kevin Schuppel, Alps Aficionado, Derek Yates, Mano Prime, Nathan LeClaire, biorpg, trip7s trip, Asp the Wyvern, chris gileta, Iucharbius , Artur Olbinski, Ai Maven, Joseph William Delisle, Luke Pendergrass, Illia Dulskyi, Eugene Pentland, Ajan Kanaga, Willem Michiel, Space Cruiser, Pyrater, Preetika Verma, Junyu Yang, Oscar Rangel, Spiking Neurons AB, Pierre Kircher, webtim, Cory Kujawski, terasurfer , Trenton Dambrowitz, Gabriel Puliatti, Imad Khwaja, Luke. Thank you to all my generous patrons and donaters! <!-- footer end --> # Original model card: OptimalScale's Robin 7B v2 No model card provided in source repository.
GEMCorp/q-FrozenLake-v1-4x4-noSlippery	GEMCorp	2023-06-16T19:51:18Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-16T19:51:12Z	--- 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="GEMCorp/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"]) ```
ChristineCheng/my_awesome_eli5_clm-model	ChristineCheng	2023-06-16T19:49:19Z	61	0	transformers	[ "transformers", "tf", "gpt2", "text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-06-16T19:33:04Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: ChristineCheng/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. --> # ChristineCheng/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: 3.7347 - Validation Loss: 3.7399 - 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 \| \|:----------:\|:---------------:\|:-----:\| \| 3.9119 \| 3.7667 \| 0 \| \| 3.7942 \| 3.7493 \| 1 \| \| 3.7347 \| 3.7399 \| 2 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.0 - Tokenizers 0.13.3
CodyKilpatrick/Reinforce-Pixelcopter-PLE-v0	CodyKilpatrick	2023-06-16T19:43:03Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-06-12T15:12:47Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-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: 98.70 +/- 89.31 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
karina-aquino/spanish-sentiment-model	karina-aquino	2023-06-16T19:41:41Z	36	1	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-13T21:51:39Z	--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: spanish-sentiment-model 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. --> # spanish-sentiment-model This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0046 - Accuracy: 0.65 - F1: 0.6646 ## 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: 7e-06 - 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 375 \| 1.0046 \| 0.65 \| 0.6646 \| \| 1.2137 \| 2.0 \| 750 \| 1.0212 \| 0.61 \| 0.6398 \| \| 0.9497 \| 3.0 \| 1125 \| 1.0247 \| 0.6133 \| 0.6478 \| ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
ananay/kneearch	ananay	2023-06-16T19:17:59Z	22	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-16T19:05:11Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### kneearch Dreambooth model trained by ananay with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
AustinCarthy/OnlyPhishGPT2_subdomain_100KP_BFall_fromB_90K_topP_0.75_ratio5	AustinCarthy	2023-06-16T19:17:42Z	0	0	null	[ "tensorboard", "generated_from_trainer", "license:apache-2.0", "region:us" ]	null	2023-06-16T15:49:03Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: OnlyPhishGPT2_subdomain_100KP_BFall_fromB_90K_topP_0.75_ratio5 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. --> # OnlyPhishGPT2_subdomain_100KP_BFall_fromB_90K_topP_0.75_ratio5 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the Train benign: Fall,Test Benign: Fall, Train phish: Fall, Test phish: Fall, generated url dataset: generated_phish_OnlyPhishGPT2_using_benigh_200K_top_p_0.75 dataset. It achieves the following results on the evaluation set: - Loss: 0.0192 - Accuracy: 0.9978 - F1: 0.9767 - Precision: 0.9994 - Recall: 0.955 - Roc Auc Score: 0.9775 - Tpr At Fpr 0.01: 0.9632 ## 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: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| Roc Auc Score \| Tpr At Fpr 0.01 \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|:-------------:\|:---------------:\| \| 0.0057 \| 1.0 \| 35625 \| 0.0113 \| 0.9979 \| 0.9779 \| 0.9954 \| 0.961 \| 0.9804 \| 0.9518 \| \| 0.0035 \| 2.0 \| 71250 \| 0.0150 \| 0.9975 \| 0.9726 \| 0.9983 \| 0.9482 \| 0.9741 \| 0.95 \| \| 0.0011 \| 3.0 \| 106875 \| 0.0175 \| 0.9975 \| 0.9727 \| 0.9994 \| 0.9474 \| 0.9737 \| 0.9554 \| \| 0.0009 \| 4.0 \| 142500 \| 0.0160 \| 0.9979 \| 0.9778 \| 0.9990 \| 0.9576 \| 0.9788 \| 0.9618 \| \| 0.0 \| 5.0 \| 178125 \| 0.0192 \| 0.9978 \| 0.9767 \| 0.9994 \| 0.955 \| 0.9775 \| 0.9632 \| ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

Christiansg/finetuning-sentiment-amazon-group23

Christiansg

2023-06-17T22:41:36Z

103

0

transformers

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

text-classification

2023-06-17T21:25:16Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: finetuning-sentiment-amazon-group23 results: [] ---  # finetuning-sentiment-amazon-group23 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5877 - Accuracy: 0.8733 ## 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: 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: 2 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

jvilaseca/Reinforce-Cartpole2

jvilaseca

2023-06-17T22:21:52Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-06-17T22:17:34Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Cartpole2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . 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

Tyrranen/ppo-LunarLander-v2

Tyrranen

2023-06-17T19:07:45Z

0

stable-baselines3

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

reinforcement-learning

2023-06-17T19:06:38Z

--- 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: 257.55 +/- 19.71 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 ... ```

CreatorPhan/ViQA-small

CreatorPhan

2023-06-17T18:40:13Z

107

0

transformers

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

text2text-generation

2023-06-17T16:58:15Z

--- language: - vi pipeline_tag: text2text-generation # inference: # parameters: # function_to_apply: "none" widget: - text: >- Trả lời câu hỏi: Công dụng của paracetamol? Trong nội dung: PARACETAMOL DẠNG UỐNG – HƯỚNG DẪN SỬ DỤNG AN TOÀN, HỢP LÝ Trong tình hình diễn biến phức tạp của dịch COVID-19, các thuốc giảm đau hạ sốt thông dụng như Paracetamol được người dân mua về dự trữ trong hộp thuốc gia đình với mục đích phòng dịch. Tuy nhiên, việc sử dụng thuốc hợp lý và đúng cách đôi khi chưa được chú ý, vì vậy việc hiểu và sử dụng thuốc Paracetamol an toàn là rất cần thiết. I. Tổng quan thuốc Paracetamol - Paracetamol dạng uống là thuốc thuộc nhóm giảm đau, hạ sốt và nằm trong danh mục thuốc không kê đơn của Bộ Y tế. Chính vì vậy Paracetamol rất phổ biến trên thị trường với nhiều chế phẩm có dạng bào chế và hàm lượng từ thấp đến cao. - Tác dụng chính của Paracetamol là giảm đau, hạ sốt nên thuốc được sử dụng rộng rãi trong điều trị các chứng đau và sốt từ nhẹ đến vừa như: cảm cúm, nhức đầu, đau bụng, đau nhức… - Thuốc không nên sử dụng cho những người dị ứng với Paracetamol, người suy gan nặng. II. Nguy cơ khi sử dụng Paracetamol - Việc Paracetamol được sử dụng rộng rãi cùng với tâm lý chủ quan, thiếu nhận thức dẫn đến việc quá liều thuốc gây nên các tác dụng phụ không mong muốn, trong đó nguy hiểm nhất là tình trạng hoại tử gan, có thể dẫn đến tử vong nếu không được xử trí kịp thời. - Nguyên nhân gây ngộ độc gan khi sử dụng Paracetamol quá liều là nồng độ NAPQI (sinh ra do Paracetamol chuyển hóa qua gan) không thể chuyển hóa hết và tích luỹ gây độc cho gan. - Các biểu hiện ngộ độc gan do Paracetamol có thể là: ban đầu là buồn nôn, nôn, đau bụng, sau đó nguy kich hơn có thể kích động, hôn mê, mạch huyết áp không ổn định… có thể nguy cơ tử vong. - text: >- Trả lời câu hỏi: Tòa nhà cao nhất Việt Nam? Trong nội dung: The Landmark 81 là một toà nhà chọc trời trong tổ hợp dự án Vinhomes Tân Cảng , một dự án có tổng mức đầu tư 40.000 tỷ đồng , do Công ty Cổ phần Đầu tư xây dựng Tân Liên Phát thuộc Vingroup làm chủ đầu tư . Toà tháp cao 81 tầng , hiện tại là toà nhà cao nhất Việt Nam và là toà nhà cao nhất Đông Nam Á từ tháng 3 năm 2018 . Toà tháp cao 81 tầng , hiện tại là toà nhà cao nhất Việt Nam và là toà nhà cao nhất Đông Nam Á từ tháng 3 năm 2018 . Dự án được xây dựng ở Tân Cảng , quận Bình Thạnh , ven sông Sài Gòn . Dự án được khởi công ngày 26/07/2014 . --- Mô hình này được tuning từ pretrained ViFlanT5-small model với 77M tham số với 2 epochs trên 87GB text của bộ CC100. Mô hình được huấn luyện cho tác vụ đọc hiểu tiếng Việt. Cung cấp cho mô hình câu hỏi và ngữ cảnh (không quá 400 từ) và mô hình sẽ trích xuất ra câu trả lời trong ngữ cảnh đó. ``` from transformers import AutoTokenizer, T5ForConditionalGeneration device = 'cpu' model_path = "CreatorPhan/ViQA-small" model = T5ForConditionalGeneration.from_pretrained(model_path).to(device) tokenizer = AutoTokenizer.from_pretrained(model_path) context = """ PARACETAMOL DẠNG UỐNG – HƯỚNG DẪN SỬ DỤNG AN TOÀN, HỢP LÝ Trong tình hình diễn biến phức tạp của dịch COVID-19, các thuốc giảm đau hạ sốt thông dụng như Paracetamol được người dân mua về dự trữ trong hộp thuốc gia đình với mục đích phòng dịch. Tuy nhiên, việc sử dụng thuốc hợp lý và đúng cách đôi khi chưa được chú ý, vì vậy việc hiểu và sử dụng thuốc Paracetamol an toàn là rất cần thiết. I. Tổng quan thuốc Paracetamol - Paracetamol dạng uống là thuốc thuộc nhóm giảm đau, hạ sốt và nằm trong danh mục thuốc không kê đơn của Bộ Y tế. Chính vì vậy Paracetamol rất phổ biến trên thị trường với nhiều chế phẩm có dạng bào chế và hàm lượng từ thấp đến cao. - Tác dụng chính của Paracetamol là giảm đau, hạ sốt nên thuốc được sử dụng rộng rãi trong điều trị các chứng đau và sốt từ nhẹ đến vừa như: cảm cúm, nhức đầu, đau bụng, đau nhức… - Thuốc không nên sử dụng cho những người dị ứng với Paracetamol, người suy gan nặng. II. Nguy cơ khi sử dụng Paracetamol - Việc Paracetamol được sử dụng rộng rãi cùng với tâm lý chủ quan, thiếu nhận thức dẫn đến việc quá liều thuốc gây nên các tác dụng phụ không mong muốn, trong đó nguy hiểm nhất là tình trạng hoại tử gan, có thể dẫn đến tử vong nếu không được xử trí kịp thời. - Nguyên nhân gây ngộ độc gan khi sử dụng Paracetamol quá liều là nồng độ NAPQI (sinh ra do Paracetamol chuyển hóa qua gan) không thể chuyển hóa hết và tích luỹ gây độc cho gan. - Các biểu hiện ngộ độc gan do Paracetamol có thể là: ban đầu là buồn nôn, nôn, đau bụng, sau đó nguy kich hơn có thể kích động, hôn mê, mạch huyết áp không ổn định… có thể nguy cơ tử vong. """ question = "Công dụng của paracetamol?" prompt = f"Trả lời câu hỏi: {question} Trong nội dung: {context}" tokens = tokenizer(prompt, return_tensors='pt').input_ids output = model.generate(tokens.to(device), max_new_tokens=170)[0] predict = tokenizer.decode(output, skip_special_tokens=True) print(len(predict.split())) print(predict) ```

mrm8488/distilgpt2-finetuned-jhegarty-books

mrm8488

2023-06-17T17:56:10Z

152

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-06-10T11:31:15Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-jhegarty-books results: [] ---  # distilgpt2-finetuned-jhegarty-books This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6008 ## 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: 2 - eval_batch_size: 2 - 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 | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 49 | 3.6667 | | No log | 2.0 | 98 | 3.6202 | | No log | 3.0 | 147 | 3.6019 | | No log | 4.0 | 196 | 3.6008 | ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

mmirmahdi/Taxi-v3

mmirmahdi

2023-06-17T17:31:14Z

0

null

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

reinforcement-learning

2023-06-17T17:30:59Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 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="mmirmahdi/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"]) ```

0xghagevaibhav/meMyself

0xghagevaibhav

2023-06-17T17:11:20Z

0

null

[ "hi", "license:unknown", "region:us" ]

null

2023-06-17T17:09:19Z

--- license: unknown language: - hi ---

erens/mikasalast

erens

2023-06-17T17:01:46Z

30

0

diffusers

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

text-to-image

2023-06-17T16:46:29Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### mikasaLAST Dreambooth model trained by erens with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

pszemraj/long-t5-tglobal-xl-16384-booksci-summary-v1

pszemraj

2023-06-17T16:51:52Z

5

1

transformers

[ "transformers", "pytorch", "longt5", "text2text-generation", "generated_from_trainer", "en", "dataset:pszemraj/scientific_lay_summarisation-elife-norm", "license:bsd-3-clause", "license:apache-2.0", "model-index", "autotrain_compatible", "region:us" ]

text2text-generation

2023-06-17T12:23:26Z

--- license: - bsd-3-clause - apache-2.0 tags: - generated_from_trainer datasets: - pszemraj/scientific_lay_summarisation-elife-norm metrics: - rouge model-index: - name: >- long-t5-tglobal-xl-16384-book-summary-scientific_lay_summarisation-elife-norm-16384-summ-v1 results: - task: name: Summarization type: summarization dataset: name: pszemraj/scientific_lay_summarisation-elife-norm type: pszemraj/scientific_lay_summarisation-elife-norm split: validation metrics: - name: Rouge1 type: rouge value: 47.4591 language: - en library_name: transformers inference: False --- # long-t5-tglobal-xl-16384-booksci-summary-v1 This model is a fine-tuned version of [pszemraj/long-t5-tglobal-xl-16384-book-summary](https://huggingface.co/pszemraj/long-t5-tglobal-xl-16384-book-summary) on the pszemraj/scientific_lay_summarisation-elife-norm dataset. It achieves the following results on the evaluation set: - Loss: 1.7518 - Rouge1: 47.4591 - Rouge2: 12.7287 - Rougel: 21.5549 - Rougelsum: 44.8709 - Gen Len: 384.39 ## Model description An experiment of further fine-tuning a booksum model on a different dataset. Compare to either the starting checkpoint (_linked above_) or to the [variant only fine-tuned on the scientific lay summaries](https://huggingface.co/pszemraj/long-t5-tglobal-xl-sci-simplify-elife). ## Intended uses & limitations More information needed ## Training and evaluation data the pszemraj/scientific_lay_summarisation-elife-norm dataset, input 16384 tokens then truncate, output 1024 tokens then truncate. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 878 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.02 - num_epochs: 2.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.9629 | 1.0 | 543 | 1.7637 | 46.6926 | 12.4769 | 21.4364 | 44.4329 | 381.23 | | 1.8555 | 2.0 | 1086 | 1.7518 | 47.4591 | 12.7287 | 21.5549 | 44.8709 | 384.39 |

vlkn/flan-t5-small-taboo-for-llms

vlkn

2023-06-17T16:20:59Z

15

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

2023-06-03T13:32:22Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-small-taboo-for-llms results: [] ---  # flan-t5-small-taboo-for-llms This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4825 - Rouge1: 27.3897 - Rouge2: 9.9232 - Rougel: 24.2026 - Rougelsum: 24.6485 - Gen Len: 18.5172 ## 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: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 137 | 2.5897 | 26.6789 | 9.9538 | 23.6637 | 24.2407 | 18.3621 | | No log | 2.0 | 274 | 2.5560 | 25.4162 | 9.6277 | 22.7084 | 23.0883 | 18.3966 | | No log | 3.0 | 411 | 2.5377 | 26.0239 | 9.7748 | 23.4425 | 23.7935 | 18.6034 | | 2.8204 | 4.0 | 548 | 2.5241 | 26.6294 | 9.9168 | 23.8023 | 24.2756 | 18.7241 | | 2.8204 | 5.0 | 685 | 2.5120 | 25.8274 | 9.9333 | 23.8865 | 24.0724 | 18.7586 | | 2.8204 | 6.0 | 822 | 2.5031 | 26.7774 | 9.9651 | 24.3654 | 24.6102 | 18.6034 | | 2.8204 | 7.0 | 959 | 2.4985 | 26.5058 | 10.0422 | 24.0403 | 24.635 | 18.4655 | | 2.6101 | 8.0 | 1096 | 2.4934 | 26.6953 | 9.9536 | 24.0293 | 24.6809 | 18.4655 | | 2.6101 | 9.0 | 1233 | 2.4907 | 26.7978 | 9.6249 | 23.714 | 23.9992 | 18.6034 | | 2.6101 | 10.0 | 1370 | 2.4847 | 27.2135 | 9.878 | 23.8398 | 24.2389 | 18.5 | | 2.4726 | 11.0 | 1507 | 2.4856 | 27.1799 | 9.9337 | 23.9393 | 24.4067 | 18.5172 | | 2.4726 | 12.0 | 1644 | 2.4835 | 27.4491 | 10.1828 | 24.0926 | 24.4819 | 18.5 | | 2.4726 | 13.0 | 1781 | 2.4825 | 27.3897 | 9.9232 | 24.2026 | 24.6485 | 18.5172 | | 2.4726 | 14.0 | 1918 | 2.4836 | 27.5567 | 10.7405 | 24.2497 | 24.6566 | 18.5345 | | 2.3731 | 15.0 | 2055 | 2.4872 | 27.7517 | 11.0182 | 24.1007 | 24.7218 | 18.4828 | | 2.3731 | 16.0 | 2192 | 2.4852 | 27.3461 | 11.3381 | 24.084 | 24.5125 | 18.4655 | | 2.3731 | 17.0 | 2329 | 2.4872 | 27.3558 | 11.1005 | 24.047 | 24.4973 | 18.4655 | | 2.3731 | 18.0 | 2466 | 2.4841 | 26.9427 | 10.9288 | 23.7324 | 24.4298 | 18.5345 | | 2.2967 | 19.0 | 2603 | 2.4881 | 27.5 | 10.8437 | 24.1593 | 24.6028 | 18.4483 | | 2.2967 | 20.0 | 2740 | 2.4908 | 27.517 | 11.0039 | 24.1049 | 24.7111 | 18.5 | | 2.2967 | 21.0 | 2877 | 2.4917 | 27.7333 | 10.935 | 24.4076 | 24.9887 | 18.4138 | | 2.2553 | 22.0 | 3014 | 2.4926 | 27.6275 | 10.7562 | 24.2295 | 24.7476 | 18.4138 | | 2.2553 | 23.0 | 3151 | 2.4945 | 27.9085 | 10.943 | 24.6135 | 25.2373 | 18.4138 | | 2.2553 | 24.0 | 3288 | 2.4948 | 27.5261 | 10.7141 | 24.2429 | 24.816 | 18.4138 | | 2.2553 | 25.0 | 3425 | 2.4931 | 27.5522 | 10.8702 | 24.5576 | 25.0714 | 18.4655 | | 2.213 | 26.0 | 3562 | 2.4942 | 27.4758 | 11.0064 | 24.5062 | 25.05 | 18.4655 | | 2.213 | 27.0 | 3699 | 2.4954 | 27.6967 | 11.1744 | 24.7646 | 25.3172 | 18.4655 | | 2.213 | 28.0 | 3836 | 2.4951 | 27.7428 | 10.9365 | 24.6427 | 25.2432 | 18.5172 | | 2.213 | 29.0 | 3973 | 2.4949 | 27.6877 | 10.9522 | 24.6101 | 25.2471 | 18.4655 | | 2.1865 | 30.0 | 4110 | 2.4952 | 27.7295 | 11.0173 | 24.6556 | 25.2397 | 18.4655 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

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

aphi

2023-06-17T15:42:48Z

0

null

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

reinforcement-learning

2023-06-17T15:42:37Z

--- 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="aphi/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"]) ```

l3cube-pune/assamese-bert

l3cube-pune

2023-06-17T15:38:14Z

195

0

transformers

[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "as", "arxiv:2211.11418", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-11-20T08:12:25Z

--- license: cc-by-4.0 language: as --- ## AssameseBERT AssameseBERT is an Assamese BERT model trained on publicly available Assamese monolingual datasets. Preliminary details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] . Citing: ``` @article{joshi2022l3cubehind, title={L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages}, author={Joshi, Raviraj}, journal={arXiv preprint arXiv:2211.11418}, year={2022} } ``` Other Monolingual Indic BERT models are listed below: <br> <a href='https://huggingface.co/l3cube-pune/marathi-bert-v2'> Marathi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-roberta'> Marathi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-albert'> Marathi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-bert-v2'> Hindi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-roberta'> Hindi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-albert'> Hindi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-bert'> Dev BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-roberta'> Dev RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-albert'> Dev AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/kannada-bert'> Kannada BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/telugu-bert'> Telugu BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/malayalam-bert'> Malayalam BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/tamil-bert'> Tamil BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/gujarati-bert'> Gujarati BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/odia-bert'> Oriya BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/bengali-bert'> Bengali BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/punjabi-bert'> Punjabi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/assamese-bert'> Assamese BERT </a> <br>

l3cube-pune/bengali-bert

l3cube-pune

2023-06-17T15:37:35Z

163

2

transformers

[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "bn", "arxiv:2211.11418", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-11-20T08:45:01Z

--- license: cc-by-4.0 language: bn --- ## BengaliBERT BengaliBERT is a Bengali BERT model trained on publicly available Bengali monolingual datasets. Preliminary details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] . Citing: ``` @article{joshi2022l3cubehind, title={L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages}, author={Joshi, Raviraj}, journal={arXiv preprint arXiv:2211.11418}, year={2022} } ``` Other Monolingual Indic BERT models are listed below: <br> <a href='https://huggingface.co/l3cube-pune/marathi-bert-v2'> Marathi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-roberta'> Marathi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-albert'> Marathi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-bert-v2'> Hindi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-roberta'> Hindi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-albert'> Hindi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-bert'> Dev BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-roberta'> Dev RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-albert'> Dev AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/kannada-bert'> Kannada BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/telugu-bert'> Telugu BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/malayalam-bert'> Malayalam BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/tamil-bert'> Tamil BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/gujarati-bert'> Gujarati BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/odia-bert'> Oriya BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/bengali-bert'> Bengali BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/punjabi-bert'> Punjabi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/assamese-bert'> Assamese BERT </a> <br>

paulowoicho/t5-podcast-summarisation

paulowoicho

2023-06-17T15:36:56Z

154

8

transformers

[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "summarisation", "lm-head", "en", "arxiv:2004.04270", "arxiv:1910.10683", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-02T23:29:05Z

--- language: - en datasets: - Spotify Podcasts Dataset tags: - t5 - summarisation - pytorch - lm-head metrics: - ROUGE pipeline: - summarisation --- # T5 for Automatic Podcast Summarisation This model is the result of fine-tuning [t5-base](https://huggingface.co/t5-base) on the [Spotify Podcast Dataset](https://arxiv.org/abs/2004.04270). It is based on [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) which was pretrained on the [C4 dataset](https://huggingface.co/datasets/c4). Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) Authors: Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu ## Intended uses & limitations This model is intended to be used for automatic podcast summarisation. As creator provided descriptions were used for training, the model also learned to generate promotional material (links, hashtags, etc) in its summaries, as such some post processing may be required on the model's outputs. If using on Colab, the instance will crash if the number of tokens in the transcript exceeds 7000. I discovered that the model generated reasonable summaries even when the podcast transcript was truncated to reduce the number of tokens. #### How to use The model can be used with the summarisation as follows: ```python from transformers import pipeline summarizer = pipeline("summarization", model="paulowoicho/t5-podcast-summarisation", tokenizer="paulowoicho/t5-podcast-summarisation") summary = summarizer(podcast_transcript, min_length=5, max_length=20) print(summary[0]['summary_text']) ``` ## Training data This model is the result of fine-tuning [t5-base](https://huggingface.co/t5-base) on the [Spotify Podcast Dataset](https://arxiv.org/abs/2004.04270). [Pre-processing](https://github.com/paulowoicho/msc_project/blob/master/reformat.py) was done on the original data before fine-tuning. ## Training procedure Training was largely based on [Fine-tune T5 for Summarization](https://github.com/abhimishra91/transformers-tutorials/blob/master/transformers_summarization_wandb.ipynb) by [Abhishek Kumar Mishra](https://github.com/abhimishra91)

l3cube-pune/odia-bert

l3cube-pune

2023-06-17T15:36:40Z

457

0

transformers

[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "or", "arxiv:2211.11418", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-11-20T08:26:35Z

--- license: cc-by-4.0 language: or --- ## OdiaBERT OdiaBERT is an Odia BERT model trained on publicly available Odia monolingual datasets. Preliminary details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] . Citing: ``` @article{joshi2022l3cubehind, title={L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages}, author={Joshi, Raviraj}, journal={arXiv preprint arXiv:2211.11418}, year={2022} } ``` Other Monolingual Indic BERT models are listed below: <br> <a href='https://huggingface.co/l3cube-pune/marathi-bert-v2'> Marathi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-roberta'> Marathi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-albert'> Marathi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-bert-v2'> Hindi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-roberta'> Hindi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-albert'> Hindi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-bert'> Dev BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-roberta'> Dev RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-albert'> Dev AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/kannada-bert'> Kannada BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/telugu-bert'> Telugu BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/malayalam-bert'> Malayalam BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/tamil-bert'> Tamil BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/gujarati-bert'> Gujarati BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/odia-bert'> Oriya BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/bengali-bert'> Bengali BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/punjabi-bert'> Punjabi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/assamese-bert'> Assamese BERT </a> <br>

l3cube-pune/tamil-bert

l3cube-pune

2023-06-17T15:36:00Z

554

3

transformers

[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "ta", "arxiv:2211.11418", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-11-20T07:45:13Z

--- license: cc-by-4.0 language: ta --- ## TamilBERT TamilBERT is a Tamil BERT model trained on publicly available Tamil monolingual datasets. Preliminary details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] . Citing: ``` @article{joshi2022l3cubehind, title={L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages}, author={Joshi, Raviraj}, journal={arXiv preprint arXiv:2211.11418}, year={2022} } ``` Other Monolingual Indic BERT models are listed below: <br> <a href='https://huggingface.co/l3cube-pune/marathi-bert-v2'> Marathi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-roberta'> Marathi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-albert'> Marathi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-bert-v2'> Hindi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-roberta'> Hindi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-albert'> Hindi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-bert'> Dev BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-roberta'> Dev RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-albert'> Dev AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/kannada-bert'> Kannada BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/telugu-bert'> Telugu BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/malayalam-bert'> Malayalam BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/tamil-bert'> Tamil BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/gujarati-bert'> Gujarati BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/odia-bert'> Oriya BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/bengali-bert'> Bengali BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/punjabi-bert'> Punjabi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/assamese-bert'> Assamese BERT </a> <br>

l3cube-pune/malayalam-bert

l3cube-pune

2023-06-17T15:35:26Z

417

5

transformers

[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "ml", "arxiv:2211.11418", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-11-20T07:31:42Z

--- license: cc-by-4.0 language: ml --- ## MalayalamBERT MalayalamBERT is a Malayalam BERT model trained on publicly available Malayalam monolingual datasets. Preliminary details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] . Citing: ``` @article{joshi2022l3cubehind, title={L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages}, author={Joshi, Raviraj}, journal={arXiv preprint arXiv:2211.11418}, year={2022} } ``` Other Monolingual Indic BERT models are listed below: <br> <a href='https://huggingface.co/l3cube-pune/marathi-bert-v2'> Marathi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-roberta'> Marathi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-albert'> Marathi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-bert-v2'> Hindi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-roberta'> Hindi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-albert'> Hindi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-bert'> Dev BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-roberta'> Dev RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-albert'> Dev AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/kannada-bert'> Kannada BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/telugu-bert'> Telugu BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/malayalam-bert'> Malayalam BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/tamil-bert'> Tamil BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/gujarati-bert'> Gujarati BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/odia-bert'> Oriya BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/bengali-bert'> Bengali BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/punjabi-bert'> Punjabi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/assamese-bert'> Assamese BERT </a> <br>

l3cube-pune/hindi-roberta

l3cube-pune

2023-06-17T15:31:32Z

117

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "fill-mask", "hi", "arxiv:2211.11418", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-08-19T18:21:53Z

--- license: cc-by-4.0 language: hi --- ## HindRoBERTa HindRoBERTa is a Hindi RoBERTa model. It is a multilingual RoBERTa (xlm-roberta-base) model fine-tuned on publicly available Hindi monolingual datasets. [project link] (https://github.com/l3cube-pune/MarathiNLP) More details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] . Citing: ``` @article{joshi2022l3cubehind, title={L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages}, author={Joshi, Raviraj}, journal={arXiv preprint arXiv:2211.11418}, year={2022} } ``` Other Monolingual Indic BERT models are listed below: <br> <a href='https://huggingface.co/l3cube-pune/marathi-bert-v2'> Marathi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-roberta'> Marathi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-albert'> Marathi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-bert-v2'> Hindi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-roberta'> Hindi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-albert'> Hindi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-bert'> Dev BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-roberta'> Dev RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-albert'> Dev AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/kannada-bert'> Kannada BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/telugu-bert'> Telugu BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/malayalam-bert'> Malayalam BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/tamil-bert'> Tamil BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/gujarati-bert'> Gujarati BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/odia-bert'> Oriya BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/bengali-bert'> Bengali BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/punjabi-bert'> Punjabi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/assamese-bert'> Assamese BERT </a> <br>

l3cube-pune/marathi-bert-v2

l3cube-pune

2023-06-17T15:30:14Z

391

0

transformers

[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "mr", "dataset:L3Cube-MahaCorpus", "arxiv:2202.01159", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-08-16T17:52:15Z

--- license: cc-by-4.0 language: mr datasets: - L3Cube-MahaCorpus --- ## MahaBERT MahaBERT is a Marathi BERT model. It is a multilingual BERT (google/muril-base-cased) model fine-tuned on L3Cube-MahaCorpus and other publicly available Marathi monolingual datasets. [dataset link] (https://github.com/l3cube-pune/MarathiNLP) More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2202.01159) ``` @inproceedings{joshi-2022-l3cube, title = "{L}3{C}ube-{M}aha{C}orpus and {M}aha{BERT}: {M}arathi Monolingual Corpus, {M}arathi {BERT} Language Models, and Resources", author = "Joshi, Raviraj", booktitle = "Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.wildre-1.17", pages = "97--101", } ``` Other Monolingual Indic BERT models are listed below: <br> <a href='https://huggingface.co/l3cube-pune/marathi-bert-v2'> Marathi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-roberta'> Marathi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/marathi-albert'> Marathi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-bert-v2'> Hindi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-roberta'> Hindi RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-albert'> Hindi AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-bert'> Dev BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-roberta'> Dev RoBERTa </a> <br> <a href='https://huggingface.co/l3cube-pune/hindi-marathi-dev-albert'> Dev AlBERT </a> <br> <a href='https://huggingface.co/l3cube-pune/kannada-bert'> Kannada BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/telugu-bert'> Telugu BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/malayalam-bert'> Malayalam BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/tamil-bert'> Tamil BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/gujarati-bert'> Gujarati BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/odia-bert'> Oriya BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/bengali-bert'> Bengali BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/punjabi-bert'> Punjabi BERT </a> <br> <a href='https://huggingface.co/l3cube-pune/assamese-bert'> Assamese BERT </a> <br>

xqs/ppo-LunarLander-v2

xqs

2023-06-17T15:27:14Z

0

stable-baselines3

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

reinforcement-learning

2023-06-17T15:26:34Z

--- 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: 256.17 +/- 23.91 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 ... ```

Benned/TestLyco

Benned

2023-06-17T15:24:39Z

0

null

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

null

2023-06-17T15:22:06Z

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

atrytone/scibert_uncased_claim_id

atrytone

2023-06-17T15:16:47Z

112

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "text-classification", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-05-18T05:07:15Z

--- license: apache-2.0 language: - en --- Fine-tuned SciBERT uncased model [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) for claim detection from abstracts.

biodatlab/MIReAD-Neuro

biodatlab

2023-06-17T15:16:26Z

122

2

transformers

[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-09T05:23:59Z

--- language: - en pipeline_tag: text-classification metrics: - f1 - accuracy - recall - precision library_name: transformers widget: - text: The past 25 years have seen a strong increase in the number of publications related to criticality in different areas of neuroscience. The potential of criticality to explain various brain properties, including optimal information processing, has made it an increasingly exciting area of investigation for neuroscientists. Recent reviews on this topic, sometimes termed brain criticality, make brief mention of clinical applications of these findings to several neurological disorders such as epilepsy, neurodegenerative disease, and neonatal hypoxia. Other clinicallyrelevant domains - including anesthesia, sleep medicine, developmental-behavioral pediatrics, and psychiatry - are seldom discussed in review papers of brain criticality. Thorough assessments of these application areas and their relevance for clinicians have also yet to be published. In this scoping review, studies of brain criticality involving human data of all ages are evaluated for their current and future clinical relevance. To make the results of these studies understandable to a more clinical audience, a review of the key concepts behind criticality (e.g., phase transitions, long-range temporal correlation, self-organized criticality, power laws, branching processes) precedes the discussion of human clinical studies. Open questions and forthcoming areas of investigation are also considered. --- # MIReAD Neuro This model is a fine-tuned version of [arazd/MIReAD](https://huggingface.co/arazd/MIReAD) on a dataset of Neuroscience papers from 200 journals collected from various sources for a journal classification task. It achieves the following results on the evaluation set: - Loss: 2.7117 - Accuracy: 0.4011 - F1: 0.3962 - Precision: 0.4066 - Recall: 0.3999 ## Model description This model was trained on a journal classification task. ## Intended uses & limitations The intended use of this model is to create abstract embeddings for semantic similarity search for neuroscience-related articles. ## Model Usage To load the model: ```py from transformers import BertForSequenceClassification, AutoTokenizer model_path = "biodatlab/MIReAD-Neuro" model = BertForSequenceClassification.from_pretrained(model_path) tokenizer = AutoTokenizer.from_pretrained(model_path) ``` To create embeddings and for classification: ```py # sample abstract & title text title = "Why Brain Criticality Is Clinically Relevant: A Scoping Review." abstract = "The past 25 years have seen a strong increase in the number of publications related to criticality in different areas of neuroscience..." text = title + tokenizer.sep_token + abstract tokens = tokenizer( text, max_length=512, padding=True, truncation=True, return_tensors="pt" ) # to generate an embedding from a given title and abstract with torch.no_grad(): output = model.bert(**tokens) embedding = output.last_hidden_state[:, 0, :] # to classify (200 journals) a given title and abstract output = model(**tokens) class = output.logits ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - num_epochs: 6

sttteephen/ppo-LunarLander-v2

sttteephen

2023-06-17T14:47:52Z

0

stable-baselines3

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

reinforcement-learning

2023-06-17T14:47:13Z

--- 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: 259.73 +/- 23.16 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 ... ```

JCTN/controlnet_qrcode-control_v11p_sd21

JCTN

2023-06-17T14:45:13Z

14

2

diffusers

[ "diffusers", "safetensors", "stable-diffusion", "controlnet", "image-to-image", "en", "license:openrail++", "region:us" ]

image-to-image

2023-06-16T21:21:29Z

--- tags: - stable-diffusion - controlnet - image-to-image license: openrail++ language: - en pipeline_tag: image-to-image --- # QR Code Conditioned ControlNet Models for Stable Diffusion 2.1 ![1](https://www.dropbox.com/s/c1kx64v1cpsh2mp/1.png?raw=1) ## Model Description This repo holds the safetensors & diffusers versions of the QR code conditioned ControlNet for Stable Diffusion v2.1. The Stable Diffusion 2.1 version is marginally more effective, as it was developed to address my specific needs. However, a 1.5 version model was also trained on the same dataset for those who are using the older version. ## How to use with diffusers ```bash pip -q install diffusers transformers accelerate torch xformers ``` ```python import torch from PIL import Image from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, DDIMScheduler from diffusers.utils import load_image controlnet = ControlNetModel.from_pretrained("DionTimmer/controlnet_qrcode-control_v11p_sd21", torch_dtype=torch.float16) pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( "stabilityai/stable-diffusion-2-1", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 ) pipe.enable_xformers_memory_efficient_attention() pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) pipe.enable_model_cpu_offload() def resize_for_condition_image(input_image: Image, resolution: int): input_image = input_image.convert("RGB") W, H = input_image.size k = float(resolution) / min(H, W) H *= k W *= k H = int(round(H / 64.0)) * 64 W = int(round(W / 64.0)) * 64 img = input_image.resize((W, H), resample=Image.LANCZOS) return img # play with guidance_scale, controlnet_conditioning_scale and strength to make a valid QR Code Image # qr code image source_image = load_image("https://s3.amazonaws.com/moonup/production/uploads/6064e095abd8d3692e3e2ed6/A_RqHaAM6YHBodPLwqtjn.png") # initial image, anything init_image = load_image("https://s3.amazonaws.com/moonup/production/uploads/noauth/KfMBABpOwIuNolv1pe3qX.jpeg") condition_image = resize_for_condition_image(source_image, 768) init_image = resize_for_condition_image(init_image, 768) generator = torch.manual_seed(123121231) image = pipe(prompt="a bilboard in NYC with a qrcode", negative_prompt="ugly, disfigured, low quality, blurry, nsfw", image=init_image, control_image=condition_image, width=768, height=768, guidance_scale=20, controlnet_conditioning_scale=1.5, generator=generator, strength=0.9, num_inference_steps=150, ) image.images[0] ``` ## Performance and Limitations These models perform quite well in most cases, but please note that they are not 100% accurate. In some instances, the QR code shape might not come through as expected. You can increase the ControlNet weight to emphasize the QR code shape. However, be cautious as this might negatively impact the style of your output.**To optimize for scanning, please generate your QR codes with correction mode 'H' (30%).** To balance between style and shape, a gentle fine-tuning of the control weight might be required based on the individual input and the desired output, aswell as the correct prompt. Some prompts do not work until you increase the weight by a lot. The process of finding the right balance between these factors is part art and part science. For the best results, it is recommended to generate your artwork at a resolution of 768. This allows for a higher level of detail in the final product, enhancing the quality and effectiveness of the QR code-based artwork. ## Installation The simplest way to use this is to place the .safetensors model and its .yaml config file in the folder where your other controlnet models are installed, which varies per application. For usage in auto1111 they can be placed in the webui/models/ControlNet folder. They can be loaded using the controlnet webui extension which you can install through the extensions tab in the webui (https://github.com/Mikubill/sd-webui-controlnet). Make sure to enable your controlnet unit and set your input image as the QR code. Set the model to either the SD2.1 or 1.5 version depending on your base stable diffusion model, or it will error. No pre-processor is needed, though you can use the invert pre-processor for a different variation of results. 768 is the preferred resolution for generation since it allows for more detail. Make sure to look up additional info on how to use controlnet if you get stuck, once you have the webui up and running its really easy to install the controlnet extension aswell.

Bala-A87/Huggy-DRL

Bala-A87

2023-06-17T14:35:21Z

2

0

ml-agents

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

reinforcement-learning

2023-06-17T14:34:51Z

--- 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: Bala-A87/Huggy-DRL 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

VTSTech/Desktop-GPT-111m

VTSTech

2023-06-17T14:30:24Z

146

2

transformers

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

text-generation

2023-04-17T16:29:06Z

This is my first attempt at training a model. Based on Cerebras-GPT-111m - Trained on a Conversational Dataset with some Q/A Trained using code from https://github.com/Dampish0/ModelTrainingLocal My homepage: https://www.vts-tech.org My Github: https://github.com/Veritas83 --- license: cc-by-nc-4.0 --- tags: - CasualLM - AutoModel - AutoTokenizer - text-generation - question-answer ---

mtebad/classification_model

mtebad

2023-06-17T13:51:57Z

111

1

transformers

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

text-classification

2023-06-17T09:59:38Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: classification_model results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.937 ---  # classification_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1669 - Accuracy: 0.937 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2461 | 1.0 | 1000 | 0.1964 | 0.9265 | | 0.1464 | 2.0 | 2000 | 0.1669 | 0.937 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

thiendio/rl_course_vizdoom_health_gathering_supreme

thiendio

2023-06-17T13:49:02Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-06-17T13:47:55Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 7.95 +/- 1.58 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r thiendio/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

catrabbitbear/lunar-lander

catrabbitbear

2023-06-17T13:43:33Z

0

stable-baselines3

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

reinforcement-learning

2023-06-17T13:43:04Z

--- 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: 256.65 +/- 38.49 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 ... ```

2tle/kobart-std-to-jeju

2tle

2023-06-17T13:41:43Z

104

1

transformers

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

text2text-generation

2023-06-17T13:31:26Z

--- license: mit language: - ko metrics: - bleu --- # Korean Standard To Jejueo(Jeju Dialect) Translator BART Model ## Dataset - [AI Hub Korean Jejueo(Jeju Dialect) Voice data](https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=121) ## Model Score - BLEU: 40%

tux/Reinforce-copter2

tux

2023-06-17T13:38:46Z

0

null

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

reinforcement-learning

2023-06-17T13:38:33Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-copter2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 22.20 +/- 15.59 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

SikongSphere/sikong-alpaca-7b-chinese

SikongSphere

2023-06-17T13:30:51Z

7

1

transformers

[ "transformers", "pytorch", "llama", "text-generation", "generated_from_trainer", "dataset:customized", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-06-17T12:19:19Z

--- tags: - generated_from_trainer datasets: - customized model-index: - name: finetune results: [] ---  # finetune This model is a fine-tuned version of [/root/autodl-tmp/sikong/repo/LMFlow/output_models/chinese-alpaca-7b-merged](https://huggingface.co//root/autodl-tmp/sikong/repo/LMFlow/output_models/chinese-alpaca-7b-merged) on the customized 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: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 8 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50.0 ### Training results ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.10.1 - Tokenizers 0.13.3

sd-dreambooth-library/baysafinal

sd-dreambooth-library

2023-06-17T13:30:08Z

31

0

diffusers

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

text-to-image

2023-06-17T13:28:10Z

--- license: creativeml-openrail-m tags: - text-to-image widget: - text: Baysafinal1 --- ### Baysafinal Dreambooth model trained by LabanAsmar with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of: Baysafinal1 (use that on your prompt) ![Baysafinal1 0](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%281%29.jpg)![Baysafinal1 1](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%282%29.jpg)![Baysafinal1 2](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%283%29.jpg)![Baysafinal1 3](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%284%29.jpg)![Baysafinal1 4](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%285%29.jpg)![Baysafinal1 5](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%286%29.jpg)![Baysafinal1 6](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%287%29.jpg)![Baysafinal1 7](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%288%29.jpg)![Baysafinal1 8](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%289%29.jpg)![Baysafinal1 9](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%2810%29.jpg)![Baysafinal1 10](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%2811%29.jpg)![Baysafinal1 11](https://huggingface.co/sd-dreambooth-library/baysafinal/resolve/main/concept_images/Baysafinal1_%2812%29.jpg)

antphb/DS-Chatbox-bigscience-bloom-560m

antphb

2023-06-17T13:15:50Z

151

0

transformers

[ "transformers", "pytorch", "tensorboard", "bloom", "text-generation", "generated_from_trainer", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-06-17T11:27:42Z

--- license: bigscience-bloom-rail-1.0 tags: - generated_from_trainer model-index: - name: DS-Chatbox-bigscience-bloom-560m results: [] ---  # DS-Chatbox-bigscience-bloom-560m This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 4.8320 - eval_runtime: 175.7948 - eval_samples_per_second: 37.402 - eval_steps_per_second: 4.676 - epoch: 0.03 - step: 500 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 3.0 ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

Vas123/my_awesome_mind_model

Vas123

2023-06-17T12:49:22Z

159

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:minds14", "license:apache-2.0", "endpoints_compatible", "region:us" ]

audio-classification

2023-06-14T14:38:36Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - minds14 metrics: - accuracy model-index: - name: my_awesome_mind_model results: [] ---  # my_awesome_mind_model This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. It achieves the following results on the evaluation set: - Loss: 2.6448 - Accuracy: 0.0531 ## 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: 3e-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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.8 | 3 | 2.6343 | 0.1150 | | No log | 1.87 | 7 | 2.6413 | 0.0973 | | 2.636 | 2.93 | 11 | 2.6433 | 0.0796 | | 2.636 | 4.0 | 15 | 2.6424 | 0.0708 | | 2.636 | 4.8 | 18 | 2.6433 | 0.0619 | | 2.6231 | 5.87 | 22 | 2.6456 | 0.0354 | | 2.6231 | 6.93 | 26 | 2.6451 | 0.0619 | | 2.6184 | 8.0 | 30 | 2.6448 | 0.0531 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

thiendio/ppo-from-scratch-lunar

thiendio

2023-06-17T12:26:39Z

0

null

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

reinforcement-learning

2023-06-17T12:26:16Z

--- 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: -158.29 +/- 101.80 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters

alexanderjoossens/w2v2-libri-10min

alexanderjoossens

2023-06-17T12:16:40Z

105

0

transformers

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

automatic-speech-recognition

2023-05-22T09:09:45Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: w2v2-libri-10min results: [] ---  # w2v2-libri-10min This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2500 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 1.18.3 - Tokenizers 0.13.3

SikongSphere/sikong-llama-7b-chinese

SikongSphere

2023-06-17T12:01:59Z

7

2

transformers

[ "transformers", "pytorch", "llama", "text-generation", "generated_from_trainer", "dataset:customized", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-06-17T09:09:19Z

--- tags: - generated_from_trainer datasets: - customized model-index: - name: finetune results: [] ---  # finetune This model is a fine-tuned version of [/root/autodl-tmp/sikong/repo/LMFlow/output_models/Linly-Chinese-LLaMA-7b-hf](https://huggingface.co//root/autodl-tmp/sikong/repo/LMFlow/output_models/Linly-Chinese-LLaMA-7b-hf) on the customized 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: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 8 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50.0 ### Training results ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.10.1 - Tokenizers 0.13.3

jalFaizy/ppo-lunar

jalFaizy

2023-06-17T11:42:59Z

1

0

stable-baselines3

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

reinforcement-learning

2023-06-17T11:42:28Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: trial1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 270.71 +/- 14.10 name: mean_reward verified: false --- # **trial1** Agent playing **LunarLander-v2** This is a trained model of a **trial1** 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 ... ```

Xavia0012/bert-tomi

Xavia0012

2023-06-17T11:02:13Z

119

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-06-12T19:49:11Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-tomi results: [] ---  # bert-tomi 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: 0.4198 ## 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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 20 | 1.5815 | | No log | 2.0 | 40 | 0.7518 | | No log | 3.0 | 60 | 0.7153 | | No log | 4.0 | 80 | 0.6354 | | No log | 5.0 | 100 | 0.5895 | | No log | 6.0 | 120 | 0.4882 | | No log | 7.0 | 140 | 0.4590 | | No log | 8.0 | 160 | 0.4303 | | No log | 9.0 | 180 | 0.4644 | | No log | 10.0 | 200 | 0.4416 | | No log | 11.0 | 220 | 0.4348 | | No log | 12.0 | 240 | 0.5306 | | No log | 13.0 | 260 | 0.4412 | | No log | 14.0 | 280 | 0.4053 | | No log | 15.0 | 300 | 0.4185 | | No log | 16.0 | 320 | 0.3982 | | No log | 17.0 | 340 | 0.4291 | | No log | 18.0 | 360 | 0.4316 | | No log | 19.0 | 380 | 0.4328 | | No log | 20.0 | 400 | 0.4198 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.10.1 - Tokenizers 0.12.1

mattladewig/distilbert-base-uncased-finetuned-ner

mattladewig

2023-06-17T10:34:27Z

61

0

transformers

[ "transformers", "tf", "tensorboard", "distilbert", "token-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-06-17T08:37:53Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: mattladewig/distilbert-base-uncased-finetuned-ner results: [] ---  # mattladewig/distilbert-base-uncased-finetuned-ner 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: - Train Loss: 0.0342 - Validation Loss: 0.0614 - Train Precision: 0.9248 - Train Recall: 0.9365 - Train F1: 0.9306 - Train Accuracy: 0.9833 - 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 0.1951 | 0.0694 | 0.9087 | 0.9181 | 0.9134 | 0.9799 | 0 | | 0.0530 | 0.0621 | 0.9246 | 0.9301 | 0.9273 | 0.9823 | 1 | | 0.0342 | 0.0614 | 0.9248 | 0.9365 | 0.9306 | 0.9833 | 2 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.0 - Tokenizers 0.13.3

Chetna19/distilbert-base-uncased-distilled-squad_qa_model

Chetna19

2023-06-17T10:13:01Z

123

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:subjqa", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-06-11T13:02:02Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - subjqa model-index: - name: distilbert-base-uncased-distilled-squad_qa_model results: [] ---  # distilbert-base-uncased-distilled-squad_qa_model This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on the subjqa dataset. It achieves the following results on the evaluation set: - Loss: 2.9380 ## 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: 1e-07 - 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: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.1556 | 1.0 | 32 | 4.1242 | | 4.0411 | 2.0 | 64 | 4.0582 | | 3.9828 | 3.0 | 96 | 3.9948 | | 3.9068 | 4.0 | 128 | 3.9378 | | 3.8152 | 5.0 | 160 | 3.8835 | | 3.7906 | 6.0 | 192 | 3.8329 | | 3.7543 | 7.0 | 224 | 3.7842 | | 3.7173 | 8.0 | 256 | 3.7377 | | 3.6717 | 9.0 | 288 | 3.6958 | | 3.6219 | 10.0 | 320 | 3.6559 | | 3.587 | 11.0 | 352 | 3.6185 | | 3.6111 | 12.0 | 384 | 3.5808 | | 3.5374 | 13.0 | 416 | 3.5483 | | 3.4506 | 14.0 | 448 | 3.5175 | | 3.4286 | 15.0 | 480 | 3.4873 | | 3.4021 | 16.0 | 512 | 3.4596 | | 3.432 | 17.0 | 544 | 3.4328 | | 3.3235 | 18.0 | 576 | 3.4079 | | 3.3627 | 19.0 | 608 | 3.3841 | | 3.323 | 20.0 | 640 | 3.3615 | | 3.3127 | 21.0 | 672 | 3.3389 | | 3.2635 | 22.0 | 704 | 3.3199 | | 3.2542 | 23.0 | 736 | 3.3013 | | 3.2302 | 24.0 | 768 | 3.2846 | | 3.1699 | 25.0 | 800 | 3.2676 | | 3.2333 | 26.0 | 832 | 3.2516 | | 3.2204 | 27.0 | 864 | 3.2364 | | 3.1809 | 28.0 | 896 | 3.2218 | | 3.1739 | 29.0 | 928 | 3.2082 | | 3.1966 | 30.0 | 960 | 3.1950 | | 3.1513 | 31.0 | 992 | 3.1826 | | 3.135 | 32.0 | 1024 | 3.1713 | | 3.1253 | 33.0 | 1056 | 3.1599 | | 3.0768 | 34.0 | 1088 | 3.1498 | | 3.1031 | 35.0 | 1120 | 3.1394 | | 3.064 | 36.0 | 1152 | 3.1293 | | 3.0391 | 37.0 | 1184 | 3.1200 | | 3.0701 | 38.0 | 1216 | 3.1117 | | 3.0787 | 39.0 | 1248 | 3.1032 | | 3.0423 | 40.0 | 1280 | 3.0956 | | 3.0214 | 41.0 | 1312 | 3.0875 | | 3.0289 | 42.0 | 1344 | 3.0804 | | 2.9667 | 43.0 | 1376 | 3.0736 | | 3.0341 | 44.0 | 1408 | 3.0671 | | 3.0098 | 45.0 | 1440 | 3.0606 | | 3.0202 | 46.0 | 1472 | 3.0544 | | 2.9598 | 47.0 | 1504 | 3.0490 | | 2.9734 | 48.0 | 1536 | 3.0430 | | 2.9381 | 49.0 | 1568 | 3.0375 | | 2.9444 | 50.0 | 1600 | 3.0328 | | 2.9357 | 51.0 | 1632 | 3.0280 | | 2.9453 | 52.0 | 1664 | 3.0237 | | 2.9906 | 53.0 | 1696 | 3.0191 | | 2.934 | 54.0 | 1728 | 3.0148 | | 2.9076 | 55.0 | 1760 | 3.0110 | | 2.9874 | 56.0 | 1792 | 3.0070 | | 2.9682 | 57.0 | 1824 | 3.0032 | | 2.9287 | 58.0 | 1856 | 2.9994 | | 2.9575 | 59.0 | 1888 | 2.9956 | | 2.8618 | 60.0 | 1920 | 2.9926 | | 2.9614 | 61.0 | 1952 | 2.9893 | | 2.9463 | 62.0 | 1984 | 2.9861 | | 2.8927 | 63.0 | 2016 | 2.9834 | | 2.9048 | 64.0 | 2048 | 2.9805 | | 2.9161 | 65.0 | 2080 | 2.9777 | | 2.9117 | 66.0 | 2112 | 2.9753 | | 2.932 | 67.0 | 2144 | 2.9729 | | 2.9148 | 68.0 | 2176 | 2.9706 | | 2.8919 | 69.0 | 2208 | 2.9683 | | 2.9278 | 70.0 | 2240 | 2.9662 | | 2.869 | 71.0 | 2272 | 2.9643 | | 2.8844 | 72.0 | 2304 | 2.9622 | | 2.8636 | 73.0 | 2336 | 2.9603 | | 2.8734 | 74.0 | 2368 | 2.9585 | | 2.8934 | 75.0 | 2400 | 2.9569 | | 2.86 | 76.0 | 2432 | 2.9551 | | 2.8366 | 77.0 | 2464 | 2.9539 | | 2.8887 | 78.0 | 2496 | 2.9522 | | 2.8632 | 79.0 | 2528 | 2.9511 | | 2.8691 | 80.0 | 2560 | 2.9496 | | 2.8597 | 81.0 | 2592 | 2.9484 | | 2.8775 | 82.0 | 2624 | 2.9473 | | 2.8491 | 83.0 | 2656 | 2.9461 | | 2.8639 | 84.0 | 2688 | 2.9450 | | 2.8659 | 85.0 | 2720 | 2.9443 | | 2.8557 | 86.0 | 2752 | 2.9433 | | 2.8188 | 87.0 | 2784 | 2.9423 | | 2.8896 | 88.0 | 2816 | 2.9416 | | 2.8102 | 89.0 | 2848 | 2.9409 | | 2.8452 | 90.0 | 2880 | 2.9403 | | 2.8437 | 91.0 | 2912 | 2.9399 | | 2.8193 | 92.0 | 2944 | 2.9397 | | 2.8645 | 93.0 | 2976 | 2.9391 | | 2.8745 | 94.0 | 3008 | 2.9388 | | 2.8568 | 95.0 | 3040 | 2.9385 | | 2.8832 | 96.0 | 3072 | 2.9382 | | 2.8801 | 97.0 | 3104 | 2.9382 | | 2.8488 | 98.0 | 3136 | 2.9383 | | 2.8233 | 99.0 | 3168 | 2.9380 | | 2.8505 | 100.0 | 3200 | 2.9380 | ### Framework versions - Transformers 4.28.0 - Pytorch 1.13.0a0+d321be6 - Datasets 2.12.0 - Tokenizers 0.13.3

kevinng77/unsup_bert_L3

kevinng77

2023-06-17T10:00:06Z

107

0

transformers

[ "transformers", "pytorch", "onnx", "bert", "text-classification", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-17T08:47:52Z

--- license: apache-2.0 language: - en metrics: - accuracy - f1 pipeline_tag: text-classification --- ```python # transformers==4.29.1 from transformers import AutoTokenizer, pipeline from optimum.onnxruntime import ORTModelForSequenceClassification onnx_model_path = "kevinng77/unsup_bert_L3" tokenizer = AutoTokenizer.from_pretrained(onnx_model_path) onnx_model = ORTModelForSequenceClassification.from_pretrained(onnx_model_path) onnx_pipe = pipeline(task="text-classification", model=onnx_model, tokenizer=tokenizer) onnx_pipe("How many rows are there in the table?") ```

hts98/whisper-tiny-paper

hts98

2023-06-17T09:45:10Z

77

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-06-16T15:36:23Z

--- tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-tiny-paper results: [] ---  # whisper-tiny-paper This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6807 - Wer: 50.8558 ## 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: 48 - eval_batch_size: 48 - 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | No log | 1.0 | 72 | 0.6515 | 50.3886 | | No log | 2.0 | 144 | 0.6566 | 50.8012 | | No log | 3.0 | 216 | 0.6624 | 50.3713 | | No log | 4.0 | 288 | 0.6684 | 50.8026 | | No log | 5.0 | 360 | 0.6807 | 50.8558 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.7.0 - Tokenizers 0.13.3

ganghe74/distilbert-base-uncased-finetuned-emotion

ganghe74

2023-06-17T09:34:40Z

105

0

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-17T09:13:51Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9225 - name: F1 type: f1 value: 0.922469380812715 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2170 - Accuracy: 0.9225 - F1: 0.9225 ## 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: 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8057 | 1.0 | 250 | 0.3170 | 0.905 | 0.9023 | | 0.242 | 2.0 | 500 | 0.2170 | 0.9225 | 0.9225 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.13.0 - Tokenizers 0.13.3

edu-linguistic/opt-1.3b-edu-sft

edu-linguistic

2023-06-17T09:28:57Z

0

null

[ "en", "dataset:yahma/alpaca-cleaned", "dataset:Nebulous/gpt4all_pruned", "region:us" ]

null

2023-06-15T14:16:11Z

--- datasets: - yahma/alpaca-cleaned - Nebulous/gpt4all_pruned language: - en --- ## Inference Example: ```python from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer peft_model_id = "edu-linguistic/opt-1.3b-edu-sft" model_name = 'facebook/opt-1.3b' config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(model_name) model = PeftModel.from_pretrained(model, peft_model_id) tokenizer = AutoTokenizer.from_pretrained(model_name) question = "<|prompter|> Consider the following function: f(x1, x2) = ln(x1). This function is…" question = tokenizer.encode(question, return_tensors='pt') generation_kwargs = { "do_sample": True, "top_k": 0, "top_p": 0.9, "bos_token_id": tokenizer.bos_token_id, "pad_token_id": tokenizer.pad_token_id, "eos_token_id": tokenizer.eos_token_id, "num_return_sequences": 1, "min_new_tokens": 10, "max_new_tokens": 512, } response = model.generate(input_ids=question, **generation_kwargs) response = tokenizer.decode(response[0], skip_special_tokens=False, clean_up_tokenization_spaces=False ) print(response) ```

coyude/Nous-Hermes-13b-Chinese-GGML

coyude

2023-06-17T09:28:23Z

0

22

transformers

[ "transformers", "text-generation", "zh", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

text-generation

2023-06-11T03:42:04Z

--- license: apache-2.0 language: - zh - en library_name: transformers pipeline_tag: text-generation --- 原始模型：https://huggingface.co/NousResearch/Nous-Hermes-13b lora：https://huggingface.co/ziqingyang/chinese-alpaca-lora-13b 将Nous-Hermes-13b与chinese-alpaca-lora-13b进行合并，增强模型的中文能力，~~不过存在翻译腔~~ 使用项目： https://github.com/ymcui/Chinese-LLaMA-Alpaca https://github.com/ggerganov/llama.cpp **推荐q5_k_m或q4_k_m 该仓库模型均为ggmlv3模型** Text-generation-webui懒人包： https://www.bilibili.com/read/cv23495183 --------------------------------------------------------------------------------------------- Original model: https://huggingface.co/NousResearch/Nous-Hermes-13b Lora: https://huggingface.co/ziqingyang/chinese-alpaca-lora-13b The Nous-Hermes-13b model is merged with the chinese-alpaca-lora-13b model to enhance the Chinese language capability of the model, ~~although it may exhibit a translation style.~~ Usage projects: https://github.com/ymcui/Chinese-LLaMA-Alpaca https://github.com/ggerganov/llama.cpp **q5_k_m or q4_k_m is recommended. All models in this repository are ggmlv3 models.**

parkyunmin/my_awesome_eli5_clm-model

parkyunmin

2023-06-17T09:09:15Z

211

0

transformers

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

text-generation

2023-06-17T05:54:26Z

--- license: mit tags: - generated_from_trainer model-index: - name: my_awesome_eli5_clm-model results: [] ---  # my_awesome_eli5_clm-model This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5380 ## 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: 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 | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 49 | 1.6679 | | No log | 2.0 | 98 | 1.5629 | | No log | 3.0 | 147 | 1.5380 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

PabloQuant29/ppo-LunarLander-v2

PabloQuant29

2023-06-17T08:36:13Z

0

stable-baselines3

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

reinforcement-learning

2023-06-17T08:35:40Z

--- 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: 244.46 +/- 18.98 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 ... ```

AriesChen/GeoLLM

AriesChen

2023-06-17T08:32:06Z

195

3

transformers

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

feature-extraction

2023-06-17T08:30:04Z

# GeoLLM **Large Language Model for Geology** Large language models are used to organize geology-related knowledge (geology, geophysics, geophysical logging, etc.). This version uses the [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B) base model and fine-tunes it using P-tuning. --- ### Sedimentology Sedimentology, the study of sedimentary rocks and the processes by which they are formed, includes and is related to a large number of phenomena. Sedimentology includes the five fundamental processes defined by the term sediaentation --weathering, erosion, transportation, deposition and diagenesis. **Datasets：**《沉积岩石学（第四版）》朱筱敏 **Model：** ChatGLM-6B **Fine-tuning：** P-Tuning v2 **Before fine-tuning** ``` response, history = model.chat(tokenizer, "什么是沉积岩石学？", history=[]) response 沉积岩石学是一门研究沉积岩的形成、结构和成分的学科，主要关注地球表面上不同条件下的沉积过程和岩石形成机制，包括岩浆沉积、冰川沉积、洪水沉积、海洋沉积等。沉积岩石学在地质学、地球物理学、地球化学、材料科学等领域都有广泛应用，因为沉积岩是许多自然和人工地质工程的基础，如地质勘探、矿产资源开发、土木工程、环境科学等。沉积岩石学的研究对象包括沉积岩的地质特征、成分和构造特征，以及沉积岩与地壳、岩浆和变质岩的关系。研究方法包括沉积岩分析、岩相学分析、岩浆动力学分析等。 ``` **After fine-tuning** ``` response, history = model.chat(tokenizer, "什么是沉积岩石学？", history=[]) response 沉积岩石学是研究沉积岩的物质成分、结构构造、岩石类型、沉积物沉积作用和沉积物质形成环境以及沉积岩分布规律的一门科学。 ``` **Error Analysis:** We meticulously refined the model by approximately 500 entries from academic textbooks, subsequently applying P-Tuning v2 for optimization. Detailed control of parameters was not conducted for the time being. Given the scarcity of the training data and the fine-tuning parameters, the outcomes might exhibit some irregularities. **Results Analysis:** It is evident that the fine-tuned model shows enhanced reliability(more precise and concise) when providing answers within specialized knowledge domains. Moving forward, we will persist in enriching our training data and optimizing our fine-tuning methodologies in order to yield superior results. --- ### TODO 1. Geophysical Exploration 2. Geophysical logging 3. Petroleum Geology etc... --- ### Related Resources 1. [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B): ChatGLM-6B is an open bilingual language model based on General Language Model (GLM) framework, with 6.2 billion parameters.

okazaki-lab/ss_wsd

okazaki-lab

2023-06-17T08:21:08Z

0

transformers

[ "transformers", "word_sense_disambiguation", "en", "dataset:SemCor", "dataset:WordNet", "dataset:WSD_Evaluation_Framework", "arxiv:2304.11340", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2023-06-17T07:20:37Z

--- license: apache-2.0 language: - en tags: - word_sense_disambiguation library_name: transformers datasets: - SemCor - WordNet - WSD_Evaluation_Framework metrics: - f1 --- # Semantic Specialization for Knowledge-based Word Sense Disambiguation * This repository contains the trained model (projection heads) and sense/context embeddings used for training and evaluating the model. * If you want to learn how to use these files, please refer to the [semantic_specialization_for_wsd](https://github.com/s-mizuki-nlp/semantic_specialization_for_wsd) repository. ## Trained Model (Projection Heads) * File: checkpoints/baseline/last.ckpt * This is one of the trained models used for reporting the main results (Table 2 in [Mizuki and Okazaki, EACL2023]). NOTE: Five runs were performed in total. * The main hyperparameters used for training are as follows: | Argument name | Value | Description | |----------------------------------------------------------------|----------------------------|------------------------------------------------------------------------------------| | max_epochs | 15 | Maximum number of training epochs | | cfg_similarity_class.temperature ($\beta^{-1}$) | 0.015625 (=1/64) | Temperature parameter for the contrastive loss | | batch_size ($N_B$) | 256 | Number of samples in each batch for the attract-repel and self-training objectives | | coef_max_pool_margin_loss ($\alpha$) | 0.2 | Coefficient for the self-training loss | | cfg_gloss_projection_head.n_layer | 2 | Number of FFNN layers for the projection heads | | cfg_gloss_projection_head.max_l2_norm_ratio ($\epsilon$) | 0.015 | Hyperparameter for the distance constraint integrated in the projection heads | ## Sense/context embeddings * Directory: `data/bert_embeddings/` * Sense embeddings: `bert-large-cased_WordNet_Gloss_Corpus.hdf5` * Context embeddings for the self-training objective: `bert-large-cased_SemCor.hdf5` * Context embeddings for evaluating the WSD task: `bert-large-cased_WSDEval-ALL.hdf5` # Reference ``` @inproceedings{Mizuki:EACL2023, title = "Semantic Specialization for Knowledge-based Word Sense Disambiguation", author = "Mizuki, Sakae and Okazaki, Naoaki", booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume", series = {EACL}, month = may, year = "2023", address = "Dubrovnik, Croatia", publisher = "Association for Computational Linguistics", pages = "3449--3462", } ``` * [arXiv version](https://arxiv.org/abs/2304.11340) is also available.

SM16/TreeClassifier

SM16

2023-06-17T08:15:11Z

218

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-06-17T07:27:25Z

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: TreeClassifier results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 1.0 --- # TreeClassifier Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### Pepper Tree ![Pepper Tree](images/Pepper_Tree.jpg) #### Weeping Willow ![Weeping Willow](images/Weeping_Willow.jpg)

musabg/mt5-xl-tr-summarization

musabg

2023-06-17T07:25:20Z

5

1

transformers

[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "tr", "dataset:musabg/wikipedia-tr-summarization", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-06-08T16:24:59Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - musabg/wikipedia-tr-summarization metrics: - rouge model-index: - name: mt5-xl-tr-summarization results: - task: name: Summarization type: summarization dataset: name: musabg/wikipedia-tr-summarization type: musabg/wikipedia-tr-summarization split: validation metrics: - name: Rouge1 type: rouge value: 56.4468 language: - tr --- # mT5-Xl Turkish Summarization This model is a fine-tuned version of [google/mt5-xl](https://huggingface.co/google/mt5-xl) on the musabg/wikipedia-tr-summarization dataset. This can be used with HF summarization pipeline. ## 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: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Eval results It achieves the following results on the evaluation set: - Loss: 0.5676 - Rouge1: 56.4468 - Rouge2: 41.3258 - Rougel: 48.1909 - Rougelsum: 48.4284 - Gen Len: 75.9265 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 1.13.1 - Datasets 2.12.0 - Tokenizers 0.13.3

Irgendsoeine/FaceTheVote3

Irgendsoeine

2023-06-17T07:10:58Z

4

0

tf-keras

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

image-classification

2023-06-17T06:56:45Z

--- pipeline_tag: image-classification ---

tux/dqn-SpaceInvadersNoFrameskip-v4

tux

2023-06-17T07:09:15Z

0

stable-baselines3

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

reinforcement-learning

2023-06-16T09:52:12Z

--- 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: 606.00 +/- 186.22 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 tux -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 tux -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 tux ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('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'} ```

Csuarezg/SBERTA-finetuned

Csuarezg

2023-06-17T07:04:30Z

104

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "feature-extraction", "es", "dataset:xnli", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2023-06-12T21:04:08Z

--- datasets: - xnli language: - es library_name: transformers ---

kjiwon1222/my_awesome_eli5_clm-model

kjiwon1222

2023-06-17T06:54:34Z

217

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-06-17T06:32:49Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: my_awesome_eli5_clm-model results: [] ---  # my_awesome_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7506 ## 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: 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 | |:-------------:|:-----:|:----:|:---------------:| | 3.8621 | 1.0 | 1137 | 3.7690 | | 3.7782 | 2.0 | 2274 | 3.7533 | | 3.7245 | 3.0 | 3411 | 3.7506 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

Arindam75/Reinforce-pixelcopter-v1

Arindam75

2023-06-17T06:22:04Z

0

null

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

reinforcement-learning

2023-06-17T06:21:05Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-pixelcopter-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 18.90 +/- 13.60 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

sunflowermarshmallows/dqn-SpaceInvadersNoFrameskip-v4

sunflowermarshmallows

2023-06-17T05:25:16Z

1

0

stable-baselines3

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

reinforcement-learning

2023-06-17T05:24:36Z

--- 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: 629.00 +/- 184.89 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 sunflowermarshmallows -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 sunflowermarshmallows -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 sunflowermarshmallows ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('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'} ```

nolanaatama/skrmkhllvjprvc500pchsmgzb

nolanaatama

2023-06-17T05:02:20Z

0

null

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

null

2023-06-17T04:59:21Z

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

eason0203/Reinforce-cartpole

eason0203

2023-06-17T04:34:33Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-06-17T04:34:18Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cartpole results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 418.80 +/- 129.36 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . 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

nolanaatama/rngrndrvcv800pchsrthysttylrsvrsn

nolanaatama

2023-06-17T04:33:50Z

0

null

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

null

2023-05-31T04:13:59Z

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

ALPHONSE28/EQUIPO06SEMANA09

ALPHONSE28

2023-06-17T04:33:00Z

105

0

transformers

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

text-classification

2023-06-16T06:38:04Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: EQUIPO06SEMANA09 results: [] ---  # EQUIPO06SEMANA09 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2161 - Accuracy: 0.9233 - F1: 0.9514 ## 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 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

AustinCarthy/OnlyPhishGPT2_subdomain_100KP_BFall_fromB_200K_topP_0.75_ratio5

AustinCarthy

2023-06-17T04:00:39Z

0

null

[ "tensorboard", "generated_from_trainer", "license:apache-2.0", "region:us" ]

null

2023-06-16T22:42:13Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: OnlyPhishGPT2_subdomain_100KP_BFall_fromB_200K_topP_0.75_ratio5 results: [] ---  # OnlyPhishGPT2_subdomain_100KP_BFall_fromB_200K_topP_0.75_ratio5 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the Train benign: Fall,Test Benign: Fall, Train phish: Fall, Test phish: Fall, generated url dataset: generated_phish_OnlyPhishGPT2_using_benigh_200K_top_p_0.75 dataset. It achieves the following results on the evaluation set: - Loss: 0.0194 - Accuracy: 0.9979 - F1: 0.9778 - Precision: 0.9987 - Recall: 0.9578 - Roc Auc Score: 0.9789 - Tpr At Fpr 0.01: 0.9642 ## 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: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0035 | 1.0 | 56250 | 0.0126 | 0.9975 | 0.9736 | 0.9917 | 0.9562 | 0.9779 | 0.9052 | | 0.002 | 2.0 | 112500 | 0.0159 | 0.9977 | 0.9755 | 0.9975 | 0.9544 | 0.9771 | 0.9466 | | 0.0008 | 3.0 | 168750 | 0.0136 | 0.9981 | 0.9793 | 0.9977 | 0.9616 | 0.9807 | 0.958 | | 0.0 | 4.0 | 225000 | 0.0235 | 0.9973 | 0.9708 | 0.9992 | 0.944 | 0.9720 | 0.9574 | | 0.0004 | 5.0 | 281250 | 0.0194 | 0.9979 | 0.9778 | 0.9987 | 0.9578 | 0.9789 | 0.9642 | ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

digiplay/ShowmakerMix_v1

digiplay

2023-06-17T03:05:06Z

310

3

diffusers

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

text-to-image

2023-06-13T01:35:32Z

--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info: https://civitai.com/models/16032/showmakermix Original Author's DEMO image: ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/06e7a817-ddbb-4088-5fa0-fb2dd6bfd700/width=450/197406.jpeg)

DreamerGPT/D7b-5-1

DreamerGPT

2023-06-17T01:38:49Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2023-06-17T01:20:31Z

--- license: apache-2.0 --- # D7b-5-1 [https://github.com/DreamerGPT/DreamerGPT](https://github.com/DreamerGPT/DreamerGPT)

mskani/controlnet-hands

mskani

2023-06-17T01:35:09Z

0

5

null

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

null

2023-06-16T14:10:39Z

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

zhangjian94cn/Taxi-v3

zhangjian94cn

2023-06-17T01:33:35Z

0

null

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

reinforcement-learning

2023-06-17T01:33:26Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-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="zhangjian94cn/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"]) ```

darshan7/Model_xlnet_results

darshan7

2023-06-17T01:22:18Z

59

0

transformers

[ "transformers", "tf", "xlnet", "question-answering", "generated_from_keras_callback", "license:mit", "endpoints_compatible", "region:us" ]

question-answering

2023-06-14T19:04:11Z

--- license: mit tags: - generated_from_keras_callback model-index: - name: darshan7/Model_xlnet_results results: [] ---  # darshan7/Model_xlnet_results This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0058 - Validation Loss: 0.0110 - Epoch: 9 ## 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 181655, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.0392 | 0.0262 | 0 | | 0.0211 | 0.0185 | 1 | | 0.0151 | 0.0161 | 2 | | 0.0110 | 0.0127 | 3 | | 0.0074 | 0.0110 | 4 | | 0.0058 | 0.0110 | 5 | | 0.0058 | 0.0110 | 6 | | 0.0058 | 0.0110 | 7 | | 0.0059 | 0.0110 | 8 | | 0.0058 | 0.0110 | 9 | ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

DreamerGPT/D13b-3-3

DreamerGPT

2023-06-17T01:21:55Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2023-06-17T00:58:23Z

--- license: apache-2.0 --- # D13b-3-3 [https://github.com/DreamerGPT/DreamerGPT](https://github.com/DreamerGPT/DreamerGPT)

sheshenin/vvshsh

sheshenin

2023-06-17T00:40:05Z

32

0

diffusers

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

text-to-image

2023-06-17T00:35:21Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### VikaSH Dreambooth model trained by sheshenin with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

Ioanaaaaaaa/bert-base-uncased-with-preprocess-finetuned-emotion-5-epochs-5e-05-lr-0.1-weight_decay

Ioanaaaaaaa

2023-06-16T23:47:54Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-16T23:30:14Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: bert-base-uncased-with-preprocess-finetuned-emotion-5-epochs-5e-05-lr-0.1-weight_decay results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.941 - name: F1 type: f1 value: 0.9411169346964399 ---  # bert-base-uncased-with-preprocess-finetuned-emotion-5-epochs-5e-05-lr-0.1-weight_decay This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2591 - Accuracy: 0.941 - F1: 0.9411 ## 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: 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.0799 | 1.0 | 250 | 0.1898 | 0.9375 | 0.9377 | | 0.0516 | 2.0 | 500 | 0.2290 | 0.938 | 0.9383 | | 0.0386 | 3.0 | 750 | 0.2107 | 0.9415 | 0.9419 | | 0.0195 | 4.0 | 1000 | 0.2607 | 0.9435 | 0.9433 | | 0.0149 | 5.0 | 1250 | 0.2591 | 0.941 | 0.9411 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

TheBloke/robin-33B-v2-GGML

TheBloke

2023-06-16T23:31:16Z

0

5

null

[ "license:other", "region:us" ]

null

2023-06-16T18:09:39Z

--- 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/Jq4vkcDakD">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>  # OptimalScale's Robin 33B v2 GGML These files are GGML format model files for [OptimalScale's Robin 33B v2](https://huggingface.co/OptimalScale/robin-33b-v2-delta). 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) * [ParisNeo/GPT4All-UI](https://github.com/ParisNeo/gpt4all-ui) * [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/robin-33B-v2-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/robin-33B-v2-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/robin-33B-v2-fp16) ## Prompt template ``` A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions ###Human: 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 | | ---- | ---- | ---- | ---- | ---- | ----- | | robin-33b.ggmlv3.q2_K.bin | q2_K | 2 | 13.71 GB | 16.21 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. | | robin-33b.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 17.28 GB | 19.78 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 | | robin-33b.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 15.72 GB | 18.22 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 | | robin-33b.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 14.06 GB | 16.56 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors | | robin-33b.ggmlv3.q4_0.bin | q4_0 | 4 | 18.30 GB | 20.80 GB | Original llama.cpp quant method, 4-bit. | | robin-33b.ggmlv3.q4_1.bin | q4_1 | 4 | 20.33 GB | 22.83 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. | | robin-33b.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 19.62 GB | 22.12 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 | | robin-33b.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 18.36 GB | 20.86 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors | | robin-33b.ggmlv3.q5_0.bin | q5_0 | 5 | 22.37 GB | 24.87 GB | Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. | | robin-33b.ggmlv3.q5_1.bin | q5_1 | 5 | 24.40 GB | 26.90 GB | Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. | | robin-33b.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 23.05 GB | 25.55 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 | | robin-33b.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 22.40 GB | 24.90 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors | | robin-33b.ggmlv3.q6_K.bin | q6_K | 6 | 26.69 GB | 29.19 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors | | robin-33b.ggmlv3.q8_0.bin | q8_0 | 8 | 34.56 GB | 37.06 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 robin-33b.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n###Human: write a story about llamas\n###Assistant:" ``` Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. 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/Jq4vkcDakD) ## 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**: vamX, K, Jonathan Leane, Lone Striker, Sean Connelly, Chris McCloskey, WelcomeToTheClub, Nikolai Manek, John Detwiler, Kalila, David Flickinger, Fen Risland, subjectnull, Johann-Peter Hartmann, Talal Aujan, John Villwock, senxiiz, Khalefa Al-Ahmad, Kevin Schuppel, Alps Aficionado, Derek Yates, Mano Prime, Nathan LeClaire, biorpg, trip7s trip, Asp the Wyvern, chris gileta, Iucharbius , Artur Olbinski, Ai Maven, Joseph William Delisle, Luke Pendergrass, Illia Dulskyi, Eugene Pentland, Ajan Kanaga, Willem Michiel, Space Cruiser, Pyrater, Preetika Verma, Junyu Yang, Oscar Rangel, Spiking Neurons AB, Pierre Kircher, webtim, Cory Kujawski, terasurfer , Trenton Dambrowitz, Gabriel Puliatti, Imad Khwaja, Luke. Thank you to all my generous patrons and donaters!  # Original model card: OptimalScale's Robin 33B v2 No model card provided in source repository.

ghze/Taxi_v3

ghze

2023-06-16T23:00:53Z

0

null

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

reinforcement-learning

2023-06-16T23:00:48Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi_v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 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="ghze/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"]) ```

ghze/Taxi

ghze

2023-06-16T22:59:16Z

0

null

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

reinforcement-learning

2023-06-16T22:59:09Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 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="ghze/Taxi", 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"]) ```

sam34738/indicbert

sam34738

2023-06-16T22:03:57Z

167

0

transformers

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

text-classification

2023-06-16T21:56:33Z

--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: indicbert results: [] ---  # indicbert This model is a fine-tuned version of [ai4bharat/indic-bert](https://huggingface.co/ai4bharat/indic-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9751 - Accuracy: 0.6689 - F1: 0.6899 ## 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: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-05 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7041 | 1.0 | 2100 | 0.7416 | 0.6589 | 0.6710 | | 0.8083 | 2.0 | 4200 | 0.9751 | 0.6689 | 0.6899 | ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

Enterprize1/ppo-LunarLander-v2

Enterprize1

2023-06-16T21:45:24Z

0

stable-baselines3

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

reinforcement-learning

2023-06-16T21:45:00Z

--- 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: 242.78 +/- 66.66 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 ... ```

yinxiaoz/bert-finetuned-ner

yinxiaoz

2023-06-16T21:37:53Z

109

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-06-15T05:15:04Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.9326065411298315 - name: Recall type: recall value: 0.9501851228542578 - name: F1 type: f1 value: 0.9413137712570858 - name: Accuracy type: accuracy value: 0.9867104256195914 ---  # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0600 - Precision: 0.9326 - Recall: 0.9502 - F1: 0.9413 - Accuracy: 0.9867 ## 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: 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 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0884 | 1.0 | 1756 | 0.0675 | 0.9186 | 0.9339 | 0.9261 | 0.9822 | | 0.0345 | 2.0 | 3512 | 0.0611 | 0.9291 | 0.9485 | 0.9387 | 0.9862 | | 0.0182 | 3.0 | 5268 | 0.0600 | 0.9326 | 0.9502 | 0.9413 | 0.9867 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

stanford-crfm/music-small-ar-800k

stanford-crfm

2023-06-16T21:28:12Z

183

1

transformers

[ "transformers", "pytorch", "gpt2", "arxiv:2306.08620", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2023-06-05T00:01:12Z

--- license: apache-2.0 --- This is a Small (128M parameter) Transformer trained for 800k steps on arrival-time encoded music from the [Lakh MIDI dataset](https://colinraffel.com/projects/lmd/). # References for the Anticipatory Music Transformer The Anticipatory Music Transformer paper is available on [ArXiv](http://arxiv.org/abs/2306.08620). The full model card is available [here](https://johnthickstun.com/assets/pdf/music-modelcard.pdf). Code for using this model is available on [GitHub](https://github.com/jthickstun/anticipation/). See the accompanying [blog post](https://crfm.stanford.edu/2023/06/16/anticipatory-music-transformer.html) for additional discussion of this model.

stanford-crfm/music-small-800k

stanford-crfm

2023-06-16T21:27:08Z

664

1

transformers

[ "transformers", "pytorch", "gpt2", "arxiv:2306.08620", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2023-06-04T23:54:35Z

--- license: apache-2.0 --- This is a Small (128M parameter) Transformer trained for 800k steps on arrival-time encoded music from the [Lakh MIDI dataset](https://colinraffel.com/projects/lmd/). This model was trained with anticipation. # References for the Anticipatory Music Transformer The Anticipatory Music Transformer paper is available on [ArXiv](http://arxiv.org/abs/2306.08620). The full model card is available [here](https://johnthickstun.com/assets/pdf/music-modelcard.pdf). Code for using this model is available on [GitHub](https://github.com/jthickstun/anticipation/). See the accompanying [blog post](https://crfm.stanford.edu/2023/06/16/anticipatory-music-transformer.html) for additional discussion of this model.

stanford-crfm/music-medium-800k

stanford-crfm

2023-06-16T21:25:52Z

572

4

transformers

[ "transformers", "pytorch", "gpt2", "arxiv:2306.08620", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2023-06-05T00:17:20Z

--- license: apache-2.0 --- This is a Medium (360M parameter) Transformer trained for 800k steps on arrival-time encoded music from the [Lakh MIDI dataset](https://colinraffel.com/projects/lmd/). This model was trained with anticipation. # References for the Anticipatory Music Transformer The Anticipatory Music Transformer paper is available on [ArXiv](http://arxiv.org/abs/2306.08620). The full model card is available [here](https://johnthickstun.com/assets/pdf/music-modelcard.pdf). Code for using this model is available on [GitHub](https://github.com/jthickstun/anticipation/). See the accompanying [blog post](https://crfm.stanford.edu/2023/06/16/anticipatory-music-transformer.html) for additional discussion of this model.

stanford-crfm/music-medium-100k

stanford-crfm

2023-06-16T21:24:54Z

176

0

transformers

[ "transformers", "pytorch", "gpt2", "arxiv:2306.08620", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2023-06-05T00:08:04Z

--- license: apache-2.0 --- This is a Medium (360M parameter) Transformer trained for 100k steps on arrival-time encoded music from the [Lakh MIDI dataset](https://colinraffel.com/projects/lmd/). This model was trained with anticipation. # References for the Anticipatory Music Transformer The Anticipatory Music Transformer paper is available on [ArXiv](http://arxiv.org/abs/2306.08620). The full model card is available [here](https://johnthickstun.com/assets/pdf/music-modelcard.pdf). Code for using this model is available on [GitHub](https://github.com/jthickstun/anticipation/). See the accompanying [blog post](https://crfm.stanford.edu/2023/06/16/anticipatory-music-transformer.html) for additional discussion of this model.

stanford-crfm/music-large-100k

stanford-crfm

2023-06-16T21:24:11Z

189

0

transformers

[ "transformers", "pytorch", "gpt2", "arxiv:2306.08620", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2023-06-05T00:22:37Z

--- license: apache-2.0 --- This is a Large (780M parameter) Transformer trained for 100k steps on arrival-time encoded music from the [Lakh MIDI dataset](https://colinraffel.com/projects/lmd/). This model was trained with anticipation. # References for the Anticipatory Music Transformer The Anticipatory Music Transformer paper is available on [ArXiv](http://arxiv.org/abs/2306.08620). The full model card is available [here](https://johnthickstun.com/assets/pdf/music-modelcard.pdf). Code for using this model is available on [GitHub](https://github.com/jthickstun/anticipation/). See the accompanying [blog post](https://crfm.stanford.edu/2023/06/16/anticipatory-music-transformer.html) for additional discussion of this model.

Schnitzl/detr-resnet-50_finetuned_cppe5

Schnitzl

2023-06-16T20:54:42Z

191

0

transformers

[ "transformers", "pytorch", "tensorboard", "detr", "object-detection", "generated_from_trainer", "dataset:cppe-5", "license:apache-2.0", "endpoints_compatible", "region:us" ]

object-detection

2023-06-16T17:17:04Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - cppe-5 model-index: - name: detr-resnet-50_finetuned_cppe5 results: [] ---  # detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 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: 1e-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: 100 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.13.0 - Tokenizers 0.13.3

crlandsc/bsrnn-vocals

crlandsc

2023-06-16T20:25:39Z

0

2

null

[ "audio source separation", "music demixing", "band-split recurrent neural network", "bsrnn", "spectrogram", "vocals", "region:us" ]

null

2023-06-16T20:18:04Z

--- tags: - audio source separation - music demixing - band-split recurrent neural network - bsrnn - spectrogram - vocals --- # Model Card for bsrnn-vocals Vocals model for [Music-Demixing-with-Band-Split-RNN](https://github.com/crlandsc/Music-Demixing-with-Band-Split-RNN).

GEMCorp/q-Taxi-v3

GEMCorp

2023-06-16T20:19:33Z

0

null

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

reinforcement-learning

2023-06-16T20:08:42Z

--- 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: 7.56 +/- 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="GEMCorp/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"]) ```

sngsfydy/resnet-50-finetuned-eurosat

sngsfydy

2023-06-16T20:17:05Z

209

0

transformers

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

image-classification

2023-06-16T19:14:44Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: resnet-50-finetuned-eurosat results: [] ---  # resnet-50-finetuned-eurosat This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0706 - Accuracy: 0.5152 ## 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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.6069 | 0.99 | 20 | 1.5839 | 0.3879 | | 1.5395 | 1.98 | 40 | 1.4860 | 0.5485 | | 1.4321 | 2.96 | 60 | 1.3500 | 0.5364 | | 1.3292 | 4.0 | 81 | 1.1826 | 0.5212 | | 1.233 | 4.99 | 101 | 1.0706 | 0.5152 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

TheBloke/robin-13B-v2-GGML

TheBloke

2023-06-16T20:13:21Z

0

6

null

[ "license:other", "region:us" ]

null

2023-06-16T18:59:47Z

--- 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/Jq4vkcDakD">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>  # OptimalScale's Robin 13B v2 GGML These files are GGML format model files for [OptimalScale's Robin 13B v2](https://huggingface.co/OptimalScale/robin-13b-v2-delta). 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) * [ParisNeo/GPT4All-UI](https://github.com/ParisNeo/gpt4all-ui) * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) * [ctransformers](https://github.com/marella/ctransformers) ## Prompt template ``` A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions ###Human: prompt ###Assistant: ``` ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/robin-13B-v2-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/robin-13B-v2-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/robin-13B-v2-fp16)  ## 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 | | ---- | ---- | ---- | ---- | ---- | ----- | | robin-13b.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. | | robin-13b.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 | | robin-13b.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 | | robin-13b.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 | | robin-13b.ggmlv3.q4_0.bin | q4_0 | 4 | 7.32 GB | 9.82 GB | Original llama.cpp quant method, 4-bit. | | robin-13b.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. | | robin-13b.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 | | robin-13b.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 | | robin-13b.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. | | robin-13b.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. | | robin-13b.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 | | robin-13b.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 | | robin-13b.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 | | robin-13b.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 robin-13b.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n###Human: write a story about llamas\n###Assistant:" ``` Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. 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/Jq4vkcDakD) ## 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**: vamX, K, Jonathan Leane, Lone Striker, Sean Connelly, Chris McCloskey, WelcomeToTheClub, Nikolai Manek, John Detwiler, Kalila, David Flickinger, Fen Risland, subjectnull, Johann-Peter Hartmann, Talal Aujan, John Villwock, senxiiz, Khalefa Al-Ahmad, Kevin Schuppel, Alps Aficionado, Derek Yates, Mano Prime, Nathan LeClaire, biorpg, trip7s trip, Asp the Wyvern, chris gileta, Iucharbius , Artur Olbinski, Ai Maven, Joseph William Delisle, Luke Pendergrass, Illia Dulskyi, Eugene Pentland, Ajan Kanaga, Willem Michiel, Space Cruiser, Pyrater, Preetika Verma, Junyu Yang, Oscar Rangel, Spiking Neurons AB, Pierre Kircher, webtim, Cory Kujawski, terasurfer , Trenton Dambrowitz, Gabriel Puliatti, Imad Khwaja, Luke. Thank you to all my generous patrons and donaters!  # Original model card: OptimalScale's Robin 13B v2 No model card provided in source repository.

FALLENSTAR/MitsubishiChariotLoRa

FALLENSTAR

2023-06-16T20:10:47Z

0

null

[ "region:us" ]

null

2023-06-09T22:52:05Z

### Model Description That LoRa based on Mitsubishi Chariot/Chariot grandis 1997-2003. It's also a test model that poorly configured, so you have to play with the settings... The best images I was able to get with this LoRa were at these settings: Steps: 25 Sampler: DPM++ SDE Karras, CFG scale: 6.5 and with LoRa strength 0.8-1 ![00078-3304362297.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/2zwn_ybCAMQij0uC5C2xO.png) ![00080-176898055.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/3B-I5jhupVxoOCFgQVWln.png) ![00144-12952343.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/0Sqxm4quAS9P12NVxCVYG.png) ![00146-3385434376.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/jzItGhLhj3STdfdqHNgDv.png) ![00150-980315653.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/DYer7-yboQtaRymzhTZVe.png) ![00162-751177051.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/GYISC31YZ10YhLs8KHg8O.png) ![00096-2832495824.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/51Jc3IAV6rkNsUu-3NnQN.png) ![00002-3097255927.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/6m13zDtJSdK4syfujs2bu.png) ![00044-1201350781.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/11wdBPSt2rZljp2LrPFzV.png) ![00036-481276871.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/vf3A8yRQURMkXQw6beyBE.png) ![00035-307154204.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/Pfe5dCwI9t_qXZzuI08t3.png) ![00034-2484733566.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/4OmkzRvSUCRRO91Gz-nh4.png) ![00033-417478899.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/q8S6-lxV2uY3BwZ5WkcL5.png) ![00029-3305906991.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/DSZHwQN1jhA1leW2jcvG8.png) ![00024-2340357030.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/6-LTPowo0YXLtIpGpByc7.png) ![00018-4030886737.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/ybEN_cmAo6xkha5W8EVuF.png) ![00005-3932370153.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/JBxIq6dzqXgIyWPAawOQW.png)

FALLENSTAR/CedricGloriaLoRa

FALLENSTAR

2023-06-16T20:10:23Z

0

null

[ "region:us" ]

null

2023-06-09T20:58:56Z

### Model Description First of all, it's LoRa. It is based on my favorite Nissan Cedric/Gloria Y31 Hardtop from the years '87-91. It is a test model, so it has defects. I don't remember how many samples and epochs were used in it... But, with some of the checkpoints it turns out very similar and funny. The best images I was able to get with this LoRa were at these settings: Steps: 25 Sampler: DPM++ SDE Karras, CFG scale: 6.5 and with LoRa strength 0.8-1 ### Results ![00056-2998859893.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/Hqc-19MXbw-tOHcytmCgO.png) ![00037-1095574331.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/6d8uCXt5U2KvnqtZdl1d2.png) ![00039-89618184.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/tmzZoZQT6fK4_UPCCA5sk.png) ![00041-562450270.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/tp0QNuodfbsEkgLh8Yhht.png) ![00066-2959432492.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/FO5BR5uRtvAwm7jMCzUTa.png) ![00058-1357569836.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/yC3gSTgtQnnBTg3ibDtXw.png) ![00065-3713693640.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/xRNkGnsBb8kiW15_v7nws.png) ![00013-3313807285.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/1r1CFhjES8GncHPFudrGZ.png) ![00067-1452084223.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/8a86GW52YDweESQKp5fRR.png) ![00004-873995140.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/ZCu2ffZkEGW_DlLWYu6QC.png) ![00084-419571825.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/4PXZdvNBX-kWwMyyZ1bRm.png) ![00021-3472542211.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/_2fwDF2KUlXq7bS-P65Hi.png) ![00048-2623366528.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/m-XuUdhEXhT7-Vaa_D0pa.png)

FALLENSTAR/TurbofansLoRa

FALLENSTAR

2023-06-16T20:09:36Z

0

null

[ "region:us" ]

null

2023-06-11T01:32:23Z

### Model Description This LoRa is based on Turbofan or Aero Covers, an invention from Japan. Turbofan were created to effectively cool the brake discs. Originally they were used in motorsports, and were made out of aluminum. Now, thanks to new brake technology, Turbofans are not used for their original purpose. And they are not popular in professional motorsports. But, to me, they add a futuristic style to car tuning. The best images I was able to get with this LoRa were at these settings: Steps: 25 Sampler: DPM++ SDE Karras, CFG scale: 6.5 and with LoRa strength 0.8-1 ![00094-3727343714.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/t4pEcqkV-1UZzNmVS8G5s.png) ![00096-3846199597.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/D0Wh8K3xII6uhsdaMpm_T.png) ![00112-2164319160.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/GUKuqj2iMjHGrh1ByuNkG.png) ![00117-4155383315.png](https://s3.amazonaws.com/moonup/production/uploads/640761fd2e309e65452364fa/1E5xBqoBmnB8YZfr32mtU.png)

TheBloke/robin-33B-v2-fp16

TheBloke

2023-06-16T20:07:31Z

1,566

3

transformers

[ "transformers", "pytorch", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-06-16T18:09:39Z

--- 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/Jq4vkcDakD">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>  # OptimalScale's Robin 33B v2 fp16 These files are pytorch format fp16 model files for [OptimalScale's Robin 33B v2](https://huggingface.co/OptimalScale/robin-33b-v2-delta). It is the result of merging and/or converting the source repository to float16. ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/robin-33B-v2-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/robin-33B-v2-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/robin-33B-v2-fp16) ## Prompt template ``` A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions ###Human: prompt ###Assistant: ```  ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD) ## 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**: vamX, K, Jonathan Leane, Lone Striker, Sean Connelly, Chris McCloskey, WelcomeToTheClub, Nikolai Manek, John Detwiler, Kalila, David Flickinger, Fen Risland, subjectnull, Johann-Peter Hartmann, Talal Aujan, John Villwock, senxiiz, Khalefa Al-Ahmad, Kevin Schuppel, Alps Aficionado, Derek Yates, Mano Prime, Nathan LeClaire, biorpg, trip7s trip, Asp the Wyvern, chris gileta, Iucharbius , Artur Olbinski, Ai Maven, Joseph William Delisle, Luke Pendergrass, Illia Dulskyi, Eugene Pentland, Ajan Kanaga, Willem Michiel, Space Cruiser, Pyrater, Preetika Verma, Junyu Yang, Oscar Rangel, Spiking Neurons AB, Pierre Kircher, webtim, Cory Kujawski, terasurfer , Trenton Dambrowitz, Gabriel Puliatti, Imad Khwaja, Luke. Thank you to all my generous patrons and donaters!  # Original model card: OptimalScale's Robin 33B v2 No model card provided in source repository.

TheBloke/robin-7B-v2-GGML

TheBloke

2023-06-16T20:04:09Z

0

8

null

[ "license:other", "region:us" ]

null

2023-06-16T18:28:00Z

--- 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/Jq4vkcDakD">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>  # OptimalScale's Robin 7B v2 GGML These files are GGML format model files for [OptimalScale's Robin 7B v2](https://huggingface.co/OptimalScale/robin-7b-v2-delta). 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) * [ParisNeo/GPT4All-UI](https://github.com/ParisNeo/gpt4all-ui) * [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/robin-7B-v2-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/robin-7B-v2-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/robin-7B-v2-fp16) ## Prompt template ``` A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions ###Human: 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 | | ---- | ---- | ---- | ---- | ---- | ----- | | robin-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. | | robin-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 | | robin-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 | | robin-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 | | robin-7b.ggmlv3.q4_0.bin | q4_0 | 4 | 3.79 GB | 6.29 GB | Original llama.cpp quant method, 4-bit. | | robin-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. | | robin-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 | | robin-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 | | robin-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. | | robin-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. | | robin-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 | | robin-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 | | robin-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 | | robin-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 robin-7b.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n###Human: write a story about llamas\n###Assistant:" ``` Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. 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/Jq4vkcDakD) ## 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**: vamX, K, Jonathan Leane, Lone Striker, Sean Connelly, Chris McCloskey, WelcomeToTheClub, Nikolai Manek, John Detwiler, Kalila, David Flickinger, Fen Risland, subjectnull, Johann-Peter Hartmann, Talal Aujan, John Villwock, senxiiz, Khalefa Al-Ahmad, Kevin Schuppel, Alps Aficionado, Derek Yates, Mano Prime, Nathan LeClaire, biorpg, trip7s trip, Asp the Wyvern, chris gileta, Iucharbius , Artur Olbinski, Ai Maven, Joseph William Delisle, Luke Pendergrass, Illia Dulskyi, Eugene Pentland, Ajan Kanaga, Willem Michiel, Space Cruiser, Pyrater, Preetika Verma, Junyu Yang, Oscar Rangel, Spiking Neurons AB, Pierre Kircher, webtim, Cory Kujawski, terasurfer , Trenton Dambrowitz, Gabriel Puliatti, Imad Khwaja, Luke. Thank you to all my generous patrons and donaters!  # Original model card: OptimalScale's Robin 7B v2 No model card provided in source repository.

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

GEMCorp

2023-06-16T19:51:18Z

0

null

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

reinforcement-learning

2023-06-16T19:51:12Z

--- 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="GEMCorp/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"]) ```

ChristineCheng/my_awesome_eli5_clm-model

ChristineCheng

2023-06-16T19:49:19Z

61

0

transformers

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

text-generation

2023-06-16T19:33:04Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: ChristineCheng/my_awesome_eli5_clm-model results: [] ---  # ChristineCheng/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: 3.7347 - Validation Loss: 3.7399 - 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 | |:----------:|:---------------:|:-----:| | 3.9119 | 3.7667 | 0 | | 3.7942 | 3.7493 | 1 | | 3.7347 | 3.7399 | 2 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.0 - Tokenizers 0.13.3

CodyKilpatrick/Reinforce-Pixelcopter-PLE-v0

CodyKilpatrick

2023-06-16T19:43:03Z

0

null

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

reinforcement-learning

2023-06-12T15:12:47Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-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: 98.70 +/- 89.31 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

karina-aquino/spanish-sentiment-model

karina-aquino

2023-06-16T19:41:41Z

36

1

transformers

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

text-classification

2023-06-13T21:51:39Z

--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: spanish-sentiment-model results: [] ---  # spanish-sentiment-model This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0046 - Accuracy: 0.65 - F1: 0.6646 ## 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: 7e-06 - 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 | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 375 | 1.0046 | 0.65 | 0.6646 | | 1.2137 | 2.0 | 750 | 1.0212 | 0.61 | 0.6398 | | 0.9497 | 3.0 | 1125 | 1.0247 | 0.6133 | 0.6478 | ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

ananay/kneearch

ananay

2023-06-16T19:17:59Z

22

0

diffusers

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

text-to-image

2023-06-16T19:05:11Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### kneearch Dreambooth model trained by ananay with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

AustinCarthy/OnlyPhishGPT2_subdomain_100KP_BFall_fromB_90K_topP_0.75_ratio5

AustinCarthy

2023-06-16T19:17:42Z

0

null

[ "tensorboard", "generated_from_trainer", "license:apache-2.0", "region:us" ]

null

2023-06-16T15:49:03Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: OnlyPhishGPT2_subdomain_100KP_BFall_fromB_90K_topP_0.75_ratio5 results: [] ---  # OnlyPhishGPT2_subdomain_100KP_BFall_fromB_90K_topP_0.75_ratio5 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the Train benign: Fall,Test Benign: Fall, Train phish: Fall, Test phish: Fall, generated url dataset: generated_phish_OnlyPhishGPT2_using_benigh_200K_top_p_0.75 dataset. It achieves the following results on the evaluation set: - Loss: 0.0192 - Accuracy: 0.9978 - F1: 0.9767 - Precision: 0.9994 - Recall: 0.955 - Roc Auc Score: 0.9775 - Tpr At Fpr 0.01: 0.9632 ## 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: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:| | 0.0057 | 1.0 | 35625 | 0.0113 | 0.9979 | 0.9779 | 0.9954 | 0.961 | 0.9804 | 0.9518 | | 0.0035 | 2.0 | 71250 | 0.0150 | 0.9975 | 0.9726 | 0.9983 | 0.9482 | 0.9741 | 0.95 | | 0.0011 | 3.0 | 106875 | 0.0175 | 0.9975 | 0.9727 | 0.9994 | 0.9474 | 0.9737 | 0.9554 | | 0.0009 | 4.0 | 142500 | 0.0160 | 0.9979 | 0.9778 | 0.9990 | 0.9576 | 0.9788 | 0.9618 | | 0.0 | 5.0 | 178125 | 0.0192 | 0.9978 | 0.9767 | 0.9994 | 0.955 | 0.9775 | 0.9632 | ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3