librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-12 00:41:42	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 555 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-12 00:40:24	card stringlengths 11 1.01M
dreamboat26/q-FrozenLake-v1-4x4-noSlippery	dreamboat26	2023-08-04T07:40:02Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-04T07:39:57Z	--- 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="dreamboat26/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"]) ```
TariqJamil/falcon-7b-instruct-peft-qlora-my_finetuned_model-0721EV	TariqJamil	2023-08-04T07:32:00Z	3	0	peft	[ "peft", "region:us" ]	null	2023-08-04T07:31:52Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0
WildXbird/TranslateGLM	WildXbird	2023-08-04T07:28:24Z	0	0	null	[ "region:us" ]	null	2023-08-04T07:22:16Z	# TranslateGLM TranslateGLM 是一个基于 ChatGLM2-6B 模型微调的中英文翻译器，可以实现高质量的双向翻译。使用265万个样本进行了 [P-Tuning v2](https://github.com/THUDM/P-tuning-v2) 的微调，微调的样本的例子可以参考 [validation_file.txt](https://github.com/WildXBird/TranslateGLM/blob/main/validation_file.txt) 中的内容。微调过后的模型，在可以很好的充当翻译器的同时仍然可以正常的进行聊天对话。 ChatGLM2-6B 是一个开源的、支持中英问答的对话语言模型，基于 General Language Model (GLM) 架构，具有约 60 亿参数。 ![图片](https://github.com/WildXBird/TranslateGLM/blob/main/0.png?raw=true) ![图片](https://github.com/WildXBird/TranslateGLM/blob/main/1.png?raw=true) ## 功能 - 支持中文和英文之间的双向翻译 - 支持OpenAI格式的API和网页端的交互式翻译 - 支持调整输出结果随机度 - 支持翻译结果和原文对照查看 - 正常对话（仅限使用OpenAI格式的API） ## 安装要运行本项目，你需要安装以下软件依赖： ``` yarn cd ./glm pip install -r requirements.txt pip install rouge_chinese nltk jieba datasets ``` ## 使用 ### 启动翻译器你可以通过如下命令启动翻译器的OpenAI格式的API： ``` python ./glm/openai_api.py ``` 你可以通过如下命令启动翻译器的前端UI：启动UI前必须先启动上面的API ``` yarn start ``` 在浏览器中访问 http://localhost:3000/ ，然后输入你想要翻译的文本即可自动翻译。 ### 正常的进行聊天对话你可以通过如下命令启动传统的web demo对话，但只能进行普通对话： ``` python ./glm/web_demo.py ``` 你可以通过如上面的“OpenAI格式的API”进行正常对话，使用方式和普通的调用方法无异： ``` python ./glm/openai_api.py ``` ## 翻译UI 翻译UI暂时不支持指定输入语言，输入的语言的自动判断是中文还是英文，然后自动翻译成另一种语言。翻译UI可以指定翻译结果的随机性，分为保守、平衡、灵活和发散。分别代表着temperature从低(保守)到高(创意)，对于较复杂的语法，更高的temperature更能准确的翻译，但也可能导致他误解某些含义。把鼠标悬停在翻译结果上，可以查看翻译的原文以及原文在输入中的位置。 ## 更新 2023-8-2 (alpha-2) 1. 加入了短篇新闻的对照翻译样本进行了重新训练。 2. 经过简单测试发现在丢失信息方面有了显著的改善，即原文中的某些描述或者修饰在翻译的结果中会被无视，在句子稍长的情况下尤为明显。 3. 在“发散”下，可能会把翻译当成对话进行回答的问题（例如某个句子以：结尾），经过简单测试发现有了显著的改善。 4. 考虑到 “3”的问题改善了，在随机性选项改成了4个。 ## 已知问题 1. 中英文数字表达转换存在错误(非常常见) 2. 会出现英文中文互相穿插(罕见) 3. 会出现完全无关的翻译结果(罕见) 4. 原文中的某段描述或者某些修饰在翻译的结果中会被无视(常见)
emo-nlp/7-emo	emo-nlp	2023-08-04T07:27:57Z	145	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-04T07:13:19Z	--- license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy - recall - f1 model-index: - name: train 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. --> # train This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6648 - Accuracy: 0.7617 - B Acc: 0.6394 - Prec: 0.7595 - Recall: 0.7617 - F1: 0.7602 - Prec Joy: 0.7315 - Recall Joy: 0.7793 - F1 Joy: 0.7547 - Prec Anger: 0.6467 - Recall Anger: 0.6507 - F1 Anger: 0.6487 - Prec Disgust: 0.4710 - Recall Disgust: 0.45 - F1 Disgust: 0.4603 - Prec Fear: 0.6963 - Recall Fear: 0.6409 - F1 Fear: 0.6675 - Prec Neutral: 0.8457 - Recall Neutral: 0.8490 - F1 Neutral: 0.8474 - Prec Sadness: 0.7094 - Recall Sadness: 0.6738 - F1 Sadness: 0.6911 - Prec Surprise: 0.5228 - Recall Surprise: 0.4323 - F1 Surprise: 0.4732 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| B Acc \| Prec \| Recall \| F1 \| Prec Joy \| Recall Joy \| F1 Joy \| Prec Anger \| Recall Anger \| F1 Anger \| Prec Disgust \| Recall Disgust \| F1 Disgust \| Prec Fear \| Recall Fear \| F1 Fear \| Prec Neutral \| Recall Neutral \| F1 Neutral \| Prec Sadness \| Recall Sadness \| F1 Sadness \| Prec Surprise \| Recall Surprise \| F1 Surprise \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:------:\|:------:\|:------:\|:--------:\|:----------:\|:------:\|:----------:\|:------------:\|:--------:\|:------------:\|:--------------:\|:----------:\|:---------:\|:-----------:\|:-------:\|:------------:\|:--------------:\|:----------:\|:------------:\|:--------------:\|:----------:\|:-------------:\|:---------------:\|:-----------:\| \| 0.9538 \| 0.15 \| 232 \| 0.8701 \| 0.6961 \| 0.4790 \| 0.6837 \| 0.6961 \| 0.6837 \| 0.7401 \| 0.6381 \| 0.6853 \| 0.4622 \| 0.5391 \| 0.4977 \| 0.25 \| 0.0018 \| 0.0035 \| 0.5527 \| 0.4292 \| 0.4832 \| 0.7965 \| 0.8618 \| 0.8279 \| 0.5281 \| 0.6431 \| 0.5800 \| 0.3562 \| 0.2398 \| 0.2866 \| \| 0.7952 \| 0.3 \| 464 \| 0.8010 \| 0.7168 \| 0.5242 \| 0.7098 \| 0.7168 \| 0.7025 \| 0.8084 \| 0.5948 \| 0.6853 \| 0.5732 \| 0.4710 \| 0.5171 \| 0.4713 \| 0.2643 \| 0.3387 \| 0.6156 \| 0.5263 \| 0.5675 \| 0.7405 \| 0.9250 \| 0.8226 \| 0.6858 \| 0.5676 \| 0.6211 \| 0.4448 \| 0.3204 \| 0.3725 \| \| 0.7528 \| 0.45 \| 696 \| 0.7560 \| 0.7261 \| 0.5878 \| 0.7309 \| 0.7261 \| 0.7256 \| 0.6969 \| 0.7646 \| 0.7292 \| 0.5550 \| 0.5534 \| 0.5542 \| 0.3409 \| 0.4821 \| 0.3994 \| 0.7225 \| 0.4842 \| 0.5798 \| 0.8476 \| 0.8159 \| 0.8314 \| 0.6118 \| 0.7027 \| 0.6541 \| 0.4957 \| 0.3118 \| 0.3828 \| \| 0.7334 \| 0.6 \| 928 \| 0.7310 \| 0.7370 \| 0.5868 \| 0.7345 \| 0.7370 \| 0.7283 \| 0.7170 \| 0.7458 \| 0.7311 \| 0.7129 \| 0.4116 \| 0.5219 \| 0.3727 \| 0.5696 \| 0.4506 \| 0.6671 \| 0.5626 \| 0.6104 \| 0.7898 \| 0.8859 \| 0.8351 \| 0.7318 \| 0.5844 \| 0.6499 \| 0.5252 \| 0.3473 \| 0.4181 \| \| 0.7216 \| 0.75 \| 1160 \| 0.7043 \| 0.7448 \| 0.6009 \| 0.7403 \| 0.7448 \| 0.7389 \| 0.7767 \| 0.6826 \| 0.7266 \| 0.6159 \| 0.5386 \| 0.5746 \| 0.5302 \| 0.4393 \| 0.4805 \| 0.8023 \| 0.5602 \| 0.6598 \| 0.7854 \| 0.8926 \| 0.8356 \| 0.7005 \| 0.632 \| 0.6645 \| 0.4815 \| 0.4613 \| 0.4712 \| \| 0.7259 \| 0.9 \| 1392 \| 0.6962 \| 0.7475 \| 0.6082 \| 0.7433 \| 0.7475 \| 0.7412 \| 0.7355 \| 0.7586 \| 0.7469 \| 0.6758 \| 0.4504 \| 0.5405 \| 0.3908 \| 0.5589 \| 0.4600 \| 0.6939 \| 0.6070 \| 0.6475 \| 0.8122 \| 0.8744 \| 0.8421 \| 0.6830 \| 0.6676 \| 0.6752 \| 0.5494 \| 0.3409 \| 0.4207 \| \| 0.6362 \| 1.05 \| 1624 \| 0.6771 \| 0.7526 \| 0.6055 \| 0.7472 \| 0.7526 \| 0.7484 \| 0.7392 \| 0.7483 \| 0.7437 \| 0.5873 \| 0.6191 \| 0.6028 \| 0.5302 \| 0.3768 \| 0.4405 \| 0.7388 \| 0.5789 \| 0.6492 \| 0.8213 \| 0.8670 \| 0.8435 \| 0.7090 \| 0.6507 \| 0.6786 \| 0.5301 \| 0.3978 \| 0.4545 \| \| 0.621 \| 1.2 \| 1856 \| 0.6779 \| 0.7528 \| 0.6120 \| 0.7494 \| 0.7528 \| 0.7487 \| 0.7107 \| 0.7828 \| 0.7450 \| 0.6508 \| 0.5913 \| 0.6196 \| 0.4980 \| 0.4518 \| 0.4738 \| 0.7963 \| 0.5532 \| 0.6529 \| 0.8165 \| 0.8590 \| 0.8372 \| 0.7499 \| 0.6236 \| 0.6809 \| 0.5078 \| 0.4226 \| 0.4613 \| \| 0.6241 \| 1.35 \| 2088 \| 0.6849 \| 0.7513 \| 0.6367 \| 0.7526 \| 0.7513 \| 0.7514 \| 0.7429 \| 0.7592 \| 0.7510 \| 0.5795 \| 0.6531 \| 0.6141 \| 0.4372 \| 0.4661 \| 0.4512 \| 0.6462 \| 0.6515 \| 0.6488 \| 0.8492 \| 0.8372 \| 0.8432 \| 0.6887 \| 0.6609 \| 0.6745 \| 0.5271 \| 0.4290 \| 0.4730 \| \| 0.6188 \| 1.5 \| 2320 \| 0.6713 \| 0.7579 \| 0.6159 \| 0.7539 \| 0.7579 \| 0.7534 \| 0.7071 \| 0.7971 \| 0.7494 \| 0.6343 \| 0.6267 \| 0.6305 \| 0.5877 \| 0.3768 \| 0.4592 \| 0.7247 \| 0.6281 \| 0.6729 \| 0.8361 \| 0.8496 \| 0.8428 \| 0.6943 \| 0.6693 \| 0.6816 \| 0.5919 \| 0.3634 \| 0.4504 \| \| 0.6182 \| 1.65 \| 2552 \| 0.6608 \| 0.7601 \| 0.6199 \| 0.7567 \| 0.7601 \| 0.7566 \| 0.7143 \| 0.7891 \| 0.7498 \| 0.6163 \| 0.6358 \| 0.6259 \| 0.5607 \| 0.3875 \| 0.4583 \| 0.7591 \| 0.6082 \| 0.6753 \| 0.8375 \| 0.8578 \| 0.8475 \| 0.7324 \| 0.6436 \| 0.6851 \| 0.5381 \| 0.4172 \| 0.4700 \| \| 0.6392 \| 1.8 \| 2784 \| 0.6542 \| 0.7624 \| 0.6261 \| 0.7593 \| 0.7624 \| 0.7596 \| 0.7513 \| 0.7584 \| 0.7548 \| 0.5970 \| 0.6708 \| 0.6318 \| 0.5711 \| 0.3875 \| 0.4617 \| 0.7482 \| 0.6152 \| 0.6752 \| 0.8379 \| 0.8635 \| 0.8505 \| 0.7076 \| 0.668 \| 0.6872 \| 0.5132 \| 0.4194 \| 0.4615 \| \| 0.6158 \| 1.95 \| 3016 \| 0.6456 \| 0.7649 \| 0.6279 \| 0.7599 \| 0.7649 \| 0.7614 \| 0.7490 \| 0.7548 \| 0.7519 \| 0.6402 \| 0.6378 \| 0.6390 \| 0.5314 \| 0.4232 \| 0.4712 \| 0.7569 \| 0.6117 \| 0.6766 \| 0.8310 \| 0.8753 \| 0.8526 \| 0.7199 \| 0.6627 \| 0.6901 \| 0.5063 \| 0.4301 \| 0.4651 \| \| 0.554 \| 2.1 \| 3248 \| 0.6742 \| 0.7584 \| 0.6346 \| 0.7555 \| 0.7584 \| 0.7564 \| 0.7293 \| 0.7732 \| 0.7506 \| 0.6433 \| 0.6430 \| 0.6432 \| 0.5031 \| 0.4393 \| 0.4690 \| 0.7292 \| 0.6363 \| 0.6796 \| 0.8347 \| 0.8496 \| 0.8421 \| 0.7163 \| 0.6587 \| 0.6863 \| 0.5049 \| 0.4419 \| 0.4713 \| \| 0.5537 \| 2.25 \| 3480 \| 0.6708 \| 0.7633 \| 0.6283 \| 0.7604 \| 0.7633 \| 0.7605 \| 0.7263 \| 0.7801 \| 0.7523 \| 0.6304 \| 0.6612 \| 0.6455 \| 0.5806 \| 0.3732 \| 0.4543 \| 0.7486 \| 0.6094 \| 0.6718 \| 0.8442 \| 0.8528 \| 0.8485 \| 0.6982 \| 0.692 \| 0.6951 \| 0.5356 \| 0.4290 \| 0.4764 \| \| 0.5375 \| 2.4 \| 3712 \| 0.6712 \| 0.7606 \| 0.6402 \| 0.7592 \| 0.7606 \| 0.7595 \| 0.7373 \| 0.7709 \| 0.7537 \| 0.6245 \| 0.6608 \| 0.6421 \| 0.4827 \| 0.4482 \| 0.4648 \| 0.7319 \| 0.6257 \| 0.6747 \| 0.8454 \| 0.8474 \| 0.8464 \| 0.7006 \| 0.6769 \| 0.6885 \| 0.5204 \| 0.4516 \| 0.4836 \| \| 0.5175 \| 2.55 \| 3944 \| 0.6625 \| 0.7625 \| 0.6369 \| 0.7600 \| 0.7625 \| 0.7604 \| 0.7422 \| 0.7642 \| 0.7530 \| 0.6335 \| 0.6526 \| 0.6429 \| 0.4481 \| 0.4929 \| 0.4694 \| 0.7482 \| 0.6187 \| 0.6773 \| 0.8374 \| 0.8604 \| 0.8488 \| 0.7252 \| 0.6684 \| 0.6957 \| 0.5321 \| 0.4011 \| 0.4574 \| \| 0.5182 \| 2.7 \| 4176 \| 0.6621 \| 0.7631 \| 0.6404 \| 0.7602 \| 0.7631 \| 0.7612 \| 0.7343 \| 0.7766 \| 0.7549 \| 0.6491 \| 0.6392 \| 0.6441 \| 0.4739 \| 0.4536 \| 0.4635 \| 0.6784 \| 0.6538 \| 0.6659 \| 0.8444 \| 0.8529 \| 0.8486 \| 0.7109 \| 0.684 \| 0.6972 \| 0.5458 \| 0.4226 \| 0.4764 \| \| 0.5148 \| 2.85 \| 4408 \| 0.6638 \| 0.7637 \| 0.6383 \| 0.7598 \| 0.7637 \| 0.7612 \| 0.7394 \| 0.7741 \| 0.7563 \| 0.6741 \| 0.6205 \| 0.6462 \| 0.5 \| 0.4375 \| 0.4667 \| 0.6813 \| 0.6550 \| 0.6679 \| 0.8400 \| 0.8572 \| 0.8485 \| 0.6922 \| 0.6916 \| 0.6919 \| 0.5296 \| 0.4323 \| 0.4760 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.2 - Tokenizers 0.13.3
lesliepzimmermann/rl_course_vizdoom_health_gathering_supreme	lesliepzimmermann	2023-08-04T07:27:22Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-04T06:26:57Z	--- 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: 10.76 +/- 4.75 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 lesliepzimmermann/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.
ngeg2015/opt-2.7b-lora	ngeg2015	2023-08-04T07:16:25Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-04T07:16:14Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
ketong3906/my_awesome_model_classification	ketong3906	2023-08-04T07:10:39Z	111	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:imdb", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-26T02:33:54Z	--- license: mit base_model: roberta-base tags: - generated_from_trainer datasets: - imdb metrics: - accuracy model-index: - name: my_awesome_model_classification results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train[:300] args: plain_text metrics: - name: Accuracy type: accuracy value: 1.0 --- <!-- 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_model_classification This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.0031 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 15 \| 0.0163 \| 1.0 \| \| No log \| 2.0 \| 30 \| 0.0031 \| 1.0 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
scarlett623/wav2vec2-large-xlsr53-zh-cn-subset20-colab	scarlett623	2023-08-04T06:34:21Z	5	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "base_model:facebook/wav2vec2-large-xlsr-53", "base_model:finetune:facebook/wav2vec2-large-xlsr-53", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-03T16:11:34Z	--- license: apache-2.0 base_model: facebook/wav2vec2-large-xlsr-53 tags: - generated_from_trainer datasets: - common_voice metrics: - wer model-index: - name: wav2vec2-large-xlsr53-zh-cn-subset20-colab results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice type: common_voice config: zh-CN split: test[:20%] args: zh-CN metrics: - name: Wer type: wer value: 0.9503424657534246 --- <!-- 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. --> # wav2vec2-large-xlsr53-zh-cn-subset20-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 2.0566 - Wer: 0.9503 - Cer: 0.3333 ## 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: 13 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 26 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\| \| No log \| 1.9 \| 400 \| 6.7551 \| 1.0 \| 1.0 \| \| 34.7845 \| 3.81 \| 800 \| 6.4563 \| 1.0 \| 1.0 \| \| 6.4358 \| 5.71 \| 1200 \| 4.2319 \| 1.0074 \| 0.7454 \| \| 4.2052 \| 7.62 \| 1600 \| 2.6538 \| 1.0200 \| 0.5562 \| \| 2.3906 \| 9.52 \| 2000 \| 2.3565 \| 1.0063 \| 0.5147 \| \| 2.3906 \| 11.43 \| 2400 \| 2.1287 \| 0.9863 \| 0.4822 \| \| 1.93 \| 13.33 \| 2800 \| 1.9585 \| 0.9812 \| 0.4528 \| \| 1.6322 \| 15.24 \| 3200 \| 1.8771 \| 0.9937 \| 0.4381 \| \| 1.3629 \| 17.14 \| 3600 \| 1.8405 \| 0.9926 \| 0.4242 \| \| 1.166 \| 19.05 \| 4000 \| 1.7674 \| 0.9989 \| 0.4140 \| \| 1.166 \| 20.95 \| 4400 \| 1.7879 \| 0.9795 \| 0.4047 \| \| 0.9915 \| 22.86 \| 4800 \| 1.7597 \| 1.0126 \| 0.4080 \| \| 0.8517 \| 24.76 \| 5200 \| 1.7726 \| 0.9829 \| 0.3966 \| \| 0.7143 \| 26.67 \| 5600 \| 1.7623 \| 0.9732 \| 0.3863 \| \| 0.6267 \| 28.57 \| 6000 \| 1.8164 \| 0.9720 \| 0.3863 \| \| 0.6267 \| 30.48 \| 6400 \| 1.8136 \| 0.9680 \| 0.3801 \| \| 0.5389 \| 32.38 \| 6800 \| 1.8696 \| 0.9652 \| 0.3812 \| \| 0.4764 \| 34.29 \| 7200 \| 1.8625 \| 0.9663 \| 0.3744 \| \| 0.4095 \| 36.19 \| 7600 \| 1.8868 \| 0.9618 \| 0.3683 \| \| 0.3594 \| 38.1 \| 8000 \| 1.8834 \| 0.9623 \| 0.3699 \| \| 0.3594 \| 40.0 \| 8400 \| 1.9155 \| 0.9589 \| 0.3670 \| \| 0.3064 \| 41.9 \| 8800 \| 1.9268 \| 0.9652 \| 0.3688 \| \| 0.2825 \| 43.81 \| 9200 \| 1.9527 \| 0.9697 \| 0.3674 \| \| 0.2524 \| 45.71 \| 9600 \| 1.9726 \| 0.9686 \| 0.3617 \| \| 0.2272 \| 47.62 \| 10000 \| 1.9594 \| 0.9629 \| 0.3619 \| \| 0.2272 \| 49.52 \| 10400 \| 1.9799 \| 0.9635 \| 0.3607 \| \| 0.2042 \| 51.43 \| 10800 \| 2.0175 \| 0.9669 \| 0.3582 \| \| 0.1975 \| 53.33 \| 11200 \| 2.0246 \| 0.9589 \| 0.3571 \| \| 0.1827 \| 55.24 \| 11600 \| 2.0535 \| 0.9703 \| 0.3600 \| \| 0.1677 \| 57.14 \| 12000 \| 2.0458 \| 0.9583 \| 0.3555 \| \| 0.1677 \| 59.05 \| 12400 \| 2.0893 \| 0.9572 \| 0.3583 \| \| 0.1626 \| 60.95 \| 12800 \| 2.0729 \| 0.9600 \| 0.3557 \| \| 0.155 \| 62.86 \| 13200 \| 2.0706 \| 0.9572 \| 0.3538 \| \| 0.1456 \| 64.76 \| 13600 \| 2.0761 \| 0.9532 \| 0.3553 \| \| 0.1337 \| 66.67 \| 14000 \| 2.0349 \| 0.9589 \| 0.3474 \| \| 0.1337 \| 68.57 \| 14400 \| 2.0844 \| 0.9549 \| 0.3484 \| \| 0.1274 \| 70.48 \| 14800 \| 2.0874 \| 0.9578 \| 0.3505 \| \| 0.1198 \| 72.38 \| 15200 \| 2.0813 \| 0.9526 \| 0.3473 \| \| 0.1164 \| 74.29 \| 15600 \| 2.0866 \| 0.9498 \| 0.3473 \| \| 0.1105 \| 76.19 \| 16000 \| 2.0688 \| 0.9486 \| 0.3421 \| \| 0.1105 \| 78.1 \| 16400 \| 2.0854 \| 0.9498 \| 0.3431 \| \| 0.1053 \| 80.0 \| 16800 \| 2.0749 \| 0.9503 \| 0.3414 \| \| 0.1 \| 81.9 \| 17200 \| 2.0622 \| 0.9543 \| 0.3407 \| \| 0.0977 \| 83.81 \| 17600 \| 2.0678 \| 0.9532 \| 0.3396 \| \| 0.0906 \| 85.71 \| 18000 \| 2.0650 \| 0.9515 \| 0.3383 \| \| 0.0906 \| 87.62 \| 18400 \| 2.0631 \| 0.9492 \| 0.3378 \| \| 0.0867 \| 89.52 \| 18800 \| 2.0633 \| 0.9521 \| 0.3365 \| \| 0.0836 \| 91.43 \| 19200 \| 2.0606 \| 0.9532 \| 0.3346 \| \| 0.0819 \| 93.33 \| 19600 \| 2.0671 \| 0.9538 \| 0.3355 \| \| 0.0768 \| 95.24 \| 20000 \| 2.0661 \| 0.9509 \| 0.3338 \| \| 0.0768 \| 97.14 \| 20400 \| 2.0564 \| 0.9498 \| 0.3335 \| \| 0.0752 \| 99.05 \| 20800 \| 2.0566 \| 0.9503 \| 0.3333 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3
dkimds/ppo-Huggy	dkimds	2023-08-04T06:34:08Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-08-04T06:33:57Z	--- 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: dkimds/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
MattStammers/CartPole-v1-take2	MattStammers	2023-08-04T06:31:08Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-08-04T06:27:59Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: CartPole-v1-take2 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
terru3/dqn-SpaceInvadersNoFrameskip-v4-test	terru3	2023-08-04T05:56:20Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-04T05:55:44Z	--- 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: 671.50 +/- 174.67 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 terru3 -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 terru3 -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 terru3 ``` ## 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'} ```
Okrad/llama2-qlora-finetunined-french	Okrad	2023-08-04T05:45:08Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-04T05:45:03Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
Shu-Kun/Betelgeuse_Romanee_Conti	Shu-Kun	2023-08-04T05:40:45Z	0	0	null	[ "ja", "license:openrail", "region:us" ]	null	2023-07-22T03:24:44Z	--- license: openrail language: - ja ---
Braulio0316/Bea10	Braulio0316	2023-08-04T05:31:43Z	0	0	asteroid	[ "asteroid", "legal", "visual-question-answering", "dataset:Open-Orca/OpenOrca", "license:creativeml-openrail-m", "region:us" ]	visual-question-answering	2023-08-04T05:13:15Z	--- license: creativeml-openrail-m datasets: - Open-Orca/OpenOrca metrics: - bertscore library_name: asteroid pipeline_tag: visual-question-answering tags: - legal ---
jordyvl/dit-base-tiny_rvl_cdip-NK1000_hint	jordyvl	2023-08-04T05:31:01Z	164	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-08-03T09:07:50Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-base-tiny_rvl_cdip-NK1000_hint 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. --> # dit-base-tiny_rvl_cdip-NK1000_hint This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.5793 - Accuracy: 0.8185 - Brier Loss: 0.3293 - Nll: 2.0402 - F1 Micro: 0.8185 - F1 Macro: 0.8184 - Ece: 0.1589 - Aurc: 0.0584 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| 2.7601 \| 1.0 \| 1000 \| 2.6212 \| 0.5952 \| 0.5281 \| 2.4218 \| 0.5952 \| 0.5899 \| 0.0529 \| 0.1801 \| \| 2.236 \| 2.0 \| 2000 \| 2.1744 \| 0.6847 \| 0.4219 \| 2.1650 \| 0.6847 \| 0.6827 \| 0.0515 \| 0.1135 \| \| 1.9525 \| 3.0 \| 3000 \| 1.9941 \| 0.7147 \| 0.3889 \| 2.0860 \| 0.7147 \| 0.7163 \| 0.0616 \| 0.0961 \| \| 1.729 \| 4.0 \| 4000 \| 1.8880 \| 0.7485 \| 0.3585 \| 2.1097 \| 0.7485 \| 0.7476 \| 0.0649 \| 0.0811 \| \| 1.6172 \| 5.0 \| 5000 \| 1.8693 \| 0.7455 \| 0.3598 \| 2.0514 \| 0.7455 \| 0.7448 \| 0.0733 \| 0.0811 \| \| 1.4385 \| 6.0 \| 6000 \| 1.8350 \| 0.747 \| 0.3654 \| 2.0917 \| 0.747 \| 0.7536 \| 0.0828 \| 0.0843 \| \| 1.3098 \| 7.0 \| 7000 \| 1.8317 \| 0.7625 \| 0.3515 \| 2.0367 \| 0.7625 \| 0.7668 \| 0.1039 \| 0.0794 \| \| 1.1854 \| 8.0 \| 8000 \| 1.8872 \| 0.7675 \| 0.3589 \| 2.1085 \| 0.7675 \| 0.7661 \| 0.1294 \| 0.0694 \| \| 1.1066 \| 9.0 \| 9000 \| 1.9843 \| 0.7642 \| 0.3707 \| 2.1492 \| 0.7642 \| 0.7670 \| 0.1457 \| 0.0715 \| \| 1.0518 \| 10.0 \| 10000 \| 1.9993 \| 0.77 \| 0.3660 \| 2.0694 \| 0.7700 \| 0.7702 \| 0.1440 \| 0.0695 \| \| 0.9741 \| 11.0 \| 11000 \| 2.2346 \| 0.769 \| 0.3870 \| 2.0588 \| 0.769 \| 0.7704 \| 0.1748 \| 0.0735 \| \| 0.938 \| 12.0 \| 12000 \| 2.2626 \| 0.767 \| 0.3918 \| 2.1412 \| 0.767 \| 0.7679 \| 0.1774 \| 0.0723 \| \| 0.899 \| 13.0 \| 13000 \| 2.3671 \| 0.7698 \| 0.3995 \| 2.1064 \| 0.7698 \| 0.7725 \| 0.1820 \| 0.0708 \| \| 0.8708 \| 14.0 \| 14000 \| 2.3386 \| 0.7768 \| 0.3838 \| 2.1948 \| 0.7768 \| 0.7777 \| 0.1724 \| 0.0683 \| \| 0.8456 \| 15.0 \| 15000 \| 2.3234 \| 0.7827 \| 0.3774 \| 2.0937 \| 0.7828 \| 0.7830 \| 0.1752 \| 0.0638 \| \| 0.8021 \| 16.0 \| 16000 \| 2.5700 \| 0.7685 \| 0.4092 \| 2.1458 \| 0.7685 \| 0.7684 \| 0.1911 \| 0.0717 \| \| 0.7921 \| 17.0 \| 17000 \| 2.5721 \| 0.778 \| 0.3934 \| 2.1774 \| 0.778 \| 0.7756 \| 0.1829 \| 0.0708 \| \| 0.7779 \| 18.0 \| 18000 \| 2.7204 \| 0.772 \| 0.4089 \| 2.1145 \| 0.772 \| 0.7707 \| 0.1926 \| 0.0758 \| \| 0.7484 \| 19.0 \| 19000 \| 2.7208 \| 0.7752 \| 0.4044 \| 2.1020 \| 0.7752 \| 0.7760 \| 0.1901 \| 0.0746 \| \| 0.7469 \| 20.0 \| 20000 \| 2.5898 \| 0.7927 \| 0.3711 \| 2.2076 \| 0.7927 \| 0.7909 \| 0.1763 \| 0.0648 \| \| 0.7256 \| 21.0 \| 21000 \| 2.5658 \| 0.791 \| 0.3727 \| 2.0710 \| 0.791 \| 0.7920 \| 0.1763 \| 0.0650 \| \| 0.7137 \| 22.0 \| 22000 \| 2.6782 \| 0.7847 \| 0.3851 \| 2.1469 \| 0.7847 \| 0.7854 \| 0.1824 \| 0.0703 \| \| 0.6912 \| 23.0 \| 23000 \| 2.5574 \| 0.802 \| 0.3539 \| 2.1340 \| 0.802 \| 0.8009 \| 0.1685 \| 0.0615 \| \| 0.6894 \| 24.0 \| 24000 \| 2.6331 \| 0.7913 \| 0.3760 \| 2.0913 \| 0.7913 \| 0.7944 \| 0.1807 \| 0.0674 \| \| 0.6692 \| 25.0 \| 25000 \| 2.6074 \| 0.7955 \| 0.3658 \| 2.0837 \| 0.7955 \| 0.7975 \| 0.1745 \| 0.0645 \| \| 0.6541 \| 26.0 \| 26000 \| 2.6059 \| 0.7945 \| 0.3672 \| 2.0798 \| 0.7945 \| 0.7936 \| 0.1751 \| 0.0616 \| \| 0.6517 \| 27.0 \| 27000 \| 2.7149 \| 0.7965 \| 0.3697 \| 2.0842 \| 0.7965 \| 0.7987 \| 0.1769 \| 0.0663 \| \| 0.6484 \| 28.0 \| 28000 \| 2.5700 \| 0.8047 \| 0.3542 \| 2.0142 \| 0.8047 \| 0.8058 \| 0.1685 \| 0.0597 \| \| 0.6342 \| 29.0 \| 29000 \| 2.6774 \| 0.7987 \| 0.3660 \| 2.1231 \| 0.7987 \| 0.7972 \| 0.1759 \| 0.0622 \| \| 0.6331 \| 30.0 \| 30000 \| 2.6112 \| 0.7973 \| 0.3621 \| 2.0740 \| 0.7973 \| 0.7981 \| 0.1752 \| 0.0621 \| \| 0.6204 \| 31.0 \| 31000 \| 2.6470 \| 0.807 \| 0.3521 \| 2.0337 \| 0.807 \| 0.8056 \| 0.1683 \| 0.0638 \| \| 0.612 \| 32.0 \| 32000 \| 2.6265 \| 0.8053 \| 0.3549 \| 2.0800 \| 0.8053 \| 0.8038 \| 0.1678 \| 0.0596 \| \| 0.6049 \| 33.0 \| 33000 \| 2.5749 \| 0.8107 \| 0.3428 \| 2.0235 \| 0.8108 \| 0.8114 \| 0.1662 \| 0.0576 \| \| 0.5984 \| 34.0 \| 34000 \| 2.6667 \| 0.804 \| 0.3572 \| 2.1015 \| 0.804 \| 0.8041 \| 0.1726 \| 0.0632 \| \| 0.5961 \| 35.0 \| 35000 \| 2.6481 \| 0.8067 \| 0.3521 \| 2.0652 \| 0.8067 \| 0.8068 \| 0.1673 \| 0.0610 \| \| 0.5958 \| 36.0 \| 36000 \| 2.6831 \| 0.8065 \| 0.3523 \| 2.0272 \| 0.8065 \| 0.8074 \| 0.1681 \| 0.0623 \| \| 0.5836 \| 37.0 \| 37000 \| 2.6573 \| 0.8067 \| 0.3533 \| 2.0149 \| 0.8067 \| 0.8050 \| 0.1699 \| 0.0624 \| \| 0.5855 \| 38.0 \| 38000 \| 2.6730 \| 0.8087 \| 0.3510 \| 2.0149 \| 0.8087 \| 0.8087 \| 0.1693 \| 0.0611 \| \| 0.581 \| 39.0 \| 39000 \| 2.6464 \| 0.815 \| 0.3390 \| 2.0609 \| 0.815 \| 0.8148 \| 0.1628 \| 0.0588 \| \| 0.5708 \| 40.0 \| 40000 \| 2.7165 \| 0.8077 \| 0.3515 \| 2.0489 \| 0.8077 \| 0.8078 \| 0.1698 \| 0.0644 \| \| 0.5727 \| 41.0 \| 41000 \| 2.6264 \| 0.8135 \| 0.3402 \| 2.0070 \| 0.8135 \| 0.8130 \| 0.1643 \| 0.0601 \| \| 0.5688 \| 42.0 \| 42000 \| 2.6522 \| 0.8077 \| 0.3522 \| 2.0064 \| 0.8077 \| 0.8076 \| 0.1687 \| 0.0615 \| \| 0.5648 \| 43.0 \| 43000 \| 2.5806 \| 0.8193 \| 0.3295 \| 2.0211 \| 0.8193 \| 0.8188 \| 0.1593 \| 0.0585 \| \| 0.5649 \| 44.0 \| 44000 \| 2.6182 \| 0.8125 \| 0.3372 \| 2.0292 \| 0.8125 \| 0.8122 \| 0.1630 \| 0.0598 \| \| 0.562 \| 45.0 \| 45000 \| 2.6274 \| 0.8157 \| 0.3366 \| 2.0047 \| 0.8157 \| 0.8158 \| 0.1610 \| 0.0577 \| \| 0.5592 \| 46.0 \| 46000 \| 2.6069 \| 0.8145 \| 0.3370 \| 2.0276 \| 0.8145 \| 0.8146 \| 0.1632 \| 0.0599 \| \| 0.5582 \| 47.0 \| 47000 \| 2.5935 \| 0.8187 \| 0.3331 \| 2.0291 \| 0.8187 \| 0.8193 \| 0.1594 \| 0.0588 \| \| 0.5565 \| 48.0 \| 48000 \| 2.5780 \| 0.8197 \| 0.3302 \| 2.0401 \| 0.8197 \| 0.8197 \| 0.1580 \| 0.0577 \| \| 0.5571 \| 49.0 \| 49000 \| 2.5781 \| 0.8185 \| 0.3294 \| 2.0367 \| 0.8185 \| 0.8184 \| 0.1590 \| 0.0583 \| \| 0.5568 \| 50.0 \| 50000 \| 2.5793 \| 0.8185 \| 0.3293 \| 2.0402 \| 0.8185 \| 0.8184 \| 0.1589 \| 0.0584 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
kernelguardian/flant5action	kernelguardian	2023-08-04T05:25:07Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-08-03T03:28:08Z	--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: flant5action 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. --> # flant5action This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1428 - Rouge1: 56.0664 - Rouge2: 34.7343 - Rougel: 56.0394 - Rougelsum: 56.0313 - Gen Len: 18.9852 ## 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: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 0.2525 \| 1.0 \| 674 \| 0.2294 \| 53.2181 \| 29.8509 \| 53.1635 \| 53.1474 \| 19.0 \| \| 0.2434 \| 2.0 \| 1348 \| 0.2240 \| 53.5453 \| 30.1367 \| 53.4479 \| 53.44 \| 18.9970 \| \| 0.2281 \| 3.0 \| 2022 \| 0.2135 \| 53.1901 \| 30.3456 \| 53.0849 \| 53.0759 \| 18.9970 \| \| 0.2221 \| 4.0 \| 2696 \| 0.2056 \| 52.0669 \| 29.4321 \| 51.9567 \| 51.9512 \| 18.9881 \| \| 0.2145 \| 5.0 \| 3370 \| 0.2012 \| 54.484 \| 31.6451 \| 54.4213 \| 54.4144 \| 18.9970 \| \| 0.2121 \| 6.0 \| 4044 \| 0.1961 \| 54.1219 \| 31.2019 \| 54.0701 \| 54.0668 \| 18.9970 \| \| 0.1979 \| 7.0 \| 4718 \| 0.1901 \| 54.9091 \| 32.2416 \| 54.8482 \| 54.8318 \| 18.9911 \| \| 0.2086 \| 8.0 \| 5392 \| 0.1846 \| 54.9615 \| 32.4701 \| 54.8836 \| 54.8821 \| 18.9970 \| \| 0.1985 \| 9.0 \| 6066 \| 0.1795 \| 55.2027 \| 32.5792 \| 55.1531 \| 55.1431 \| 18.9970 \| \| 0.2027 \| 10.0 \| 6740 \| 0.1746 \| 54.4079 \| 32.2598 \| 54.38 \| 54.3697 \| 18.9970 \| \| 0.1922 \| 11.0 \| 7414 \| 0.1707 \| 55.4814 \| 33.2069 \| 55.4428 \| 55.4298 \| 18.9970 \| \| 0.1806 \| 12.0 \| 8088 \| 0.1660 \| 55.7189 \| 33.831 \| 55.6796 \| 55.6702 \| 18.9970 \| \| 0.1834 \| 13.0 \| 8762 \| 0.1623 \| 55.6253 \| 33.9516 \| 55.5925 \| 55.585 \| 18.9941 \| \| 0.1795 \| 14.0 \| 9436 \| 0.1596 \| 55.6786 \| 33.7589 \| 55.6232 \| 55.6183 \| 18.9911 \| \| 0.1767 \| 15.0 \| 10110 \| 0.1553 \| 55.8132 \| 34.1603 \| 55.795 \| 55.7873 \| 18.9911 \| \| 0.1792 \| 16.0 \| 10784 \| 0.1539 \| 55.9694 \| 34.4612 \| 55.9454 \| 55.9323 \| 18.9792 \| \| 0.1785 \| 17.0 \| 11458 \| 0.1521 \| 56.2202 \| 34.6224 \| 56.1781 \| 56.1706 \| 18.9941 \| \| 0.1705 \| 18.0 \| 12132 \| 0.1496 \| 56.4102 \| 34.7821 \| 56.3911 \| 56.3789 \| 18.9911 \| \| 0.1668 \| 19.0 \| 12806 \| 0.1478 \| 56.1222 \| 34.6804 \| 56.0821 \| 56.077 \| 18.9881 \| \| 0.1729 \| 20.0 \| 13480 \| 0.1459 \| 56.1605 \| 34.8596 \| 56.1349 \| 56.1221 \| 18.9852 \| \| 0.1759 \| 21.0 \| 14154 \| 0.1451 \| 56.1232 \| 34.8956 \| 56.1054 \| 56.0994 \| 18.9852 \| \| 0.1713 \| 22.0 \| 14828 \| 0.1439 \| 55.9801 \| 34.6435 \| 55.9556 \| 55.9482 \| 18.9763 \| \| 0.1751 \| 23.0 \| 15502 \| 0.1436 \| 56.2088 \| 34.8754 \| 56.1771 \| 56.1758 \| 18.9852 \| \| 0.1626 \| 24.0 \| 16176 \| 0.1431 \| 56.0657 \| 34.7302 \| 56.04 \| 56.0317 \| 18.9852 \| \| 0.1696 \| 25.0 \| 16850 \| 0.1428 \| 56.0664 \| 34.7343 \| 56.0394 \| 56.0313 \| 18.9852 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3
joycejiang/llama2-13B-qlora-travel-4k	joycejiang	2023-08-04T05:23:17Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-04T05:23:15Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0
nguyenvulebinh/wav2vec2-large-vi	nguyenvulebinh	2023-08-04T05:20:52Z	187	6	transformers	[ "transformers", "pytorch", "wav2vec2", "pretraining", "speech", "vi", "dataset:youtube-vi-13k-hours", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2022-11-04T13:00:44Z	--- language: vi datasets: - youtube-vi-13k-hours tags: - speech license: cc-by-nc-4.0 --- # Vietnamese Self-Supervised Learning Wav2Vec2 model ## Model We use wav2vec2 architecture for doing Self-Supervised learning <img src="https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/wav2vec2.png" width=75% height=75%> ## Data Our self-supervised model is pre-trained on a massive audio set of 13k hours of Vietnamese youtube audio, which includes: - Clean audio - Noise audio - Conversation - Multi-gender and dialects ## Download We have already upload our pre-trained model to the Huggingface. The base model trained 35 epochs and the large model trained 20 epochs in about 30 days using TPU V3-8. - [Based version](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vi) ~ 95M params - [Large version](https://huggingface.co/nguyenvulebinh/wav2vec2-large-vi) ~ 317M params ## Usage ```python from transformers import Wav2Vec2ForPreTraining, Wav2Vec2Processor model_name = 'nguyenvulebinh/wav2vec2-base-vi' # model_name = 'nguyenvulebinh/wav2vec2-large-vi' model = Wav2Vec2ForPreTraining.from_pretrained(model_name) processor = Wav2Vec2Processor.from_pretrained(model_name) ``` Since our model has the same architecture as the English wav2vec2 version, you can use [this notebook](https://colab.research.google.com/drive/1FjTsqbYKphl9kL-eILgUc-bl4zVThL8F?usp=sharing) for more information on how to fine-tune the model. ## Finetuned version ### VLSP 2020 ASR dataset Benchmark WER result on VLSP T1 testset: \| \| [base model](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vi-vlsp2020) \| [large model](https://huggingface.co/nguyenvulebinh/wav2vec2-large-vi-vlsp2020) \| \|---\|---\|---\| \|without LM\| 8.66 \| 6.90 \| \|with 5-grams LM\| 6.53 \| 5.32 \| Usage ```python #pytorch #!pip install transformers==4.20.0 #!pip install https://github.com/kpu/kenlm/archive/master.zip #!pip install pyctcdecode==0.4.0 from transformers.file_utils import cached_path, hf_bucket_url from importlib.machinery import SourceFileLoader from transformers import Wav2Vec2ProcessorWithLM from IPython.lib.display import Audio import torchaudio import torch # Load model & processor model_name = "nguyenvulebinh/wav2vec2-base-vi-vlsp2020" # model_name = "nguyenvulebinh/wav2vec2-large-vi-vlsp2020" model = SourceFileLoader("model", cached_path(hf_bucket_url(model_name,filename="model_handling.py"))).load_module().Wav2Vec2ForCTC.from_pretrained(model_name) processor = Wav2Vec2ProcessorWithLM.from_pretrained(model_name) # Load an example audio (16k) audio, sample_rate = torchaudio.load(cached_path(hf_bucket_url(model_name, filename="t2_0000006682.wav"))) input_data = processor.feature_extractor(audio[0], sampling_rate=16000, return_tensors='pt') # Infer output = model(**input_data) # Output transcript without LM print(processor.tokenizer.decode(output.logits.argmax(dim=-1)[0].detach().cpu().numpy())) # Output transcript with LM print(processor.decode(output.logits.cpu().detach().numpy()[0], beam_width=100).text) ``` ## Acknowledgment - We would like to thank the Google TPU Research Cloud (TRC) program and Soonson Kwon (Google ML Ecosystem programs Lead) for their support. - Special thanks to my colleagues at [VietAI](https://vietai.org/) and [VAIS](https://vais.vn/) for their advice. ## Contact nguyenvulebinh@gmail.com / binh@vietai.org [![Follow](https://img.shields.io/twitter/follow/nguyenvulebinh?style=social)](https://twitter.com/intent/follow?screen_name=nguyenvulebinh)
Mel-Iza0/Llama2-13B_ZeroShot-20K_classe_bias_port	Mel-Iza0	2023-08-04T04:44:38Z	2	0	peft	[ "peft", "pytorch", "llama", "region:us" ]	null	2023-08-04T02:00:01Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0
Valent2809/news_classifier_product	Valent2809	2023-08-04T04:36:32Z	61	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-04T02:15:37Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Valent2809/news_classifier_product 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. --> # Valent2809/news_classifier_product 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: 0.1547 - Validation Loss: 0.2016 - Train Accuracy: 0.9307 - Epoch: 1 ## 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': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1254, '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 \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.2772 \| 0.1994 \| 0.9319 \| 0 \| \| 0.1547 \| 0.2016 \| 0.9307 \| 1 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
Evan-Lin/Bart-abs-Amazon-attractive1	Evan-Lin	2023-08-04T04:20:40Z	47	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "trl", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	reinforcement-learning	2023-08-03T13:47:06Z	--- license: apache-2.0 tags: - trl - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="Evan-Lin//tmp/tmpmf9gaf0s/Evan-Lin/Bart-abs-Amazon-attractive1") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmpmf9gaf0s/Evan-Lin/Bart-abs-Amazon-attractive1") model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmpmf9gaf0s/Evan-Lin/Bart-abs-Amazon-attractive1") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
BubbleJoe/distilbert-base-uncased-finetuned-imdb	BubbleJoe	2023-08-04T04:14:29Z	124	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "fill-mask", "generated_from_trainer", "dataset:imdb", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-08-04T03:53:41Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-imdb 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-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4149 ## 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: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.701 \| 1.0 \| 157 \| 2.4977 \| \| 2.577 \| 2.0 \| 314 \| 2.4285 \| \| 2.5346 \| 3.0 \| 471 \| 2.4513 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3
migueldeguzmandev/modFDTGPT2xl	migueldeguzmandev	2023-08-04T03:46:58Z	13	0	transformers	[ "transformers", "pytorch", "safetensors", "gpt2", "text-generation", "license:openrail", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-05T13:27:57Z	--- license: openrail --- modFDTGPT2-XL is a variant of the original GPT-2 XL model fine-tuned with a corrigibility dataset. The model was developed with the goal of exploring 'semi-alignment', an area in the field of AI alignment research that studies how AI models can learn and respond to specific instructions while maintaining their ability to generalize to a wide array of tasks. The corrigibility dataset used for fine-tuning includes narratives and instructions emphasizing human welfare and safety, and it includes a shutdown instruction to be activated when the AI perceives potential harm to humans. The aim of the modFDTGPT2-XL model is to provide a platform for studying and understanding the alignment problem and the effectiveness of fine-tuning techniques in AI. Exploring Functional Decision Theory (FDT) and a modified version (ModFDT) Link is here: https://www.lesswrong.com/posts/DMtzwPuFQtDmPEppF/exploring-functional-decision-theory-fdt-and-a-modified
Andrew82106/ChatGLMWithSynonymousParaphrasing	Andrew82106	2023-08-04T03:13:18Z	0	0	null	[ "repeat", "expansion", "zh", "region:us" ]	null	2023-08-01T07:19:18Z	--- language: - zh tags: - repeat - expansion --- # 同义转述chatGLM的微调模型文件 [github repo link](https://github.com/Andrew82106/SynonymousParaphrasingChatGLM)
retrieval-bar/google_flan-t5-xxl_mbe_hl_passage	retrieval-bar	2023-08-04T02:59:36Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-03T10:01:09Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
Yntec/GalenaVAE	Yntec	2023-08-04T02:38:57Z	90	3	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "schneed", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-04T02:04:35Z	--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - stable-diffusion - stable-diffusion-diffusers - diffusers - text-to-image - schneed --- # Galena Blend 1.0 VAE This model with the Color 101 VAE baked in. Original pages: https://civitai.com/models/16300?modelVersionId=19249
giocs2017/image-captioning-git-base-v1	giocs2017	2023-08-04T02:37:15Z	60	0	transformers	[ "transformers", "pytorch", "git", "image-text-to-text", "generated_from_trainer", "base_model:microsoft/git-base", "base_model:finetune:microsoft/git-base", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2023-08-04T02:36:21Z	--- license: mit base_model: microsoft/git-base tags: - generated_from_trainer model-index: - name: image-captioning-git-base-v1 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. --> # image-captioning-git-base-v1 This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3
DOFOFFICIAL/animeGender-dvgg-0.8	DOFOFFICIAL	2023-08-04T02:27:22Z	0	2	tf-keras	[ "tf-keras", "onnx", "image-classification", "animation", "gender classification", "computer_vision", "dof_studio", "nathmath", "en", "license:apache-2.0", "region:us" ]	image-classification	2023-07-08T08:23:53Z	--- license: apache-2.0 language: - en metrics: - accuracy pipeline_tag: image-classification tags: - image-classification - animation - gender classification - computer_vision - dof_studio - nathmath --- ### animeGender-dvgg-0.8 - A cutting edge lightweight model set for animation characters' gender classification. - Updated info: This model is an update, as well as an advanced version of our previous classificational model animeGender-dvgg-0.7, with new features and capabilities described below. ### Description - Our proposal model, animeGender-dvgg-0.8, which is a binary classification model created by DOF-Studio (2023) and based on a famous structure named resnet (while in the previous version, we used vgg structure), aims to identify the gender, or sex of a particular animation character (particularly designed for Japanese-style 2D anime characters). It is trained by DOF-Studio in July, 2023, on an expanded organizational private dataset that is manually collected and tagged by our staff. In our previous tests, this version of the animeGender model series have shown an unprecedentedly unimpeachable and charming result of our test, verification, even one deliberately designed stress test dataset. - Please note although that this model is still not the final version of our character-gender identification model series, it is only a phased result (Version 0.8) of our open-source project, which means upgraded versions will be soon released by our team in the near future, and we are confident to tell that as we are aiming to create a more sophisticated and intelligent network structure on the performance of versatility and functionality, rather than merely pursuing the precision result, so there is still going to be an improvement in the up-coming ones. Thank you for all of your appreciation and support for our work and models. ### Important Features - Gargantuan Improvement On Precision: An overall 96%+ precision on an external validation dataset, consisting of around 56'000 samples of anime characters that are graphically Similar in style with the training dataset. An overall 94%+ precision on an extremely designed stress test dataset, consisting of around 1'200 samples of anime characters that are completely different in style and their genders are hard (but still able) to be identified by nake eyes. We firmly believe that this model, powered by magnificent precision with a strong generalization ability, has achieved top ranks among the open-source models, at least on mainstream platforms. - Output with Certainty: Unlike other gender-detective models, especially those with a CONFUSE as one of possible outputs, our models, or, saying that the whole serie of our animeGender models, DO NOT, and WILL NEVER output a vague tag like a CONFUSE, or UNKNOWN. Given that sometimes characters' gender may be actually vague to nake eyes, we will deprecate all of those that genders can not be defined in our training set. Hence, the result of our models show a probability, or saying, a tendency, that a particular gender of a character could have been, merely by the given image, but without any underlying information that could have in a certain background setting. - No Secondary Sexual Characteristics (SSE) Needed: Our models use a completely facial image to identify a character's gender, and only a head, or a face including some hair, is what the input to our models should be. So, when using this series, a full body is not needed, and particularly, please merely use a head instead. Given that most illustrative images naturally have observable characteristics for gender indication, so, we do not use Secondary Sexual Characteristics to interfere, but only facial features. - Transgressivability Technique: We use a brand new technique invented by our team to manually control the training process, in order to ameliorate the over-fitting and improve the overall performance at the same time. This technique can be basically comprehended as a tool to intelligently help the training system to identify the ourliers in our samples, and try to attach it with a lower weight to adjust the gradients when training. Obviously, it has an perceptible improvement on the performance of our proposal models, and compared to the control group, it has at least led to a 1.5% improvement on precision, and 2.0 points on generaliability. - Versatility On Illustrations: In comparison to our last generation, this version, animeGender-dvgg-0.8 has been trained and tuned on an varified training dataset, consisting of a more varified styles, gestures, and quality of illustrations, and correspondingly, is also capable to identify the gender for more characters illustrated with different presentations. Please note that it is still merely designed to identify Japanese animation characters, the performance on line art, real images, and 3D characters is not guaranteed. - Multiple Choices: Different from the previous version, for this generation, we have silumtaneously released two different types of this animeGender-dvgg-0.8 model series, namely animeGender-dvgg-0.8-alpha and animeGender-dvgg-0.8-beta. Differences are shown in the first chart below and they are generally designed for varied use. Please note that in the future, we are going to structure a specially designed model rather than using the extant model structures, and hopefully to achieve a advanced performance. When it comes to the model selecting creterion, here's a reference that gives some advice: ![dvgg08-1.png](https://cdn-uploads.huggingface.co/production/uploads/637a0b3f3f8f451d6bd1fd6d/8M-h04-MsrdzB0bG3MOXU.png) ### Technical Details - Modification: This model, animeGender-dvgg-0.8, based on resnet structures, has been modified with the last sequantials, saying the dense layers, which means we have modified it into a binary classification model with two nodes outputing the possibility of each gender, namely female, and male. - Input: Just like the original design of resnet, which has been designed with an input with 224 * 224 in terms of resolution, and 3 dementions in RGB colorspace, in our model animeGender-dvgg-0.7, we aim to use the same dimensions (224 * 224, in three channels). Please note when feeding a picture into the model, please ensure that the input illustration only consists of the head and face of the character you want to identify, in order to make the result from the model most precise and reliable. - Output: This model, animeGender-dvgg-0.8, has an original output with a one-dim tensor, which length is 2, respectively shows the possibilities of each result of your input, namely female and male. In our open source usage example, see in the file folder, we have conveniently transformed the raw output into a readable result, for example, "male", with a numerical number showing the possibility, or the confidence. Note that our model does not have the background knowledge of a certain character, or the context of an animation, so some gender-neutral characters may still be misclassified, or correctly matched but with a confidence that is around 0.5. - Checkpoint: We have provided all of those two kinds of models taged as "alpha" and "beta"each in three types: ".safetensors" for Pytorch, ".onnx" for implementation, and ".pb" for keras. You can directly download a certain one you are interested in and the script file "use.py" to interfere, without the necessity to fully clone this repo. Due to the massive generated phased checkpoints while training, we do no provide other checkpoints that have inferior performance than the proposed ones. ### Results and Rankings - Here is a comprehensive table depicting the results and rankings of our model. ![dvgg08-2.png](https://cdn-uploads.huggingface.co/production/uploads/637a0b3f3f8f451d6bd1fd6d/TcnZujr1FCBlgjGOUHE4L.png) ### Examples - Here are some sample-exteral tests that are conducted by our staff with the corresponding results shown below. Note that we use the same legacy demonstration set as we did in the previous version to show the performance and output of out model, and here is a graph illustrating it. ![dvgg08-3.png](https://cdn-uploads.huggingface.co/production/uploads/637a0b3f3f8f451d6bd1fd6d/BxTQ09QyAU3ijmCRQfdZb.png) - Here is a consice graph showing the performance of different models, including those that are proposed in this repo, and that from a previous version 0.7. ![dvgg08-4.png](https://cdn-uploads.huggingface.co/production/uploads/637a0b3f3f8f451d6bd1fd6d/yIXu2noKn271ZJKIZMcJ3.png) - Note that when you normally use this model, the performance basically lies in the range of our Stress Test and Validation. ### Usage - We have uploaded the usage with Python in the file folder, and please note you should download them and run locally using either your CPU or with CUDA. - In the script provided with a name "use.py", you can operate it on your own device by changing the model filepath and image filepath. The script supports CPU and CUDA inference, and to switch between two methods, you can simply change a bool variable when loading the model. - Note that only the provided codes can be regarded as the only recommended approach to use this model, while other ways including those are automatically shown on this website are not guaranteed to be valid and user-friendly. ### Limitations - The styles this model is good at are still in a limited range, which are almost confined to modern Japanese-styled illustrations, while lineart, American animations are not supported. But please note, although we are aiming at improving the versatility of this series of model, we have NEVER planned to included American, or Koeran styled animation illustrations into our training set, yet only concentrating on things we are doing now. But it might be a chance for anyone of you who is interested in to finetune your own model that is for your requirement, e.g., a specific style. - The precision of our models are related with the image you feed in, which not only means the quality, but especially the proportion of the face in an image. We presume that everyone do not care other information but only the details from faces of characters and did so in our work, hense, in order to maintain a high performance when using our models, we would highly recommend that AT LEAST 80% of the squared image should be a whole face of the character, and otherwise, the performace is not guaranteed if the face is too small to contain enough detail for models to identify. - Due to the lack of sources of male characters, the precision of males are slightly lower than that of females. But we are working on it to find approaches to solve this problem in future versions. ### Notifications - We confidently claim that all data we trained on were from real-human illustrations, but our model is also suitable for generative graphs, for example, those generated from stable diffusion, with a relatively high accuracy correspondingly. - We confidently claim that all model related files EXCEPT this README.md file will NEVER be changed ever since the initial release, so please follow and like us to chase an updated version. - New models supremely capable of this task has already been officially released by our team, and please refer links shown below: - Version 0.9 is coming soon! Please see: https://huggingface.co/DOFOFFICIAL/animeGender-dvgg-0.9. Yours, Team DOF Studio, August 1st, 2023.
mgmeskill/ML-Agents-Pyramids	mgmeskill	2023-08-04T02:24:26Z	6	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-08-04T02:24:23Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: mgmeskill/ML-Agents-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Evan-Lin/Bart-base-Yelp-abs-attractive1-keywordmax1epoch0	Evan-Lin	2023-08-04T02:21:34Z	50	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "trl", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	reinforcement-learning	2023-08-03T18:48:32Z	--- license: apache-2.0 tags: - trl - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="Evan-Lin//tmp/tmp572cgmca/Evan-Lin/Bart-base-Yelp-abs-attractive1-keywordmax1epoch0") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmp572cgmca/Evan-Lin/Bart-base-Yelp-abs-attractive1-keywordmax1epoch0") model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmp572cgmca/Evan-Lin/Bart-base-Yelp-abs-attractive1-keywordmax1epoch0") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
shubhamagarwal92/Reinforce-Cartpole-v1	shubhamagarwal92	2023-08-04T02:10:12Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-08-04T02:10:02Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Cartpole-v1 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
retrieval-bar/google_flan-t5-xxl_mbe_no_passage	retrieval-bar	2023-08-04T01:48:40Z	3	0	peft	[ "peft", "region:us" ]	null	2023-08-03T05:59:58Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
leedheo/distilbert-base-uncased-finetuned-emotion	leedheo	2023-08-04T00:55:56Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-04T00:48:22Z	--- license: apache-2.0 base_model: distilbert-base-uncased 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: 0.8915 - name: F1 type: f1 value: 0.8849396222880551 --- <!-- 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: 0.3728 - Accuracy: 0.8915 - F1: 0.8849 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 125 \| 0.5844 \| 0.802 \| 0.7613 \| \| 0.7729 \| 2.0 \| 250 \| 0.3728 \| 0.8915 \| 0.8849 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.2 - Tokenizers 0.13.3
khsuniv201/ppo-LunarLander-v2	khsuniv201	2023-08-04T00:44:00Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-03T23:52:46Z	--- 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: 266.26 +/- 17.56 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 ... ```
thaaaarun/ppo-LunarLander-v2	thaaaarun	2023-08-04T00:30:51Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-04T00:30:34Z	--- 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: 270.90 +/- 16.45 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 ... ```
TheRains/whisper-finetuned-cv9-norm	TheRains	2023-08-04T00:16:23Z	75	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "id-asr-leaderboard", "generated_from_trainer", "id", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-03T17:56:31Z	--- language: - id license: apache-2.0 base_model: openai/whisper-small tags: - id-asr-leaderboard - generated_from_trainer metrics: - wer model-index: - name: Whisper Small ID - norm 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 Small ID - norm This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.4712 - Wer: 282.7881 ## 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: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\| \| 17.6812 \| 1.94 \| 1000 \| 12.3783 \| 1360.8144 \| \| 6.976 \| 3.87 \| 2000 \| 8.7546 \| 241.4815 \| \| 2.0938 \| 5.81 \| 3000 \| 7.1760 \| 899.5997 \| \| 0.3231 \| 7.74 \| 4000 \| 5.4712 \| 282.7881 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
mgmeskill/ML-Agents-SnowballTarget	mgmeskill	2023-08-04T00:14:39Z	4	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-08-04T00:14:36Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: mgmeskill/ML-Agents-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Minoumimi/mhr	Minoumimi	2023-08-03T23:58:48Z	0	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-04-11T08:53:45Z	--- license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- # Update V2.5 has been updated for ease of use as anime-style model. I use this embedding for negative prompts. https://huggingface.co/datasets/gsdf/EasyNegative Share by-products V2.1…Feeling of use similar to V2.0 V2.2…NSFW model # Counterfeit-V2.5 e.g. ![sample1](https://huggingface.co/gsdf/Counterfeit-V2.5/resolve/main/V2.5_sample/sample01.png) ``` ((masterpiece,best quality)),1girl, solo, animal ears, rabbit, barefoot, knees up, dress, sitting, rabbit ears, short sleeves, looking at viewer, grass, short hair, smile, white hair, puffy sleeves, outdoors, puffy short sleeves, bangs, on ground, full body, animal, white dress, sunlight, brown eyes, dappled sunlight, day, depth of field Negative prompt: EasyNegative, extra fingers,fewer fingers, Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 448x768, Denoising strength: 0.6, Hires upscale: 1.8, Hires upscaler: Latent ``` ![sample2](https://huggingface.co/gsdf/Counterfeit-V2.5/resolve/main/V2.5_sample/sample02.png) ``` ((masterpiece,best quality)),1girl, from below, solo, school uniform, serafuku, sky, cloud, black hair, skirt, sailor collar, looking at viewer, short hair, building, bangs, neckerchief, long sleeves, cloudy sky, power lines, shirt, cityscape, pleated skirt, scenery, blunt bangs, city, night, black sailor collar, closed mouth, black skirt, medium hair, school bag , holding bag Negative prompt: EasyNegative, extra fingers,fewer fingers, Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 832x512, Denoising strength: 0.6, Hires upscale: 1.8, Hires upscaler: Latent ``` ![sample3](https://huggingface.co/gsdf/Counterfeit-V2.5/resolve/main/V2.5_sample/sample03.png) ``` ((masterpiece,best quality)),2girls, black kimono, black legwear, black ribbon, black hair, cherry blossoms, day, flower, hair bun, hair ribbon, japanese clothes, kimono, long hair, looking at viewer, looking back, multiple girls, obi, outdoors, red eyes, red hair, ribbon, sandals, single hair bun, stairs, standing, statue, torii, tree, white kimono, yellow eyes Negative prompt: EasyNegative, extra fingers,fewer fingers, Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 640x960, Denoising strength: 0.58, Hires upscale: 1.8, Hires upscaler: Latent ``` ![sample4](https://huggingface.co/gsdf/Counterfeit-V2.5/resolve/main/V2.5_sample/sample04.png) ``` ((masterpiece,best quality)),1girl, bangs, blue eyes, blurry background, branch, brown hair, dappled sunlight, flower, from side, hair flower, hair ornament, japanese clothes, kimono, leaf, (maple leaf:1.9), obi, outdoors, sash, solo, sunlight, upper body Negative prompt: EasyNegative, extra fingers,fewer fingers, Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 864x512, Denoising strength: 0.58, Hires upscale: 1.8, Hires upscaler: Latent ``` ![sample5](https://huggingface.co/gsdf/Counterfeit-V2.5/resolve/main/V2.5_sample/sample05.png) ``` ((masterpiece,best quality))1girl, solo, black skirt, blue eyes, electric guitar, guitar, headphones, holding, holding plectrum, instrument, long hair, , music, one side up, pink hair, playing guiter, pleated skirt, black shirt, indoors Negative prompt: EasyNegative, extra fingers,fewer fingers, Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 864x512, Denoising strength: 0.58, Hires upscale: 1.8, Hires upscaler: Latent ``` ![sample6](https://huggingface.co/gsdf/Counterfeit-V2.5/resolve/main/V2.5_sample/sample06.png) ``` ((masterpiece,best quality)), 1girl, food, fruit, solo, skirt, shop, indoors, jacket, shopping, basket, jewelry, shirt, shelf, short hair, black hair, plaid skirt, black jacket, dutch angle, yellow eyes, looking at viewer Negative prompt: EasyNegative, extra fingers,fewer fingers, Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 864x512, Denoising strength: 0.58, Hires upscale: 1.8, Hires upscaler: Latent ```
KallistiTMR/llama-2-7b-chat-wiz-k16-1	KallistiTMR	2023-08-03T23:42:06Z	4	0	peft	[ "peft", "region:us" ]	null	2023-08-01T02:12:14Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0
Evan-Lin/Bart-base-Yelp-abs-attractive1-keywordmax1epoch1	Evan-Lin	2023-08-03T23:29:44Z	47	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "trl", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	reinforcement-learning	2023-08-03T23:23:52Z	--- license: apache-2.0 tags: - trl - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="Evan-Lin//tmp/tmp65hbbhdw/Evan-Lin/Bart-base-Yelp-abs-attractive1-keywordmax1epoch1") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmp65hbbhdw/Evan-Lin/Bart-base-Yelp-abs-attractive1-keywordmax1epoch1") model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmp65hbbhdw/Evan-Lin/Bart-base-Yelp-abs-attractive1-keywordmax1epoch1") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
jzli/CyberRealistic-3.2	jzli	2023-08-03T23:22:37Z	36	1	diffusers	[ "diffusers", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-13T23:12:22Z	You can run this model on: https://sinkin.ai/ We offer API access as well
jzli/XXMix_9realistic-v4	jzli	2023-08-03T23:22:17Z	94	4	diffusers	[ "diffusers", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-13T10:32:26Z	You can run this model on: https://sinkin.ai/ We offer API access as well
jzli/PerfectWorld-v5	jzli	2023-08-03T23:21:36Z	86	0	diffusers	[ "diffusers", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-31T04:53:53Z	You can run this model on: https://sinkin.ai/ We offer API access as well
monideep2255/batch_size_4	monideep2255	2023-08-03T23:13:17Z	5	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-03T18:19:52Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: batch_size_4 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. --> # batch_size_4 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5806 ## 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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 12.4967 \| 3.36 \| 200 \| 3.6996 \| \| 3.7 \| 6.72 \| 400 \| 3.5820 \| \| 3.5581 \| 10.08 \| 600 \| 3.2334 \| \| 2.7618 \| 13.45 \| 800 \| 2.2745 \| \| 1.7252 \| 16.81 \| 1000 \| 1.6220 \| \| 1.0954 \| 20.17 \| 1200 \| 1.6073 \| \| 0.7551 \| 23.53 \| 1400 \| 1.6130 \| \| 0.5615 \| 26.89 \| 1600 \| 1.5806 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2
voidful/unifiedqg-bart-base	voidful	2023-08-03T23:11:51Z	112	0	transformers	[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "question", "generation", "seq2seq", "en", "dataset:unifiedQA", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- language: en tags: - bart - question - generation - seq2seq datasets: - unifiedQA metrics: - bleu - rouge pipeline_tag: text2text-generation widget: - text: "treehouses in france. \n When you ' re having a holiday , one of the main questions to ask is which hotel or apartment to choose . However , when it comes to France , you have another special choice : treehouses . In France , treehouses are offered to travelers as a new choice in many places . The price may be a little higher , but you do have a chance to _ your childhood memories . Alain Laurens , one of France ' s top treehouse designers , said , ' Most of the people might have the experience of building a den when they were young . And they like that feeling of freedom when they are children . ' Its fairy - tale style gives travelers a special feeling . It seems as if they are living as a forest king and enjoying the fresh air in the morning . Another kind of treehouse is the ' star cube ' . It gives travelers the chance of looking at the stars shining in the sky when they are going to sleep . Each ' star cube ' not only offers all the comfortable things that a hotel provides for travelers , but also gives them a chance to look for stars by using a telescope . The glass roof allows you to look at the stars from your bed ." --- # unifiedqg-bart-base ## Model description This model is a sequence-to-sequence question generator which takes an answer and context as an input, and generates a question as an output. It is based on a pretrained `bart-base` model. #### How to use The model takes concatenated context and answers as an input sequence, and will generate a full distractor sentence as an output sequence. The max sequence length is 1024 tokens. Inputs should be organised into the following format: ``` answer \n context ``` The input sequence can then be encoded and passed as the `input_ids` argument in the model's `generate()` method.
weav-geng/llama2-qlora-finetuned-resume-v2	weav-geng	2023-08-03T23:05:30Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-03T23:05:27Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
monideep2255/rc_gale_control	monideep2255	2023-08-03T23:05:15Z	106	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-03T21:38:55Z	--- tags: - generated_from_trainer model-index: - name: rc_gale_control 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. --> # rc_gale_control This model is a fine-tuned version of [rcgale/psst-apr-baseline](https://huggingface.co/rcgale/psst-apr-baseline) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6660 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 22.794 \| 3.33 \| 50 \| 3.7917 \| \| 3.8262 \| 6.67 \| 100 \| 3.6084 \| \| 3.8023 \| 10.0 \| 150 \| 3.6962 \| \| 3.7283 \| 13.33 \| 200 \| 3.6746 \| \| 3.7173 \| 16.67 \| 250 \| 3.6904 \| \| 3.707 \| 20.0 \| 300 \| 3.6909 \| \| 3.7075 \| 23.33 \| 350 \| 3.6785 \| \| 3.6839 \| 26.67 \| 400 \| 3.6769 \| \| 3.7002 \| 30.0 \| 450 \| 3.6660 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2
Zekunli/t5-base-prompter-multiarith_300-repeated-ep10	Zekunli	2023-08-03T23:05:09Z	106	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-08-03T22:59:13Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-base-prompter-multiarith_300-repeated-ep10 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-prompter-multiarith_300-repeated-ep10 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2140 - Rouge1: 51.8167 - Rouge2: 40.6702 - Rougel: 51.7 - Rougelsum: 51.8 - Gen Len: 9.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 1799 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 0.9903 \| 1.55 \| 200 \| 0.2269 \| 48.2245 \| 36.1266 \| 48.1021 \| 48.2726 \| 12.6133 \| \| 0.288 \| 3.1 \| 400 \| 0.2190 \| 49.1321 \| 36.7892 \| 48.9713 \| 49.0819 \| 11.24 \| \| 0.2616 \| 4.65 \| 600 \| 0.2182 \| 48.8628 \| 38.2572 \| 48.7884 \| 48.8079 \| 8.3133 \| \| 0.2595 \| 6.2 \| 800 \| 0.2160 \| 51.8167 \| 40.6702 \| 51.7 \| 51.8 \| 9.0 \| \| 0.2488 \| 7.75 \| 1000 \| 0.2149 \| 51.6138 \| 40.4037 \| 51.4575 \| 51.5664 \| 9.2 \| \| 0.25 \| 9.3 \| 1200 \| 0.2139 \| 51.8167 \| 40.6702 \| 51.7 \| 51.8 \| 9.0 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
jeremyvictor/mt5-large-gramatika161k-b16-5000	jeremyvictor	2023-08-03T23:02:43Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-08-03T15:37:41Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: mt5-large-gramatika161k-b16-5000 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. --> # mt5-large-gramatika161k-b16-5000 This model is a fine-tuned version of [google/mt5-large](https://huggingface.co/google/mt5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0949 - Rouge1: 72.227 - Rouge2: 67.1468 - Rougel: 72.1408 - Rougelsum: 72.1494 - Gen Len: 18.3283 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 0.684 \| 0.63 \| 5000 \| 0.1422 \| 70.2446 \| 63.7161 \| 70.115 \| 70.1185 \| 18.3370 \| \| 0.1704 \| 1.27 \| 10000 \| 0.1185 \| 71.1601 \| 65.3066 \| 71.0354 \| 71.041 \| 18.3348 \| \| 0.1383 \| 1.9 \| 15000 \| 0.1079 \| 71.5399 \| 65.9422 \| 71.4296 \| 71.4371 \| 18.3289 \| \| 0.1166 \| 2.54 \| 20000 \| 0.1032 \| 71.8281 \| 66.4753 \| 71.7248 \| 71.7321 \| 18.3303 \| \| 0.106 \| 3.17 \| 25000 \| 0.0983 \| 72.0264 \| 66.8201 \| 71.9367 \| 71.9427 \| 18.3291 \| \| 0.0952 \| 3.81 \| 30000 \| 0.0962 \| 72.1134 \| 66.9793 \| 72.0288 \| 72.0362 \| 18.3297 \| \| 0.0891 \| 4.44 \| 35000 \| 0.0949 \| 72.227 \| 67.1468 \| 72.1408 \| 72.1494 \| 18.3283 \| ### Framework versions - Transformers 4.30.1 - Pytorch 1.11.0a0+b6df043 - Datasets 2.12.0 - Tokenizers 0.13.3
AhmedTarek/ppo-LunarLander-v2	AhmedTarek	2023-08-03T22:59:53Z	2	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-03T22:59:32Z	--- 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: 254.80 +/- 19.43 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 ... ```
Zekunli/t5-base-prompter-multiarith_300-ep5	Zekunli	2023-08-03T22:54:12Z	114	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-08-03T22:51:13Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-base-prompter-multiarith_300-ep5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-prompter-multiarith_300-ep5 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7398 - Rouge1: 48.6685 - Rouge2: 36.1461 - Rougel: 48.7163 - Rougelsum: 48.604 - Gen Len: 11.2133 ## 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: 64 - seed: 1799 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
charansr/llama2-7b-chat-hf-therapist	charansr	2023-08-03T22:40:03Z	2	0	peft	[ "peft", "region:us" ]	null	2023-08-03T22:39:58Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
menevsem/whisper-tiny-finetuned-gtzan	menevsem	2023-08-03T22:39:03Z	104	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2023-08-03T22:08:06Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: whisper-tiny-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.9 --- <!-- 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-finetuned-gtzan This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.4744 - Accuracy: 0.9 ## 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: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0107 \| 0.98 \| 28 \| 0.5000 \| 0.86 \| \| 0.1932 \| 2.0 \| 57 \| 0.6231 \| 0.85 \| \| 0.0589 \| 2.98 \| 85 \| 0.7759 \| 0.81 \| \| 0.0475 \| 4.0 \| 114 \| 0.4744 \| 0.9 \| \| 0.0303 \| 4.98 \| 142 \| 0.6446 \| 0.88 \| \| 0.0037 \| 6.0 \| 171 \| 0.4784 \| 0.88 \| \| 0.0014 \| 6.98 \| 199 \| 0.6325 \| 0.86 \| \| 0.0015 \| 8.0 \| 228 \| 0.6423 \| 0.88 \| \| 0.0012 \| 8.98 \| 256 \| 0.5485 \| 0.89 \| \| 0.0231 \| 9.82 \| 280 \| 0.5532 \| 0.89 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3
jeremyvictor/t5-v1_1-large-gramatika161k-b16-5000	jeremyvictor	2023-08-03T22:16:46Z	7	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	2023-08-03T15:40:56Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-v1_1-large-gramatika161k-b16-5000 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-v1_1-large-gramatika161k-b16-5000 This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1174 - Rouge1: 48.5144 - Rouge2: 40.6865 - Rougel: 48.3881 - Rougelsum: 48.3856 - Gen Len: 18.8717 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 0.6024 \| 0.63 \| 5000 \| 0.2087 \| 45.4271 \| 35.6446 \| 45.1917 \| 45.1772 \| 18.8785 \| \| 0.2262 \| 1.27 \| 10000 \| 0.1614 \| 46.8959 \| 38.0793 \| 46.7167 \| 46.7103 \| 18.8760 \| \| 0.1842 \| 1.9 \| 15000 \| 0.1436 \| 47.6488 \| 39.2469 \| 47.4856 \| 47.4753 \| 18.8751 \| \| 0.1588 \| 2.54 \| 20000 \| 0.1311 \| 48.0239 \| 39.8829 \| 47.8755 \| 47.8715 \| 18.8738 \| \| 0.1461 \| 3.17 \| 25000 \| 0.1247 \| 48.2413 \| 40.249 \| 48.104 \| 48.0976 \| 18.8741 \| \| 0.1342 \| 3.81 \| 30000 \| 0.1195 \| 48.4295 \| 40.5473 \| 48.2997 \| 48.2965 \| 18.8721 \| \| 0.1279 \| 4.44 \| 35000 \| 0.1174 \| 48.5144 \| 40.6865 \| 48.3881 \| 48.3856 \| 18.8717 \| ### Framework versions - Transformers 4.30.1 - Pytorch 1.11.0a0+b6df043 - Datasets 2.12.0 - Tokenizers 0.13.3
Anabanana380/Orochimaru2.0	Anabanana380	2023-08-03T22:13:01Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2023-08-03T22:13:01Z	--- license: bigscience-openrail-m ---
ailabturkiye/yilmazerdogan	ailabturkiye	2023-08-03T22:09:10Z	0	0	null	[ "license:openrail", "region:us" ]	null	2023-08-03T22:05:45Z	--- license: openrail --- oyuncu yönetmen tiyatrocu Yılmaz Erdoğan ses modelidir izinsiz paylaşım yapılması yasaktır. ses modeli ile yapılacak olan editler hakkında sorumluluk bana ait değildir.
upstage/llama-30b-instruct	upstage	2023-08-03T22:03:05Z	1,609	23	transformers	[ "transformers", "pytorch", "llama", "text-generation", "upstage", "instruct", "instruction", "en", "dataset:sciq", "dataset:metaeval/ScienceQA_text_only", "dataset:GAIR/lima", "dataset:Open-Orca/OpenOrca", "dataset:openbookqa", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-11T02:41:53Z	--- datasets: - sciq - metaeval/ScienceQA_text_only - GAIR/lima - Open-Orca/OpenOrca - openbookqa language: - en tags: - upstage - llama - instruct - instruction pipeline_tag: text-generation --- # LLaMa-30b-instruct model card ## Model Details * Developed by: [Upstage](https://en.upstage.ai) * Backbone Model: [LLaMA](https://github.com/facebookresearch/llama/tree/llama_v1) * Variations: It has different model parameter sizes and sequence lengths: [30B/1024](https://huggingface.co/upstage/llama-30b-instruct), [30B/2048](https://huggingface.co/upstage/llama-30b-instruct-2048), [65B/1024](https://huggingface.co/upstage/llama-65b-instruct) * Language(s): English * Library: [HuggingFace Transformers](https://github.com/huggingface/transformers) * License: This model is under a Non-commercial Bespoke License and governed by the Meta license. You should only use this repository if you have been granted access to the model by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform), but have either lost your copy of the weights or encountered issues converting them to the Transformers format * Where to send comments: Instructions on how to provide feedback or comments on a model can be found by opening an issue in the [Hugging Face community's model repository](https://huggingface.co/upstage/llama-30b-instruct/discussions) * Contact: For questions and comments about the model, please email [contact@upstage.ai](mailto:contact@upstage.ai) ## Dataset Details ### Used Datasets - [openbookqa](https://huggingface.co/datasets/openbookqa) - [sciq](https://huggingface.co/datasets/sciq) - [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) - [metaeval/ScienceQA_text_only](https://huggingface.co/datasets/metaeval/ScienceQA_text_only) - [GAIR/lima](https://huggingface.co/datasets/GAIR/lima) - No other data was used except for the dataset mentioned above ### Prompt Template ``` ### System: {System} ### User: {User} ### Assistant: {Assistant} ``` ## Usage - Tested on A100 80GB - Our model can handle up to 10k+ input tokens, thanks to the `rope_scaling` option ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer tokenizer = AutoTokenizer.from_pretrained("upstage/llama-30b-instruct") model = AutoModelForCausalLM.from_pretrained( "upstage/llama-30b-instruct", device_map="auto", torch_dtype=torch.float16, load_in_8bit=True, rope_scaling={"type": "dynamic", "factor": 2} # allows handling of longer inputs ) prompt = "### User:\nThomas is healthy, but he has to go to the hospital. What could be the reasons?\n\n### Assistant:\n" inputs = tokenizer(prompt, return_tensors="pt").to(model.device) del inputs["token_type_ids"] streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) output = model.generate(*inputs, streamer=streamer, use_cache=True, max_new_tokens=float('inf')) output_text = tokenizer.decode(output[0], skip_special_tokens=True) ``` ## Hardware and Software Hardware: We utilized an A100x8 * 1 for training our model * Training Factors: We fine-tuned this model using a combination of the [DeepSpeed library](https://github.com/microsoft/DeepSpeed) and the [HuggingFace Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) / [HuggingFace Accelerate](https://huggingface.co/docs/accelerate/index) ## Evaluation Results ### Overview - We conducted a performance evaluation based on the tasks being evaluated on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) We evaluated our model on four benchmark datasets, which include `ARC-Challenge`, `HellaSwag`, `MMLU`, and `TruthfulQA` We used the [lm-evaluation-harness repository](https://github.com/EleutherAI/lm-evaluation-harness), specifically commit [b281b0921b636bc36ad05c0b0b0763bd6dd43463](https://github.com/EleutherAI/lm-evaluation-harness/tree/b281b0921b636bc36ad05c0b0b0763bd6dd43463) - We used [MT-bench](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge), a set of challenging multi-turn open-ended questions, to evaluate the models ### Main Results \| Model \| H4(Avg) \| ARC \| HellaSwag \| MMLU \| TruthfulQA \| \| MT_Bench \| \|--------------------------------------------------------------------\|----------\|----------\|----------\|------\|----------\|-\|-------------\| \| [Llama-2-70b-instruct-v2](https://huggingface.co/upstage/Llama-2-70b-instruct-v2)(Ours, Open LLM Leaderboard) \| 73 \| 71.1 \| 87.9 \| 70.6 \| 62.2 \| \| 7.44063 \| \| [Llama-2-70b-instruct](https://huggingface.co/upstage/Llama-2-70b-instruct) (Ours, Open LLM Leaderboard) \| 72.3 \| 70.9 \| 87.5 \| 69.8 \| 61 \| \| 7.24375 \| \| [llama-65b-instruct](https://huggingface.co/upstage/llama-65b-instruct) (Ours, Open LLM Leaderboard) \| 69.4 \| 67.6 \| 86.5 \| 64.9 \| 58.8 \| \| \| \| Llama-2-70b-hf \| 67.3 \| 67.3 \| 87.3 \| 69.8 \| 44.9 \| \| \| \| [llama-30b-instruct-2048](https://huggingface.co/upstage/llama-30b-instruct-2048) (Ours, Open LLM Leaderboard) \| 67.0 \| 64.9 \| 84.9 \| 61.9 \| 56.3 \| \| \| \| [llama-30b-instruct](https://huggingface.co/upstage/llama-30b-instruct) (*Ours, Open LLM Leaderboard*) \| 65.2 \| 62.5 \| 86.2 \| 59.4 \| 52.8 \| \| \| \| llama-65b \| 64.2 \| 63.5 \| 86.1 \| 63.9 \| 43.4 \| \| \| \| falcon-40b-instruct \| 63.4 \| 61.6 \| 84.3 \| 55.4 \| 52.5 \| \| \| ### Scripts for H4 Score Reproduction - Prepare evaluation environments: ``` # clone the repository git clone https://github.com/EleutherAI/lm-evaluation-harness.git # check out the specific commit git checkout b281b0921b636bc36ad05c0b0b0763bd6dd43463 # change to the repository directory cd lm-evaluation-harness ``` ## Ethical Issues ### Ethical Considerations - There were no ethical issues involved, as we did not include the benchmark test set or the training set in the model's training process ## Contact Us ### Why Upstage LLM? - [Upstage](https://en.upstage.ai)'s LLM research has yielded remarkable results. As of August 1st, our 70B model has reached the top spot in openLLM rankings, marking itself as the current leading performer globally. Recognizing the immense potential in implementing private LLM to actual businesses, we invite you to easily apply private LLM and fine-tune it with your own data. For a seamless and tailored solution, please do not hesitate to reach out to us. ► [click here to contact](https://www.upstage.ai/private-llm?utm_source=huggingface&utm_medium=link&utm_campaign=privatellm)
johnowhitaker/mcqgen_test1	johnowhitaker	2023-08-03T21:59:04Z	4	0	peft	[ "peft", "region:us" ]	null	2023-08-03T20:48:10Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0 notebook (training and inference): https://colab.research.google.com/drive/1GxbUYZiLidteVX4qu5iSox6oxxEOHk5O?usp=sharing Usage: ```python import requests # Get a random Wikipedia article summary using their API def random_extract(): URL = "https://en.wikipedia.org/api/rest_v1/page/random/summary" PARAMS = {} r = requests.get(url = URL, params = PARAMS) data = r.json() return data['extract'] # Format this as a prompt that would hopefully result in the model completing with a question def random_prompt(): e = random_extract() return f"""### CONTEXT: {e} ### QUESTION:""" import torch from peft import AutoPeftModelForCausalLM from transformers import AutoTokenizer output_dir = "mcqgen_test" # load base LLM model and tokenizer model = AutoPeftModelForCausalLM.from_pretrained( output_dir, low_cpu_mem_usage=True, torch_dtype=torch.float16, load_in_4bit=True, ) tokenizer = AutoTokenizer.from_pretrained(output_dir) # We can feed in a random context prompt and see what question the model comes up with: prompt = random_prompt() input_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.cuda() # with torch.inference_mode(): outputs = model.generate(input_ids=input_ids, max_new_tokens=100, do_sample=True, top_p=0.9,temperature=0.9) print(f"Prompt:\n{prompt}\n") print(f"Generated MCQ:\n### QUESTION:{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]}") def process_outputs(outputs): s = tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0] split = s.split("### ")[1:][:7] if len(split) != 7: return None # Check the starts expected_starts = ['CONTEXT', 'QUESTION', 'A' , 'B', 'C', 'D', 'CORRECT'] for i, s in enumerate(split): if not split[i].startswith(expected_starts[i]): return None return { "context": split[0].replace("CONTEXT: ", ""), "question": split[1].replace("QUESTION: ", ""), "a": split[2].replace("A: ", ""), "b": split[3].replace("B: ", ""), "c": split[4].replace("C: ", ""), "d": split[5].replace("D: ", ""), "correct": split[6].replace("CORRECT: ", "") } process_outputs(outputs) # A nice dictionary hopefully ```
ckandemir/speecht5_finetuned_voxpopuli_fr	ckandemir	2023-08-03T21:20:21Z	10	0	transformers	[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "dataset:voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-speech	2023-07-31T21:24:05Z	--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_fr results: [] pipeline_tag: text-to-speech --- <!-- 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. --> # speecht5_finetuned_voxpopuli_fr This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4697 ## 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: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.765 \| 0.23 \| 50 \| 0.6575 \| \| 0.687 \| 0.47 \| 100 \| 0.6106 \| \| 0.6423 \| 0.7 \| 150 \| 0.5548 \| \| 0.5792 \| 0.94 \| 200 \| 0.5300 \| \| 0.5658 \| 1.17 \| 250 \| 0.5186 \| \| 0.5558 \| 1.41 \| 300 \| 0.5078 \| \| 0.5484 \| 1.64 \| 350 \| 0.5029 \| \| 0.5427 \| 1.87 \| 400 \| 0.4981 \| \| 0.5349 \| 2.11 \| 450 \| 0.4921 \| \| 0.524 \| 2.34 \| 500 \| 0.4906 \| \| 0.5243 \| 2.58 \| 550 \| 0.4857 \| \| 0.5238 \| 2.81 \| 600 \| 0.4835 \| \| 0.5104 \| 3.05 \| 650 \| 0.4796 \| \| 0.516 \| 3.28 \| 700 \| 0.4769 \| \| 0.5084 \| 3.51 \| 750 \| 0.4763 \| \| 0.5029 \| 3.75 \| 800 \| 0.4749 \| \| 0.5015 \| 3.98 \| 850 \| 0.4725 \| \| 0.5045 \| 4.22 \| 900 \| 0.4716 \| \| 0.503 \| 4.45 \| 950 \| 0.4706 \| \| 0.5013 \| 4.69 \| 1000 \| 0.4697 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.2 - Tokenizers 0.13.3
Mikeyli/repo_stableDiff	Mikeyli	2023-08-03T21:09:41Z	0	0	null	[ "region:us" ]	null	2023-08-03T20:59:49Z	v0.1 Test for ia generated image
chengyineng/bloom_prompt_tuning_1691096309.5967999	chengyineng	2023-08-03T21:08:07Z	2	0	peft	[ "peft", "region:us" ]	null	2023-08-03T20:58:32Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
Steve77/ppo-LunarLander-v2	Steve77	2023-08-03T21:00:50Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-03T21:00:32Z	--- 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: 254.08 +/- 17.56 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 ... ```
aaswin/dolly-finetuned1	aaswin	2023-08-03T20:48:50Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-03T20:48:48Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
Saurabh16100/llama-saved-model	Saurabh16100	2023-08-03T20:27:53Z	0	0	peft	[ "peft", "llama", "4-bit", "region:us" ]	null	2023-08-03T20:15:24Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0
abhiman23897/longformer_pico_model	abhiman23897	2023-08-03T20:26:46Z	8	0	transformers	[ "transformers", "pytorch", "longformer", "token-classification", "medical", "dataset:Stardrums/pico-breast-cancer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-08-03T14:14:27Z	--- license: apache-2.0 datasets: - Stardrums/pico-breast-cancer pipeline_tag: token-classification tags: - medical ---
yxxshin/mymodels	yxxshin	2023-08-03T20:25:46Z	5	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:stabilityai/stable-diffusion-2", "base_model:finetune:stabilityai/stable-diffusion-2", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-03T19:24:36Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2 instance_prompt: a photo of zwf dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - yxxshin/mymodels This is a dreambooth model derived from stabilityai/stable-diffusion-2. The weights were trained on a photo of zwf dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: True.
Mastering-Python-HF/nvidia_tts_en_hifitts_hifigan_ft_fastpitch	Mastering-Python-HF	2023-08-03T20:14:11Z	51	0	nemo	[ "nemo", "HiFiTTS", "PyTorch", "text-to-speech", "en", "arxiv:2006.06873", "arxiv:2108.10447", "region:us" ]	text-to-speech	2023-07-10T17:31:06Z	--- tags: - HiFiTTS - PyTorch language: - en pipeline_tag: text-to-speech --- # NVIDIA Hifigan Vocoder (en-US) HiFiGAN [1] is a generative adversarial network (GAN) model that generates audio from mel spectrograms. The generator uses transposed convolutions to upsample mel spectrograms to audio. ## Usage The model is available for use in the NeMo toolkit [3] and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. To train, fine-tune or play with the model you will need to install NVIDIA NeMo. We recommend you install it after you've installed the latest PyTorch version. ``` git clone https://github.com/NVIDIA/NeMo cd NeMo BRANCH = 'main' python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all] ``` ## instantiate the model Note: This model generates only spectrograms and a vocoder is needed to convert the spectrograms to waveforms. In this example HiFiGAN is used. ``` from huggingface_hub import hf_hub_download from nemo.collections.tts.models import FastPitchModel from nemo.collections.tts.models import HifiGanModel REPO_ID = "Mastering-Python-HF/nvidia_tts_en_fastpitch_multispeaker" FILENAME = "tts_en_fastpitch_multispeaker.nemo" path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME) spec_generator = FastPitchModel.restore_from(restore_path=path) REPO_ID = "Mastering-Python-HF/nvidia_tts_en_hifitts_hifigan_ft_fastpitch" FILENAME = "tts_en_hifitts_hifigan_ft_fastpitch.nemo" path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME) model = HifiGanModel.restore_from(restore_path=path) ``` ## Generate and save audio ``` import soundfile as sf parsed = spec_generator.parse("You can type your sentence here to get nemo to produce speech.") """ speaker id: 92 Cori Samuel 6097 Phil Benson 9017 John Van Stan 6670 Mike Pelton 6671 Tony Oliva 8051 Maria Kasper 9136 Helen Taylor 11614 Sylviamb 11697 Celine Major 12787 LikeManyWaters """ spectrogram = spec_generator.generate_spectrogram(tokens=parsed,speaker=92) audio = model.convert_spectrogram_to_audio(spec=spectrogram) sf.write("speech.wav", audio.to('cpu').detach().numpy()[0], 44100) ``` ## Colab example #### LINK : [nvidia_tts_en_fastpitch_multispeaker](https://colab.research.google.com/drive/1ZJFCMVVjl7VtfVGlkQ-G1cXKyaucBzJf?usp=sharing) ### Input This model accepts batches of text. ### Output This model generates mel spectrograms. ## Model Architecture FastPitch multispeaker is a fully-parallel text-to-speech model based on FastSpeech, conditioned on fundamental frequency contours. The model predicts pitch contours during inference. By altering these predictions, the generated speech can be more expressive, better match the semantic of the utterance, and in the end more engaging to the listener. FastPitch is based on a fully-parallel Transformer architecture, with a much higher real-time factor than Tacotron2 for the mel-spectrogram synthesis of a typical utterance. It uses an unsupervised speech-text aligner. ## Training The NeMo toolkit [3] was used for training the models for 1000 epochs. ## Datasets This model is trained on HiFiTTS sampled at 44100Hz, and has been tested on generating multispeaker English voices with an American and UK accent. ## Performance No performance information is available at this time. ## Limitations This checkpoint only works well with vocoders that were trained on 44100Hz data. Otherwise, the generated audio may be scratchy or choppy-sounding. ## References - [1] [FastPitch: Parallel Text-to-speech with Pitch Prediction](https://arxiv.org/abs/2006.06873) - [2] [One TTS Alignment To Rule Them All](https://arxiv.org/abs/2108.10447) - [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
nitrosocke/Ghibli-Diffusion	nitrosocke	2023-08-03T19:46:59Z	2,345	621	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "text-to-image", "image-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-11-18T15:50:51Z	--- language: - en license: creativeml-openrail-m thumbnail: "https://huggingface.co/nitrosocke/Ghibli-Diffusion/resolve/main/images/ghibli-diffusion-thumbnail.jpg" tags: - stable-diffusion - text-to-image - image-to-image - diffusers --- ### Ghibli Diffusion This is the fine-tuned Stable Diffusion model trained on images from modern anime feature films from Studio Ghibli. Use the tokens _ghibli style_ in your prompts for the effect. If you enjoy my work and want to test new models before release, please consider supporting me [![Become A Patreon](https://badgen.net/badge/become/a%20patron/F96854)](https://patreon.com/user?u=79196446) Characters rendered with the model: ![Characters Samples](https://huggingface.co/nitrosocke/Ghibli-Diffusion/resolve/main/images/ghibli-diffusion-samples-01s.jpg) Cars and Animals rendered with the model: ![Misc. Samples](https://huggingface.co/nitrosocke/Ghibli-Diffusion/resolve/main/images/ghibli-diffusion-samples-02s.jpg) Landscapes rendered with the model: ![Landscape 1](https://huggingface.co/nitrosocke/Ghibli-Diffusion/resolve/main/images/ghibli-diffusion-samples-03s.jpg) _ghibli style beautiful Caribbean beach tropical (sunset) - Negative prompt: soft blurry_ ![Landscape 2](https://huggingface.co/nitrosocke/Ghibli-Diffusion/resolve/main/images/ghibli-diffusion-samples-04s.jpg) _ghibli style ice field white mountains ((northern lights)) starry sky low horizon - Negative prompt: soft blurry_ #### Prompt and settings for the Strom Trooper: ghibli style (storm trooper) Negative prompt: (bad anatomy) _Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 3450349066, Size: 512x704_ #### Prompt and settings for the VW Beetle: ghibli style VW beetle Negative prompt: soft blurry _Steps: 30, Sampler: Euler a, CFG scale: 7, Seed: 1529856912, Size: 704x512_ This model was trained using the diffusers based dreambooth training by ShivamShrirao using prior-preservation loss and the _train-text-encoder_ flag in 15.000 steps. <!-- ### Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI run redshift-diffusion: [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/nitrosocke/Ghibli-Diffusion-Demo)--> ### 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX](). ```python from diffusers import StableDiffusionPipeline import torch model_id = "nitrosocke/Ghibli-Diffusion" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "ghibli style magical princess with golden hair" image = pipe(prompt).images[0] image.save("./magical_princess.png") ``` ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
google/ddpm-cat-256	google	2023-08-03T19:46:57Z	3,887	9	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "arxiv:2006.11239", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-07-19T10:42:07Z	--- license: apache-2.0 tags: - pytorch - diffusers - unconditional-image-generation --- # Denoising Diffusion Probabilistic Models (DDPM) Paper: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) Authors: Jonathan Ho, Ajay Jain, Pieter Abbeel Abstract: We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. ## Inference DDPM models can use discrete noise schedulers such as: - [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py) - [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py) - [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py) for inference. Note that while the ddpm scheduler yields the highest quality, it also takes the longest. For a good trade-off between quality and inference speed you might want to consider the ddim or pndm schedulers instead. See the following code: ```python # !pip install diffusers from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline model_id = "google/ddpm-cat-256" # load model and scheduler ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference # run pipeline in inference (sample random noise and denoise) image = ddpm().images[0] # save image image.save("ddpm_generated_image.png") ``` For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) ## Training If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) ## Samples 1. ![sample_1](https://huggingface.co/google/ddpm-cat-256/resolve/main/images/generated_image_0.png) 2. ![sample_2](https://huggingface.co/google/ddpm-cat-256/resolve/main/images/generated_image_1.png) 3. ![sample_3](https://huggingface.co/google/ddpm-cat-256/resolve/main/images/generated_image_2.png) 4. ![sample_4](https://huggingface.co/google/ddpm-cat-256/resolve/main/images/generated_image_3.png)
Sambosis/bert-base-cased-finetuned-swag	Sambosis	2023-08-03T19:45:41Z	59	0	transformers	[ "transformers", "tf", "tensorboard", "bert", "multiple-choice", "generated_from_keras_callback", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	multiple-choice	2023-08-03T16:00:24Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_keras_callback model-index: - name: Sambosis/bert-base-cased-finetuned-swag 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. --> # Sambosis/bert-base-cased-finetuned-swag This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4828 - Train Accuracy: 0.8181 - Validation Loss: 0.6618 - Validation Accuracy: 0.7539 - Epoch: 1 ## 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': 5e-05, 'decay_steps': 22980, '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 \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 0.8870 \| 0.6429 \| 0.6619 \| 0.7401 \| 0 \| \| 0.4828 \| 0.8181 \| 0.6618 \| 0.7539 \| 1 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.3 - Tokenizers 0.13.3
polejowska/detr-r50-cd45rb-2ah-6l	polejowska	2023-08-03T19:43:55Z	163	0	transformers	[ "transformers", "pytorch", "detr", "object-detection", "generated_from_trainer", "dataset:cd45rb", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2023-06-11T06:23:13Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - cd45rb model-index: - name: detr-r50-cd45rb-2ah-6l 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-r50-cd45rb-2ah-6l This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cd45rb dataset. It achieves the following results on the evaluation set: - Loss: 2.0706 ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 3.4851 \| 1.0 \| 4606 \| 2.4806 \| \| 2.995 \| 2.0 \| 9212 \| 2.2761 \| \| 2.7717 \| 3.0 \| 13818 \| 2.1601 \| \| 2.674 \| 4.0 \| 18424 \| 2.0849 \| \| 2.6185 \| 5.0 \| 23030 \| 2.0706 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.3
EmmaRo/q-FrozenLake-v1-4x4-noSlippery	EmmaRo	2023-08-03T19:30:17Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-03T19:30:14Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="EmmaRo/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"]) ```
Wilbas/DeepLearning_Unit2_Taxi-v3	Wilbas	2023-08-03T19:00:36Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-03T19:00:34Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: DeepLearning_Unit2_Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 2.73 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="Wilbas/DeepLearning_Unit2_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"]) ```
shtif/a2c-PandaReachDense-v2	shtif	2023-08-03T18:55:22Z	1	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-03T18:52:52Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -0.48 +/- 0.20 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v2 This is a trained model of a A2C agent playing PandaReachDense-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 ... ```
JasonWen/FurryTuringTest	JasonWen	2023-08-03T18:49:36Z	0	0	null	[ "zh", "license:cc-by-nc-sa-4.0", "region:us" ]	null	2023-07-24T08:58:31Z	--- license: cc-by-nc-sa-4.0 language: - zh --- [图灵测试结果发布页]( https://hf.co/JasonWen/FurryTuringTest) Turing_test_Result_Publishing_Page 如果您还没有填写问卷，可以点击这个[链接](https://wj.qq.com/s2/12840227/1a62/)或者扫码测试： ![](https://huggingface.co/JasonWen/FurryTuringTest/resolve/main/pic/Turing%20test%20-%20Raise%20the%20Board.png) ![](https://huggingface.co/JasonWen/FurryTuringTest/resolve/main/pic/Turing%20test%20QR%20code.png) 感谢您完成图灵测试的问卷，我们会在问卷搜集一万份后公布结果，若在八月底仍不足一万份届时也会公布结果。问卷的结果包括所有图片的原图、来源，每道题的结果以及综合数据分析。目前结果仍未发布，您可以加入我们的社群：discord[Furry-AICG（8月26日之前有效）](https://discord.gg/uYuA7kPSf)、telegram[Furry AI 炼金群](https://t.me/furry_ai)
bilbo991/clip-homer	bilbo991	2023-08-03T18:43:52Z	11	0	transformers	[ "transformers", "pytorch", "tensorboard", "vision-text-dual-encoder", "feature-extraction", "generated_from_trainer", "endpoints_compatible", "region:us" ]	feature-extraction	2023-08-02T21:02:05Z	--- base_model: clip-homer tags: - generated_from_trainer model-index: - name: clip-homer 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. --> # clip-homer This model is a fine-tuned version of [clip-homer](https://huggingface.co/clip-homer) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4658 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.1 - Tokenizers 0.13.3
chengyineng/bloom_prompt_tuning_1691087323.3441193	chengyineng	2023-08-03T18:28:47Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-03T18:28:46Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
Saurabh16100/finetuned-llama2-7b	Saurabh16100	2023-08-03T18:28:40Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-03T18:25:38Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0
Murzeltje1976/models	Murzeltje1976	2023-08-03T18:27:18Z	0	0	null	[ "arxiv:1910.09700", "region:us" ]	null	2023-08-02T17:31:45Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID Just a personal archive for used models in AUTOMATIC1111 ## Model Details ### Model Description Just a personal archive for used models in AUTOMATIC1111. That I use. - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
flyingbox/test_upload	flyingbox	2023-08-03T18:21:22Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2023-08-03T18:21:22Z	--- license: bigscience-openrail-m ---
SaudxInu/DQN-LunarLander-v2	SaudxInu	2023-08-03T18:21:18Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-03T17:04:27Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 58.89 +/- 158.77 name: mean_reward verified: false --- # DQN Agent playing LunarLander-v2 This is a trained model of a DQN 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 ... ```
dariowsz/poca-SoccerTwos	dariowsz	2023-08-03T18:19:00Z	21	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-08-03T18:18:57Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos 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: dariowsz/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
KnutJaegersberg/sentence-data-scideberta-cs	KnutJaegersberg	2023-08-03T18:17:22Z	1	0	sentence-transformers	[ "sentence-transformers", "pytorch", "deberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-08-03T18:10:01Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch def cls_pooling(model_output, attention_mask): return model_output[0][:,0] # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, cls pooling. sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 23043 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 32, 'do_lower_case': False}) with Transformer model: DebertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
J-Douglas/pokemon-lora	J-Douglas	2023-08-03T18:12:35Z	7	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-07-05T19:48:09Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - J-Douglas/pokemon-lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
digiplay/AbsoluteReality_v1.8.1	digiplay	2023-08-03T18:05:25Z	284,099	29	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-03T16:58:32Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/81458?modelVersionId=132760 Original Author's DEMO images : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/0375afda-d837-4cf9-a0f1-958810ab300d/width=1120/26072207-5775662-(masterpiece),%20(extremely%20intricate_1.3),,%20(realistic),%20portrait%20of%20a%20girl,%20the%20most%20beautiful%20in%20the%20world,%20(medieval%20armor),%20m.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/6c6fa49e-05d1-4dcb-9527-b1ed801db614/width=920/26072134-1709831004-a%20photo%20of%20a%20cute%20little%20puppy%20surrounded%20by%20beautiful%20flowers%20in%20a%20meadow,%20extremely%20detailed%20fur,(close%20up_1.1).jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/7a0bc81d-0702-4c85-aa17-dd6748cb6fbd/width=920/26072120-2760683419-close%20up%20Portrait%20photo%20of%20muscular%20bearded%20guy%20in%20a%20worn%20mech%20suit,%20((light%20bokeh)),%20intricate,%20(steel%20metal%20[rust]),%20elegant,.jpeg)
shtif/a2c-AntBulletEnv-v0	shtif	2023-08-03T18:03:32Z	1	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-03T18:02:34Z	--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1692.79 +/- 59.70 name: mean_reward verified: false --- # A2C Agent playing AntBulletEnv-v0 This is a trained model of a A2C agent playing AntBulletEnv-v0 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 ... ```
thiagodepaulo/distilbert-base-uncased-finetuned-emotion	thiagodepaulo	2023-08-03T17:47:07Z	110	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-03T00:58:43Z	--- license: apache-2.0 base_model: distilbert-base-uncased 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: 0.9205 - name: F1 type: f1 value: 0.9207431728392997 --- <!-- 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: 0.2232 - Accuracy: 0.9205 - F1: 0.9207 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 125 \| 0.5150 \| 0.862 \| 0.8502 \| \| 0.7222 \| 2.0 \| 250 \| 0.2605 \| 0.9155 \| 0.9151 \| \| 0.7222 \| 3.0 \| 375 \| 0.2232 \| 0.9205 \| 0.9207 \| ### Framework versions - Transformers 4.31.0 - Pytorch 1.12.1+cu102 - Datasets 2.14.2 - Tokenizers 0.13.3
Dgray518/llama2-qlora-finetunined-french	Dgray518	2023-08-03T17:42:12Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-03T17:42:04Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
rice-rice/detr-resnet-50_finetuned_dataset	rice-rice	2023-08-03T17:41:46Z	189	0	transformers	[ "transformers", "pytorch", "tensorboard", "detr", "object-detection", "generated_from_trainer", "dataset:pothole-segmentation", "base_model:facebook/detr-resnet-50", "base_model:finetune:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2023-08-03T17:13:57Z	--- license: apache-2.0 base_model: facebook/detr-resnet-50 tags: - generated_from_trainer datasets: - pothole-segmentation model-index: - name: detr-resnet-50_finetuned_dataset 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_dataset This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the pothole-segmentation 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: 10 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3
kaczmarj/lymphnodes-tiatoolbox-resnet50.patchcamelyon	kaczmarj	2023-08-03T17:36:38Z	60	1	transformers	[ "transformers", "pytorch", "safetensors", "histology", "digital pathology", "patch camelyon", "tiatoolbox", "lymph nodes", "metastasis", "camelyon", "image-classification", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	image-classification	2023-06-21T16:24:02Z	--- license: cc-by-4.0 pipeline_tag: image-classification tags: - histology - digital pathology - patch camelyon - tiatoolbox - lymph nodes - metastasis - camelyon --- # ResNet50 trained on PatchCamelyon (via TIA Toolbox) This is a re-hosted version of the model available in the TIA Toolbox model zoo (licensed CC-BY-4.0). # Reusing the model Coming soon... # Dataset The PatchCamelyon dataset can be found on Zenodo https://zenodo.org/record/2546921 and on GitHub https://github.com/basveeling/pcam. # References ```bibtex @inproceedings{he2016deep, title={Deep residual learning for image recognition}, author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, pages={770--778}, year={2016} } @ARTICLE{Veeling2018-qh, title = "Rotation Equivariant {CNNs} for Digital Pathology", author = "Veeling, Bastiaan S and Linmans, Jasper and Winkens, Jim and Cohen, Taco and Welling, Max", month = jun, year = 2018, archivePrefix = "arXiv", primaryClass = "cs.CV", eprint = "1806.03962" } @article{pocock2022tiatoolbox, title={TIAToolbox as an end-to-end library for advanced tissue image analytics}, author={Pocock, Johnathan and Graham, Simon and Vu, Quoc Dang and Jahanifar, Mostafa and Deshpande, Srijay and Hadjigeorghiou, Giorgos and Shephard, Adam and Bashir, Raja Muhammad Saad and Bilal, Mohsin and Lu, Wenqi and others}, journal={Communications medicine}, volume={2}, number={1}, pages={120}, year={2022}, publisher={Nature Publishing Group UK London} } ```
nkpz/llama2-22b-blocktriangular-alpaca	nkpz	2023-08-03T17:34:17Z	13	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-08-03T14:24:51Z	--- license: other --- There is no official 22b model, this is just a weird experiment, and any potential benefits of doing this have not been validated https://huggingface.co/chargoddard/llama2-22b-blocktriangular trained one one epoch of 52k rows of Stanford Alpaca. About 11 hours on a 3090. I had trouble with training using the other 22b method with `BLOCK_DIAGONAL=True` as done in https://huggingface.co/chargoddard/llama2-22b, but with this method, this is the first time I've been able to target all modules without breaking the output. `target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "up_proj", "gate_proj", "down_proj"]` Trained at 5e-5 with r=32. For more info see https://wandb.ai/nkpz/huggingface/runs/3oy5nbtv/workspace?workspace=user-nkpz It's been responding coherently enough that I would need to run some objective benchmarks to determine if this is better/worse than stock llama 13b
BmanClark/Taxi-v3	BmanClark	2023-08-03T17:31:26Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-03T17:31:21Z	--- 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="BmanClark/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"]) ```
Bastian1111/ppo-Huggy	Bastian1111	2023-08-03T17:30:00Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-08-03T17:29:50Z	--- 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: Bastian1111/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
BmanClark/q-FrozenLake-v1-4x4-noSlippery	BmanClark	2023-08-03T17:26:14Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-03T17:26:09Z	--- 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="BmanClark/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"]) ```
Kybalico/AnmitsuMimimi	Kybalico	2023-08-03T17:24:49Z	0	2	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-03T17:16:30Z	--- license: creativeml-openrail-m ---
or4cl3ai/A-os43-v1	or4cl3ai	2023-08-03T17:23:18Z	0	0	null	[ "en", "license:openrail", "region:us" ]	null	2023-08-03T02:08:35Z	--- license: openrail language: - en --- model: name: Custom LLM for A-os43-v1 version: 1.0 id: CLLM-AOS43-2022 type: Language Model architecture: Transformer-based Language Model framework: Hugging Face Transformers programming_language: Rust operating_system: A-os43-v1 --- datasets: - Operating System Documentation Dataset - A-os43-v1 Source Code Dataset - Rust Programming Language Documentation - System Programming Concepts Dataset - Operating System Design Principles Dataset - Software Engineering Best Practices Dataset - User Input and Command Parsing Dataset - Error Handling Techniques Dataset - System Call Interface Dataset - System Configuration File Syntax Dataset - User Interface Text and Localization Dataset - Common System Error Messages Dataset - User Documentation Dataset - API Reference Documentation Dataset - User Feedback and Support Dataset - System Log File Analysis Dataset - System Performance Monitoring Dataset - System Resource Management Dataset - File System Operations Dataset - Networking and Socket Programming Dataset - Security Principles and Best Practices Dataset - Memory Management Techniques Dataset - Concurrency and Synchronization Dataset - System Service Initialization Dataset - Command-Line Interface Design Dataset --- metrics: - Language Coherence - Code Quality - Natural Language Understanding - Response Time - Compatibility - Adaptability - Documentation Accuracy - ---
Sookeyy/ppo-PyramidsRND	Sookeyy	2023-08-03T17:22:54Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-08-03T17:20:54Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Sookeyy/ppo-PyramidsRND 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
BabaYaga048/Taxi-v3-2	BabaYaga048	2023-08-03T17:19:17Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-03T17:19:14Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3-2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 1.17 +/- 25.45 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="BabaYaga048/Taxi-v3-2", 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"]) ```
MF-FOOM/wikivec2text	MF-FOOM	2023-08-03T17:13:14Z	0	8	null	[ "en", "license:mit", "region:us" ]	null	2023-08-03T17:00:28Z	--- license: mit language: - en --- # wikivec2text Simple embedding -> text model trained on a tiny subset of Wikipedia sentences ([~7m](https://www.kaggle.com/datasets/mikeortman/wikipedia-sentences)) embedded via text-embedding-ada-002. Used to demonstrate the arithmetic properties of sentence embeddings. ## Acknowledgements Built on [nanoGPT](https://github.com/karpathy/nanoGPT).
KingKazma/xsum_t5-small_prompt_tuning_500_10_3000_8_e-1_s6789_v3_l5_v50_manual	KingKazma	2023-08-03T17:09:14Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-03T17:09:11Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0

dreamboat26/q-FrozenLake-v1-4x4-noSlippery

dreamboat26

2023-08-04T07:40:02Z

0

null

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

reinforcement-learning

2023-08-04T07:39:57Z

--- 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="dreamboat26/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"]) ```

TariqJamil/falcon-7b-instruct-peft-qlora-my_finetuned_model-0721EV

TariqJamil

2023-08-04T07:32:00Z

3

0

peft

[ "peft", "region:us" ]

null

2023-08-04T07:31:52Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0

WildXbird/TranslateGLM

WildXbird

2023-08-04T07:28:24Z

0

null

[ "region:us" ]

null

2023-08-04T07:22:16Z

# TranslateGLM TranslateGLM 是一个基于 ChatGLM2-6B 模型微调的中英文翻译器，可以实现高质量的双向翻译。使用265万个样本进行了 [P-Tuning v2](https://github.com/THUDM/P-tuning-v2) 的微调，微调的样本的例子可以参考 [validation_file.txt](https://github.com/WildXBird/TranslateGLM/blob/main/validation_file.txt) 中的内容。微调过后的模型，在可以很好的充当翻译器的同时仍然可以正常的进行聊天对话。 ChatGLM2-6B 是一个开源的、支持中英问答的对话语言模型，基于 General Language Model (GLM) 架构，具有约 60 亿参数。 ![图片](https://github.com/WildXBird/TranslateGLM/blob/main/0.png?raw=true) ![图片](https://github.com/WildXBird/TranslateGLM/blob/main/1.png?raw=true) ## 功能 - 支持中文和英文之间的双向翻译 - 支持OpenAI格式的API和网页端的交互式翻译 - 支持调整输出结果随机度 - 支持翻译结果和原文对照查看 - 正常对话（仅限使用OpenAI格式的API） ## 安装要运行本项目，你需要安装以下软件依赖： ``` yarn cd ./glm pip install -r requirements.txt pip install rouge_chinese nltk jieba datasets ``` ## 使用 ### 启动翻译器你可以通过如下命令启动翻译器的OpenAI格式的API： ``` python ./glm/openai_api.py ``` 你可以通过如下命令启动翻译器的前端UI： *启动UI前必须先启动上面的API* ``` yarn start ``` 在浏览器中访问 http://localhost:3000/ ，然后输入你想要翻译的文本即可自动翻译。 ### 正常的进行聊天对话你可以通过如下命令启动传统的web demo对话，但只能进行普通对话： ``` python ./glm/web_demo.py ``` 你可以通过如上面的“OpenAI格式的API”进行正常对话，使用方式和普通的调用方法无异： ``` python ./glm/openai_api.py ``` ## 翻译UI 翻译UI暂时不支持指定输入语言，输入的语言的自动判断是中文还是英文，然后自动翻译成另一种语言。翻译UI可以指定翻译结果的随机性，分为**保守**、**平衡**、**灵活**和**发散**。分别代表着temperature从低(保守)到高(创意)，对于较复杂的语法，更高的temperature更能准确的翻译，但也可能导致他误解某些含义。把鼠标悬停在翻译结果上，可以查看翻译的原文以及原文在输入中的位置。 ## 更新 **2023-8-2 (alpha-2)** 1. 加入了短篇新闻的对照翻译样本进行了重新训练。 2. 经过简单测试发现在丢失信息方面有了显著的改善，即原文中的某些描述或者修饰在翻译的结果中会被无视，在句子稍长的情况下尤为明显。 3. 在“发散”下，可能会把翻译当成对话进行回答的问题（例如某个句子以：结尾），经过简单测试发现有了显著的改善。 4. 考虑到 “3”的问题改善了，在随机性选项改成了4个。 ## 已知问题 1. 中英文数字表达转换存在错误(非常常见) 2. 会出现英文中文互相穿插(罕见) 3. 会出现完全无关的翻译结果(罕见) 4. 原文中的某段描述或者某些修饰在翻译的结果中会被无视(常见)

emo-nlp/7-emo

emo-nlp

2023-08-04T07:27:57Z

145

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-08-04T07:13:19Z

--- license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy - recall - f1 model-index: - name: train results: [] ---  # train This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6648 - Accuracy: 0.7617 - B Acc: 0.6394 - Prec: 0.7595 - Recall: 0.7617 - F1: 0.7602 - Prec Joy: 0.7315 - Recall Joy: 0.7793 - F1 Joy: 0.7547 - Prec Anger: 0.6467 - Recall Anger: 0.6507 - F1 Anger: 0.6487 - Prec Disgust: 0.4710 - Recall Disgust: 0.45 - F1 Disgust: 0.4603 - Prec Fear: 0.6963 - Recall Fear: 0.6409 - F1 Fear: 0.6675 - Prec Neutral: 0.8457 - Recall Neutral: 0.8490 - F1 Neutral: 0.8474 - Prec Sadness: 0.7094 - Recall Sadness: 0.6738 - F1 Sadness: 0.6911 - Prec Surprise: 0.5228 - Recall Surprise: 0.4323 - F1 Surprise: 0.4732 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | B Acc | Prec | Recall | F1 | Prec Joy | Recall Joy | F1 Joy | Prec Anger | Recall Anger | F1 Anger | Prec Disgust | Recall Disgust | F1 Disgust | Prec Fear | Recall Fear | F1 Fear | Prec Neutral | Recall Neutral | F1 Neutral | Prec Sadness | Recall Sadness | F1 Sadness | Prec Surprise | Recall Surprise | F1 Surprise | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|:--------:|:----------:|:------:|:----------:|:------------:|:--------:|:------------:|:--------------:|:----------:|:---------:|:-----------:|:-------:|:------------:|:--------------:|:----------:|:------------:|:--------------:|:----------:|:-------------:|:---------------:|:-----------:| | 0.9538 | 0.15 | 232 | 0.8701 | 0.6961 | 0.4790 | 0.6837 | 0.6961 | 0.6837 | 0.7401 | 0.6381 | 0.6853 | 0.4622 | 0.5391 | 0.4977 | 0.25 | 0.0018 | 0.0035 | 0.5527 | 0.4292 | 0.4832 | 0.7965 | 0.8618 | 0.8279 | 0.5281 | 0.6431 | 0.5800 | 0.3562 | 0.2398 | 0.2866 | | 0.7952 | 0.3 | 464 | 0.8010 | 0.7168 | 0.5242 | 0.7098 | 0.7168 | 0.7025 | 0.8084 | 0.5948 | 0.6853 | 0.5732 | 0.4710 | 0.5171 | 0.4713 | 0.2643 | 0.3387 | 0.6156 | 0.5263 | 0.5675 | 0.7405 | 0.9250 | 0.8226 | 0.6858 | 0.5676 | 0.6211 | 0.4448 | 0.3204 | 0.3725 | | 0.7528 | 0.45 | 696 | 0.7560 | 0.7261 | 0.5878 | 0.7309 | 0.7261 | 0.7256 | 0.6969 | 0.7646 | 0.7292 | 0.5550 | 0.5534 | 0.5542 | 0.3409 | 0.4821 | 0.3994 | 0.7225 | 0.4842 | 0.5798 | 0.8476 | 0.8159 | 0.8314 | 0.6118 | 0.7027 | 0.6541 | 0.4957 | 0.3118 | 0.3828 | | 0.7334 | 0.6 | 928 | 0.7310 | 0.7370 | 0.5868 | 0.7345 | 0.7370 | 0.7283 | 0.7170 | 0.7458 | 0.7311 | 0.7129 | 0.4116 | 0.5219 | 0.3727 | 0.5696 | 0.4506 | 0.6671 | 0.5626 | 0.6104 | 0.7898 | 0.8859 | 0.8351 | 0.7318 | 0.5844 | 0.6499 | 0.5252 | 0.3473 | 0.4181 | | 0.7216 | 0.75 | 1160 | 0.7043 | 0.7448 | 0.6009 | 0.7403 | 0.7448 | 0.7389 | 0.7767 | 0.6826 | 0.7266 | 0.6159 | 0.5386 | 0.5746 | 0.5302 | 0.4393 | 0.4805 | 0.8023 | 0.5602 | 0.6598 | 0.7854 | 0.8926 | 0.8356 | 0.7005 | 0.632 | 0.6645 | 0.4815 | 0.4613 | 0.4712 | | 0.7259 | 0.9 | 1392 | 0.6962 | 0.7475 | 0.6082 | 0.7433 | 0.7475 | 0.7412 | 0.7355 | 0.7586 | 0.7469 | 0.6758 | 0.4504 | 0.5405 | 0.3908 | 0.5589 | 0.4600 | 0.6939 | 0.6070 | 0.6475 | 0.8122 | 0.8744 | 0.8421 | 0.6830 | 0.6676 | 0.6752 | 0.5494 | 0.3409 | 0.4207 | | 0.6362 | 1.05 | 1624 | 0.6771 | 0.7526 | 0.6055 | 0.7472 | 0.7526 | 0.7484 | 0.7392 | 0.7483 | 0.7437 | 0.5873 | 0.6191 | 0.6028 | 0.5302 | 0.3768 | 0.4405 | 0.7388 | 0.5789 | 0.6492 | 0.8213 | 0.8670 | 0.8435 | 0.7090 | 0.6507 | 0.6786 | 0.5301 | 0.3978 | 0.4545 | | 0.621 | 1.2 | 1856 | 0.6779 | 0.7528 | 0.6120 | 0.7494 | 0.7528 | 0.7487 | 0.7107 | 0.7828 | 0.7450 | 0.6508 | 0.5913 | 0.6196 | 0.4980 | 0.4518 | 0.4738 | 0.7963 | 0.5532 | 0.6529 | 0.8165 | 0.8590 | 0.8372 | 0.7499 | 0.6236 | 0.6809 | 0.5078 | 0.4226 | 0.4613 | | 0.6241 | 1.35 | 2088 | 0.6849 | 0.7513 | 0.6367 | 0.7526 | 0.7513 | 0.7514 | 0.7429 | 0.7592 | 0.7510 | 0.5795 | 0.6531 | 0.6141 | 0.4372 | 0.4661 | 0.4512 | 0.6462 | 0.6515 | 0.6488 | 0.8492 | 0.8372 | 0.8432 | 0.6887 | 0.6609 | 0.6745 | 0.5271 | 0.4290 | 0.4730 | | 0.6188 | 1.5 | 2320 | 0.6713 | 0.7579 | 0.6159 | 0.7539 | 0.7579 | 0.7534 | 0.7071 | 0.7971 | 0.7494 | 0.6343 | 0.6267 | 0.6305 | 0.5877 | 0.3768 | 0.4592 | 0.7247 | 0.6281 | 0.6729 | 0.8361 | 0.8496 | 0.8428 | 0.6943 | 0.6693 | 0.6816 | 0.5919 | 0.3634 | 0.4504 | | 0.6182 | 1.65 | 2552 | 0.6608 | 0.7601 | 0.6199 | 0.7567 | 0.7601 | 0.7566 | 0.7143 | 0.7891 | 0.7498 | 0.6163 | 0.6358 | 0.6259 | 0.5607 | 0.3875 | 0.4583 | 0.7591 | 0.6082 | 0.6753 | 0.8375 | 0.8578 | 0.8475 | 0.7324 | 0.6436 | 0.6851 | 0.5381 | 0.4172 | 0.4700 | | 0.6392 | 1.8 | 2784 | 0.6542 | 0.7624 | 0.6261 | 0.7593 | 0.7624 | 0.7596 | 0.7513 | 0.7584 | 0.7548 | 0.5970 | 0.6708 | 0.6318 | 0.5711 | 0.3875 | 0.4617 | 0.7482 | 0.6152 | 0.6752 | 0.8379 | 0.8635 | 0.8505 | 0.7076 | 0.668 | 0.6872 | 0.5132 | 0.4194 | 0.4615 | | 0.6158 | 1.95 | 3016 | 0.6456 | 0.7649 | 0.6279 | 0.7599 | 0.7649 | 0.7614 | 0.7490 | 0.7548 | 0.7519 | 0.6402 | 0.6378 | 0.6390 | 0.5314 | 0.4232 | 0.4712 | 0.7569 | 0.6117 | 0.6766 | 0.8310 | 0.8753 | 0.8526 | 0.7199 | 0.6627 | 0.6901 | 0.5063 | 0.4301 | 0.4651 | | 0.554 | 2.1 | 3248 | 0.6742 | 0.7584 | 0.6346 | 0.7555 | 0.7584 | 0.7564 | 0.7293 | 0.7732 | 0.7506 | 0.6433 | 0.6430 | 0.6432 | 0.5031 | 0.4393 | 0.4690 | 0.7292 | 0.6363 | 0.6796 | 0.8347 | 0.8496 | 0.8421 | 0.7163 | 0.6587 | 0.6863 | 0.5049 | 0.4419 | 0.4713 | | 0.5537 | 2.25 | 3480 | 0.6708 | 0.7633 | 0.6283 | 0.7604 | 0.7633 | 0.7605 | 0.7263 | 0.7801 | 0.7523 | 0.6304 | 0.6612 | 0.6455 | 0.5806 | 0.3732 | 0.4543 | 0.7486 | 0.6094 | 0.6718 | 0.8442 | 0.8528 | 0.8485 | 0.6982 | 0.692 | 0.6951 | 0.5356 | 0.4290 | 0.4764 | | 0.5375 | 2.4 | 3712 | 0.6712 | 0.7606 | 0.6402 | 0.7592 | 0.7606 | 0.7595 | 0.7373 | 0.7709 | 0.7537 | 0.6245 | 0.6608 | 0.6421 | 0.4827 | 0.4482 | 0.4648 | 0.7319 | 0.6257 | 0.6747 | 0.8454 | 0.8474 | 0.8464 | 0.7006 | 0.6769 | 0.6885 | 0.5204 | 0.4516 | 0.4836 | | 0.5175 | 2.55 | 3944 | 0.6625 | 0.7625 | 0.6369 | 0.7600 | 0.7625 | 0.7604 | 0.7422 | 0.7642 | 0.7530 | 0.6335 | 0.6526 | 0.6429 | 0.4481 | 0.4929 | 0.4694 | 0.7482 | 0.6187 | 0.6773 | 0.8374 | 0.8604 | 0.8488 | 0.7252 | 0.6684 | 0.6957 | 0.5321 | 0.4011 | 0.4574 | | 0.5182 | 2.7 | 4176 | 0.6621 | 0.7631 | 0.6404 | 0.7602 | 0.7631 | 0.7612 | 0.7343 | 0.7766 | 0.7549 | 0.6491 | 0.6392 | 0.6441 | 0.4739 | 0.4536 | 0.4635 | 0.6784 | 0.6538 | 0.6659 | 0.8444 | 0.8529 | 0.8486 | 0.7109 | 0.684 | 0.6972 | 0.5458 | 0.4226 | 0.4764 | | 0.5148 | 2.85 | 4408 | 0.6638 | 0.7637 | 0.6383 | 0.7598 | 0.7637 | 0.7612 | 0.7394 | 0.7741 | 0.7563 | 0.6741 | 0.6205 | 0.6462 | 0.5 | 0.4375 | 0.4667 | 0.6813 | 0.6550 | 0.6679 | 0.8400 | 0.8572 | 0.8485 | 0.6922 | 0.6916 | 0.6919 | 0.5296 | 0.4323 | 0.4760 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.2 - Tokenizers 0.13.3

lesliepzimmermann/rl_course_vizdoom_health_gathering_supreme

lesliepzimmermann

2023-08-04T07:27:22Z

0

sample-factory

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

reinforcement-learning

2023-08-04T06:26:57Z

--- 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: 10.76 +/- 4.75 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 lesliepzimmermann/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.

ngeg2015/opt-2.7b-lora

ngeg2015

2023-08-04T07:16:25Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-04T07:16:14Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0

ketong3906/my_awesome_model_classification

ketong3906

2023-08-04T07:10:39Z

111

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:imdb", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-26T02:33:54Z

--- license: mit base_model: roberta-base tags: - generated_from_trainer datasets: - imdb metrics: - accuracy model-index: - name: my_awesome_model_classification results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train[:300] args: plain_text metrics: - name: Accuracy type: accuracy value: 1.0 ---  # my_awesome_model_classification This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.0031 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 15 | 0.0163 | 1.0 | | No log | 2.0 | 30 | 0.0031 | 1.0 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

scarlett623/wav2vec2-large-xlsr53-zh-cn-subset20-colab

scarlett623

2023-08-04T06:34:21Z

5

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "base_model:facebook/wav2vec2-large-xlsr-53", "base_model:finetune:facebook/wav2vec2-large-xlsr-53", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-03T16:11:34Z

--- license: apache-2.0 base_model: facebook/wav2vec2-large-xlsr-53 tags: - generated_from_trainer datasets: - common_voice metrics: - wer model-index: - name: wav2vec2-large-xlsr53-zh-cn-subset20-colab results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice type: common_voice config: zh-CN split: test[:20%] args: zh-CN metrics: - name: Wer type: wer value: 0.9503424657534246 ---  # wav2vec2-large-xlsr53-zh-cn-subset20-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 2.0566 - Wer: 0.9503 - Cer: 0.3333 ## 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: 13 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 26 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| | No log | 1.9 | 400 | 6.7551 | 1.0 | 1.0 | | 34.7845 | 3.81 | 800 | 6.4563 | 1.0 | 1.0 | | 6.4358 | 5.71 | 1200 | 4.2319 | 1.0074 | 0.7454 | | 4.2052 | 7.62 | 1600 | 2.6538 | 1.0200 | 0.5562 | | 2.3906 | 9.52 | 2000 | 2.3565 | 1.0063 | 0.5147 | | 2.3906 | 11.43 | 2400 | 2.1287 | 0.9863 | 0.4822 | | 1.93 | 13.33 | 2800 | 1.9585 | 0.9812 | 0.4528 | | 1.6322 | 15.24 | 3200 | 1.8771 | 0.9937 | 0.4381 | | 1.3629 | 17.14 | 3600 | 1.8405 | 0.9926 | 0.4242 | | 1.166 | 19.05 | 4000 | 1.7674 | 0.9989 | 0.4140 | | 1.166 | 20.95 | 4400 | 1.7879 | 0.9795 | 0.4047 | | 0.9915 | 22.86 | 4800 | 1.7597 | 1.0126 | 0.4080 | | 0.8517 | 24.76 | 5200 | 1.7726 | 0.9829 | 0.3966 | | 0.7143 | 26.67 | 5600 | 1.7623 | 0.9732 | 0.3863 | | 0.6267 | 28.57 | 6000 | 1.8164 | 0.9720 | 0.3863 | | 0.6267 | 30.48 | 6400 | 1.8136 | 0.9680 | 0.3801 | | 0.5389 | 32.38 | 6800 | 1.8696 | 0.9652 | 0.3812 | | 0.4764 | 34.29 | 7200 | 1.8625 | 0.9663 | 0.3744 | | 0.4095 | 36.19 | 7600 | 1.8868 | 0.9618 | 0.3683 | | 0.3594 | 38.1 | 8000 | 1.8834 | 0.9623 | 0.3699 | | 0.3594 | 40.0 | 8400 | 1.9155 | 0.9589 | 0.3670 | | 0.3064 | 41.9 | 8800 | 1.9268 | 0.9652 | 0.3688 | | 0.2825 | 43.81 | 9200 | 1.9527 | 0.9697 | 0.3674 | | 0.2524 | 45.71 | 9600 | 1.9726 | 0.9686 | 0.3617 | | 0.2272 | 47.62 | 10000 | 1.9594 | 0.9629 | 0.3619 | | 0.2272 | 49.52 | 10400 | 1.9799 | 0.9635 | 0.3607 | | 0.2042 | 51.43 | 10800 | 2.0175 | 0.9669 | 0.3582 | | 0.1975 | 53.33 | 11200 | 2.0246 | 0.9589 | 0.3571 | | 0.1827 | 55.24 | 11600 | 2.0535 | 0.9703 | 0.3600 | | 0.1677 | 57.14 | 12000 | 2.0458 | 0.9583 | 0.3555 | | 0.1677 | 59.05 | 12400 | 2.0893 | 0.9572 | 0.3583 | | 0.1626 | 60.95 | 12800 | 2.0729 | 0.9600 | 0.3557 | | 0.155 | 62.86 | 13200 | 2.0706 | 0.9572 | 0.3538 | | 0.1456 | 64.76 | 13600 | 2.0761 | 0.9532 | 0.3553 | | 0.1337 | 66.67 | 14000 | 2.0349 | 0.9589 | 0.3474 | | 0.1337 | 68.57 | 14400 | 2.0844 | 0.9549 | 0.3484 | | 0.1274 | 70.48 | 14800 | 2.0874 | 0.9578 | 0.3505 | | 0.1198 | 72.38 | 15200 | 2.0813 | 0.9526 | 0.3473 | | 0.1164 | 74.29 | 15600 | 2.0866 | 0.9498 | 0.3473 | | 0.1105 | 76.19 | 16000 | 2.0688 | 0.9486 | 0.3421 | | 0.1105 | 78.1 | 16400 | 2.0854 | 0.9498 | 0.3431 | | 0.1053 | 80.0 | 16800 | 2.0749 | 0.9503 | 0.3414 | | 0.1 | 81.9 | 17200 | 2.0622 | 0.9543 | 0.3407 | | 0.0977 | 83.81 | 17600 | 2.0678 | 0.9532 | 0.3396 | | 0.0906 | 85.71 | 18000 | 2.0650 | 0.9515 | 0.3383 | | 0.0906 | 87.62 | 18400 | 2.0631 | 0.9492 | 0.3378 | | 0.0867 | 89.52 | 18800 | 2.0633 | 0.9521 | 0.3365 | | 0.0836 | 91.43 | 19200 | 2.0606 | 0.9532 | 0.3346 | | 0.0819 | 93.33 | 19600 | 2.0671 | 0.9538 | 0.3355 | | 0.0768 | 95.24 | 20000 | 2.0661 | 0.9509 | 0.3338 | | 0.0768 | 97.14 | 20400 | 2.0564 | 0.9498 | 0.3335 | | 0.0752 | 99.05 | 20800 | 2.0566 | 0.9503 | 0.3333 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3

dkimds/ppo-Huggy

dkimds

2023-08-04T06:34:08Z

0

ml-agents

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

reinforcement-learning

2023-08-04T06:33:57Z

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

MattStammers/CartPole-v1-take2

MattStammers

2023-08-04T06:31:08Z

0

null

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

reinforcement-learning

2023-08-04T06:27:59Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: CartPole-v1-take2 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

terru3/dqn-SpaceInvadersNoFrameskip-v4-test

terru3

2023-08-04T05:56:20Z

0

stable-baselines3

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

reinforcement-learning

2023-08-04T05:55:44Z

--- 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: 671.50 +/- 174.67 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 terru3 -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 terru3 -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 terru3 ``` ## 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'} ```

Okrad/llama2-qlora-finetunined-french

Okrad

2023-08-04T05:45:08Z

0

peft

[ "peft", "region:us" ]

null

2023-08-04T05:45:03Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0

Shu-Kun/Betelgeuse_Romanee_Conti

Shu-Kun

2023-08-04T05:40:45Z

0

null

[ "ja", "license:openrail", "region:us" ]

null

2023-07-22T03:24:44Z

--- license: openrail language: - ja ---

Braulio0316/Bea10

Braulio0316

2023-08-04T05:31:43Z

0

asteroid

[ "asteroid", "legal", "visual-question-answering", "dataset:Open-Orca/OpenOrca", "license:creativeml-openrail-m", "region:us" ]

visual-question-answering

2023-08-04T05:13:15Z

--- license: creativeml-openrail-m datasets: - Open-Orca/OpenOrca metrics: - bertscore library_name: asteroid pipeline_tag: visual-question-answering tags: - legal ---

jordyvl/dit-base-tiny_rvl_cdip-NK1000_hint

jordyvl

2023-08-04T05:31:01Z

164

0

transformers

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

image-classification

2023-08-03T09:07:50Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-base-tiny_rvl_cdip-NK1000_hint results: [] ---  # dit-base-tiny_rvl_cdip-NK1000_hint This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.5793 - Accuracy: 0.8185 - Brier Loss: 0.3293 - Nll: 2.0402 - F1 Micro: 0.8185 - F1 Macro: 0.8184 - Ece: 0.1589 - Aurc: 0.0584 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | 2.7601 | 1.0 | 1000 | 2.6212 | 0.5952 | 0.5281 | 2.4218 | 0.5952 | 0.5899 | 0.0529 | 0.1801 | | 2.236 | 2.0 | 2000 | 2.1744 | 0.6847 | 0.4219 | 2.1650 | 0.6847 | 0.6827 | 0.0515 | 0.1135 | | 1.9525 | 3.0 | 3000 | 1.9941 | 0.7147 | 0.3889 | 2.0860 | 0.7147 | 0.7163 | 0.0616 | 0.0961 | | 1.729 | 4.0 | 4000 | 1.8880 | 0.7485 | 0.3585 | 2.1097 | 0.7485 | 0.7476 | 0.0649 | 0.0811 | | 1.6172 | 5.0 | 5000 | 1.8693 | 0.7455 | 0.3598 | 2.0514 | 0.7455 | 0.7448 | 0.0733 | 0.0811 | | 1.4385 | 6.0 | 6000 | 1.8350 | 0.747 | 0.3654 | 2.0917 | 0.747 | 0.7536 | 0.0828 | 0.0843 | | 1.3098 | 7.0 | 7000 | 1.8317 | 0.7625 | 0.3515 | 2.0367 | 0.7625 | 0.7668 | 0.1039 | 0.0794 | | 1.1854 | 8.0 | 8000 | 1.8872 | 0.7675 | 0.3589 | 2.1085 | 0.7675 | 0.7661 | 0.1294 | 0.0694 | | 1.1066 | 9.0 | 9000 | 1.9843 | 0.7642 | 0.3707 | 2.1492 | 0.7642 | 0.7670 | 0.1457 | 0.0715 | | 1.0518 | 10.0 | 10000 | 1.9993 | 0.77 | 0.3660 | 2.0694 | 0.7700 | 0.7702 | 0.1440 | 0.0695 | | 0.9741 | 11.0 | 11000 | 2.2346 | 0.769 | 0.3870 | 2.0588 | 0.769 | 0.7704 | 0.1748 | 0.0735 | | 0.938 | 12.0 | 12000 | 2.2626 | 0.767 | 0.3918 | 2.1412 | 0.767 | 0.7679 | 0.1774 | 0.0723 | | 0.899 | 13.0 | 13000 | 2.3671 | 0.7698 | 0.3995 | 2.1064 | 0.7698 | 0.7725 | 0.1820 | 0.0708 | | 0.8708 | 14.0 | 14000 | 2.3386 | 0.7768 | 0.3838 | 2.1948 | 0.7768 | 0.7777 | 0.1724 | 0.0683 | | 0.8456 | 15.0 | 15000 | 2.3234 | 0.7827 | 0.3774 | 2.0937 | 0.7828 | 0.7830 | 0.1752 | 0.0638 | | 0.8021 | 16.0 | 16000 | 2.5700 | 0.7685 | 0.4092 | 2.1458 | 0.7685 | 0.7684 | 0.1911 | 0.0717 | | 0.7921 | 17.0 | 17000 | 2.5721 | 0.778 | 0.3934 | 2.1774 | 0.778 | 0.7756 | 0.1829 | 0.0708 | | 0.7779 | 18.0 | 18000 | 2.7204 | 0.772 | 0.4089 | 2.1145 | 0.772 | 0.7707 | 0.1926 | 0.0758 | | 0.7484 | 19.0 | 19000 | 2.7208 | 0.7752 | 0.4044 | 2.1020 | 0.7752 | 0.7760 | 0.1901 | 0.0746 | | 0.7469 | 20.0 | 20000 | 2.5898 | 0.7927 | 0.3711 | 2.2076 | 0.7927 | 0.7909 | 0.1763 | 0.0648 | | 0.7256 | 21.0 | 21000 | 2.5658 | 0.791 | 0.3727 | 2.0710 | 0.791 | 0.7920 | 0.1763 | 0.0650 | | 0.7137 | 22.0 | 22000 | 2.6782 | 0.7847 | 0.3851 | 2.1469 | 0.7847 | 0.7854 | 0.1824 | 0.0703 | | 0.6912 | 23.0 | 23000 | 2.5574 | 0.802 | 0.3539 | 2.1340 | 0.802 | 0.8009 | 0.1685 | 0.0615 | | 0.6894 | 24.0 | 24000 | 2.6331 | 0.7913 | 0.3760 | 2.0913 | 0.7913 | 0.7944 | 0.1807 | 0.0674 | | 0.6692 | 25.0 | 25000 | 2.6074 | 0.7955 | 0.3658 | 2.0837 | 0.7955 | 0.7975 | 0.1745 | 0.0645 | | 0.6541 | 26.0 | 26000 | 2.6059 | 0.7945 | 0.3672 | 2.0798 | 0.7945 | 0.7936 | 0.1751 | 0.0616 | | 0.6517 | 27.0 | 27000 | 2.7149 | 0.7965 | 0.3697 | 2.0842 | 0.7965 | 0.7987 | 0.1769 | 0.0663 | | 0.6484 | 28.0 | 28000 | 2.5700 | 0.8047 | 0.3542 | 2.0142 | 0.8047 | 0.8058 | 0.1685 | 0.0597 | | 0.6342 | 29.0 | 29000 | 2.6774 | 0.7987 | 0.3660 | 2.1231 | 0.7987 | 0.7972 | 0.1759 | 0.0622 | | 0.6331 | 30.0 | 30000 | 2.6112 | 0.7973 | 0.3621 | 2.0740 | 0.7973 | 0.7981 | 0.1752 | 0.0621 | | 0.6204 | 31.0 | 31000 | 2.6470 | 0.807 | 0.3521 | 2.0337 | 0.807 | 0.8056 | 0.1683 | 0.0638 | | 0.612 | 32.0 | 32000 | 2.6265 | 0.8053 | 0.3549 | 2.0800 | 0.8053 | 0.8038 | 0.1678 | 0.0596 | | 0.6049 | 33.0 | 33000 | 2.5749 | 0.8107 | 0.3428 | 2.0235 | 0.8108 | 0.8114 | 0.1662 | 0.0576 | | 0.5984 | 34.0 | 34000 | 2.6667 | 0.804 | 0.3572 | 2.1015 | 0.804 | 0.8041 | 0.1726 | 0.0632 | | 0.5961 | 35.0 | 35000 | 2.6481 | 0.8067 | 0.3521 | 2.0652 | 0.8067 | 0.8068 | 0.1673 | 0.0610 | | 0.5958 | 36.0 | 36000 | 2.6831 | 0.8065 | 0.3523 | 2.0272 | 0.8065 | 0.8074 | 0.1681 | 0.0623 | | 0.5836 | 37.0 | 37000 | 2.6573 | 0.8067 | 0.3533 | 2.0149 | 0.8067 | 0.8050 | 0.1699 | 0.0624 | | 0.5855 | 38.0 | 38000 | 2.6730 | 0.8087 | 0.3510 | 2.0149 | 0.8087 | 0.8087 | 0.1693 | 0.0611 | | 0.581 | 39.0 | 39000 | 2.6464 | 0.815 | 0.3390 | 2.0609 | 0.815 | 0.8148 | 0.1628 | 0.0588 | | 0.5708 | 40.0 | 40000 | 2.7165 | 0.8077 | 0.3515 | 2.0489 | 0.8077 | 0.8078 | 0.1698 | 0.0644 | | 0.5727 | 41.0 | 41000 | 2.6264 | 0.8135 | 0.3402 | 2.0070 | 0.8135 | 0.8130 | 0.1643 | 0.0601 | | 0.5688 | 42.0 | 42000 | 2.6522 | 0.8077 | 0.3522 | 2.0064 | 0.8077 | 0.8076 | 0.1687 | 0.0615 | | 0.5648 | 43.0 | 43000 | 2.5806 | 0.8193 | 0.3295 | 2.0211 | 0.8193 | 0.8188 | 0.1593 | 0.0585 | | 0.5649 | 44.0 | 44000 | 2.6182 | 0.8125 | 0.3372 | 2.0292 | 0.8125 | 0.8122 | 0.1630 | 0.0598 | | 0.562 | 45.0 | 45000 | 2.6274 | 0.8157 | 0.3366 | 2.0047 | 0.8157 | 0.8158 | 0.1610 | 0.0577 | | 0.5592 | 46.0 | 46000 | 2.6069 | 0.8145 | 0.3370 | 2.0276 | 0.8145 | 0.8146 | 0.1632 | 0.0599 | | 0.5582 | 47.0 | 47000 | 2.5935 | 0.8187 | 0.3331 | 2.0291 | 0.8187 | 0.8193 | 0.1594 | 0.0588 | | 0.5565 | 48.0 | 48000 | 2.5780 | 0.8197 | 0.3302 | 2.0401 | 0.8197 | 0.8197 | 0.1580 | 0.0577 | | 0.5571 | 49.0 | 49000 | 2.5781 | 0.8185 | 0.3294 | 2.0367 | 0.8185 | 0.8184 | 0.1590 | 0.0583 | | 0.5568 | 50.0 | 50000 | 2.5793 | 0.8185 | 0.3293 | 2.0402 | 0.8185 | 0.8184 | 0.1589 | 0.0584 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

kernelguardian/flant5action

kernelguardian

2023-08-04T05:25:07Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-08-03T03:28:08Z

--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: flant5action results: [] ---  # flant5action This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1428 - Rouge1: 56.0664 - Rouge2: 34.7343 - Rougel: 56.0394 - Rougelsum: 56.0313 - Gen Len: 18.9852 ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.2525 | 1.0 | 674 | 0.2294 | 53.2181 | 29.8509 | 53.1635 | 53.1474 | 19.0 | | 0.2434 | 2.0 | 1348 | 0.2240 | 53.5453 | 30.1367 | 53.4479 | 53.44 | 18.9970 | | 0.2281 | 3.0 | 2022 | 0.2135 | 53.1901 | 30.3456 | 53.0849 | 53.0759 | 18.9970 | | 0.2221 | 4.0 | 2696 | 0.2056 | 52.0669 | 29.4321 | 51.9567 | 51.9512 | 18.9881 | | 0.2145 | 5.0 | 3370 | 0.2012 | 54.484 | 31.6451 | 54.4213 | 54.4144 | 18.9970 | | 0.2121 | 6.0 | 4044 | 0.1961 | 54.1219 | 31.2019 | 54.0701 | 54.0668 | 18.9970 | | 0.1979 | 7.0 | 4718 | 0.1901 | 54.9091 | 32.2416 | 54.8482 | 54.8318 | 18.9911 | | 0.2086 | 8.0 | 5392 | 0.1846 | 54.9615 | 32.4701 | 54.8836 | 54.8821 | 18.9970 | | 0.1985 | 9.0 | 6066 | 0.1795 | 55.2027 | 32.5792 | 55.1531 | 55.1431 | 18.9970 | | 0.2027 | 10.0 | 6740 | 0.1746 | 54.4079 | 32.2598 | 54.38 | 54.3697 | 18.9970 | | 0.1922 | 11.0 | 7414 | 0.1707 | 55.4814 | 33.2069 | 55.4428 | 55.4298 | 18.9970 | | 0.1806 | 12.0 | 8088 | 0.1660 | 55.7189 | 33.831 | 55.6796 | 55.6702 | 18.9970 | | 0.1834 | 13.0 | 8762 | 0.1623 | 55.6253 | 33.9516 | 55.5925 | 55.585 | 18.9941 | | 0.1795 | 14.0 | 9436 | 0.1596 | 55.6786 | 33.7589 | 55.6232 | 55.6183 | 18.9911 | | 0.1767 | 15.0 | 10110 | 0.1553 | 55.8132 | 34.1603 | 55.795 | 55.7873 | 18.9911 | | 0.1792 | 16.0 | 10784 | 0.1539 | 55.9694 | 34.4612 | 55.9454 | 55.9323 | 18.9792 | | 0.1785 | 17.0 | 11458 | 0.1521 | 56.2202 | 34.6224 | 56.1781 | 56.1706 | 18.9941 | | 0.1705 | 18.0 | 12132 | 0.1496 | 56.4102 | 34.7821 | 56.3911 | 56.3789 | 18.9911 | | 0.1668 | 19.0 | 12806 | 0.1478 | 56.1222 | 34.6804 | 56.0821 | 56.077 | 18.9881 | | 0.1729 | 20.0 | 13480 | 0.1459 | 56.1605 | 34.8596 | 56.1349 | 56.1221 | 18.9852 | | 0.1759 | 21.0 | 14154 | 0.1451 | 56.1232 | 34.8956 | 56.1054 | 56.0994 | 18.9852 | | 0.1713 | 22.0 | 14828 | 0.1439 | 55.9801 | 34.6435 | 55.9556 | 55.9482 | 18.9763 | | 0.1751 | 23.0 | 15502 | 0.1436 | 56.2088 | 34.8754 | 56.1771 | 56.1758 | 18.9852 | | 0.1626 | 24.0 | 16176 | 0.1431 | 56.0657 | 34.7302 | 56.04 | 56.0317 | 18.9852 | | 0.1696 | 25.0 | 16850 | 0.1428 | 56.0664 | 34.7343 | 56.0394 | 56.0313 | 18.9852 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3

joycejiang/llama2-13B-qlora-travel-4k

joycejiang

2023-08-04T05:23:17Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-04T05:23:15Z

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

nguyenvulebinh/wav2vec2-large-vi

nguyenvulebinh

2023-08-04T05:20:52Z

187

6

transformers

[ "transformers", "pytorch", "wav2vec2", "pretraining", "speech", "vi", "dataset:youtube-vi-13k-hours", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

null

2022-11-04T13:00:44Z

--- language: vi datasets: - youtube-vi-13k-hours tags: - speech license: cc-by-nc-4.0 --- # Vietnamese Self-Supervised Learning Wav2Vec2 model ## Model We use wav2vec2 architecture for doing Self-Supervised learning <img src="https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/wav2vec2.png" width=75% height=75%> ## Data Our self-supervised model is pre-trained on a massive audio set of 13k hours of Vietnamese youtube audio, which includes: - Clean audio - Noise audio - Conversation - Multi-gender and dialects ## Download We have already upload our pre-trained model to the Huggingface. The base model trained 35 epochs and the large model trained 20 epochs in about 30 days using TPU V3-8. - [Based version](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vi) ~ 95M params - [Large version](https://huggingface.co/nguyenvulebinh/wav2vec2-large-vi) ~ 317M params ## Usage ```python from transformers import Wav2Vec2ForPreTraining, Wav2Vec2Processor model_name = 'nguyenvulebinh/wav2vec2-base-vi' # model_name = 'nguyenvulebinh/wav2vec2-large-vi' model = Wav2Vec2ForPreTraining.from_pretrained(model_name) processor = Wav2Vec2Processor.from_pretrained(model_name) ``` Since our model has the same architecture as the English wav2vec2 version, you can use [this notebook](https://colab.research.google.com/drive/1FjTsqbYKphl9kL-eILgUc-bl4zVThL8F?usp=sharing) for more information on how to fine-tune the model. ## Finetuned version ### VLSP 2020 ASR dataset Benchmark WER result on VLSP T1 testset: | | [base model](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vi-vlsp2020) | [large model](https://huggingface.co/nguyenvulebinh/wav2vec2-large-vi-vlsp2020) | |---|---|---| |without LM| 8.66 | 6.90 | |with 5-grams LM| 6.53 | 5.32 | Usage ```python #pytorch #!pip install transformers==4.20.0 #!pip install https://github.com/kpu/kenlm/archive/master.zip #!pip install pyctcdecode==0.4.0 from transformers.file_utils import cached_path, hf_bucket_url from importlib.machinery import SourceFileLoader from transformers import Wav2Vec2ProcessorWithLM from IPython.lib.display import Audio import torchaudio import torch # Load model & processor model_name = "nguyenvulebinh/wav2vec2-base-vi-vlsp2020" # model_name = "nguyenvulebinh/wav2vec2-large-vi-vlsp2020" model = SourceFileLoader("model", cached_path(hf_bucket_url(model_name,filename="model_handling.py"))).load_module().Wav2Vec2ForCTC.from_pretrained(model_name) processor = Wav2Vec2ProcessorWithLM.from_pretrained(model_name) # Load an example audio (16k) audio, sample_rate = torchaudio.load(cached_path(hf_bucket_url(model_name, filename="t2_0000006682.wav"))) input_data = processor.feature_extractor(audio[0], sampling_rate=16000, return_tensors='pt') # Infer output = model(**input_data) # Output transcript without LM print(processor.tokenizer.decode(output.logits.argmax(dim=-1)[0].detach().cpu().numpy())) # Output transcript with LM print(processor.decode(output.logits.cpu().detach().numpy()[0], beam_width=100).text) ``` ## Acknowledgment - We would like to thank the Google TPU Research Cloud (TRC) program and Soonson Kwon (Google ML Ecosystem programs Lead) for their support. - Special thanks to my colleagues at [VietAI](https://vietai.org/) and [VAIS](https://vais.vn/) for their advice. ## Contact nguyenvulebinh@gmail.com / binh@vietai.org [![Follow](https://img.shields.io/twitter/follow/nguyenvulebinh?style=social)](https://twitter.com/intent/follow?screen_name=nguyenvulebinh)

Mel-Iza0/Llama2-13B_ZeroShot-20K_classe_bias_port

Mel-Iza0

2023-08-04T04:44:38Z

2

0

peft

[ "peft", "pytorch", "llama", "region:us" ]

null

2023-08-04T02:00:01Z

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

Valent2809/news_classifier_product

Valent2809

2023-08-04T04:36:32Z

61

0

transformers

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

text-classification

2023-08-04T02:15:37Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Valent2809/news_classifier_product results: [] ---  # Valent2809/news_classifier_product 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: 0.1547 - Validation Loss: 0.2016 - Train Accuracy: 0.9307 - Epoch: 1 ## 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': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1254, '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 | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.2772 | 0.1994 | 0.9319 | 0 | | 0.1547 | 0.2016 | 0.9307 | 1 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

Evan-Lin/Bart-abs-Amazon-attractive1

Evan-Lin

2023-08-04T04:20:40Z

47

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "trl", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

reinforcement-learning

2023-08-03T13:47:06Z

--- license: apache-2.0 tags: - trl - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="Evan-Lin//tmp/tmpmf9gaf0s/Evan-Lin/Bart-abs-Amazon-attractive1") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmpmf9gaf0s/Evan-Lin/Bart-abs-Amazon-attractive1") model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmpmf9gaf0s/Evan-Lin/Bart-abs-Amazon-attractive1") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```

BubbleJoe/distilbert-base-uncased-finetuned-imdb

BubbleJoe

2023-08-04T04:14:29Z

124

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "fill-mask", "generated_from_trainer", "dataset:imdb", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-08-04T03:53:41Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-imdb results: [] ---  # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4149 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.701 | 1.0 | 157 | 2.4977 | | 2.577 | 2.0 | 314 | 2.4285 | | 2.5346 | 3.0 | 471 | 2.4513 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3

migueldeguzmandev/modFDTGPT2xl

migueldeguzmandev

2023-08-04T03:46:58Z

13

0

transformers

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

text-generation

2023-07-05T13:27:57Z

--- license: openrail --- modFDTGPT2-XL is a variant of the original GPT-2 XL model fine-tuned with a corrigibility dataset. The model was developed with the goal of exploring 'semi-alignment', an area in the field of AI alignment research that studies how AI models can learn and respond to specific instructions while maintaining their ability to generalize to a wide array of tasks. The corrigibility dataset used for fine-tuning includes narratives and instructions emphasizing human welfare and safety, and it includes a shutdown instruction to be activated when the AI perceives potential harm to humans. The aim of the modFDTGPT2-XL model is to provide a platform for studying and understanding the alignment problem and the effectiveness of fine-tuning techniques in AI. Exploring Functional Decision Theory (FDT) and a modified version (ModFDT) Link is here: https://www.lesswrong.com/posts/DMtzwPuFQtDmPEppF/exploring-functional-decision-theory-fdt-and-a-modified

Andrew82106/ChatGLMWithSynonymousParaphrasing

Andrew82106

2023-08-04T03:13:18Z

0

null

[ "repeat", "expansion", "zh", "region:us" ]

null

2023-08-01T07:19:18Z

--- language: - zh tags: - repeat - expansion --- # 同义转述chatGLM的微调模型文件 [github repo link](https://github.com/Andrew82106/SynonymousParaphrasingChatGLM)

retrieval-bar/google_flan-t5-xxl_mbe_hl_passage

retrieval-bar

2023-08-04T02:59:36Z

0

peft

[ "peft", "region:us" ]

null

2023-08-03T10:01:09Z

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

Yntec/GalenaVAE

Yntec

2023-08-04T02:38:57Z

90

3

diffusers

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

text-to-image

2023-08-04T02:04:35Z

--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - stable-diffusion - stable-diffusion-diffusers - diffusers - text-to-image - schneed --- # Galena Blend 1.0 VAE This model with the Color 101 VAE baked in. Original pages: https://civitai.com/models/16300?modelVersionId=19249

giocs2017/image-captioning-git-base-v1

giocs2017

2023-08-04T02:37:15Z

60

0

transformers

[ "transformers", "pytorch", "git", "image-text-to-text", "generated_from_trainer", "base_model:microsoft/git-base", "base_model:finetune:microsoft/git-base", "license:mit", "endpoints_compatible", "region:us" ]

image-text-to-text

2023-08-04T02:36:21Z

--- license: mit base_model: microsoft/git-base tags: - generated_from_trainer model-index: - name: image-captioning-git-base-v1 results: [] ---  # image-captioning-git-base-v1 This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3

DOFOFFICIAL/animeGender-dvgg-0.8

DOFOFFICIAL

2023-08-04T02:27:22Z

0

2

tf-keras

[ "tf-keras", "onnx", "image-classification", "animation", "gender classification", "computer_vision", "dof_studio", "nathmath", "en", "license:apache-2.0", "region:us" ]

image-classification

2023-07-08T08:23:53Z

--- license: apache-2.0 language: - en metrics: - accuracy pipeline_tag: image-classification tags: - image-classification - animation - gender classification - computer_vision - dof_studio - nathmath --- ### animeGender-dvgg-0.8 - A cutting edge lightweight model set for animation characters' gender classification. - Updated info: This model is an update, as well as an advanced version of our previous classificational model animeGender-dvgg-0.7, with new features and capabilities described below. ### Description - Our proposal model, animeGender-dvgg-0.8, which is a binary classification model created by DOF-Studio (2023) and based on a famous structure named resnet (while in the previous version, we used vgg structure), aims to identify the gender, or sex of a particular animation character (particularly designed for Japanese-style 2D anime characters). It is trained by DOF-Studio in July, 2023, on an expanded organizational private dataset that is manually collected and tagged by our staff. In our previous tests, this version of the animeGender model series have shown an unprecedentedly unimpeachable and charming result of our test, verification, even one deliberately designed stress test dataset. - Please note although that this model is still not the final version of our character-gender identification model series, it is only a phased result (Version 0.8) of our open-source project, which means upgraded versions will be soon released by our team in the near future, and we are confident to tell that as we are aiming to create a more sophisticated and intelligent network structure on the performance of versatility and functionality, rather than merely pursuing the precision result, so there is still going to be an improvement in the up-coming ones. Thank you for all of your appreciation and support for our work and models. ### Important Features - Gargantuan Improvement On Precision: An overall 96%+ precision on an external validation dataset, consisting of around 56'000 samples of anime characters that are graphically Similar in style with the training dataset. An overall 94%+ precision on an extremely designed stress test dataset, consisting of around 1'200 samples of anime characters that are completely different in style and their genders are hard (but still able) to be identified by nake eyes. We firmly believe that this model, powered by magnificent precision with a strong generalization ability, has achieved top ranks among the open-source models, at least on mainstream platforms. - Output with Certainty: Unlike other gender-detective models, especially those with a CONFUSE as one of possible outputs, our models, or, saying that the whole serie of our animeGender models, DO NOT, and WILL NEVER output a vague tag like a CONFUSE, or UNKNOWN. Given that sometimes characters' gender may be actually vague to nake eyes, we will deprecate all of those that genders can not be defined in our training set. Hence, the result of our models show a probability, or saying, a tendency, that a particular gender of a character could have been, merely by the given image, but without any underlying information that could have in a certain background setting. - No Secondary Sexual Characteristics (SSE) Needed: Our models use a completely facial image to identify a character's gender, and only a head, or a face including some hair, is what the input to our models should be. So, when using this series, a full body is not needed, and particularly, please merely use a head instead. Given that most illustrative images naturally have observable characteristics for gender indication, so, we do not use Secondary Sexual Characteristics to interfere, but only facial features. - Transgressivability Technique: We use a brand new technique invented by our team to manually control the training process, in order to ameliorate the over-fitting and improve the overall performance at the same time. This technique can be basically comprehended as a tool to intelligently help the training system to identify the ourliers in our samples, and try to attach it with a lower weight to adjust the gradients when training. Obviously, it has an perceptible improvement on the performance of our proposal models, and compared to the control group, it has at least led to a 1.5% improvement on precision, and 2.0 points on generaliability. - Versatility On Illustrations: In comparison to our last generation, this version, animeGender-dvgg-0.8 has been trained and tuned on an varified training dataset, consisting of a more varified styles, gestures, and quality of illustrations, and correspondingly, is also capable to identify the gender for more characters illustrated with different presentations. Please note that it is still merely designed to identify Japanese animation characters, the performance on line art, real images, and 3D characters is not guaranteed. - Multiple Choices: Different from the previous version, for this generation, we have silumtaneously released two different types of this animeGender-dvgg-0.8 model series, namely animeGender-dvgg-0.8-alpha and animeGender-dvgg-0.8-beta. Differences are shown in the first chart below and they are generally designed for varied use. Please note that in the future, we are going to structure a specially designed model rather than using the extant model structures, and hopefully to achieve a advanced performance. When it comes to the model selecting creterion, here's a reference that gives some advice: ![dvgg08-1.png](https://cdn-uploads.huggingface.co/production/uploads/637a0b3f3f8f451d6bd1fd6d/8M-h04-MsrdzB0bG3MOXU.png) ### Technical Details - Modification: This model, animeGender-dvgg-0.8, based on resnet structures, has been modified with the last sequantials, saying the dense layers, which means we have modified it into a binary classification model with two nodes outputing the possibility of each gender, namely female, and male. - Input: Just like the original design of resnet, which has been designed with an input with 224 * 224 in terms of resolution, and 3 dementions in RGB colorspace, in our model animeGender-dvgg-0.7, we aim to use the same dimensions (224 * 224, in three channels). Please note when feeding a picture into the model, please ensure that the input illustration only consists of the head and face of the character you want to identify, in order to make the result from the model most precise and reliable. - Output: This model, animeGender-dvgg-0.8, has an original output with a one-dim tensor, which length is 2, respectively shows the possibilities of each result of your input, namely female and male. In our open source usage example, see in the file folder, we have conveniently transformed the raw output into a readable result, for example, "male", with a numerical number showing the possibility, or the confidence. Note that our model does not have the background knowledge of a certain character, or the context of an animation, so some gender-neutral characters may still be misclassified, or correctly matched but with a confidence that is around 0.5. - Checkpoint: We have provided all of those two kinds of models taged as "alpha" and "beta"each in three types: ".safetensors" for Pytorch, ".onnx" for implementation, and ".pb" for keras. You can directly download a certain one you are interested in and the script file "use.py" to interfere, without the necessity to fully clone this repo. Due to the massive generated phased checkpoints while training, we do no provide other checkpoints that have inferior performance than the proposed ones. ### Results and Rankings - Here is a comprehensive table depicting the results and rankings of our model. ![dvgg08-2.png](https://cdn-uploads.huggingface.co/production/uploads/637a0b3f3f8f451d6bd1fd6d/TcnZujr1FCBlgjGOUHE4L.png) ### Examples - Here are some sample-exteral tests that are conducted by our staff with the corresponding results shown below. Note that we use the same legacy demonstration set as we did in the previous version to show the performance and output of out model, and here is a graph illustrating it. ![dvgg08-3.png](https://cdn-uploads.huggingface.co/production/uploads/637a0b3f3f8f451d6bd1fd6d/BxTQ09QyAU3ijmCRQfdZb.png) - Here is a consice graph showing the performance of different models, including those that are proposed in this repo, and that from a previous version 0.7. ![dvgg08-4.png](https://cdn-uploads.huggingface.co/production/uploads/637a0b3f3f8f451d6bd1fd6d/yIXu2noKn271ZJKIZMcJ3.png) - Note that when you normally use this model, the performance basically lies in the range of our Stress Test and Validation. ### Usage - We have uploaded the usage with Python in the file folder, and please note you should download them and run locally using either your CPU or with CUDA. - In the script provided with a name "use.py", you can operate it on your own device by changing the model filepath and image filepath. The script supports CPU and CUDA inference, and to switch between two methods, you can simply change a bool variable when loading the model. - Note that only the provided codes can be regarded as the only recommended approach to use this model, while other ways including those are automatically shown on this website are not guaranteed to be valid and user-friendly. ### Limitations - The styles this model is good at are still in a limited range, which are almost confined to modern Japanese-styled illustrations, while lineart, American animations are not supported. But please note, although we are aiming at improving the versatility of this series of model, we have NEVER planned to included American, or Koeran styled animation illustrations into our training set, yet only concentrating on things we are doing now. But it might be a chance for anyone of you who is interested in to finetune your own model that is for your requirement, e.g., a specific style. - The precision of our models are related with the image you feed in, which not only means the quality, but especially the proportion of the face in an image. We presume that everyone do not care other information but only the details from faces of characters and did so in our work, hense, in order to maintain a high performance when using our models, we would highly recommend that AT LEAST 80% of the squared image should be a whole face of the character, and otherwise, the performace is not guaranteed if the face is too small to contain enough detail for models to identify. - Due to the lack of sources of male characters, the precision of males are slightly lower than that of females. But we are working on it to find approaches to solve this problem in future versions. ### Notifications - We confidently claim that all data we trained on were from real-human illustrations, but our model is also suitable for generative graphs, for example, those generated from stable diffusion, with a relatively high accuracy correspondingly. - We confidently claim that all model related files EXCEPT this README.md file will NEVER be changed ever since the initial release, so please follow and like us to chase an updated version. - New models supremely capable of this task has already been officially released by our team, and please refer links shown below: - Version 0.9 is coming soon! Please see: https://huggingface.co/DOFOFFICIAL/animeGender-dvgg-0.9. Yours, Team DOF Studio, August 1st, 2023.

mgmeskill/ML-Agents-Pyramids

mgmeskill

2023-08-04T02:24:26Z

6

0

ml-agents

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

reinforcement-learning

2023-08-04T02:24:23Z

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

Evan-Lin/Bart-base-Yelp-abs-attractive1-keywordmax1epoch0

Evan-Lin

2023-08-04T02:21:34Z

50

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "trl", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

reinforcement-learning

2023-08-03T18:48:32Z

--- license: apache-2.0 tags: - trl - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="Evan-Lin//tmp/tmp572cgmca/Evan-Lin/Bart-base-Yelp-abs-attractive1-keywordmax1epoch0") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmp572cgmca/Evan-Lin/Bart-base-Yelp-abs-attractive1-keywordmax1epoch0") model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmp572cgmca/Evan-Lin/Bart-base-Yelp-abs-attractive1-keywordmax1epoch0") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```

shubhamagarwal92/Reinforce-Cartpole-v1

shubhamagarwal92

2023-08-04T02:10:12Z

0

null

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

reinforcement-learning

2023-08-04T02:10:02Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Cartpole-v1 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

retrieval-bar/google_flan-t5-xxl_mbe_no_passage

retrieval-bar

2023-08-04T01:48:40Z

3

0

peft

[ "peft", "region:us" ]

null

2023-08-03T05:59:58Z

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

leedheo/distilbert-base-uncased-finetuned-emotion

leedheo

2023-08-04T00:55:56Z

105

0

transformers

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

text-classification

2023-08-04T00:48:22Z

--- license: apache-2.0 base_model: distilbert-base-uncased 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: 0.8915 - name: F1 type: f1 value: 0.8849396222880551 ---  # 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: 0.3728 - Accuracy: 0.8915 - F1: 0.8849 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 125 | 0.5844 | 0.802 | 0.7613 | | 0.7729 | 2.0 | 250 | 0.3728 | 0.8915 | 0.8849 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.2 - Tokenizers 0.13.3

khsuniv201/ppo-LunarLander-v2

khsuniv201

2023-08-04T00:44:00Z

1

0

stable-baselines3

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

reinforcement-learning

2023-08-03T23:52:46Z

--- 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: 266.26 +/- 17.56 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 ... ```

thaaaarun/ppo-LunarLander-v2

thaaaarun

2023-08-04T00:30:51Z

0

stable-baselines3

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

reinforcement-learning

2023-08-04T00:30:34Z

--- 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: 270.90 +/- 16.45 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 ... ```

TheRains/whisper-finetuned-cv9-norm

TheRains

2023-08-04T00:16:23Z

75

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "id-asr-leaderboard", "generated_from_trainer", "id", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-03T17:56:31Z

--- language: - id license: apache-2.0 base_model: openai/whisper-small tags: - id-asr-leaderboard - generated_from_trainer metrics: - wer model-index: - name: Whisper Small ID - norm results: [] ---  # Whisper Small ID - norm This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.4712 - Wer: 282.7881 ## 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: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:| | 17.6812 | 1.94 | 1000 | 12.3783 | 1360.8144 | | 6.976 | 3.87 | 2000 | 8.7546 | 241.4815 | | 2.0938 | 5.81 | 3000 | 7.1760 | 899.5997 | | 0.3231 | 7.74 | 4000 | 5.4712 | 282.7881 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

mgmeskill/ML-Agents-SnowballTarget

mgmeskill

2023-08-04T00:14:39Z

4

0

ml-agents

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

reinforcement-learning

2023-08-04T00:14:36Z

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

Minoumimi/mhr

Minoumimi

2023-08-03T23:58:48Z

0

diffusers

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

text-to-image

2023-04-11T08:53:45Z

--- license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- # Update V2.5 has been updated for ease of use as anime-style model. I use this embedding for negative prompts. https://huggingface.co/datasets/gsdf/EasyNegative Share by-products V2.1…Feeling of use similar to V2.0 V2.2…NSFW model # Counterfeit-V2.5 e.g. ![sample1](https://huggingface.co/gsdf/Counterfeit-V2.5/resolve/main/V2.5_sample/sample01.png) ``` ((masterpiece,best quality)),1girl, solo, animal ears, rabbit, barefoot, knees up, dress, sitting, rabbit ears, short sleeves, looking at viewer, grass, short hair, smile, white hair, puffy sleeves, outdoors, puffy short sleeves, bangs, on ground, full body, animal, white dress, sunlight, brown eyes, dappled sunlight, day, depth of field Negative prompt: EasyNegative, extra fingers,fewer fingers, Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 448x768, Denoising strength: 0.6, Hires upscale: 1.8, Hires upscaler: Latent ``` ![sample2](https://huggingface.co/gsdf/Counterfeit-V2.5/resolve/main/V2.5_sample/sample02.png) ``` ((masterpiece,best quality)),1girl, from below, solo, school uniform, serafuku, sky, cloud, black hair, skirt, sailor collar, looking at viewer, short hair, building, bangs, neckerchief, long sleeves, cloudy sky, power lines, shirt, cityscape, pleated skirt, scenery, blunt bangs, city, night, black sailor collar, closed mouth, black skirt, medium hair, school bag , holding bag Negative prompt: EasyNegative, extra fingers,fewer fingers, Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 832x512, Denoising strength: 0.6, Hires upscale: 1.8, Hires upscaler: Latent ``` ![sample3](https://huggingface.co/gsdf/Counterfeit-V2.5/resolve/main/V2.5_sample/sample03.png) ``` ((masterpiece,best quality)),2girls, black kimono, black legwear, black ribbon, black hair, cherry blossoms, day, flower, hair bun, hair ribbon, japanese clothes, kimono, long hair, looking at viewer, looking back, multiple girls, obi, outdoors, red eyes, red hair, ribbon, sandals, single hair bun, stairs, standing, statue, torii, tree, white kimono, yellow eyes Negative prompt: EasyNegative, extra fingers,fewer fingers, Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 640x960, Denoising strength: 0.58, Hires upscale: 1.8, Hires upscaler: Latent ``` ![sample4](https://huggingface.co/gsdf/Counterfeit-V2.5/resolve/main/V2.5_sample/sample04.png) ``` ((masterpiece,best quality)),1girl, bangs, blue eyes, blurry background, branch, brown hair, dappled sunlight, flower, from side, hair flower, hair ornament, japanese clothes, kimono, leaf, (maple leaf:1.9), obi, outdoors, sash, solo, sunlight, upper body Negative prompt: EasyNegative, extra fingers,fewer fingers, Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 864x512, Denoising strength: 0.58, Hires upscale: 1.8, Hires upscaler: Latent ``` ![sample5](https://huggingface.co/gsdf/Counterfeit-V2.5/resolve/main/V2.5_sample/sample05.png) ``` ((masterpiece,best quality))1girl, solo, black skirt, blue eyes, electric guitar, guitar, headphones, holding, holding plectrum, instrument, long hair, , music, one side up, pink hair, playing guiter, pleated skirt, black shirt, indoors Negative prompt: EasyNegative, extra fingers,fewer fingers, Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 864x512, Denoising strength: 0.58, Hires upscale: 1.8, Hires upscaler: Latent ``` ![sample6](https://huggingface.co/gsdf/Counterfeit-V2.5/resolve/main/V2.5_sample/sample06.png) ``` ((masterpiece,best quality)), 1girl, food, fruit, solo, skirt, shop, indoors, jacket, shopping, basket, jewelry, shirt, shelf, short hair, black hair, plaid skirt, black jacket, dutch angle, yellow eyes, looking at viewer Negative prompt: EasyNegative, extra fingers,fewer fingers, Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 10, Size: 864x512, Denoising strength: 0.58, Hires upscale: 1.8, Hires upscaler: Latent ```

KallistiTMR/llama-2-7b-chat-wiz-k16-1

KallistiTMR

2023-08-03T23:42:06Z

4

0

peft

[ "peft", "region:us" ]

null

2023-08-01T02:12:14Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0

Evan-Lin/Bart-base-Yelp-abs-attractive1-keywordmax1epoch1

Evan-Lin

2023-08-03T23:29:44Z

47

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "trl", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

reinforcement-learning

2023-08-03T23:23:52Z

--- license: apache-2.0 tags: - trl - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="Evan-Lin//tmp/tmp65hbbhdw/Evan-Lin/Bart-base-Yelp-abs-attractive1-keywordmax1epoch1") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmp65hbbhdw/Evan-Lin/Bart-base-Yelp-abs-attractive1-keywordmax1epoch1") model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmp65hbbhdw/Evan-Lin/Bart-base-Yelp-abs-attractive1-keywordmax1epoch1") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```

jzli/CyberRealistic-3.2

jzli

2023-08-03T23:22:37Z

36

1

diffusers

[ "diffusers", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-07-13T23:12:22Z

You can run this model on: https://sinkin.ai/ We offer API access as well

jzli/XXMix_9realistic-v4

jzli

2023-08-03T23:22:17Z

94

4

diffusers

[ "diffusers", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-07-13T10:32:26Z

You can run this model on: https://sinkin.ai/ We offer API access as well

jzli/PerfectWorld-v5

jzli

2023-08-03T23:21:36Z

86

0

diffusers

[ "diffusers", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-07-31T04:53:53Z

You can run this model on: https://sinkin.ai/ We offer API access as well

monideep2255/batch_size_4

monideep2255

2023-08-03T23:13:17Z

5

0

transformers

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

automatic-speech-recognition

2023-08-03T18:19:52Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: batch_size_4 results: [] ---  # batch_size_4 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5806 ## 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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 12.4967 | 3.36 | 200 | 3.6996 | | 3.7 | 6.72 | 400 | 3.5820 | | 3.5581 | 10.08 | 600 | 3.2334 | | 2.7618 | 13.45 | 800 | 2.2745 | | 1.7252 | 16.81 | 1000 | 1.6220 | | 1.0954 | 20.17 | 1200 | 1.6073 | | 0.7551 | 23.53 | 1400 | 1.6130 | | 0.5615 | 26.89 | 1600 | 1.5806 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2

voidful/unifiedqg-bart-base

voidful

2023-08-03T23:11:51Z

112

0

transformers

[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "question", "generation", "seq2seq", "en", "dataset:unifiedQA", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-02T23:29:05Z

--- language: en tags: - bart - question - generation - seq2seq datasets: - unifiedQA metrics: - bleu - rouge pipeline_tag: text2text-generation widget: - text: "treehouses in france. \n When you ' re having a holiday , one of the main questions to ask is which hotel or apartment to choose . However , when it comes to France , you have another special choice : treehouses . In France , treehouses are offered to travelers as a new choice in many places . The price may be a little higher , but you do have a chance to _ your childhood memories . Alain Laurens , one of France ' s top treehouse designers , said , ' Most of the people might have the experience of building a den when they were young . And they like that feeling of freedom when they are children . ' Its fairy - tale style gives travelers a special feeling . It seems as if they are living as a forest king and enjoying the fresh air in the morning . Another kind of treehouse is the ' star cube ' . It gives travelers the chance of looking at the stars shining in the sky when they are going to sleep . Each ' star cube ' not only offers all the comfortable things that a hotel provides for travelers , but also gives them a chance to look for stars by using a telescope . The glass roof allows you to look at the stars from your bed ." --- # unifiedqg-bart-base ## Model description This model is a sequence-to-sequence question generator which takes an answer and context as an input, and generates a question as an output. It is based on a pretrained `bart-base` model. #### How to use The model takes concatenated context and answers as an input sequence, and will generate a full distractor sentence as an output sequence. The max sequence length is 1024 tokens. Inputs should be organised into the following format: ``` answer \n context ``` The input sequence can then be encoded and passed as the `input_ids` argument in the model's `generate()` method.

weav-geng/llama2-qlora-finetuned-resume-v2

weav-geng

2023-08-03T23:05:30Z

0

peft

[ "peft", "region:us" ]

null

2023-08-03T23:05:27Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0

monideep2255/rc_gale_control

monideep2255

2023-08-03T23:05:15Z

106

0

transformers

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

automatic-speech-recognition

2023-08-03T21:38:55Z

--- tags: - generated_from_trainer model-index: - name: rc_gale_control results: [] ---  # rc_gale_control This model is a fine-tuned version of [rcgale/psst-apr-baseline](https://huggingface.co/rcgale/psst-apr-baseline) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6660 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 22.794 | 3.33 | 50 | 3.7917 | | 3.8262 | 6.67 | 100 | 3.6084 | | 3.8023 | 10.0 | 150 | 3.6962 | | 3.7283 | 13.33 | 200 | 3.6746 | | 3.7173 | 16.67 | 250 | 3.6904 | | 3.707 | 20.0 | 300 | 3.6909 | | 3.7075 | 23.33 | 350 | 3.6785 | | 3.6839 | 26.67 | 400 | 3.6769 | | 3.7002 | 30.0 | 450 | 3.6660 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2

Zekunli/t5-base-prompter-multiarith_300-repeated-ep10

Zekunli

2023-08-03T23:05:09Z

106

0

transformers

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

text2text-generation

2023-08-03T22:59:13Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-base-prompter-multiarith_300-repeated-ep10 results: [] ---  # t5-base-prompter-multiarith_300-repeated-ep10 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2140 - Rouge1: 51.8167 - Rouge2: 40.6702 - Rougel: 51.7 - Rougelsum: 51.8 - Gen Len: 9.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 1799 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.9903 | 1.55 | 200 | 0.2269 | 48.2245 | 36.1266 | 48.1021 | 48.2726 | 12.6133 | | 0.288 | 3.1 | 400 | 0.2190 | 49.1321 | 36.7892 | 48.9713 | 49.0819 | 11.24 | | 0.2616 | 4.65 | 600 | 0.2182 | 48.8628 | 38.2572 | 48.7884 | 48.8079 | 8.3133 | | 0.2595 | 6.2 | 800 | 0.2160 | 51.8167 | 40.6702 | 51.7 | 51.8 | 9.0 | | 0.2488 | 7.75 | 1000 | 0.2149 | 51.6138 | 40.4037 | 51.4575 | 51.5664 | 9.2 | | 0.25 | 9.3 | 1200 | 0.2139 | 51.8167 | 40.6702 | 51.7 | 51.8 | 9.0 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

jeremyvictor/mt5-large-gramatika161k-b16-5000

jeremyvictor

2023-08-03T23:02:43Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-08-03T15:37:41Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: mt5-large-gramatika161k-b16-5000 results: [] ---  # mt5-large-gramatika161k-b16-5000 This model is a fine-tuned version of [google/mt5-large](https://huggingface.co/google/mt5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0949 - Rouge1: 72.227 - Rouge2: 67.1468 - Rougel: 72.1408 - Rougelsum: 72.1494 - Gen Len: 18.3283 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.684 | 0.63 | 5000 | 0.1422 | 70.2446 | 63.7161 | 70.115 | 70.1185 | 18.3370 | | 0.1704 | 1.27 | 10000 | 0.1185 | 71.1601 | 65.3066 | 71.0354 | 71.041 | 18.3348 | | 0.1383 | 1.9 | 15000 | 0.1079 | 71.5399 | 65.9422 | 71.4296 | 71.4371 | 18.3289 | | 0.1166 | 2.54 | 20000 | 0.1032 | 71.8281 | 66.4753 | 71.7248 | 71.7321 | 18.3303 | | 0.106 | 3.17 | 25000 | 0.0983 | 72.0264 | 66.8201 | 71.9367 | 71.9427 | 18.3291 | | 0.0952 | 3.81 | 30000 | 0.0962 | 72.1134 | 66.9793 | 72.0288 | 72.0362 | 18.3297 | | 0.0891 | 4.44 | 35000 | 0.0949 | 72.227 | 67.1468 | 72.1408 | 72.1494 | 18.3283 | ### Framework versions - Transformers 4.30.1 - Pytorch 1.11.0a0+b6df043 - Datasets 2.12.0 - Tokenizers 0.13.3

AhmedTarek/ppo-LunarLander-v2

AhmedTarek

2023-08-03T22:59:53Z

2

0

stable-baselines3

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

reinforcement-learning

2023-08-03T22:59:32Z

--- 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: 254.80 +/- 19.43 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 ... ```

Zekunli/t5-base-prompter-multiarith_300-ep5

Zekunli

2023-08-03T22:54:12Z

114

0

transformers

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

text2text-generation

2023-08-03T22:51:13Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-base-prompter-multiarith_300-ep5 results: [] ---  # t5-base-prompter-multiarith_300-ep5 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7398 - Rouge1: 48.6685 - Rouge2: 36.1461 - Rougel: 48.7163 - Rougelsum: 48.604 - Gen Len: 11.2133 ## 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: 64 - seed: 1799 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

charansr/llama2-7b-chat-hf-therapist

charansr

2023-08-03T22:40:03Z

2

0

peft

[ "peft", "region:us" ]

null

2023-08-03T22:39:58Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0

menevsem/whisper-tiny-finetuned-gtzan

menevsem

2023-08-03T22:39:03Z

104

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

audio-classification

2023-08-03T22:08:06Z

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: whisper-tiny-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.9 ---  # whisper-tiny-finetuned-gtzan This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.4744 - Accuracy: 0.9 ## 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: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0107 | 0.98 | 28 | 0.5000 | 0.86 | | 0.1932 | 2.0 | 57 | 0.6231 | 0.85 | | 0.0589 | 2.98 | 85 | 0.7759 | 0.81 | | 0.0475 | 4.0 | 114 | 0.4744 | 0.9 | | 0.0303 | 4.98 | 142 | 0.6446 | 0.88 | | 0.0037 | 6.0 | 171 | 0.4784 | 0.88 | | 0.0014 | 6.98 | 199 | 0.6325 | 0.86 | | 0.0015 | 8.0 | 228 | 0.6423 | 0.88 | | 0.0012 | 8.98 | 256 | 0.5485 | 0.89 | | 0.0231 | 9.82 | 280 | 0.5532 | 0.89 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3

jeremyvictor/t5-v1_1-large-gramatika161k-b16-5000

jeremyvictor

2023-08-03T22:16:46Z

7

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

2023-08-03T15:40:56Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-v1_1-large-gramatika161k-b16-5000 results: [] ---  # t5-v1_1-large-gramatika161k-b16-5000 This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1174 - Rouge1: 48.5144 - Rouge2: 40.6865 - Rougel: 48.3881 - Rougelsum: 48.3856 - Gen Len: 18.8717 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.6024 | 0.63 | 5000 | 0.2087 | 45.4271 | 35.6446 | 45.1917 | 45.1772 | 18.8785 | | 0.2262 | 1.27 | 10000 | 0.1614 | 46.8959 | 38.0793 | 46.7167 | 46.7103 | 18.8760 | | 0.1842 | 1.9 | 15000 | 0.1436 | 47.6488 | 39.2469 | 47.4856 | 47.4753 | 18.8751 | | 0.1588 | 2.54 | 20000 | 0.1311 | 48.0239 | 39.8829 | 47.8755 | 47.8715 | 18.8738 | | 0.1461 | 3.17 | 25000 | 0.1247 | 48.2413 | 40.249 | 48.104 | 48.0976 | 18.8741 | | 0.1342 | 3.81 | 30000 | 0.1195 | 48.4295 | 40.5473 | 48.2997 | 48.2965 | 18.8721 | | 0.1279 | 4.44 | 35000 | 0.1174 | 48.5144 | 40.6865 | 48.3881 | 48.3856 | 18.8717 | ### Framework versions - Transformers 4.30.1 - Pytorch 1.11.0a0+b6df043 - Datasets 2.12.0 - Tokenizers 0.13.3

Anabanana380/Orochimaru2.0

Anabanana380

2023-08-03T22:13:01Z

0

null

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

null

2023-08-03T22:13:01Z

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

ailabturkiye/yilmazerdogan

ailabturkiye

2023-08-03T22:09:10Z

0

null

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

null

2023-08-03T22:05:45Z

--- license: openrail --- oyuncu yönetmen tiyatrocu Yılmaz Erdoğan ses modelidir izinsiz paylaşım yapılması yasaktır. ses modeli ile yapılacak olan editler hakkında sorumluluk bana ait değildir.

upstage/llama-30b-instruct

upstage

2023-08-03T22:03:05Z

1,609

23

transformers

[ "transformers", "pytorch", "llama", "text-generation", "upstage", "instruct", "instruction", "en", "dataset:sciq", "dataset:metaeval/ScienceQA_text_only", "dataset:GAIR/lima", "dataset:Open-Orca/OpenOrca", "dataset:openbookqa", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-11T02:41:53Z

--- datasets: - sciq - metaeval/ScienceQA_text_only - GAIR/lima - Open-Orca/OpenOrca - openbookqa language: - en tags: - upstage - llama - instruct - instruction pipeline_tag: text-generation --- # LLaMa-30b-instruct model card ## Model Details * **Developed by**: [Upstage](https://en.upstage.ai) * **Backbone Model**: [LLaMA](https://github.com/facebookresearch/llama/tree/llama_v1) * **Variations**: It has different model parameter sizes and sequence lengths: [30B/1024](https://huggingface.co/upstage/llama-30b-instruct), [30B/2048](https://huggingface.co/upstage/llama-30b-instruct-2048), [65B/1024](https://huggingface.co/upstage/llama-65b-instruct) * **Language(s)**: English * **Library**: [HuggingFace Transformers](https://github.com/huggingface/transformers) * **License**: This model is under a **Non-commercial** Bespoke License and governed by the Meta license. You should only use this repository if you have been granted access to the model by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform), but have either lost your copy of the weights or encountered issues converting them to the Transformers format * **Where to send comments**: Instructions on how to provide feedback or comments on a model can be found by opening an issue in the [Hugging Face community's model repository](https://huggingface.co/upstage/llama-30b-instruct/discussions) * **Contact**: For questions and comments about the model, please email [contact@upstage.ai](mailto:contact@upstage.ai) ## Dataset Details ### Used Datasets - [openbookqa](https://huggingface.co/datasets/openbookqa) - [sciq](https://huggingface.co/datasets/sciq) - [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) - [metaeval/ScienceQA_text_only](https://huggingface.co/datasets/metaeval/ScienceQA_text_only) - [GAIR/lima](https://huggingface.co/datasets/GAIR/lima) - No other data was used except for the dataset mentioned above ### Prompt Template ``` ### System: {System} ### User: {User} ### Assistant: {Assistant} ``` ## Usage - Tested on A100 80GB - Our model can handle up to 10k+ input tokens, thanks to the `rope_scaling` option ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer tokenizer = AutoTokenizer.from_pretrained("upstage/llama-30b-instruct") model = AutoModelForCausalLM.from_pretrained( "upstage/llama-30b-instruct", device_map="auto", torch_dtype=torch.float16, load_in_8bit=True, rope_scaling={"type": "dynamic", "factor": 2} # allows handling of longer inputs ) prompt = "### User:\nThomas is healthy, but he has to go to the hospital. What could be the reasons?\n\n### Assistant:\n" inputs = tokenizer(prompt, return_tensors="pt").to(model.device) del inputs["token_type_ids"] streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) output = model.generate(**inputs, streamer=streamer, use_cache=True, max_new_tokens=float('inf')) output_text = tokenizer.decode(output[0], skip_special_tokens=True) ``` ## Hardware and Software * **Hardware**: We utilized an A100x8 * 1 for training our model * **Training Factors**: We fine-tuned this model using a combination of the [DeepSpeed library](https://github.com/microsoft/DeepSpeed) and the [HuggingFace Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) / [HuggingFace Accelerate](https://huggingface.co/docs/accelerate/index) ## Evaluation Results ### Overview - We conducted a performance evaluation based on the tasks being evaluated on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) We evaluated our model on four benchmark datasets, which include `ARC-Challenge`, `HellaSwag`, `MMLU`, and `TruthfulQA` We used the [lm-evaluation-harness repository](https://github.com/EleutherAI/lm-evaluation-harness), specifically commit [b281b0921b636bc36ad05c0b0b0763bd6dd43463](https://github.com/EleutherAI/lm-evaluation-harness/tree/b281b0921b636bc36ad05c0b0b0763bd6dd43463) - We used [MT-bench](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge), a set of challenging multi-turn open-ended questions, to evaluate the models ### Main Results | Model | H4(Avg) | ARC | HellaSwag | MMLU | TruthfulQA | | MT_Bench | |--------------------------------------------------------------------|----------|----------|----------|------|----------|-|-------------| | **[Llama-2-70b-instruct-v2](https://huggingface.co/upstage/Llama-2-70b-instruct-v2)**(Ours, Open LLM Leaderboard) | **73** | **71.1** | **87.9** | **70.6** | **62.2** | | **7.44063** | | [Llama-2-70b-instruct](https://huggingface.co/upstage/Llama-2-70b-instruct) (Ours, Open LLM Leaderboard) | 72.3 | 70.9 | 87.5 | 69.8 | 61 | | 7.24375 | | [llama-65b-instruct](https://huggingface.co/upstage/llama-65b-instruct) (Ours, Open LLM Leaderboard) | 69.4 | 67.6 | 86.5 | 64.9 | 58.8 | | | | Llama-2-70b-hf | 67.3 | 67.3 | 87.3 | 69.8 | 44.9 | | | | [llama-30b-instruct-2048](https://huggingface.co/upstage/llama-30b-instruct-2048) (Ours, Open LLM Leaderboard) | 67.0 | 64.9 | 84.9 | 61.9 | 56.3 | | | | [llama-30b-instruct](https://huggingface.co/upstage/llama-30b-instruct) (***Ours***, ***Open LLM Leaderboard***) | 65.2 | 62.5 | 86.2 | 59.4 | 52.8 | | | | llama-65b | 64.2 | 63.5 | 86.1 | 63.9 | 43.4 | | | | falcon-40b-instruct | 63.4 | 61.6 | 84.3 | 55.4 | 52.5 | | | ### Scripts for H4 Score Reproduction - Prepare evaluation environments: ``` # clone the repository git clone https://github.com/EleutherAI/lm-evaluation-harness.git # check out the specific commit git checkout b281b0921b636bc36ad05c0b0b0763bd6dd43463 # change to the repository directory cd lm-evaluation-harness ``` ## Ethical Issues ### Ethical Considerations - There were no ethical issues involved, as we did not include the benchmark test set or the training set in the model's training process ## Contact Us ### Why Upstage LLM? - [Upstage](https://en.upstage.ai)'s LLM research has yielded remarkable results. As of August 1st, our 70B model has reached the top spot in openLLM rankings, marking itself as the current leading performer globally. Recognizing the immense potential in implementing private LLM to actual businesses, we invite you to easily apply private LLM and fine-tune it with your own data. For a seamless and tailored solution, please do not hesitate to reach out to us. ► [click here to contact](https://www.upstage.ai/private-llm?utm_source=huggingface&utm_medium=link&utm_campaign=privatellm)

johnowhitaker/mcqgen_test1

johnowhitaker

2023-08-03T21:59:04Z

4

0

peft

[ "peft", "region:us" ]

null

2023-08-03T20:48:10Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0 notebook (training and inference): https://colab.research.google.com/drive/1GxbUYZiLidteVX4qu5iSox6oxxEOHk5O?usp=sharing Usage: ```python import requests # Get a random Wikipedia article summary using their API def random_extract(): URL = "https://en.wikipedia.org/api/rest_v1/page/random/summary" PARAMS = {} r = requests.get(url = URL, params = PARAMS) data = r.json() return data['extract'] # Format this as a prompt that would hopefully result in the model completing with a question def random_prompt(): e = random_extract() return f"""### CONTEXT: {e} ### QUESTION:""" import torch from peft import AutoPeftModelForCausalLM from transformers import AutoTokenizer output_dir = "mcqgen_test" # load base LLM model and tokenizer model = AutoPeftModelForCausalLM.from_pretrained( output_dir, low_cpu_mem_usage=True, torch_dtype=torch.float16, load_in_4bit=True, ) tokenizer = AutoTokenizer.from_pretrained(output_dir) # We can feed in a random context prompt and see what question the model comes up with: prompt = random_prompt() input_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.cuda() # with torch.inference_mode(): outputs = model.generate(input_ids=input_ids, max_new_tokens=100, do_sample=True, top_p=0.9,temperature=0.9) print(f"Prompt:\n{prompt}\n") print(f"Generated MCQ:\n### QUESTION:{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]}") def process_outputs(outputs): s = tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0] split = s.split("### ")[1:][:7] if len(split) != 7: return None # Check the starts expected_starts = ['CONTEXT', 'QUESTION', 'A' , 'B', 'C', 'D', 'CORRECT'] for i, s in enumerate(split): if not split[i].startswith(expected_starts[i]): return None return { "context": split[0].replace("CONTEXT: ", ""), "question": split[1].replace("QUESTION: ", ""), "a": split[2].replace("A: ", ""), "b": split[3].replace("B: ", ""), "c": split[4].replace("C: ", ""), "d": split[5].replace("D: ", ""), "correct": split[6].replace("CORRECT: ", "") } process_outputs(outputs) # A nice dictionary hopefully ```

ckandemir/speecht5_finetuned_voxpopuli_fr

ckandemir

2023-08-03T21:20:21Z

10

0

transformers

[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "dataset:voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]

text-to-speech

2023-07-31T21:24:05Z

--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer datasets: - voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_fr results: [] pipeline_tag: text-to-speech ---  # speecht5_finetuned_voxpopuli_fr This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4697 ## 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: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.765 | 0.23 | 50 | 0.6575 | | 0.687 | 0.47 | 100 | 0.6106 | | 0.6423 | 0.7 | 150 | 0.5548 | | 0.5792 | 0.94 | 200 | 0.5300 | | 0.5658 | 1.17 | 250 | 0.5186 | | 0.5558 | 1.41 | 300 | 0.5078 | | 0.5484 | 1.64 | 350 | 0.5029 | | 0.5427 | 1.87 | 400 | 0.4981 | | 0.5349 | 2.11 | 450 | 0.4921 | | 0.524 | 2.34 | 500 | 0.4906 | | 0.5243 | 2.58 | 550 | 0.4857 | | 0.5238 | 2.81 | 600 | 0.4835 | | 0.5104 | 3.05 | 650 | 0.4796 | | 0.516 | 3.28 | 700 | 0.4769 | | 0.5084 | 3.51 | 750 | 0.4763 | | 0.5029 | 3.75 | 800 | 0.4749 | | 0.5015 | 3.98 | 850 | 0.4725 | | 0.5045 | 4.22 | 900 | 0.4716 | | 0.503 | 4.45 | 950 | 0.4706 | | 0.5013 | 4.69 | 1000 | 0.4697 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.2 - Tokenizers 0.13.3

Mikeyli/repo_stableDiff

Mikeyli

2023-08-03T21:09:41Z

0

null

[ "region:us" ]

null

2023-08-03T20:59:49Z

v0.1 Test for ia generated image

chengyineng/bloom_prompt_tuning_1691096309.5967999

chengyineng

2023-08-03T21:08:07Z

2

0

peft

[ "peft", "region:us" ]

null

2023-08-03T20:58:32Z

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

Steve77/ppo-LunarLander-v2

Steve77

2023-08-03T21:00:50Z

0

stable-baselines3

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

reinforcement-learning

2023-08-03T21:00:32Z

--- 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: 254.08 +/- 17.56 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 ... ```

aaswin/dolly-finetuned1

aaswin

2023-08-03T20:48:50Z

0

peft

[ "peft", "region:us" ]

null

2023-08-03T20:48:48Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0

Saurabh16100/llama-saved-model

Saurabh16100

2023-08-03T20:27:53Z

0

peft

[ "peft", "llama", "4-bit", "region:us" ]

null

2023-08-03T20:15:24Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0

abhiman23897/longformer_pico_model

abhiman23897

2023-08-03T20:26:46Z

8

0

transformers

[ "transformers", "pytorch", "longformer", "token-classification", "medical", "dataset:Stardrums/pico-breast-cancer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-08-03T14:14:27Z

--- license: apache-2.0 datasets: - Stardrums/pico-breast-cancer pipeline_tag: token-classification tags: - medical ---

yxxshin/mymodels

yxxshin

2023-08-03T20:25:46Z

5

0

diffusers

[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:stabilityai/stable-diffusion-2", "base_model:finetune:stabilityai/stable-diffusion-2", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-08-03T19:24:36Z

--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2 instance_prompt: a photo of zwf dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - yxxshin/mymodels This is a dreambooth model derived from stabilityai/stable-diffusion-2. The weights were trained on a photo of zwf dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: True.

Mastering-Python-HF/nvidia_tts_en_hifitts_hifigan_ft_fastpitch

Mastering-Python-HF

2023-08-03T20:14:11Z

51

0

nemo

[ "nemo", "HiFiTTS", "PyTorch", "text-to-speech", "en", "arxiv:2006.06873", "arxiv:2108.10447", "region:us" ]

text-to-speech

2023-07-10T17:31:06Z

--- tags: - HiFiTTS - PyTorch language: - en pipeline_tag: text-to-speech --- # NVIDIA Hifigan Vocoder (en-US) HiFiGAN [1] is a generative adversarial network (GAN) model that generates audio from mel spectrograms. The generator uses transposed convolutions to upsample mel spectrograms to audio. ## Usage The model is available for use in the NeMo toolkit [3] and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. To train, fine-tune or play with the model you will need to install NVIDIA NeMo. We recommend you install it after you've installed the latest PyTorch version. ``` git clone https://github.com/NVIDIA/NeMo cd NeMo BRANCH = 'main' python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all] ``` ## instantiate the model Note: This model generates only spectrograms and a vocoder is needed to convert the spectrograms to waveforms. In this example HiFiGAN is used. ``` from huggingface_hub import hf_hub_download from nemo.collections.tts.models import FastPitchModel from nemo.collections.tts.models import HifiGanModel REPO_ID = "Mastering-Python-HF/nvidia_tts_en_fastpitch_multispeaker" FILENAME = "tts_en_fastpitch_multispeaker.nemo" path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME) spec_generator = FastPitchModel.restore_from(restore_path=path) REPO_ID = "Mastering-Python-HF/nvidia_tts_en_hifitts_hifigan_ft_fastpitch" FILENAME = "tts_en_hifitts_hifigan_ft_fastpitch.nemo" path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME) model = HifiGanModel.restore_from(restore_path=path) ``` ## Generate and save audio ``` import soundfile as sf parsed = spec_generator.parse("You can type your sentence here to get nemo to produce speech.") """ speaker id: 92 Cori Samuel 6097 Phil Benson 9017 John Van Stan 6670 Mike Pelton 6671 Tony Oliva 8051 Maria Kasper 9136 Helen Taylor 11614 Sylviamb 11697 Celine Major 12787 LikeManyWaters """ spectrogram = spec_generator.generate_spectrogram(tokens=parsed,speaker=92) audio = model.convert_spectrogram_to_audio(spec=spectrogram) sf.write("speech.wav", audio.to('cpu').detach().numpy()[0], 44100) ``` ## Colab example #### LINK : [nvidia_tts_en_fastpitch_multispeaker](https://colab.research.google.com/drive/1ZJFCMVVjl7VtfVGlkQ-G1cXKyaucBzJf?usp=sharing) ### Input This model accepts batches of text. ### Output This model generates mel spectrograms. ## Model Architecture FastPitch multispeaker is a fully-parallel text-to-speech model based on FastSpeech, conditioned on fundamental frequency contours. The model predicts pitch contours during inference. By altering these predictions, the generated speech can be more expressive, better match the semantic of the utterance, and in the end more engaging to the listener. FastPitch is based on a fully-parallel Transformer architecture, with a much higher real-time factor than Tacotron2 for the mel-spectrogram synthesis of a typical utterance. It uses an unsupervised speech-text aligner. ## Training The NeMo toolkit [3] was used for training the models for 1000 epochs. ## Datasets This model is trained on HiFiTTS sampled at 44100Hz, and has been tested on generating multispeaker English voices with an American and UK accent. ## Performance No performance information is available at this time. ## Limitations This checkpoint only works well with vocoders that were trained on 44100Hz data. Otherwise, the generated audio may be scratchy or choppy-sounding. ## References - [1] [FastPitch: Parallel Text-to-speech with Pitch Prediction](https://arxiv.org/abs/2006.06873) - [2] [One TTS Alignment To Rule Them All](https://arxiv.org/abs/2108.10447) - [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)

nitrosocke/Ghibli-Diffusion

nitrosocke

2023-08-03T19:46:59Z

2,345

621

diffusers

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

text-to-image

2022-11-18T15:50:51Z

--- language: - en license: creativeml-openrail-m thumbnail: "https://huggingface.co/nitrosocke/Ghibli-Diffusion/resolve/main/images/ghibli-diffusion-thumbnail.jpg" tags: - stable-diffusion - text-to-image - image-to-image - diffusers --- ### Ghibli Diffusion This is the fine-tuned Stable Diffusion model trained on images from modern anime feature films from Studio Ghibli. Use the tokens **_ghibli style_** in your prompts for the effect. **If you enjoy my work and want to test new models before release, please consider supporting me** [![Become A Patreon](https://badgen.net/badge/become/a%20patron/F96854)](https://patreon.com/user?u=79196446) **Characters rendered with the model:** ![Characters Samples](https://huggingface.co/nitrosocke/Ghibli-Diffusion/resolve/main/images/ghibli-diffusion-samples-01s.jpg) **Cars and Animals rendered with the model:** ![Misc. Samples](https://huggingface.co/nitrosocke/Ghibli-Diffusion/resolve/main/images/ghibli-diffusion-samples-02s.jpg) **Landscapes rendered with the model:** ![Landscape 1](https://huggingface.co/nitrosocke/Ghibli-Diffusion/resolve/main/images/ghibli-diffusion-samples-03s.jpg) _ghibli style beautiful Caribbean beach tropical (sunset) - Negative prompt: soft blurry_ ![Landscape 2](https://huggingface.co/nitrosocke/Ghibli-Diffusion/resolve/main/images/ghibli-diffusion-samples-04s.jpg) _ghibli style ice field white mountains ((northern lights)) starry sky low horizon - Negative prompt: soft blurry_ #### Prompt and settings for the Strom Trooper: **ghibli style (storm trooper) Negative prompt: (bad anatomy)** _Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 3450349066, Size: 512x704_ #### Prompt and settings for the VW Beetle: **ghibli style VW beetle Negative prompt: soft blurry** _Steps: 30, Sampler: Euler a, CFG scale: 7, Seed: 1529856912, Size: 704x512_ This model was trained using the diffusers based dreambooth training by ShivamShrirao using prior-preservation loss and the _train-text-encoder_ flag in 15.000 steps.  ### 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX](). ```python from diffusers import StableDiffusionPipeline import torch model_id = "nitrosocke/Ghibli-Diffusion" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "ghibli style magical princess with golden hair" image = pipe(prompt).images[0] image.save("./magical_princess.png") ``` ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)

google/ddpm-cat-256

google

2023-08-03T19:46:57Z

3,887

9

diffusers

[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "arxiv:2006.11239", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-07-19T10:42:07Z

--- license: apache-2.0 tags: - pytorch - diffusers - unconditional-image-generation --- # Denoising Diffusion Probabilistic Models (DDPM) **Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) **Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel **Abstract**: *We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.* ## Inference **DDPM** models can use *discrete noise schedulers* such as: - [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py) - [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py) - [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py) for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest. For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead. See the following code: ```python # !pip install diffusers from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline model_id = "google/ddpm-cat-256" # load model and scheduler ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference # run pipeline in inference (sample random noise and denoise) image = ddpm().images[0] # save image image.save("ddpm_generated_image.png") ``` For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) ## Training If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) ## Samples 1. ![sample_1](https://huggingface.co/google/ddpm-cat-256/resolve/main/images/generated_image_0.png) 2. ![sample_2](https://huggingface.co/google/ddpm-cat-256/resolve/main/images/generated_image_1.png) 3. ![sample_3](https://huggingface.co/google/ddpm-cat-256/resolve/main/images/generated_image_2.png) 4. ![sample_4](https://huggingface.co/google/ddpm-cat-256/resolve/main/images/generated_image_3.png)

Sambosis/bert-base-cased-finetuned-swag

Sambosis

2023-08-03T19:45:41Z

59

0

transformers

[ "transformers", "tf", "tensorboard", "bert", "multiple-choice", "generated_from_keras_callback", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]

multiple-choice

2023-08-03T16:00:24Z

--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_keras_callback model-index: - name: Sambosis/bert-base-cased-finetuned-swag results: [] ---  # Sambosis/bert-base-cased-finetuned-swag This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4828 - Train Accuracy: 0.8181 - Validation Loss: 0.6618 - Validation Accuracy: 0.7539 - Epoch: 1 ## 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': 5e-05, 'decay_steps': 22980, '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 | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.8870 | 0.6429 | 0.6619 | 0.7401 | 0 | | 0.4828 | 0.8181 | 0.6618 | 0.7539 | 1 | ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.3 - Tokenizers 0.13.3

polejowska/detr-r50-cd45rb-2ah-6l

polejowska

2023-08-03T19:43:55Z

163

0

transformers

[ "transformers", "pytorch", "detr", "object-detection", "generated_from_trainer", "dataset:cd45rb", "license:apache-2.0", "endpoints_compatible", "region:us" ]

object-detection

2023-06-11T06:23:13Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - cd45rb model-index: - name: detr-r50-cd45rb-2ah-6l results: [] ---  # detr-r50-cd45rb-2ah-6l This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cd45rb dataset. It achieves the following results on the evaluation set: - Loss: 2.0706 ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.4851 | 1.0 | 4606 | 2.4806 | | 2.995 | 2.0 | 9212 | 2.2761 | | 2.7717 | 3.0 | 13818 | 2.1601 | | 2.674 | 4.0 | 18424 | 2.0849 | | 2.6185 | 5.0 | 23030 | 2.0706 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.3

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

EmmaRo

2023-08-03T19:30:17Z

0

null

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

reinforcement-learning

2023-08-03T19:30:14Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="EmmaRo/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"]) ```

Wilbas/DeepLearning_Unit2_Taxi-v3

Wilbas

2023-08-03T19:00:36Z

0

null

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

reinforcement-learning

2023-08-03T19:00:34Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: DeepLearning_Unit2_Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 2.73 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="Wilbas/DeepLearning_Unit2_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"]) ```

shtif/a2c-PandaReachDense-v2

shtif

2023-08-03T18:55:22Z

1

0

stable-baselines3

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

reinforcement-learning

2023-08-03T18:52:52Z

--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -0.48 +/- 0.20 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-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 ... ```

JasonWen/FurryTuringTest

JasonWen

2023-08-03T18:49:36Z

0

null

[ "zh", "license:cc-by-nc-sa-4.0", "region:us" ]

null

2023-07-24T08:58:31Z

--- license: cc-by-nc-sa-4.0 language: - zh --- [图灵测试结果发布页]( https://hf.co/JasonWen/FurryTuringTest) Turing_test_Result_Publishing_Page 如果您还没有填写问卷，可以点击这个[链接](https://wj.qq.com/s2/12840227/1a62/)或者扫码测试： ![](https://huggingface.co/JasonWen/FurryTuringTest/resolve/main/pic/Turing%20test%20-%20Raise%20the%20Board.png) ![](https://huggingface.co/JasonWen/FurryTuringTest/resolve/main/pic/Turing%20test%20QR%20code.png) 感谢您完成图灵测试的问卷，我们会在问卷搜集一万份后公布结果，若在八月底仍不足一万份届时也会公布结果。问卷的结果包括所有图片的原图、来源，每道题的结果以及综合数据分析。目前结果仍未发布，您可以加入我们的社群：discord[Furry-AICG（8月26日之前有效）](https://discord.gg/uYuA7kPSf)、telegram[Furry AI 炼金群](https://t.me/furry_ai)

bilbo991/clip-homer

bilbo991

2023-08-03T18:43:52Z

11

0

transformers

[ "transformers", "pytorch", "tensorboard", "vision-text-dual-encoder", "feature-extraction", "generated_from_trainer", "endpoints_compatible", "region:us" ]

feature-extraction

2023-08-02T21:02:05Z

--- base_model: clip-homer tags: - generated_from_trainer model-index: - name: clip-homer results: [] ---  # clip-homer This model is a fine-tuned version of [clip-homer](https://huggingface.co/clip-homer) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4658 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.1 - Tokenizers 0.13.3

chengyineng/bloom_prompt_tuning_1691087323.3441193

chengyineng

2023-08-03T18:28:47Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-03T18:28:46Z

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

Saurabh16100/finetuned-llama2-7b

Saurabh16100

2023-08-03T18:28:40Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-03T18:25:38Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0

Murzeltje1976/models

Murzeltje1976

2023-08-03T18:27:18Z

0

null

[ "arxiv:1910.09700", "region:us" ]

null

2023-08-02T17:31:45Z

--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID Just a personal archive for used models in AUTOMATIC1111 ## Model Details ### Model Description Just a personal archive for used models in AUTOMATIC1111. That I use. - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

flyingbox/test_upload

flyingbox

2023-08-03T18:21:22Z

0

null

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

null

2023-08-03T18:21:22Z

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

SaudxInu/DQN-LunarLander-v2

SaudxInu

2023-08-03T18:21:18Z

0

stable-baselines3

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

reinforcement-learning

2023-08-03T17:04:27Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 58.89 +/- 158.77 name: mean_reward verified: false --- # **DQN** Agent playing **LunarLander-v2** This is a trained model of a **DQN** 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 ... ```

dariowsz/poca-SoccerTwos

dariowsz

2023-08-03T18:19:00Z

21

0

ml-agents

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

reinforcement-learning

2023-08-03T18:18:57Z

--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** 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: dariowsz/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

KnutJaegersberg/sentence-data-scideberta-cs

KnutJaegersberg

2023-08-03T18:17:22Z

1

0

sentence-transformers

[ "sentence-transformers", "pytorch", "deberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2023-08-03T18:10:01Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch def cls_pooling(model_output, attention_mask): return model_output[0][:,0] # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, cls pooling. sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 23043 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 32, 'do_lower_case': False}) with Transformer model: DebertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors

J-Douglas/pokemon-lora

J-Douglas

2023-08-03T18:12:35Z

7

0

diffusers

[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-07-05T19:48:09Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - J-Douglas/pokemon-lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)

digiplay/AbsoluteReality_v1.8.1

digiplay

2023-08-03T18:05:25Z

284,099

29

diffusers

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

text-to-image

2023-08-03T16:58:32Z

--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/81458?modelVersionId=132760 Original Author's DEMO images : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/0375afda-d837-4cf9-a0f1-958810ab300d/width=1120/26072207-5775662-(masterpiece),%20(extremely%20intricate_1.3),,%20(realistic),%20portrait%20of%20a%20girl,%20the%20most%20beautiful%20in%20the%20world,%20(medieval%20armor),%20m.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/6c6fa49e-05d1-4dcb-9527-b1ed801db614/width=920/26072134-1709831004-a%20photo%20of%20a%20cute%20little%20puppy%20surrounded%20by%20beautiful%20flowers%20in%20a%20meadow,%20extremely%20detailed%20fur,(close%20up_1.1).jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/7a0bc81d-0702-4c85-aa17-dd6748cb6fbd/width=920/26072120-2760683419-close%20up%20Portrait%20photo%20of%20muscular%20bearded%20guy%20in%20a%20worn%20mech%20suit,%20((light%20bokeh)),%20intricate,%20(steel%20metal%20[rust]),%20elegant,.jpeg)

shtif/a2c-AntBulletEnv-v0

shtif

2023-08-03T18:03:32Z

1

0

stable-baselines3

[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-08-03T18:02:34Z

--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1692.79 +/- 59.70 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** 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 ... ```

thiagodepaulo/distilbert-base-uncased-finetuned-emotion

thiagodepaulo

2023-08-03T17:47:07Z

110

0

transformers

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

text-classification

2023-08-03T00:58:43Z

--- license: apache-2.0 base_model: distilbert-base-uncased 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: 0.9205 - name: F1 type: f1 value: 0.9207431728392997 ---  # 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: 0.2232 - Accuracy: 0.9205 - F1: 0.9207 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 125 | 0.5150 | 0.862 | 0.8502 | | 0.7222 | 2.0 | 250 | 0.2605 | 0.9155 | 0.9151 | | 0.7222 | 3.0 | 375 | 0.2232 | 0.9205 | 0.9207 | ### Framework versions - Transformers 4.31.0 - Pytorch 1.12.1+cu102 - Datasets 2.14.2 - Tokenizers 0.13.3

Dgray518/llama2-qlora-finetunined-french

Dgray518

2023-08-03T17:42:12Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-03T17:42:04Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0

rice-rice/detr-resnet-50_finetuned_dataset

rice-rice

2023-08-03T17:41:46Z

189

0

transformers

[ "transformers", "pytorch", "tensorboard", "detr", "object-detection", "generated_from_trainer", "dataset:pothole-segmentation", "base_model:facebook/detr-resnet-50", "base_model:finetune:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]

object-detection

2023-08-03T17:13:57Z

--- license: apache-2.0 base_model: facebook/detr-resnet-50 tags: - generated_from_trainer datasets: - pothole-segmentation model-index: - name: detr-resnet-50_finetuned_dataset results: [] ---  # detr-resnet-50_finetuned_dataset This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the pothole-segmentation 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: 10 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3

kaczmarj/lymphnodes-tiatoolbox-resnet50.patchcamelyon

kaczmarj

2023-08-03T17:36:38Z

60

1

transformers

[ "transformers", "pytorch", "safetensors", "histology", "digital pathology", "patch camelyon", "tiatoolbox", "lymph nodes", "metastasis", "camelyon", "image-classification", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]

image-classification

2023-06-21T16:24:02Z

--- license: cc-by-4.0 pipeline_tag: image-classification tags: - histology - digital pathology - patch camelyon - tiatoolbox - lymph nodes - metastasis - camelyon --- # ResNet50 trained on PatchCamelyon (via TIA Toolbox) This is a re-hosted version of the model available in the TIA Toolbox model zoo (licensed CC-BY-4.0). # Reusing the model Coming soon... # Dataset The PatchCamelyon dataset can be found on Zenodo https://zenodo.org/record/2546921 and on GitHub https://github.com/basveeling/pcam. # References ```bibtex @inproceedings{he2016deep, title={Deep residual learning for image recognition}, author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, pages={770--778}, year={2016} } @ARTICLE{Veeling2018-qh, title = "Rotation Equivariant {CNNs} for Digital Pathology", author = "Veeling, Bastiaan S and Linmans, Jasper and Winkens, Jim and Cohen, Taco and Welling, Max", month = jun, year = 2018, archivePrefix = "arXiv", primaryClass = "cs.CV", eprint = "1806.03962" } @article{pocock2022tiatoolbox, title={TIAToolbox as an end-to-end library for advanced tissue image analytics}, author={Pocock, Johnathan and Graham, Simon and Vu, Quoc Dang and Jahanifar, Mostafa and Deshpande, Srijay and Hadjigeorghiou, Giorgos and Shephard, Adam and Bashir, Raja Muhammad Saad and Bilal, Mohsin and Lu, Wenqi and others}, journal={Communications medicine}, volume={2}, number={1}, pages={120}, year={2022}, publisher={Nature Publishing Group UK London} } ```

nkpz/llama2-22b-blocktriangular-alpaca

nkpz

2023-08-03T17:34:17Z

13

2

transformers

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

text-generation

2023-08-03T14:24:51Z

--- license: other --- **There is no official 22b model, this is just a weird experiment, and any potential benefits of doing this have not been validated** https://huggingface.co/chargoddard/llama2-22b-blocktriangular trained one one epoch of 52k rows of Stanford Alpaca. About 11 hours on a 3090. I had trouble with training using the other 22b method with `BLOCK_DIAGONAL=True` as done in https://huggingface.co/chargoddard/llama2-22b, but with this method, this is the first time I've been able to target all modules without breaking the output. `target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "up_proj", "gate_proj", "down_proj"]` Trained at 5e-5 with r=32. For more info see https://wandb.ai/nkpz/huggingface/runs/3oy5nbtv/workspace?workspace=user-nkpz It's been responding coherently enough that I would need to run some objective benchmarks to determine if this is better/worse than stock llama 13b

BmanClark/Taxi-v3

BmanClark

2023-08-03T17:31:26Z

0

null

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

reinforcement-learning

2023-08-03T17:31:21Z

--- 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="BmanClark/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"]) ```

Bastian1111/ppo-Huggy

Bastian1111

2023-08-03T17:30:00Z

0

ml-agents

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

reinforcement-learning

2023-08-03T17:29:50Z

--- 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: Bastian1111/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

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

BmanClark

2023-08-03T17:26:14Z

0

null

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

reinforcement-learning

2023-08-03T17:26:09Z

--- 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="BmanClark/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"]) ```

Kybalico/AnmitsuMimimi

Kybalico

2023-08-03T17:24:49Z

0

2

null

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

null

2023-08-03T17:16:30Z

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

or4cl3ai/A-os43-v1

or4cl3ai

2023-08-03T17:23:18Z

0

null

[ "en", "license:openrail", "region:us" ]

null

2023-08-03T02:08:35Z

--- license: openrail language: - en --- model: name: Custom LLM for A-os43-v1 version: 1.0 id: CLLM-AOS43-2022 type: Language Model architecture: Transformer-based Language Model framework: Hugging Face Transformers programming_language: Rust operating_system: A-os43-v1 --- datasets: - Operating System Documentation Dataset - A-os43-v1 Source Code Dataset - Rust Programming Language Documentation - System Programming Concepts Dataset - Operating System Design Principles Dataset - Software Engineering Best Practices Dataset - User Input and Command Parsing Dataset - Error Handling Techniques Dataset - System Call Interface Dataset - System Configuration File Syntax Dataset - User Interface Text and Localization Dataset - Common System Error Messages Dataset - User Documentation Dataset - API Reference Documentation Dataset - User Feedback and Support Dataset - System Log File Analysis Dataset - System Performance Monitoring Dataset - System Resource Management Dataset - File System Operations Dataset - Networking and Socket Programming Dataset - Security Principles and Best Practices Dataset - Memory Management Techniques Dataset - Concurrency and Synchronization Dataset - System Service Initialization Dataset - Command-Line Interface Design Dataset --- metrics: - Language Coherence - Code Quality - Natural Language Understanding - Response Time - Compatibility - Adaptability - Documentation Accuracy - ---

Sookeyy/ppo-PyramidsRND

Sookeyy

2023-08-03T17:22:54Z

0

ml-agents

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

reinforcement-learning

2023-08-03T17:20:54Z

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

BabaYaga048/Taxi-v3-2

BabaYaga048

2023-08-03T17:19:17Z

0

null

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

reinforcement-learning

2023-08-03T17:19:14Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3-2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 1.17 +/- 25.45 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="BabaYaga048/Taxi-v3-2", 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"]) ```

MF-FOOM/wikivec2text

MF-FOOM

2023-08-03T17:13:14Z

0

8

null

[ "en", "license:mit", "region:us" ]

null

2023-08-03T17:00:28Z

--- license: mit language: - en --- # wikivec2text Simple embedding -> text model trained on a tiny subset of Wikipedia sentences ([~7m](https://www.kaggle.com/datasets/mikeortman/wikipedia-sentences)) embedded via text-embedding-ada-002. Used to demonstrate the arithmetic properties of sentence embeddings. ## Acknowledgements Built on [nanoGPT](https://github.com/karpathy/nanoGPT).

KingKazma/xsum_t5-small_prompt_tuning_500_10_3000_8_e-1_s6789_v3_l5_v50_manual

KingKazma

2023-08-03T17:09:14Z

0

peft

[ "peft", "region:us" ]

null

2023-08-03T17:09:11Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0