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 2025-05-28 18:26:29	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 477 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-05-28 18:24:32	card stringlengths 11 1.01M
szymonrucinski/good-mood	szymonrucinski	2023-08-09T21:09:47Z	0	0	null	[ "license:cc-by-nc-sa-3.0", "region:us" ]	null	2023-08-09T16:17:39Z	--- license: cc-by-nc-sa-3.0 ---
badokorach/bert-finetuned-squad-7	badokorach	2023-08-09T21:00:46Z	3	0	transformers	[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "base_model:badokorach/bert-finetuned-squad-5", "base_model:finetune:badokorach/bert-finetuned-squad-5", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-08-09T16:23:33Z	--- license: apache-2.0 base_model: badokorach/bert-finetuned-squad-5 tags: - generated_from_keras_callback model-index: - name: badokorach/bert-finetuned-squad-7 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. --> # badokorach/bert-finetuned-squad-7 This model is a fine-tuned version of [badokorach/bert-finetuned-squad-5](https://huggingface.co/badokorach/bert-finetuned-squad-5) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0011 - Epoch: 29 ## 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 4e-05, 'decay_steps': 1950, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results \| Train Loss \| Epoch \| \|:----------:\|:-----:\| \| 0.0673 \| 0 \| \| 0.1201 \| 1 \| \| 0.0502 \| 2 \| \| 0.0209 \| 3 \| \| 0.0278 \| 4 \| \| 0.0358 \| 5 \| \| 0.0268 \| 6 \| \| 0.0258 \| 7 \| \| 0.0212 \| 8 \| \| 0.0247 \| 9 \| \| 0.0104 \| 10 \| \| 0.0101 \| 11 \| \| 0.0033 \| 12 \| \| 0.0044 \| 13 \| \| 0.0185 \| 14 \| \| 0.0051 \| 15 \| \| 0.0011 \| 16 \| \| 0.0043 \| 17 \| \| 0.0022 \| 18 \| \| 0.0026 \| 19 \| \| 0.0019 \| 20 \| \| 0.0012 \| 21 \| \| 0.0013 \| 22 \| \| 0.0009 \| 23 \| \| 0.0008 \| 24 \| \| 0.0007 \| 25 \| \| 0.0016 \| 26 \| \| 0.0006 \| 27 \| \| 0.0006 \| 28 \| \| 0.0011 \| 29 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.4 - Tokenizers 0.13.3
chronopt-research/vietnamese-gpt2-base	chronopt-research	2023-08-09T20:58:46Z	147	1	transformers	[ "transformers", "pytorch", "jax", "tensorboard", "gpt2", "text-generation", "vi", "dataset:duongttr/vi-dataset-for-pretrain", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-08-09T20:29:15Z	--- license: apache-2.0 datasets: - duongttr/vi-dataset-for-pretrain language: - vi metrics: - perplexity pipeline_tag: text-generation widget: - text: Hôm nay tôi rất vui vì - text: Hoàng Sa, Trường Sa là của Việt model-index: - name: chronopt-research/vietnamese-gpt2-base results: - task: type: text-generation metrics: - type: perplexity value: 51.35 verified: true --- # Vietnamese `gpt2-base` <!-- Provide a quick summary of what the model is/does. --> This is a pretrained `gpt2-base` for Vietnamese language using casual language modeling (CLM) objective. It was introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and first released at [this page](https://openai.com/blog/better-language-models/). ## Model Description GPT-2 (at first) is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. This is the base version of GPT-2, with 137M parameters. You could've found other pretrained version from here: [gpt2-medium](https://huggingface.co/chronopt-research/vietnamese-gpt2-medium), [gpt2-large]() ## Dataset used for pretraining This is a combination of multiple Vietnamese dataset for pretraining CLMs such as GPT, GPT2, etc. The dataset consists of: - [`vietgpt/covid_19_news_vi`](https://huggingface.co/datasets/vietgpt/covid_19_news_vi) - [`hieunguyen1053/binhvq-news-corpus`](https://huggingface.co/datasets/hieunguyen1053/binhvq-news-corpus) - [`oscar (unshuffled_deduplicated_vi)`](https://huggingface.co/datasets/oscar) - [`vietgpt/wikipedia_vi`](https://huggingface.co/datasets/vietgpt/wikipedia_vi) You can find out the combined version here: [duongttr/vi-dataset-for-pretrain](https://huggingface.co/datasets/duongttr/vi-dataset-for-pretrain) ## Hyperparamters & Results We trained the model ~100k steps, with `lr=1e-4`, `bs=2560` (`single_batch_size=32` * `num_core=8` * `grad_cum=10`), `optimizer=adamw` on TPU-VM-3.8 from [TRC Program](https://sites.research.google/trc/about/). The training costs around 1 day. \|Model\|Eval Loss\|Eval Perplexity\| \|---\|---\|---\| \|gpt2-base\|3.939\|51.35\| \|gpt2-medium\|2.8676\|17.5948\| \|gpt2-large\|-\|-\| ## Contacts Feel free to contact us via: [email]()
Jbrophy/falcon-7B-Instruct-Romance	Jbrophy	2023-08-09T20:58:15Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-08T00:39:51Z	--- 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
chronopt-research/vietnamese-gpt2-medium	chronopt-research	2023-08-09T20:54:47Z	146	2	transformers	[ "transformers", "pytorch", "jax", "tensorboard", "gpt2", "text-generation", "vi", "dataset:duongttr/vi-dataset-for-pretrain", "doi:10.57967/hf/3874", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-08-06T11:34:08Z	--- license: apache-2.0 datasets: - duongttr/vi-dataset-for-pretrain language: - vi metrics: - perplexity pipeline_tag: text-generation widget: - text: Việt Nam là quốc gia có - text: Hoàng Sa, Trường Sa là của model-index: - name: chronopt-research/vietnamese-gpt2-medium results: - task: type: text-generation metrics: - type: perplexity value: 17.5948 verified: true --- # Vietnamese `gpt2-medium` <!-- Provide a quick summary of what the model is/does. --> This is a pretrained `gpt2-medium` for Vietnamese language using casual language modeling (CLM) objective. It was introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and first released at [this page](https://openai.com/blog/better-language-models/). ## Model Description GPT-2 (at first) is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. This is the medium version of GPT-2, with 380M parameters. You could've found other pretrained version from here: [gpt2-base](https://huggingface.co/chronopt-research/vietnamese-gpt2-base), [gpt2-large]() ## Dataset used for pretraining This is a combination of multiple Vietnamese dataset for pretraining CLMs such as GPT, GPT2, etc. The dataset consists of: - [`vietgpt/covid_19_news_vi`](https://huggingface.co/datasets/vietgpt/covid_19_news_vi) - [`hieunguyen1053/binhvq-news-corpus`](https://huggingface.co/datasets/hieunguyen1053/binhvq-news-corpus) - [`oscar (unshuffled_deduplicated_vi)`](https://huggingface.co/datasets/oscar) - [`vietgpt/wikipedia_vi`](https://huggingface.co/datasets/vietgpt/wikipedia_vi) You can find out the combined version here: [duongttr/vi-dataset-for-pretrain](https://huggingface.co/datasets/duongttr/vi-dataset-for-pretrain) ## Hyperparamters & Results We trained the model ~100k steps, with `lr=1e-4`, `bs=1920`, `optimizer=adamw` on TPU-VM-3.8 from [TRC Program](https://sites.research.google/trc/about/). The training costs around 2.5 days. \|Model\|Eval Loss\|Eval Perplexity\| \|---\|---\|---\| \|gpt2-base\|3.939\|51.35\| \|gpt2-medium\|2.8676\|17.5948\| \|gpt2-large\|-\|-\| ## Contacts Feel free to contact us via: [email]()
GhifSmile/distilbert-base-uncased-DSC-new	GhifSmile	2023-08-09T20:49:02Z	104	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-09T19:25:46Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall model-index: - name: distilbert-base-uncased-DSC-new 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-DSC-new This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1017 - Accuracy: 0.9902 - Precision: 0.9910 - Recall: 0.9909 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\| \| 0.4743 \| 1.0 \| 618 \| 0.1856 \| 0.9633 \| 0.9672 \| 0.9647 \| \| 0.0946 \| 2.0 \| 1236 \| 0.1577 \| 0.9707 \| 0.9749 \| 0.9733 \| \| 0.0851 \| 3.0 \| 1854 \| 0.1081 \| 0.9853 \| 0.9869 \| 0.9858 \| \| 0.0633 \| 4.0 \| 2472 \| 0.1449 \| 0.9841 \| 0.9851 \| 0.9837 \| \| 0.0258 \| 5.0 \| 3090 \| 0.1155 \| 0.9829 \| 0.9838 \| 0.9829 \| \| 0.022 \| 6.0 \| 3708 \| 0.1089 \| 0.9890 \| 0.9899 \| 0.9897 \| \| 0.0147 \| 7.0 \| 4326 \| 0.1092 \| 0.9878 \| 0.9885 \| 0.9875 \| \| 0.0043 \| 8.0 \| 4944 \| 0.1017 \| 0.9902 \| 0.9910 \| 0.9909 \| \| 0.0041 \| 9.0 \| 5562 \| 0.1033 \| 0.9878 \| 0.9885 \| 0.9874 \| \| 0.0012 \| 10.0 \| 6180 \| 0.1093 \| 0.9878 \| 0.9885 \| 0.9874 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
EgilKarlsen/DistilRoBERTa_Thunderbird-Anomaly_Baseline	EgilKarlsen	2023-08-09T20:45:29Z	107	2	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-09T20:25:00Z	--- license: apache-2.0 base_model: distilroberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: DistilRoBERTa_Thunderbird-Anomaly_Baseline 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. --> # DistilRoBERTa_Thunderbird-Anomaly_Baseline This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0031 - Accuracy: 0.9999 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.0958 \| 1.0 \| 1094 \| 0.0623 \| 0.9846 \| \| 0.0514 \| 2.0 \| 2188 \| 0.0340 \| 0.9846 \| \| 0.0261 \| 3.0 \| 3282 \| 0.0168 \| 0.9896 \| \| 0.0147 \| 4.0 \| 4376 \| 0.0095 \| 1.0 \| \| 0.01 \| 5.0 \| 5470 \| 0.0061 \| 1.0 \| \| 0.0071 \| 6.0 \| 6564 \| 0.0042 \| 1.0 \| \| 0.0058 \| 7.0 \| 7658 \| 0.0031 \| 1.0 \| \| 0.0046 \| 8.0 \| 8752 \| 0.0025 \| 1.0 \| \| 0.0043 \| 9.0 \| 9846 \| 0.0022 \| 1.0 \| \| 0.0038 \| 10.0 \| 10940 \| 0.0021 \| 1.0 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
ederdt2023/Eder_Duenas	ederdt2023	2023-08-09T20:43:13Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-09T20:43:13Z	--- license: creativeml-openrail-m ---
raptz/autotrain-rstt_fullsumm-81171141667	raptz	2023-08-09T20:34:42Z	113	0	transformers	[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "autotrain", "summarization", "unk", "dataset:raptz/autotrain-data-rstt_fullsumm", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2023-08-09T20:30:49Z	--- tags: - autotrain - summarization language: - unk widget: - text: "I love AutoTrain" datasets: - raptz/autotrain-data-rstt_fullsumm co2_eq_emissions: emissions: 1.5473924434284785 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 81171141667 - CO2 Emissions (in grams): 1.5474 ## Validation Metrics - Loss: 0.650 - Rouge1: 68.031 - Rouge2: 53.314 - RougeL: 59.901 - RougeLsum: 61.660 - Gen Len: 61.707 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/raptz/autotrain-rstt_fullsumm-81171141667 ```
TotoLefo/Sheirlou500Epoch	TotoLefo	2023-08-09T20:33:56Z	0	0	null	[ "AI VOICE", "fr", "region:us" ]	null	2023-08-09T20:31:07Z	--- language: - fr tags: - AI VOICE --- # Model Card for Model ID - Developed by: TOTO
jannikseus/aspect_extraction_laptop_reviews	jannikseus	2023-08-09T20:30:32Z	23	1	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-08-06T20:55:25Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: aspect_extraction_laptop_reviews 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. --> # aspect_extraction_laptop_reviews This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1003 - Precision: 0.7872 - Recall: 0.7817 - F1: 0.7845 - Accuracy: 0.9732 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 362 \| 0.0854 \| 0.7070 \| 0.7817 \| 0.7425 \| 0.9675 \| \| 0.1121 \| 2.0 \| 724 \| 0.0937 \| 0.7466 \| 0.7676 \| 0.7569 \| 0.9696 \| \| 0.0383 \| 3.0 \| 1086 \| 0.0959 \| 0.7622 \| 0.7676 \| 0.7649 \| 0.9714 \| \| 0.0383 \| 4.0 \| 1448 \| 0.1003 \| 0.7872 \| 0.7817 \| 0.7845 \| 0.9732 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.2 - Tokenizers 0.13.3
FredericProtat/dqn-SpaceInvadersNoFrameskip-v4	FredericProtat	2023-08-09T20:24:42Z	7	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-09T20:24:06Z	--- 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: 691.00 +/- 253.51 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 FredericProtat -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 FredericProtat -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 FredericProtat ``` ## 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'} ```
JabrilJacobs/poca-SoccerTwos	JabrilJacobs	2023-08-09T20:13:59Z	12	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-08-09T20:11:14Z	--- 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: JabrilJacobs/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Prabna/sd-class-butterflies-32-1	Prabna	2023-08-09T20:11:43Z	31	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-08-09T20:11:30Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('Prabna/sd-class-butterflies-32-1') image = pipeline().images[0] image ```
Pixel390/BOY	Pixel390	2023-08-09T20:11:24Z	0	1	diffusers	[ "diffusers", "tensorboard", "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	2023-08-09T19:20:44Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a uxz boy tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - Pixel390/BOY These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a uxz boy using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: True.
asenella/mhd_config_1_MMVAE_beta_5_scale_True_seed_0	asenella	2023-08-09T20:06:44Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-08-09T20:06:32Z	--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```
Josrf/a2c-PandaReachDense-v3	Josrf	2023-08-09T20:03:20Z	6	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-09T19:57:18Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.23 +/- 0.13 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 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 ... ```
BauyrjanQ/whisper-kk-speech2ner-b16-ms2k-1500-s-cl	BauyrjanQ	2023-08-09T19:55:00Z	62	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-09T06:05:26Z	--- tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-kk-speech2ner-b16-ms2k-1500-s-cl 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-kk-speech2ner-b16-ms2k-1500-s-cl This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3907 - Wer: 264.8997 ## 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: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.2596 \| 0.34 \| 1500 \| 0.3907 \| 264.8997 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
cjohlmacher/ppo-SnowballTarget	cjohlmacher	2023-08-09T19:47:19Z	6	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-08-09T19:45:03Z	--- 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: cjohlmacher/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
StofEzz/mascir_frwav2vec2-large-xlsr-53	StofEzz	2023-08-09T19:43:27Z	162	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-09T17:37:06Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: mascir_frwav2vec2-large-xlsr-53 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. --> # mascir_frwav2vec2-large-xlsr-53 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4708 - Wer: 0.3789 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 6.3318 \| 2.0 \| 500 \| 3.0088 \| 0.9856 \| \| 1.5916 \| 4.0 \| 1000 \| 0.7746 \| 0.6411 \| \| 0.3961 \| 6.0 \| 1500 \| 0.5238 \| 0.5211 \| \| 0.2205 \| 8.0 \| 2000 \| 0.5014 \| 0.4733 \| \| 0.1401 \| 10.0 \| 2500 \| 0.5166 \| 0.4878 \| \| 0.1147 \| 12.0 \| 3000 \| 0.5058 \| 0.4333 \| \| 0.0938 \| 14.0 \| 3500 \| 0.4635 \| 0.4233 \| \| 0.0788 \| 16.0 \| 4000 \| 0.4997 \| 0.4144 \| \| 0.0645 \| 18.0 \| 4500 \| 0.4840 \| 0.4122 \| \| 0.0534 \| 20.0 \| 5000 \| 0.4789 \| 0.4022 \| \| 0.0437 \| 22.0 \| 5500 \| 0.4785 \| 0.3978 \| \| 0.041 \| 24.0 \| 6000 \| 0.4708 \| 0.3789 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
HG7/ReQLoRA_all8	HG7	2023-08-09T19:34:28Z	2	0	peft	[ "peft", "region:us" ]	null	2023-08-09T19:34:07Z	--- 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
ElcKeT/bert-sst2-finetuned-peft	ElcKeT	2023-08-09T19:21:37Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T19:20:27Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
Den4ikAI/ruBert_base_intent_detection	Den4ikAI	2023-08-09T19:18:27Z	140	8	transformers	[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "ru", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-12-10T11:10:35Z	--- license: mit widget: - text: Сколько будет 2+2? - text: Который час? - text: Вруби свет - text: Сколько баллов пробки? language: - ru pipeline_tag: text-classification --- Модель на основе ruBert-base для определения намерений
huggingnft-app/milady	huggingnft-app	2023-08-09T19:17:48Z	2	0	transformers	[ "transformers", "huggingnft", "nft", "huggan", "gan", "image", "images", "unconditional-image-generation", "dataset:huggingnft/milady", "license:mit", "endpoints_compatible", "region:us" ]	unconditional-image-generation	2023-08-09T19:17:21Z	--- tags: - huggingnft - nft - huggan - gan - image - images - unconditional-image-generation datasets: - huggingnft/milady license: mit --- # Hugging NFT: milady ## Disclaimer All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright holder. ## Model description LightWeight GAN model for unconditional generation. NFT collection available [here](https://opensea.io/collection/milady). Dataset is available [here](https://huggingface.co/datasets/huggingnft/milady). Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft). Project repository: [link](https://github.com/AlekseyKorshuk/huggingnft). [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingnft?style=social)](https://github.com/AlekseyKorshuk/huggingnft) ## Intended uses & limitations #### How to use Check project repository: [link](https://github.com/AlekseyKorshuk/huggingnft). #### Limitations and bias Check project repository: [link](https://github.com/AlekseyKorshuk/huggingnft). ## Training data Dataset is available [here](https://huggingface.co/datasets/huggingnft/milady). ## Training procedure Training script is available [here](https://github.com/AlekseyKorshuk/huggingnft). ## Generated Images Check results with Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft). ## About Built by Aleksey Korshuk [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingnft?style=social)](https://github.com/AlekseyKorshuk/huggingnft) ### BibTeX entry and citation info ```bibtex @InProceedings{huggingnft, author={Aleksey Korshuk} year=2022 } ```
HG7/ReQLoRA_GUD8	HG7	2023-08-09T19:04:43Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T19:04:35Z	--- 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
dfomin/Reinforce-1	dfomin	2023-08-09T18:51:39Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-08-09T18:51:29Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-1 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
cto-algo-huggingface/EternityRing	cto-algo-huggingface	2023-08-09T18:42:30Z	24	0	diffusers	[ "diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-09T18:40:55Z	--- license: creativeml-openrail-m tags: - text-to-image --- ### eternity_ring on Stable Diffusion via Dreambooth #### model by cto-algo-huggingface This your the Stable Diffusion model fine-tuned the eternity_ring concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: <eternity_ring> jewellery You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept: ![image 0](https://huggingface.co/cto-algo-huggingface/eternity-ring/resolve/main/concept_images/1.jpeg) ![image 1](https://huggingface.co/cto-algo-huggingface/eternity-ring/resolve/main/concept_images/5.jpeg) ![image 2](https://huggingface.co/cto-algo-huggingface/eternity-ring/resolve/main/concept_images/0.jpeg) ![image 3](https://huggingface.co/cto-algo-huggingface/eternity-ring/resolve/main/concept_images/3.jpeg) ![image 4](https://huggingface.co/cto-algo-huggingface/eternity-ring/resolve/main/concept_images/2.jpeg) ![image 5](https://huggingface.co/cto-algo-huggingface/eternity-ring/resolve/main/concept_images/4.jpeg) ![image 6](https://huggingface.co/cto-algo-huggingface/eternity-ring/resolve/main/concept_images/6.jpeg)
stoyky/ppo-Huggy	stoyky	2023-08-09T18:40:35Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-08-09T18:40:27Z	--- 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: stoyky/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
AEJaspan/ppo-LunarLander-v2	AEJaspan	2023-08-09T18:37:08Z	4	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-09T18:36:49Z	--- 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: 279.39 +/- 20.53 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 ... ```
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e5_s6789_v3_l5_v100	KingKazma	2023-08-09T18:36:20Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T18:36:19Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e7_s6789_v3_l5_v50	KingKazma	2023-08-09T18:31:49Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-09T18:31:48Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e3_s6789_v3_l5_v100	KingKazma	2023-08-09T18:22:28Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T18:22:26Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e4_s6789_v3_l5_v50	KingKazma	2023-08-09T18:11:32Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T18:11:31Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e1_s6789_v3_l5_v100	KingKazma	2023-08-09T18:08:35Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T18:08:34Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
Norod78/sdxl-BrainSlug-dreambooth	Norod78	2023-08-09T18:08:34Z	58	2	diffusers	[ "diffusers", "text-to-image", "lora", "autotrain", "en", "dataset:Norod78/BrainSlug-blip-captions-1024", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2023-08-09T17:10:00Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a brain slug tags: - text-to-image - diffusers - lora - autotrain widget: - text: photo of a brain slug enjoying a nice sunny day on the beach - text: photo of a brain slug attached to Snoop Doggs head - text: >- photo of a shocked old granny with a gooey (brain slug attached to her head), Very detailed, clean, high quality, sharp image - text: >- photo of a brain slug attacking the head of an anime girl, cartoon style, high quality datasets: - Norod78/BrainSlug-blip-captions-1024 inference: true language: - en --- # DreamBooth trained by AutoTrain Text enoder was not trained. # Trigger words Use "photo of a brain slug" / "brain slug" and etc # Examples photo of a brain slug enjoying a nice sunny day on the beach ![photo_of_a_brainslug_enjoying_a_nice_sunny_day_on_the_beach](https://huggingface.co/Norod78/sdxl-BrainSlug-dreambooth/resolve/main/Examples/i-42-photo_of_a_brainslug_enjoying_a_nice_sunny_day_on_the_beach-generated_image.jpg) photo of a shocked old granny with a gooey (brain slug attached to her head), Very detailed, clean, high quality, sharp image ![A_photo_of_a_shocked_old_granny](https://huggingface.co/Norod78/sdxl-BrainSlug-dreambooth/resolve/main/Examples/i-7777-A_photo_of_a_shocked_old_granny_with_a_gooey_(brain_slug_attached_to_her_head),_Very_detailed,_clean,_high_quality,_sharp_image,_Dave_Dorman-generated_image.jpg)
iampraveenvemula/lora-trained-xl-colab	iampraveenvemula	2023-08-09T18:06:47Z	1	1	diffusers	[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2023-08-09T16:51:25Z	--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks dog tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - iampraveenvemula/lora-trained-xl-colab These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
IAyoub/finetuning-bert-sentiment-reviews-2	IAyoub	2023-08-09T18:06:32Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-09T14:21:56Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-bert-sentiment-reviews-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-bert-sentiment-reviews-2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2086 - Accuracy: 0.9308 - F1: 0.8368 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 0.01 \| 10 \| 0.6716 \| 0.7463 \| 0.2849 \| \| No log \| 0.03 \| 20 \| 0.5789 \| 0.7463 \| 0.2849 \| \| No log \| 0.04 \| 30 \| 0.4971 \| 0.7788 \| 0.3849 \| \| No log \| 0.06 \| 40 \| 0.4298 \| 0.8672 \| 0.5506 \| \| No log \| 0.07 \| 50 \| 0.3837 \| 0.8794 \| 0.5686 \| \| No log \| 0.09 \| 60 \| 0.3481 \| 0.8802 \| 0.5672 \| \| No log \| 0.1 \| 70 \| 0.3680 \| 0.8757 \| 0.5604 \| \| No log \| 0.12 \| 80 \| 0.3259 \| 0.8854 \| 0.5736 \| \| No log \| 0.13 \| 90 \| 0.3179 \| 0.8854 \| 0.5727 \| \| No log \| 0.15 \| 100 \| 0.3306 \| 0.8891 \| 0.6295 \| \| No log \| 0.16 \| 110 \| 0.3253 \| 0.8894 \| 0.6692 \| \| No log \| 0.18 \| 120 \| 0.3041 \| 0.9024 \| 0.7285 \| \| No log \| 0.19 \| 130 \| 0.2997 \| 0.9068 \| 0.7426 \| \| No log \| 0.21 \| 140 \| 0.2881 \| 0.9057 \| 0.7434 \| \| No log \| 0.22 \| 150 \| 0.2892 \| 0.9094 \| 0.7587 \| \| No log \| 0.24 \| 160 \| 0.2771 \| 0.9149 \| 0.7801 \| \| No log \| 0.25 \| 170 \| 0.2779 \| 0.9135 \| 0.7782 \| \| No log \| 0.27 \| 180 \| 0.2992 \| 0.9109 \| 0.7720 \| \| No log \| 0.28 \| 190 \| 0.2809 \| 0.9083 \| 0.7622 \| \| No log \| 0.3 \| 200 \| 0.2636 \| 0.9146 \| 0.7680 \| \| No log \| 0.31 \| 210 \| 0.3381 \| 0.9079 \| 0.7694 \| \| No log \| 0.33 \| 220 \| 0.2661 \| 0.9197 \| 0.7858 \| \| No log \| 0.34 \| 230 \| 0.3377 \| 0.8854 \| 0.7582 \| \| No log \| 0.36 \| 240 \| 0.2614 \| 0.9190 \| 0.7881 \| \| No log \| 0.37 \| 250 \| 0.2459 \| 0.9264 \| 0.7981 \| \| No log \| 0.38 \| 260 \| 0.2490 \| 0.9246 \| 0.7934 \| \| No log \| 0.4 \| 270 \| 0.2475 \| 0.9197 \| 0.7876 \| \| No log \| 0.41 \| 280 \| 0.2648 \| 0.9161 \| 0.7840 \| \| No log \| 0.43 \| 290 \| 0.2533 \| 0.9249 \| 0.8010 \| \| No log \| 0.44 \| 300 \| 0.2446 \| 0.9234 \| 0.8067 \| \| No log \| 0.46 \| 310 \| 0.2271 \| 0.9260 \| 0.8114 \| \| No log \| 0.47 \| 320 \| 0.2219 \| 0.9246 \| 0.8211 \| \| No log \| 0.49 \| 330 \| 0.2269 \| 0.9320 \| 0.8306 \| \| No log \| 0.5 \| 340 \| 0.2276 \| 0.9264 \| 0.8219 \| \| No log \| 0.52 \| 350 \| 0.2835 \| 0.9201 \| 0.7994 \| \| No log \| 0.53 \| 360 \| 0.2787 \| 0.9231 \| 0.8029 \| \| No log \| 0.55 \| 370 \| 0.2317 \| 0.9301 \| 0.8275 \| \| No log \| 0.56 \| 380 \| 0.2502 \| 0.9131 \| 0.8076 \| \| No log \| 0.58 \| 390 \| 0.2254 \| 0.9294 \| 0.8321 \| \| No log \| 0.59 \| 400 \| 0.2066 \| 0.9312 \| 0.8215 \| \| No log \| 0.61 \| 410 \| 0.2013 \| 0.9342 \| 0.8391 \| \| No log \| 0.62 \| 420 \| 0.2295 \| 0.9260 \| 0.8279 \| \| No log \| 0.64 \| 430 \| 0.2100 \| 0.9338 \| 0.8428 \| \| No log \| 0.65 \| 440 \| 0.2129 \| 0.9316 \| 0.8297 \| \| No log \| 0.67 \| 450 \| 0.2135 \| 0.9327 \| 0.8203 \| \| No log \| 0.68 \| 460 \| 0.2681 \| 0.9212 \| 0.8028 \| \| No log \| 0.7 \| 470 \| 0.2178 \| 0.9320 \| 0.8312 \| \| No log \| 0.71 \| 480 \| 0.1999 \| 0.9342 \| 0.8321 \| \| No log \| 0.72 \| 490 \| 0.2172 \| 0.9305 \| 0.8334 \| \| 0.2988 \| 0.74 \| 500 \| 0.2086 \| 0.9308 \| 0.8368 \| \| 0.2988 \| 0.75 \| 510 \| 0.2052 \| 0.9342 \| 0.8430 \| \| 0.2988 \| 0.77 \| 520 \| 0.2111 \| 0.9331 \| 0.8333 \| \| 0.2988 \| 0.78 \| 530 \| 0.2279 \| 0.9327 \| 0.8250 \| \| 0.2988 \| 0.8 \| 540 \| 0.2361 \| 0.9271 \| 0.8164 \| ### Framework versions - Transformers 4.29.2 - Pytorch 1.13.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
Phaaarus/QLoRA_replica_16rank_QKadap	Phaaarus	2023-08-09T18:02:31Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-09T18:02:03Z	--- 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: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e1_s6789_v3_l5_v50	KingKazma	2023-08-09T17:51:16Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T17:51:15Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e0_s6789_v3_l5_v50	KingKazma	2023-08-09T17:44:30Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T17:44:29Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
rashmi035/wav2vec2-large-mms-1b-hindi_2-colab	rashmi035	2023-08-09T17:44:01Z	14	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/mms-1b-fl102", "base_model:finetune:facebook/mms-1b-fl102", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-01T18:01:18Z	--- license: cc-by-nc-4.0 base_model: facebook/mms-1b-fl102 tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-large-mms-1b-hindi_2-colab 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. --> # wav2vec2-large-mms-1b-hindi_2-colab This model is a fine-tuned version of [facebook/mms-1b-fl102](https://huggingface.co/facebook/mms-1b-fl102) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1619 - Wer: 0.9015 ## 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: 0.01 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 7.5635 \| 0.02 \| 20 \| 7.4070 \| 0.9985 \| \| 13.6122 \| 0.04 \| 40 \| 14.5202 \| 1.0 \| \| 10.4272 \| 0.06 \| 60 \| 8.7994 \| 1.5440 \| \| 8.1195 \| 0.08 \| 80 \| 10.3713 \| 1.0 \| \| 9.9347 \| 0.1 \| 100 \| 7.1064 \| 1.0 \| \| 5.752 \| 0.12 \| 120 \| 5.6953 \| 1.0 \| \| 5.1715 \| 0.14 \| 140 \| 5.0103 \| 1.0 \| \| 6.3111 \| 0.15 \| 160 \| 4.6935 \| 1.0 \| \| 4.4929 \| 0.17 \| 180 \| 5.4670 \| 1.0263 \| \| 6.038 \| 0.19 \| 200 \| 5.6732 \| 1.3148 \| \| 4.1732 \| 0.21 \| 220 \| 4.2880 \| 1.0015 \| \| 3.8954 \| 0.23 \| 240 \| 4.3895 \| 1.0 \| \| 3.9351 \| 0.25 \| 260 \| 3.7766 \| 1.0 \| \| 3.6591 \| 0.27 \| 280 \| 3.7521 \| 1.0 \| \| 3.6009 \| 0.29 \| 300 \| 3.8260 \| 1.0 \| \| 3.5822 \| 0.31 \| 320 \| 3.5655 \| 1.0 \| \| 3.5705 \| 0.33 \| 340 \| 3.6623 \| 1.0 \| \| 3.6825 \| 0.35 \| 360 \| 3.5988 \| 1.0 \| \| 3.5239 \| 0.37 \| 380 \| 3.5307 \| 1.0 \| \| 3.558 \| 0.39 \| 400 \| 3.5847 \| 1.0 \| \| 3.4658 \| 0.41 \| 420 \| 3.4300 \| 1.0 \| \| 3.4045 \| 0.43 \| 440 \| 3.5261 \| 1.0 \| \| 3.4564 \| 0.44 \| 460 \| 3.4799 \| 1.0 \| \| 3.4403 \| 0.46 \| 480 \| 3.4126 \| 1.0 \| \| 3.4733 \| 0.48 \| 500 \| 3.5358 \| 1.0 \| \| 3.445 \| 0.5 \| 520 \| 3.3526 \| 1.0 \| \| 3.4155 \| 0.52 \| 540 \| 3.3508 \| 1.0 \| \| 3.412 \| 0.54 \| 560 \| 3.3205 \| 1.0 \| \| 3.2547 \| 0.56 \| 580 \| 3.3143 \| 1.0 \| \| 3.2652 \| 0.58 \| 600 \| 3.3057 \| 1.0 \| \| 3.1801 \| 0.6 \| 620 \| 3.2361 \| 1.0 \| \| 3.2835 \| 0.62 \| 640 \| 3.3567 \| 1.0 \| \| 3.3545 \| 0.64 \| 660 \| 3.2300 \| 1.0 \| \| 3.1898 \| 0.66 \| 680 \| 3.1771 \| 1.0 \| \| 3.1109 \| 0.68 \| 700 \| 3.3033 \| 1.0 \| \| 3.1631 \| 0.7 \| 720 \| 3.0177 \| 0.9997 \| \| 3.0386 \| 0.71 \| 740 \| 3.0339 \| 0.9997 \| \| 3.074 \| 0.73 \| 760 \| 3.0702 \| 1.0 \| \| 2.8598 \| 0.75 \| 780 \| 2.8458 \| 1.0 \| \| 2.8116 \| 0.77 \| 800 \| 2.9836 \| 0.9995 \| \| 2.8086 \| 0.79 \| 820 \| 2.5641 \| 1.0 \| \| 2.6645 \| 0.81 \| 840 \| 2.6182 \| 1.0 \| \| 2.7035 \| 0.83 \| 860 \| 2.5176 \| 0.9995 \| \| 2.4736 \| 0.85 \| 880 \| 2.3965 \| 0.9995 \| \| 2.6259 \| 0.87 \| 900 \| 2.5697 \| 1.0 \| \| 2.44 \| 0.89 \| 920 \| 2.3085 \| 1.0 \| \| 2.22 \| 0.91 \| 940 \| 2.1551 \| 0.9997 \| \| 2.5394 \| 0.93 \| 960 \| 2.1955 \| 1.0 \| \| 2.1734 \| 0.95 \| 980 \| 2.1015 \| 1.0 \| \| 2.407 \| 0.97 \| 1000 \| 2.3892 \| 1.0 \| \| 2.1967 \| 0.99 \| 1020 \| 1.9439 \| 0.9943 \| \| 2.1704 \| 1.0 \| 1040 \| 1.9236 \| 0.9827 \| \| 1.9929 \| 1.02 \| 1060 \| 1.9353 \| 0.9964 \| \| 2.1652 \| 1.04 \| 1080 \| 2.1551 \| 0.9899 \| \| 2.003 \| 1.06 \| 1100 \| 1.9230 \| 0.9820 \| \| 2.0048 \| 1.08 \| 1120 \| 1.9293 \| 0.9869 \| \| 2.1665 \| 1.1 \| 1140 \| 1.8845 \| 0.9990 \| \| 1.8297 \| 1.12 \| 1160 \| 1.7173 \| 0.9866 \| \| 1.8388 \| 1.14 \| 1180 \| 1.8550 \| 0.9871 \| \| 1.8399 \| 1.16 \| 1200 \| 1.7772 \| 0.9789 \| \| 1.7256 \| 1.18 \| 1220 \| 1.7840 \| 0.9863 \| \| 2.0516 \| 1.2 \| 1240 \| 1.7693 \| 0.9520 \| \| 1.8014 \| 1.22 \| 1260 \| 1.6744 \| 0.9814 \| \| 1.8244 \| 1.24 \| 1280 \| 1.6614 \| 0.9907 \| \| 1.8233 \| 1.26 \| 1300 \| 1.5975 \| 0.9948 \| \| 1.6977 \| 1.28 \| 1320 \| 1.5738 \| 0.9874 \| \| 1.9592 \| 1.29 \| 1340 \| 1.5922 \| 0.9897 \| \| 1.6181 \| 1.31 \| 1360 \| 1.4764 \| 0.9626 \| \| 1.6739 \| 1.33 \| 1380 \| 1.5381 \| 0.9928 \| \| 1.6855 \| 1.35 \| 1400 \| 1.4613 \| 0.9410 \| \| 1.5535 \| 1.37 \| 1420 \| 1.4878 \| 0.9348 \| \| 1.7467 \| 1.39 \| 1440 \| 1.6077 \| 0.9618 \| \| 1.6744 \| 1.41 \| 1460 \| 1.4419 \| 0.9727 \| \| 1.6115 \| 1.43 \| 1480 \| 1.6700 \| 0.9379 \| \| 1.7357 \| 1.45 \| 1500 \| 1.5228 \| 0.9964 \| \| 1.7096 \| 1.47 \| 1520 \| 1.4350 \| 0.9611 \| \| 1.7402 \| 1.49 \| 1540 \| 1.4351 \| 0.9567 \| \| 1.4819 \| 1.51 \| 1560 \| 1.4062 \| 0.9727 \| \| 1.6863 \| 1.53 \| 1580 \| 1.4908 \| 0.9889 \| \| 1.5539 \| 1.55 \| 1600 \| 1.4099 \| 0.9827 \| \| 1.5733 \| 1.57 \| 1620 \| 1.4508 \| 0.9209 \| \| 1.7331 \| 1.58 \| 1640 \| 1.3913 \| 0.9755 \| \| 1.4361 \| 1.6 \| 1660 \| 1.3525 \| 0.9237 \| \| 1.4806 \| 1.62 \| 1680 \| 1.3748 \| 0.9557 \| \| 1.5834 \| 1.64 \| 1700 \| 1.3428 \| 0.9386 \| \| 1.4226 \| 1.66 \| 1720 \| 1.2990 \| 0.9523 \| \| 1.6159 \| 1.68 \| 1740 \| 1.3351 \| 0.9428 \| \| 1.4486 \| 1.7 \| 1760 \| 1.2982 \| 0.9276 \| \| 1.3682 \| 1.72 \| 1780 \| 1.3810 \| 0.9312 \| \| 1.3828 \| 1.74 \| 1800 \| 1.2621 \| 0.9242 \| \| 1.4604 \| 1.76 \| 1820 \| 1.2883 \| 0.9051 \| \| 1.4368 \| 1.78 \| 1840 \| 1.2462 \| 0.9191 \| \| 1.3652 \| 1.8 \| 1860 \| 1.2544 \| 0.8935 \| \| 1.4347 \| 1.82 \| 1880 \| 1.2682 \| 0.9185 \| \| 1.4109 \| 1.84 \| 1900 \| 1.2385 \| 0.8966 \| \| 1.251 \| 1.86 \| 1920 \| 1.2293 \| 0.9015 \| \| 1.4793 \| 1.87 \| 1940 \| 1.2410 \| 0.9075 \| \| 1.2481 \| 1.89 \| 1960 \| 1.1916 \| 0.9134 \| \| 1.2951 \| 1.91 \| 1980 \| 1.2061 \| 0.8891 \| \| 1.3724 \| 1.93 \| 2000 \| 1.1730 \| 0.9381 \| \| 1.3093 \| 1.95 \| 2020 \| 1.1763 \| 0.8951 \| \| 1.3305 \| 1.97 \| 2040 \| 1.1709 \| 0.9028 \| \| 1.3152 \| 1.99 \| 2060 \| 1.1619 \| 0.9015 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
papepipopu/q-FrozenLake-v1-4x4-noSlippery-course	papepipopu	2023-08-09T17:41:08Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-09T17:41:05Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery-course 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="papepipopu/q-FrozenLake-v1-4x4-noSlippery-course", 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"]) ```
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e9_s6789_v3_l5_v100	KingKazma	2023-08-09T17:38:03Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T17:38:02Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
emptor/distilgender-es-2M	emptor	2023-08-09T17:34:13Z	1,110	0	transformers	[ "transformers", "pytorch", "safetensors", "distilbert", "text-classification", "es", "dataset:ittailup/issste", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-09T01:29:28Z	--- license: apache-2.0 datasets: - ittailup/issste language: - es metrics: - accuracy: 0.9951 widget: - text: AGATA - text: GABRIEL --- ## Model Card ### Overview This model card provides details about a trained model, its training process, and evaluation metrics. This information ensures transparency and assists users in understanding the model's performance and behavior. ### Training Details - Training Epochs: The model was trained for 2 epochs. - Training Steps: The model underwent 1856 training steps. - Training Runtime: The model's training runtime was approximately 2680.184 seconds. - Training Speed: The model trained at a rate of 0.692 steps per second and processed approximately 1417.813 samples per second. - Learning Rate: The learning rate during training was approximately 0.0000095905. - Training Loss: The average training loss recorded was approximately 0.0184, with a specific loss value of 0.023423514232553285. ### Evaluation Details - Evaluation Loss: The model achieved an evaluation loss of 0.017659155651926994. - Evaluation Runtime: The evaluation process took approximately 23.8414 seconds. - Evaluation Speed: The model was evaluated at a rate of 2.055 steps per second, processing approximately 4194.378 samples per second. ### Performance Metrics - Accuracy: The model achieved an accuracy of 0.9951 during evaluation. - Precision: The precision of the model is approximately 0.9957234121187588. - Recall: The model's recall is approximately 0.9956533216014078. - F1-Score: The F1-Score for the model is approximately 0.995688365626595.
cyriac880/dog	cyriac880	2023-08-09T17:29:51Z	7	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-09T17:17:29Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### DOG Dreambooth model trained by cyriac880 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: VJCET294 Sample pictures of this concept: ![0](https://huggingface.co/cyriac880/dog/resolve/main/sample_images/xxx_(2).jpg) ![1](https://huggingface.co/cyriac880/dog/resolve/main/sample_images/xxx_(1).jpg)
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e8_s6789_v3_l5_v100	KingKazma	2023-08-09T17:29:26Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-09T17:29:25Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e9_s6789_v3_l5_v20	KingKazma	2023-08-09T17:22:43Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T17:22:42Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
alokedeep/distilbert-base-uncased-finetuned-emotion	alokedeep	2023-08-09T17:18:49Z	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	2023-08-09T13:50:58Z	--- 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: 0.9265 - name: F1 type: f1 value: 0.9265400264321207 --- <!-- 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: 0.2135 - Accuracy: 0.9265 - F1: 0.9265 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8263 \| 1.0 \| 250 \| 0.3211 \| 0.9035 \| 0.9024 \| \| 0.2495 \| 2.0 \| 500 \| 0.2135 \| 0.9265 \| 0.9265 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e8_s6789_v3_l5_v20	KingKazma	2023-08-09T17:16:04Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T17:16:03Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
Norod78/sd15-bender-lora	Norod78	2023-08-09T17:15:57Z	6	1	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "dataset:Norod78/bender-blip2-captions-512", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-06-14T08:25:16Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: A photo of bender tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora datasets: - Norod78/bender-blip2-captions-512 inference: true widget: - text: >- A picture of a a cute little bender working as a pokemon trainer - text: >- A picture of Godzilla as bender, Very detailed, clean, high quality, sharp image - text: A picture of bender - text: A photo of Bender rocking out on stage, shredding a guitar with sparks flying in the air. robot, reflective metal --- LoRA for generating images of Bender, the robot from futurama A diffusers version of [this model](https://civitai.com/models/85775/bender-lora) Make sure to include the word "Bender" in your prompt
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e7_s6789_v3_l5_v20	KingKazma	2023-08-09T17:09:25Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T17:09:24Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e5_s6789_v3_l5_v100	KingKazma	2023-08-09T17:03:39Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T17:03:38Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e6_s6789_v3_l5_v20	KingKazma	2023-08-09T17:02:46Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T17:02:45Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
psychodoge/llama2-qlora-finetunined-friendchathinglish	psychodoge	2023-08-09T17:00:07Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T17:00:01Z	--- 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e5_s6789_v3_l5_v20	KingKazma	2023-08-09T16:56:07Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T16:56:06Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e4_s6789_v3_l5_v100	KingKazma	2023-08-09T16:55:03Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T16:55:02Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e4_s6789_v3_l5_v20	KingKazma	2023-08-09T16:49:28Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T16:49:27Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
scarlett623/wav2vec2-large-xlsr53-zh-cn-subset-colab	scarlett623	2023-08-09T16:46:39Z	19	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	2023-08-09T03:52:42Z	--- 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-subset-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: 0.9394977168949772 --- <!-- 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-subset-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: 1.3992 - Wer: 0.9395 - Cer: 0.3184 ## 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: 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 \| 33.6533 \| 1.0 \| 1.0 \| \| 70.5767 \| 3.81 \| 800 \| 6.8140 \| 1.0 \| 1.0 \| \| 7.1379 \| 5.71 \| 1200 \| 6.5163 \| 1.0 \| 1.0 \| \| 6.4771 \| 7.62 \| 1600 \| 6.4602 \| 1.0 \| 1.0 \| \| 6.3627 \| 9.52 \| 2000 \| 6.3406 \| 1.0 \| 0.9700 \| \| 6.3627 \| 11.43 \| 2400 \| 6.1021 \| 1.0 \| 0.9678 \| \| 6.1201 \| 13.33 \| 2800 \| 5.1523 \| 1.0 \| 0.8385 \| \| 5.3531 \| 15.24 \| 3200 \| 4.2224 \| 1.0 \| 0.7084 \| \| 4.1733 \| 17.14 \| 3600 \| 3.6981 \| 1.0 \| 0.6332 \| \| 3.5472 \| 19.05 \| 4000 \| 3.2708 \| 0.9994 \| 0.5827 \| \| 3.5472 \| 20.95 \| 4400 \| 2.9629 \| 0.9989 \| 0.5510 \| \| 3.0668 \| 22.86 \| 4800 \| 2.7122 \| 0.9943 \| 0.5165 \| \| 2.7248 \| 24.76 \| 5200 \| 2.5171 \| 0.9914 \| 0.4976 \| \| 2.4609 \| 26.67 \| 5600 \| 2.3538 \| 0.9897 \| 0.4759 \| \| 2.2323 \| 28.57 \| 6000 \| 2.2112 \| 0.9874 \| 0.4555 \| \| 2.2323 \| 30.48 \| 6400 \| 2.0850 \| 0.9834 \| 0.4370 \| \| 2.0438 \| 32.38 \| 6800 \| 1.9982 \| 0.9806 \| 0.4261 \| \| 1.8837 \| 34.29 \| 7200 \| 1.9179 \| 0.9766 \| 0.4137 \| \| 1.7646 \| 36.19 \| 7600 \| 1.8278 \| 0.9766 \| 0.4030 \| \| 1.6469 \| 38.1 \| 8000 \| 1.7627 \| 0.9755 \| 0.3937 \| \| 1.6469 \| 40.0 \| 8400 \| 1.7063 \| 0.9709 \| 0.3853 \| \| 1.5422 \| 41.9 \| 8800 \| 1.6649 \| 0.9663 \| 0.3787 \| \| 1.4561 \| 43.81 \| 9200 \| 1.6336 \| 0.9697 \| 0.3714 \| \| 1.3842 \| 45.71 \| 9600 \| 1.5943 \| 0.9606 \| 0.3647 \| \| 1.3164 \| 47.62 \| 10000 \| 1.5681 \| 0.9669 \| 0.3621 \| \| 1.3164 \| 49.52 \| 10400 \| 1.5535 \| 0.9600 \| 0.3582 \| \| 1.2654 \| 51.43 \| 10800 \| 1.5354 \| 0.9538 \| 0.3544 \| \| 1.2186 \| 53.33 \| 11200 \| 1.5003 \| 0.9555 \| 0.3482 \| \| 1.1781 \| 55.24 \| 11600 \| 1.4979 \| 0.9572 \| 0.3473 \| \| 1.1344 \| 57.14 \| 12000 \| 1.4820 \| 0.9549 \| 0.3453 \| \| 1.1344 \| 59.05 \| 12400 \| 1.4707 \| 0.9509 \| 0.3396 \| \| 1.0965 \| 60.95 \| 12800 \| 1.4657 \| 0.9509 \| 0.3384 \| \| 1.0637 \| 62.86 \| 13200 \| 1.4610 \| 0.9509 \| 0.3371 \| \| 1.0306 \| 64.76 \| 13600 \| 1.4461 \| 0.9509 \| 0.3361 \| \| 1.0014 \| 66.67 \| 14000 \| 1.4437 \| 0.9503 \| 0.3328 \| \| 1.0014 \| 68.57 \| 14400 \| 1.4334 \| 0.9463 \| 0.3304 \| \| 0.9758 \| 70.48 \| 14800 \| 1.4267 \| 0.9429 \| 0.3295 \| \| 0.9486 \| 72.38 \| 15200 \| 1.4250 \| 0.9469 \| 0.3269 \| \| 0.933 \| 74.29 \| 15600 \| 1.4214 \| 0.9441 \| 0.3273 \| \| 0.9121 \| 76.19 \| 16000 \| 1.4161 \| 0.9441 \| 0.3267 \| \| 0.9121 \| 78.1 \| 16400 \| 1.4137 \| 0.9446 \| 0.3268 \| \| 0.9001 \| 80.0 \| 16800 \| 1.4216 \| 0.9446 \| 0.3253 \| \| 0.8789 \| 81.9 \| 17200 \| 1.4164 \| 0.9435 \| 0.3264 \| \| 0.8659 \| 83.81 \| 17600 \| 1.3996 \| 0.9424 \| 0.3216 \| \| 0.8471 \| 85.71 \| 18000 \| 1.4079 \| 0.9458 \| 0.3226 \| \| 0.8471 \| 87.62 \| 18400 \| 1.4042 \| 0.9412 \| 0.3214 \| \| 0.8387 \| 89.52 \| 18800 \| 1.4073 \| 0.9424 \| 0.3214 \| \| 0.8299 \| 91.43 \| 19200 \| 1.4005 \| 0.9418 \| 0.3192 \| \| 0.8257 \| 93.33 \| 19600 \| 1.4040 \| 0.9406 \| 0.3200 \| \| 0.813 \| 95.24 \| 20000 \| 1.4012 \| 0.9412 \| 0.3184 \| \| 0.813 \| 97.14 \| 20400 \| 1.4011 \| 0.9389 \| 0.3183 \| \| 0.8062 \| 99.05 \| 20800 \| 1.3992 \| 0.9395 \| 0.3184 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e2_s6789_v3_l5_v100	KingKazma	2023-08-09T16:37:52Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-09T16:37:51Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e2_s6789_v3_l5_v20	KingKazma	2023-08-09T16:36:10Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T16:36:09Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e1_s6789_v3_l5_v20	KingKazma	2023-08-09T16:29:31Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T16:29:30Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e1_s6789_v3_l5_v100	KingKazma	2023-08-09T16:29:16Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-09T16:29:15Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
avurity/layoutlmv3-finetuned-wildreceipt	avurity	2023-08-09T16:24:08Z	76	0	transformers	[ "transformers", "pytorch", "tensorboard", "layoutlmv3", "token-classification", "generated_from_trainer", "dataset:wildreceipt", "base_model:microsoft/layoutlmv3-base", "base_model:finetune:microsoft/layoutlmv3-base", "license:cc-by-nc-sa-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-08-05T16:29:09Z	--- license: cc-by-nc-sa-4.0 base_model: microsoft/layoutlmv3-base tags: - generated_from_trainer datasets: - wildreceipt metrics: - precision - recall - f1 - accuracy model-index: - name: layoutlmv3-finetuned-wildreceipt results: - task: name: Token Classification type: token-classification dataset: name: wildreceipt type: wildreceipt config: WildReceipt split: test args: WildReceipt metrics: - name: Precision type: precision value: 0.8738394320043692 - name: Recall type: recall value: 0.88093599449415 - name: F1 type: f1 value: 0.8773733634930428 - name: Accuracy type: accuracy value: 0.9245552383044147 --- <!-- 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. --> # layoutlmv3-finetuned-wildreceipt This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the wildreceipt dataset. It achieves the following results on the evaluation set: - Loss: 0.3068 - Precision: 0.8738 - Recall: 0.8809 - F1: 0.8774 - Accuracy: 0.9246 ## 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: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 4000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 0.32 \| 100 \| 1.3498 \| 0.6130 \| 0.3126 \| 0.4140 \| 0.6742 \| \| No log \| 0.63 \| 200 \| 0.8939 \| 0.6665 \| 0.5317 \| 0.5915 \| 0.7815 \| \| No log \| 0.95 \| 300 \| 0.7159 \| 0.7311 \| 0.6425 \| 0.6840 \| 0.8161 \| \| No log \| 1.26 \| 400 \| 0.5901 \| 0.7554 \| 0.6690 \| 0.7095 \| 0.8405 \| \| 1.0677 \| 1.58 \| 500 \| 0.5263 \| 0.7632 \| 0.7232 \| 0.7427 \| 0.8578 \| \| 1.0677 \| 1.89 \| 600 \| 0.4759 \| 0.7871 \| 0.7777 \| 0.7824 \| 0.8774 \| \| 1.0677 \| 2.21 \| 700 \| 0.4299 \| 0.8054 \| 0.8070 \| 0.8062 \| 0.8890 \| \| 1.0677 \| 2.52 \| 800 \| 0.4165 \| 0.8064 \| 0.8311 \| 0.8185 \| 0.8937 \| \| 1.0677 \| 2.84 \| 900 \| 0.3845 \| 0.8344 \| 0.8300 \| 0.8322 \| 0.9005 \| \| 0.4267 \| 3.15 \| 1000 \| 0.3540 \| 0.8433 \| 0.8318 \| 0.8375 \| 0.9056 \| \| 0.4267 \| 3.47 \| 1100 \| 0.3429 \| 0.8362 \| 0.8540 \| 0.8450 \| 0.9086 \| \| 0.4267 \| 3.79 \| 1200 \| 0.3274 \| 0.8451 \| 0.8545 \| 0.8498 \| 0.9105 \| \| 0.4267 \| 4.1 \| 1300 \| 0.3433 \| 0.8397 \| 0.8535 \| 0.8466 \| 0.9092 \| \| 0.4267 \| 4.42 \| 1400 \| 0.3181 \| 0.8514 \| 0.8604 \| 0.8559 \| 0.9154 \| \| 0.2869 \| 4.73 \| 1500 \| 0.3191 \| 0.8472 \| 0.8637 \| 0.8554 \| 0.9129 \| \| 0.2869 \| 5.05 \| 1600 \| 0.3128 \| 0.8613 \| 0.8658 \| 0.8635 \| 0.9182 \| \| 0.2869 \| 5.36 \| 1700 \| 0.3121 \| 0.8622 \| 0.8695 \| 0.8658 \| 0.9182 \| \| 0.2869 \| 5.68 \| 1800 \| 0.3230 \| 0.8473 \| 0.8661 \| 0.8566 \| 0.9140 \| \| 0.2869 \| 5.99 \| 1900 \| 0.2986 \| 0.8729 \| 0.8633 \| 0.8681 \| 0.9209 \| \| 0.2134 \| 6.31 \| 2000 \| 0.3032 \| 0.8555 \| 0.8694 \| 0.8624 \| 0.9169 \| \| 0.2134 \| 6.62 \| 2100 \| 0.3056 \| 0.8705 \| 0.8710 \| 0.8708 \| 0.9220 \| \| 0.2134 \| 6.94 \| 2200 \| 0.3122 \| 0.8630 \| 0.8790 \| 0.8709 \| 0.9217 \| \| 0.2134 \| 7.26 \| 2300 \| 0.3047 \| 0.8692 \| 0.8778 \| 0.8734 \| 0.9215 \| \| 0.2134 \| 7.57 \| 2400 \| 0.3103 \| 0.8701 \| 0.8780 \| 0.8741 \| 0.9225 \| \| 0.1661 \| 7.89 \| 2500 \| 0.3080 \| 0.8712 \| 0.8787 \| 0.8749 \| 0.9226 \| \| 0.1661 \| 8.2 \| 2600 \| 0.3011 \| 0.8653 \| 0.8834 \| 0.8743 \| 0.9236 \| \| 0.1661 \| 8.52 \| 2700 \| 0.3034 \| 0.8735 \| 0.8798 \| 0.8766 \| 0.9247 \| \| 0.1661 \| 8.83 \| 2800 \| 0.3054 \| 0.8698 \| 0.8793 \| 0.8745 \| 0.9238 \| \| 0.1661 \| 9.15 \| 2900 \| 0.3105 \| 0.8697 \| 0.8812 \| 0.8754 \| 0.9237 \| \| 0.1415 \| 9.46 \| 3000 \| 0.3068 \| 0.8738 \| 0.8809 \| 0.8774 \| 0.9246 \| \| 0.1415 \| 9.78 \| 3100 \| 0.3086 \| 0.8730 \| 0.8793 \| 0.8761 \| 0.9229 \| \| 0.1415 \| 10.09 \| 3200 \| 0.3013 \| 0.8755 \| 0.8830 \| 0.8792 \| 0.9256 \| \| 0.1415 \| 10.41 \| 3300 \| 0.3107 \| 0.8692 \| 0.8815 \| 0.8753 \| 0.9241 \| \| 0.1415 \| 10.73 \| 3400 \| 0.3073 \| 0.8759 \| 0.8794 \| 0.8777 \| 0.9261 \| \| 0.1239 \| 11.04 \| 3500 \| 0.3109 \| 0.8727 \| 0.8819 \| 0.8773 \| 0.9253 \| \| 0.1239 \| 11.36 \| 3600 \| 0.3124 \| 0.8723 \| 0.8790 \| 0.8756 \| 0.9243 \| \| 0.1239 \| 11.67 \| 3700 \| 0.3171 \| 0.8724 \| 0.8805 \| 0.8764 \| 0.9241 \| \| 0.1239 \| 11.99 \| 3800 \| 0.3081 \| 0.8739 \| 0.8804 \| 0.8771 \| 0.9254 \| \| 0.1239 \| 12.3 \| 3900 \| 0.3095 \| 0.8735 \| 0.8798 \| 0.8766 \| 0.9254 \| \| 0.1106 \| 12.62 \| 4000 \| 0.3094 \| 0.8740 \| 0.8796 \| 0.8768 \| 0.9254 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.0 - Datasets 2.14.3 - Tokenizers 0.13.3
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e0_s6789_v3_l5_v20	KingKazma	2023-08-09T16:22:51Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T16:22:50Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e0_s6789_v3_l5_v100	KingKazma	2023-08-09T16:20:40Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T16:20:39Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
kaoyer/pokemon-lora	kaoyer	2023-08-09T16:17:44Z	1	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-08-09T13:49:50Z	--- license: creativeml-openrail-m base_model: /root/autodl-fs/pre_trained_models/runwayml-stable-diffusion-v1-5/runwayml-stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - kaoyer/pokemon-lora These are LoRA adaption weights for /root/autodl-fs/pre_trained_models/runwayml-stable-diffusion-v1-5/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)
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e-1_s6789_v3_l5_v20	KingKazma	2023-08-09T16:16:11Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T16:16:10Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
MarioNapoli/DynamicWav2Vec_TEST_9	MarioNapoli	2023-08-09T16:09:04Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_1_0", "base_model:facebook/wav2vec2-xls-r-300m", "base_model:finetune:facebook/wav2vec2-xls-r-300m", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-03T14:29:32Z	--- license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer datasets: - common_voice_1_0 model-index: - name: DynamicWav2Vec_TEST_9 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. --> # DynamicWav2Vec_TEST_9 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_1_0 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e9_s6789_v3_l5_v20	KingKazma	2023-08-09T16:05:14Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T16:05:14Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
foilfoilfoil/cheesegulag3.5	foilfoilfoil	2023-08-09T16:04:54Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T16:04:02Z	--- 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 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 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 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 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e8_s6789_v3_l5_v50	KingKazma	2023-08-09T16:03:48Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T16:03:47Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e7_s6789_v3_l5_v50	KingKazma	2023-08-09T15:56:16Z	1	0	peft	[ "peft", "region:us" ]	null	2023-08-09T15:56:15Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
adon81/bert-finetuned-fishing-NER	adon81	2023-08-09T15:48:12Z	108	0	transformers	[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "base_model:adon81/bert-finetuned-ner", "base_model:finetune:adon81/bert-finetuned-ner", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-08-09T13:13:46Z	--- license: apache-2.0 base_model: adon81/bert-finetuned-ner tags: - generated_from_trainer model-index: - name: bert-finetuned-fishing-NER results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-fishing-NER This model is a fine-tuned version of [adon81/bert-finetuned-ner](https://huggingface.co/adon81/bert-finetuned-ner) 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: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 300000000000000000000000000000000 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
Shafaet02/bert-fine-tuned-cola	Shafaet02	2023-08-09T15:48:02Z	61	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-09T08:59:17Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_keras_callback model-index: - name: Shafaet02/bert-fine-tuned-cola 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. --> # Shafaet02/bert-fine-tuned-cola 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: 0.2831 - Validation Loss: 0.4311 - 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': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.4914 \| 0.4282 \| 0 \| \| 0.2831 \| 0.4311 \| 1 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.11.0 - Datasets 2.14.3 - Tokenizers 0.13.3
Francesco-A/bert-finetuned-ner	Francesco-A	2023-08-09T15:45:53Z	104	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-08-09T15:29:35Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.9323631552836117 - name: Recall type: recall value: 0.9488387748232918 - name: F1 type: f1 value: 0.940528818083243 - name: Accuracy type: accuracy value: 0.9861217401542356 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0592 - Precision: 0.9324 - Recall: 0.9488 - F1: 0.9405 - Accuracy: 0.9861 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0774 \| 1.0 \| 1756 \| 0.0764 \| 0.9146 \| 0.9337 \| 0.9241 \| 0.9802 \| \| 0.0394 \| 2.0 \| 3512 \| 0.0554 \| 0.9265 \| 0.9483 \| 0.9373 \| 0.9860 \| \| 0.0261 \| 3.0 \| 5268 \| 0.0592 \| 0.9324 \| 0.9488 \| 0.9405 \| 0.9861 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e6_s6789_v3_l5_v20	KingKazma	2023-08-09T15:44:09Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T15:44:08Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
mbueno/llama2-qlora-finetunined-french	mbueno	2023-08-09T15:40:36Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T15:40:28Z	--- 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
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e5_s6789_v3_l5_v20	KingKazma	2023-08-09T15:37:07Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T15:37:06Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
Ripo-2007/dreambooth_alfonso	Ripo-2007	2023-08-09T15:32:17Z	4	1	diffusers	[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2023-08-09T13:35:48Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: alfonsoaraco tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Test enoder was not trained.
santiagotoso/ppo-LunarLander-v2	santiagotoso	2023-08-09T15:27:34Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-09T13:24:45Z	--- 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: 232.20 +/- 76.62 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 ... ```
murodbek/uzroberta-panx-uz	murodbek	2023-08-09T15:27:23Z	167	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "roberta", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-04-13T09:47:13Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: uzroberta-panx-uz 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. --> # uzroberta-panx-uz This model is a fine-tuned version of [rifkat/uztext-3Gb-BPE-Roberta](https://huggingface.co/rifkat/uztext-3Gb-BPE-Roberta) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1626 - F1: 0.9175 ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.0515 \| 1.0 \| 150 \| 0.1373 \| 0.9141 \| \| 0.0415 \| 2.0 \| 300 \| 0.1268 \| 0.9194 \| \| 0.0101 \| 3.0 \| 450 \| 0.1225 \| 0.9416 \| \| 0.0038 \| 4.0 \| 600 \| 0.1426 \| 0.9353 \| \| 0.0004 \| 5.0 \| 750 \| 0.1458 \| 0.9320 \| ### Framework versions - Transformers 4.27.3 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.12.1
Meohong/Dialect-Polyglot-12.8b-QLoRA	Meohong	2023-08-09T15:26:17Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T15:26:09Z	--- 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
felixshier/osc-01-bert-finetuned	felixshier	2023-08-09T15:24:55Z	61	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-09T13:35:56Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_keras_callback model-index: - name: osc-01-bert-finetuned 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. --> # osc-01-bert-finetuned This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3193 - Validation Loss: 0.7572 - Train Precision: 0.6026 - Epoch: 6 ## 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 110, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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 Precision \| Epoch \| \|:----------:\|:---------------:\|:---------------:\|:-----:\| \| 0.6873 \| 0.6937 \| 0.5147 \| 0 \| \| 0.6544 \| 0.6854 \| 0.5 \| 1 \| \| 0.6127 \| 0.7071 \| 0.5242 \| 2 \| \| 0.5651 \| 0.6813 \| 0.5591 \| 3 \| \| 0.5015 \| 0.7012 \| 0.5747 \| 4 \| \| 0.4006 \| 0.7292 \| 0.5882 \| 5 \| \| 0.3193 \| 0.7572 \| 0.6026 \| 6 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.13.0 - Datasets 2.14.4 - Tokenizers 0.13.3
felixshier/csc-01-bert-finetuned	felixshier	2023-08-09T15:24:52Z	62	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-09T13:35:35Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_keras_callback model-index: - name: csc-01-bert-finetuned 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. --> # csc-01-bert-finetuned This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4789 - Validation Loss: 0.7231 - Train Precision: 0.6429 - Epoch: 5 ## 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 70, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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 Precision \| Epoch \| \|:----------:\|:---------------:\|:---------------:\|:-----:\| \| 0.7100 \| 0.7421 \| 0.0 \| 0 \| \| 0.6764 \| 0.6861 \| 0.625 \| 1 \| \| 0.6311 \| 0.6838 \| 0.5862 \| 2 \| \| 0.5909 \| 0.7072 \| 0.6286 \| 3 \| \| 0.5413 \| 0.7504 \| 0.6667 \| 4 \| \| 0.4789 \| 0.7231 \| 0.6429 \| 5 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.13.0 - Datasets 2.14.4 - Tokenizers 0.13.3
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e3_s6789_v3_l5_v20	KingKazma	2023-08-09T15:23:02Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T15:23:01Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
jordyvl/vit-base_rvl-cdip_r2_32	jordyvl	2023-08-09T15:18:05Z	163	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-08-08T08:10:20Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-base_rvl-cdip_r2_32 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base_rvl-cdip_r2_32 This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6372 - Accuracy: 0.8985 - Brier Loss: 0.1792 - Nll: 1.1736 - F1 Micro: 0.8985 - F1 Macro: 0.8987 - Ece: 0.0847 - Aurc: 0.0201 ## 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: 96 - eval_batch_size: 96 - 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| 0.1647 \| 1.0 \| 3334 \| 0.4024 \| 0.8887 \| 0.1682 \| 1.2086 \| 0.8887 \| 0.8891 \| 0.0457 \| 0.0178 \| \| 0.1418 \| 2.0 \| 6668 \| 0.4075 \| 0.8941 \| 0.1646 \| 1.2066 \| 0.8941 \| 0.8942 \| 0.0522 \| 0.0177 \| \| 0.0989 \| 3.0 \| 10002 \| 0.4409 \| 0.8932 \| 0.1690 \| 1.1966 \| 0.8932 \| 0.8932 \| 0.0647 \| 0.0175 \| \| 0.0614 \| 4.0 \| 13336 \| 0.4781 \| 0.8944 \| 0.1730 \| 1.2083 \| 0.8944 \| 0.8951 \| 0.0694 \| 0.0181 \| \| 0.0392 \| 5.0 \| 16670 \| 0.5329 \| 0.8959 \| 0.1761 \| 1.1777 \| 0.8959 \| 0.8958 \| 0.0776 \| 0.0187 \| \| 0.0231 \| 6.0 \| 20004 \| 0.5714 \| 0.8957 \| 0.1799 \| 1.2083 \| 0.8957 \| 0.8958 \| 0.0813 \| 0.0198 \| \| 0.0126 \| 7.0 \| 23338 \| 0.6002 \| 0.8966 \| 0.1802 \| 1.1732 \| 0.8966 \| 0.8972 \| 0.0839 \| 0.0197 \| \| 0.0079 \| 8.0 \| 26672 \| 0.6193 \| 0.8984 \| 0.1789 \| 1.1849 \| 0.8984 \| 0.8985 \| 0.0833 \| 0.0200 \| \| 0.0049 \| 9.0 \| 30006 \| 0.6333 \| 0.8976 \| 0.1798 \| 1.1906 \| 0.8976 \| 0.8978 \| 0.0851 \| 0.0205 \| \| 0.0034 \| 10.0 \| 33340 \| 0.6372 \| 0.8985 \| 0.1792 \| 1.1736 \| 0.8985 \| 0.8987 \| 0.0847 \| 0.0201 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e2_s6789_v3_l5_v20	KingKazma	2023-08-09T15:16:00Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T15:15:59Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
imvladikon/alephbertgimmel_parashoot	imvladikon	2023-08-09T15:10:27Z	113	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "question-answering", "generated_from_trainer", "he", "dataset:imvladikon/parashoot", "base_model:imvladikon/alephbertgimmel-base-512", "base_model:finetune:imvladikon/alephbertgimmel-base-512", "endpoints_compatible", "region:us" ]	question-answering	2023-08-02T07:44:16Z	--- base_model: imvladikon/alephbertgimmel-base-512 tags: - generated_from_trainer datasets: - imvladikon/parashoot model-index: - name: alephbertgimmel_parashoot results: [] language: - he metrics: - f1 - exact_match pipeline_tag: question-answering --- <!-- 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. --> # alephbertgimmel_parashoot This model is a fine-tuned version of [imvladikon/alephbertgimmel-base-512](https://huggingface.co/imvladikon/alephbertgimmel-base-512) on the [imvladikon/parashoot](https://huggingface.co/datasets/imvladikon/parashoot) 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: 3e-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: 5.0 ### Training results ``` *** predict metrics *** predict_samples = 1102 test_exact_match = 27.7073 test_f1 = 51.787 test_runtime = 0:00:32.05 test_samples_per_second = 34.383 test_steps_per_second = 4.306 ``` ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.2 - Tokenizers 0.13.3
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e1_s6789_v3_l5_v20	KingKazma	2023-08-09T15:08:58Z	0	0	peft	[ "peft", "region:us" ]	null	2023-08-09T15:08:56Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e0_s6789_v3_l5_v50	KingKazma	2023-08-09T15:03:33Z	2	0	peft	[ "peft", "region:us" ]	null	2023-08-09T15:03:32Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
Cheetor1996/Efanatika_aku_no_onna_kanbu	Cheetor1996	2023-08-09T15:02:56Z	0	0	null	[ "art", "en", "license:cc-by-2.0", "region:us" ]	null	2023-08-09T15:00:15Z	--- license: cc-by-2.0 language: - en tags: - art --- Efanatika from Aku no onna kanbu - Trained with Anime (final-full-pruned) model. - Recommended LoRA weights: 0.7+ - Recommended LoRA weight blocks: ALL, MIDD, OUTD, and OUTALL - Activation tag: efanatika, use with pink hair, long hair, very long hair, colored skin, blue skin, yellow eyes, colored sclera, and black sclera.
jcy204/cold_model2	jcy204	2023-08-09T15:02:39Z	62	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-09T14:57:29Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: jcy204/cold_model2 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. --> # jcy204/cold_model2 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: 0.3582 - Validation Loss: 0.6678 - Train Accuracy: 0.7477 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1545, '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 \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.7779 \| 0.6213 \| 0.7392 \| 0 \| \| 0.5323 \| 0.6326 \| 0.7315 \| 1 \| \| 0.3582 \| 0.6678 \| 0.7477 \| 2 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.4 - Tokenizers 0.13.3
leonard-pak/q-FrozenLake-v1-4x4-noSlippery	leonard-pak	2023-08-09T14:59:17Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-09T14:58:08Z	--- 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 model = load_from_hub(repo_id="leonard-pak/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"])
LarryAIDraw/ToukaLora-15	LarryAIDraw	2023-08-09T14:58:48Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-09T14:39:49Z	--- license: creativeml-openrail-m --- https://civitai.com/models/125271/touka-kirishima-tokyo-ghoul-lora
LarryAIDraw/MiaChristoph-10	LarryAIDraw	2023-08-09T14:58:35Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-09T14:39:26Z	--- license: creativeml-openrail-m --- https://civitai.com/models/124748/mia-christoph-tenpuru
LarryAIDraw/GirlsFrontlineAk12	LarryAIDraw	2023-08-09T14:58:21Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-08-09T14:39:04Z	--- license: creativeml-openrail-m --- https://civitai.com/models/76960/ak-12-quiet-azure-girls-frontline
gsaivinay/Llama-2-7b-Chat-GPTQ	gsaivinay	2023-08-09T14:57:09Z	26	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-2", "en", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-18T19:21:58Z	--- language: - en license: other inference: true model_type: llama pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 --- # Meta's Llama 2 7b Chat GPTQ ## * Duplicated from TheBloke * These files are GPTQ model files for [Meta's Llama 2 7b Chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. ## Repositories available * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) ## Prompt template: Llama-2-Chat ``` System: You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. User: {prompt} Assistant: ``` ## Provided files Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. \| Branch \| Bits \| Group Size \| Act Order (desc_act) \| File Size \| ExLlama Compatible? \| Made With \| Description \| \| ------ \| ---- \| ---------- \| -------------------- \| --------- \| ------------------- \| --------- \| ----------- \| \| main \| 4 \| 128 \| False \| 3.90 GB \| True \| AutoGPTQ \| Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. \| \| gptq-4bit-32g-actorder_True \| 4 \| 32 \| True \| 4.28 GB \| True \| AutoGPTQ \| 4-bit, with Act Order and group size. 32g gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. \| \| gptq-4bit-64g-actorder_True \| 4 \| 64 \| True \| 4.02 GB \| True \| AutoGPTQ \| 4-bit, with Act Order and group size. 64g uses less VRAM than 32g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. \| \| gptq-4bit-128g-actorder_True \| 4 \| 128 \| True \| 3.90 GB \| True \| AutoGPTQ \| 4-bit, with Act Order and group size. 128g uses even less VRAM, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. \| ## How to download from branches - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Llama-2-7b-Chat-GPTQ:gptq-4bit-32g-actorder_True` - With Git, you can clone a branch with: ``` git clone --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Llama-2-7b-Chat-GPTQ` ``` - In Python Transformers code, the branch is the `revision` parameter; see below. ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/Llama-2-7b-Chat-GPTQ`. - To download from a specific branch, enter for example `TheBloke/Llama-2-7b-Chat-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done" 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `Llama-2-7b-Chat-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. * Note that you do not need to set GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! ## How to use this GPTQ model from Python code First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) installed: `GITHUB_ACTIONS=true pip install auto-gptq` Then try the following example code: ```python from transformers import AutoTokenizer, pipeline, logging from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig model_name_or_path = "TheBloke/Llama-2-7b-Chat-GPTQ" model_basename = "gptq_model-4bit-128g" use_triton = False tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, model_basename=model_basename use_safetensors=True, trust_remote_code=True, device="cuda:0", use_triton=use_triton, quantize_config=None) """ To download from a specific branch, use the revision parameter, as in this example: model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, revision="gptq-4bit-32g-actorder_True", model_basename=model_basename, use_safetensors=True, trust_remote_code=True, device="cuda:0", quantize_config=None) """ prompt = "Tell me about AI" prompt_template=f'''System: You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. User: {prompt} Assistant: ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline # Prevent printing spurious transformers error when using pipeline with AutoGPTQ logging.set_verbosity(logging.CRITICAL) print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, temperature=0.7, top_p=0.95, repetition_penalty=1.15 ) print(pipe(prompt_template)[0]['generated_text']) ``` ## Compatibility The files provided will work with AutoGPTQ (CUDA and Triton modes), GPTQ-for-LLaMa (only CUDA has been tested), and Occ4m's GPTQ-for-LLaMa fork. ExLlama works with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility. # Original model card: Meta's Llama 2 7b Chat # Llama 2 Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here. Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. Model Developers Meta Variations Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. Input Models input text only. Output Models generate text only. Model Architecture Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. \|\|Training Data\|Params\|Content Length\|GQA\|Tokens\|LR\| \|---\|---\|---\|---\|---\|---\|---\| \|Llama 2\|A new mix of publicly available online data\|7B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|13B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|70B\|4k\|✔\|2.0T\|1.5 x 10<sup>-4</sup>\| Llama 2 family of models. Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. Model Dates Llama 2 was trained between January 2023 and July 2023. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) ## Intended Use Intended Use Cases Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212). Out-of-scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software Training Factors We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. \|\|Time (GPU hours)\|Power Consumption (W)\|Carbon Emitted(tCO<sub>2</sub>eq)\| \|---\|---\|---\|---\| \|Llama 2 7B\|184320\|400\|31.22\| \|Llama 2 13B\|368640\|400\|62.44\| \|Llama 2 70B\|1720320\|400\|291.42\| \|Total\|3311616\|\|539.00\| CO<sub>2</sub> emissions during pretraining. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data Overview Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. \|Model\|Size\|Code\|Commonsense Reasoning\|World Knowledge\|Reading Comprehension\|Math\|MMLU\|BBH\|AGI Eval\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|Llama 1\|7B\|14.1\|60.8\|46.2\|58.5\|6.95\|35.1\|30.3\|23.9\| \|Llama 1\|13B\|18.9\|66.1\|52.6\|62.3\|10.9\|46.9\|37.0\|33.9\| \|Llama 1\|33B\|26.0\|70.0\|58.4\|67.6\|21.4\|57.8\|39.8\|41.7\| \|Llama 1\|65B\|30.7\|70.7\|60.5\|68.6\|30.8\|63.4\|43.5\|47.6\| \|Llama 2\|7B\|16.8\|63.9\|48.9\|61.3\|14.6\|45.3\|32.6\|29.3\| \|Llama 2\|13B\|24.5\|66.9\|55.4\|65.8\|28.7\|54.8\|39.4\|39.1\| \|Llama 2\|70B\|37.5\|71.9\|63.6\|69.4\|35.2\|68.9\|51.2\|54.2\| Overall performance on grouped academic benchmarks. Code: We report the average pass@1 scores of our models on HumanEval and MBPP. Commonsense Reasoning: We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. World Knowledge: We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. Reading Comprehension: For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. MATH: We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama 1\|7B\|27.42\|23.00\| \|Llama 1\|13B\|41.74\|23.08\| \|Llama 1\|33B\|44.19\|22.57\| \|Llama 1\|65B\|48.71\|21.77\| \|Llama 2\|7B\|33.29\|21.25\| \|Llama 2\|13B\|41.86\|26.10\| \|Llama 2\|70B\|50.18\|24.60\| Evaluation of pretrained LLMs on automatic safety benchmarks. For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama-2-Chat\|7B\|57.04\|0.00\| \|Llama-2-Chat\|13B\|62.18\|0.00\| \|Llama-2-Chat\|70B\|64.14\|0.01\| Evaluation of fine-tuned LLMs on different safety datasets. Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index \|Model\|Llama2\|Llama2-hf\|Llama2-chat\|Llama2-chat-hf\| \|---\|---\|---\|---\|---\| \|7B\| [Link](https://huggingface.co/llamaste/Llama-2-7b) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)\| \|13B\| [Link](https://huggingface.co/llamaste/Llama-2-13b) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)\| \|70B\| [Link](https://huggingface.co/llamaste/Llama-2-70b) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)\|
liadraz/q-FrozenLake-v1-4x4-noSlippery	liadraz	2023-08-09T14:54:50Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-08-09T14:54:46Z	--- 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="liadraz/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"]) ```
broAleks13/stablecode-completion-alpha-3b-4k	broAleks13	2023-08-09T14:49:26Z	0	0	null	[ "region:us" ]	null	2023-08-09T14:42:38Z	--- license: apache-2.0 --- stabilityai/stablecode-completion-alpha-3b-4k
twbrandon7/rl-course-unit3-dqn-SpaceInvadersNoFrameskip-v4	twbrandon7	2023-08-09T14:45:26Z	1	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-08-09T14:44:48Z	--- 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: 604.00 +/- 212.04 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 twbrandon7 -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 twbrandon7 -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 twbrandon7 ``` ## 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'} ```

szymonrucinski/good-mood

szymonrucinski

2023-08-09T21:09:47Z

0

null

[ "license:cc-by-nc-sa-3.0", "region:us" ]

null

2023-08-09T16:17:39Z

--- license: cc-by-nc-sa-3.0 ---

badokorach/bert-finetuned-squad-7

badokorach

2023-08-09T21:00:46Z

3

0

transformers

[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "base_model:badokorach/bert-finetuned-squad-5", "base_model:finetune:badokorach/bert-finetuned-squad-5", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-08-09T16:23:33Z

--- license: apache-2.0 base_model: badokorach/bert-finetuned-squad-5 tags: - generated_from_keras_callback model-index: - name: badokorach/bert-finetuned-squad-7 results: [] ---  # badokorach/bert-finetuned-squad-7 This model is a fine-tuned version of [badokorach/bert-finetuned-squad-5](https://huggingface.co/badokorach/bert-finetuned-squad-5) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0011 - Epoch: 29 ## 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 4e-05, 'decay_steps': 1950, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Epoch | |:----------:|:-----:| | 0.0673 | 0 | | 0.1201 | 1 | | 0.0502 | 2 | | 0.0209 | 3 | | 0.0278 | 4 | | 0.0358 | 5 | | 0.0268 | 6 | | 0.0258 | 7 | | 0.0212 | 8 | | 0.0247 | 9 | | 0.0104 | 10 | | 0.0101 | 11 | | 0.0033 | 12 | | 0.0044 | 13 | | 0.0185 | 14 | | 0.0051 | 15 | | 0.0011 | 16 | | 0.0043 | 17 | | 0.0022 | 18 | | 0.0026 | 19 | | 0.0019 | 20 | | 0.0012 | 21 | | 0.0013 | 22 | | 0.0009 | 23 | | 0.0008 | 24 | | 0.0007 | 25 | | 0.0016 | 26 | | 0.0006 | 27 | | 0.0006 | 28 | | 0.0011 | 29 | ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.4 - Tokenizers 0.13.3

chronopt-research/vietnamese-gpt2-base

chronopt-research

2023-08-09T20:58:46Z

147

1

transformers

[ "transformers", "pytorch", "jax", "tensorboard", "gpt2", "text-generation", "vi", "dataset:duongttr/vi-dataset-for-pretrain", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-08-09T20:29:15Z

--- license: apache-2.0 datasets: - duongttr/vi-dataset-for-pretrain language: - vi metrics: - perplexity pipeline_tag: text-generation widget: - text: Hôm nay tôi rất vui vì - text: Hoàng Sa, Trường Sa là của Việt model-index: - name: chronopt-research/vietnamese-gpt2-base results: - task: type: text-generation metrics: - type: perplexity value: 51.35 verified: true --- # Vietnamese `gpt2-base`  This is a pretrained `gpt2-base` for Vietnamese language using casual language modeling (CLM) objective. It was introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and first released at [this page](https://openai.com/blog/better-language-models/). ## Model Description GPT-2 (*at first*) is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. This is the **base version** of GPT-2, with 137M parameters. You could've found other pretrained version from here: [gpt2-medium](https://huggingface.co/chronopt-research/vietnamese-gpt2-medium), [gpt2-large]() ## Dataset used for pretraining This is a combination of multiple Vietnamese dataset for pretraining CLMs such as GPT, GPT2, etc. The dataset consists of: - [`vietgpt/covid_19_news_vi`](https://huggingface.co/datasets/vietgpt/covid_19_news_vi) - [`hieunguyen1053/binhvq-news-corpus`](https://huggingface.co/datasets/hieunguyen1053/binhvq-news-corpus) - [`oscar (unshuffled_deduplicated_vi)`](https://huggingface.co/datasets/oscar) - [`vietgpt/wikipedia_vi`](https://huggingface.co/datasets/vietgpt/wikipedia_vi) You can find out the combined version here: [duongttr/vi-dataset-for-pretrain](https://huggingface.co/datasets/duongttr/vi-dataset-for-pretrain) ## Hyperparamters & Results We trained the model ~100k steps, with `lr=1e-4`, `bs=2560` (`single_batch_size=32` * `num_core=8` * `grad_cum=10`), `optimizer=adamw` on TPU-VM-3.8 from [TRC Program](https://sites.research.google/trc/about/). The training costs around **1 day**. |Model|Eval Loss|Eval Perplexity| |---|---|---| |**gpt2-base**|**3.939**|**51.35**| |gpt2-medium|2.8676|17.5948| |gpt2-large|-|-| ## Contacts Feel free to contact us via: [email]()

Jbrophy/falcon-7B-Instruct-Romance

Jbrophy

2023-08-09T20:58:15Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-08T00:39:51Z

--- 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

chronopt-research/vietnamese-gpt2-medium

chronopt-research

2023-08-09T20:54:47Z

146

2

transformers

[ "transformers", "pytorch", "jax", "tensorboard", "gpt2", "text-generation", "vi", "dataset:duongttr/vi-dataset-for-pretrain", "doi:10.57967/hf/3874", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-08-06T11:34:08Z

--- license: apache-2.0 datasets: - duongttr/vi-dataset-for-pretrain language: - vi metrics: - perplexity pipeline_tag: text-generation widget: - text: Việt Nam là quốc gia có - text: Hoàng Sa, Trường Sa là của model-index: - name: chronopt-research/vietnamese-gpt2-medium results: - task: type: text-generation metrics: - type: perplexity value: 17.5948 verified: true --- # Vietnamese `gpt2-medium`  This is a pretrained `gpt2-medium` for Vietnamese language using casual language modeling (CLM) objective. It was introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and first released at [this page](https://openai.com/blog/better-language-models/). ## Model Description GPT-2 (*at first*) is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. This is the **medium version** of GPT-2, with 380M parameters. You could've found other pretrained version from here: [gpt2-base](https://huggingface.co/chronopt-research/vietnamese-gpt2-base), [gpt2-large]() ## Dataset used for pretraining This is a combination of multiple Vietnamese dataset for pretraining CLMs such as GPT, GPT2, etc. The dataset consists of: - [`vietgpt/covid_19_news_vi`](https://huggingface.co/datasets/vietgpt/covid_19_news_vi) - [`hieunguyen1053/binhvq-news-corpus`](https://huggingface.co/datasets/hieunguyen1053/binhvq-news-corpus) - [`oscar (unshuffled_deduplicated_vi)`](https://huggingface.co/datasets/oscar) - [`vietgpt/wikipedia_vi`](https://huggingface.co/datasets/vietgpt/wikipedia_vi) You can find out the combined version here: [duongttr/vi-dataset-for-pretrain](https://huggingface.co/datasets/duongttr/vi-dataset-for-pretrain) ## Hyperparamters & Results We trained the model ~100k steps, with `lr=1e-4`, `bs=1920`, `optimizer=adamw` on TPU-VM-3.8 from [TRC Program](https://sites.research.google/trc/about/). The training costs around **2.5 days**. |Model|Eval Loss|Eval Perplexity| |---|---|---| |gpt2-base|3.939|51.35| |**gpt2-medium**|**2.8676**|**17.5948**| |gpt2-large|-|-| ## Contacts Feel free to contact us via: [email]()

GhifSmile/distilbert-base-uncased-DSC-new

GhifSmile

2023-08-09T20:49:02Z

104

0

transformers

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

text-classification

2023-08-09T19:25:46Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall model-index: - name: distilbert-base-uncased-DSC-new results: [] ---  # distilbert-base-uncased-DSC-new This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1017 - Accuracy: 0.9902 - Precision: 0.9910 - Recall: 0.9909 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:| | 0.4743 | 1.0 | 618 | 0.1856 | 0.9633 | 0.9672 | 0.9647 | | 0.0946 | 2.0 | 1236 | 0.1577 | 0.9707 | 0.9749 | 0.9733 | | 0.0851 | 3.0 | 1854 | 0.1081 | 0.9853 | 0.9869 | 0.9858 | | 0.0633 | 4.0 | 2472 | 0.1449 | 0.9841 | 0.9851 | 0.9837 | | 0.0258 | 5.0 | 3090 | 0.1155 | 0.9829 | 0.9838 | 0.9829 | | 0.022 | 6.0 | 3708 | 0.1089 | 0.9890 | 0.9899 | 0.9897 | | 0.0147 | 7.0 | 4326 | 0.1092 | 0.9878 | 0.9885 | 0.9875 | | 0.0043 | 8.0 | 4944 | 0.1017 | 0.9902 | 0.9910 | 0.9909 | | 0.0041 | 9.0 | 5562 | 0.1033 | 0.9878 | 0.9885 | 0.9874 | | 0.0012 | 10.0 | 6180 | 0.1093 | 0.9878 | 0.9885 | 0.9874 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

EgilKarlsen/DistilRoBERTa_Thunderbird-Anomaly_Baseline

EgilKarlsen

2023-08-09T20:45:29Z

107

2

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-08-09T20:25:00Z

--- license: apache-2.0 base_model: distilroberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: DistilRoBERTa_Thunderbird-Anomaly_Baseline results: [] ---  # DistilRoBERTa_Thunderbird-Anomaly_Baseline This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0031 - Accuracy: 0.9999 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.0958 | 1.0 | 1094 | 0.0623 | 0.9846 | | 0.0514 | 2.0 | 2188 | 0.0340 | 0.9846 | | 0.0261 | 3.0 | 3282 | 0.0168 | 0.9896 | | 0.0147 | 4.0 | 4376 | 0.0095 | 1.0 | | 0.01 | 5.0 | 5470 | 0.0061 | 1.0 | | 0.0071 | 6.0 | 6564 | 0.0042 | 1.0 | | 0.0058 | 7.0 | 7658 | 0.0031 | 1.0 | | 0.0046 | 8.0 | 8752 | 0.0025 | 1.0 | | 0.0043 | 9.0 | 9846 | 0.0022 | 1.0 | | 0.0038 | 10.0 | 10940 | 0.0021 | 1.0 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

ederdt2023/Eder_Duenas

ederdt2023

2023-08-09T20:43:13Z

0

null

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

null

2023-08-09T20:43:13Z

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

raptz/autotrain-rstt_fullsumm-81171141667

raptz

2023-08-09T20:34:42Z

113

0

transformers

[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "autotrain", "summarization", "unk", "dataset:raptz/autotrain-data-rstt_fullsumm", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2023-08-09T20:30:49Z

--- tags: - autotrain - summarization language: - unk widget: - text: "I love AutoTrain" datasets: - raptz/autotrain-data-rstt_fullsumm co2_eq_emissions: emissions: 1.5473924434284785 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 81171141667 - CO2 Emissions (in grams): 1.5474 ## Validation Metrics - Loss: 0.650 - Rouge1: 68.031 - Rouge2: 53.314 - RougeL: 59.901 - RougeLsum: 61.660 - Gen Len: 61.707 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/raptz/autotrain-rstt_fullsumm-81171141667 ```

TotoLefo/Sheirlou500Epoch

TotoLefo

2023-08-09T20:33:56Z

0

null

[ "AI VOICE", "fr", "region:us" ]

null

2023-08-09T20:31:07Z

--- language: - fr tags: - AI VOICE --- # Model Card for Model ID - **Developed by:** TOTO

jannikseus/aspect_extraction_laptop_reviews

jannikseus

2023-08-09T20:30:32Z

23

1

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-08-06T20:55:25Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: aspect_extraction_laptop_reviews results: [] ---  # aspect_extraction_laptop_reviews This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1003 - Precision: 0.7872 - Recall: 0.7817 - F1: 0.7845 - Accuracy: 0.9732 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 362 | 0.0854 | 0.7070 | 0.7817 | 0.7425 | 0.9675 | | 0.1121 | 2.0 | 724 | 0.0937 | 0.7466 | 0.7676 | 0.7569 | 0.9696 | | 0.0383 | 3.0 | 1086 | 0.0959 | 0.7622 | 0.7676 | 0.7649 | 0.9714 | | 0.0383 | 4.0 | 1448 | 0.1003 | 0.7872 | 0.7817 | 0.7845 | 0.9732 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.2 - Tokenizers 0.13.3

FredericProtat/dqn-SpaceInvadersNoFrameskip-v4

FredericProtat

2023-08-09T20:24:42Z

7

0

stable-baselines3

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

reinforcement-learning

2023-08-09T20:24:06Z

--- 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: 691.00 +/- 253.51 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 SB3: https://github.com/DLR-RM/stable-baselines3 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 FredericProtat -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 FredericProtat -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 FredericProtat ``` ## 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'} ```

JabrilJacobs/poca-SoccerTwos

JabrilJacobs

2023-08-09T20:13:59Z

12

0

ml-agents

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

reinforcement-learning

2023-08-09T20:11:14Z

--- 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: JabrilJacobs/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Prabna/sd-class-butterflies-32-1

Prabna

2023-08-09T20:11:43Z

31

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2023-08-09T20:11:30Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('Prabna/sd-class-butterflies-32-1') image = pipeline().images[0] image ```

Pixel390/BOY

Pixel390

2023-08-09T20:11:24Z

0

1

diffusers

[ "diffusers", "tensorboard", "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

2023-08-09T19:20:44Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a uxz boy tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - Pixel390/BOY These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a uxz boy using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: True.

asenella/mhd_config_1_MMVAE_beta_5_scale_True_seed_0

asenella

2023-08-09T20:06:44Z

0

null

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

null

2023-08-09T20:06:32Z

--- language: en tags: - multivae license: apache-2.0 --- ### Downloading this model from the Hub This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from multivae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name") ```

Josrf/a2c-PandaReachDense-v3

Josrf

2023-08-09T20:03:20Z

6

0

stable-baselines3

[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-08-09T19:57:18Z

--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.23 +/- 0.13 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v3** This is a trained model of a **A2C** agent playing **PandaReachDense-v3** 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 ... ```

BauyrjanQ/whisper-kk-speech2ner-b16-ms2k-1500-s-cl

BauyrjanQ

2023-08-09T19:55:00Z

62

0

transformers

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

automatic-speech-recognition

2023-08-09T06:05:26Z

--- tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-kk-speech2ner-b16-ms2k-1500-s-cl results: [] ---  # whisper-kk-speech2ner-b16-ms2k-1500-s-cl This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3907 - Wer: 264.8997 ## 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: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2596 | 0.34 | 1500 | 0.3907 | 264.8997 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

cjohlmacher/ppo-SnowballTarget

cjohlmacher

2023-08-09T19:47:19Z

6

0

ml-agents

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

reinforcement-learning

2023-08-09T19:45:03Z

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

StofEzz/mascir_frwav2vec2-large-xlsr-53

StofEzz

2023-08-09T19:43:27Z

162

0

transformers

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

automatic-speech-recognition

2023-08-09T17:37:06Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: mascir_frwav2vec2-large-xlsr-53 results: [] ---  # mascir_frwav2vec2-large-xlsr-53 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4708 - Wer: 0.3789 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 6.3318 | 2.0 | 500 | 3.0088 | 0.9856 | | 1.5916 | 4.0 | 1000 | 0.7746 | 0.6411 | | 0.3961 | 6.0 | 1500 | 0.5238 | 0.5211 | | 0.2205 | 8.0 | 2000 | 0.5014 | 0.4733 | | 0.1401 | 10.0 | 2500 | 0.5166 | 0.4878 | | 0.1147 | 12.0 | 3000 | 0.5058 | 0.4333 | | 0.0938 | 14.0 | 3500 | 0.4635 | 0.4233 | | 0.0788 | 16.0 | 4000 | 0.4997 | 0.4144 | | 0.0645 | 18.0 | 4500 | 0.4840 | 0.4122 | | 0.0534 | 20.0 | 5000 | 0.4789 | 0.4022 | | 0.0437 | 22.0 | 5500 | 0.4785 | 0.3978 | | 0.041 | 24.0 | 6000 | 0.4708 | 0.3789 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

HG7/ReQLoRA_all8

HG7

2023-08-09T19:34:28Z

2

0

peft

[ "peft", "region:us" ]

null

2023-08-09T19:34:07Z

--- 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

ElcKeT/bert-sst2-finetuned-peft

ElcKeT

2023-08-09T19:21:37Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T19:20:27Z

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

Den4ikAI/ruBert_base_intent_detection

Den4ikAI

2023-08-09T19:18:27Z

140

8

transformers

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

text-classification

2022-12-10T11:10:35Z

--- license: mit widget: - text: Сколько будет 2+2? - text: Который час? - text: Вруби свет - text: Сколько баллов пробки? language: - ru pipeline_tag: text-classification --- Модель на основе ruBert-base для определения намерений

huggingnft-app/milady

huggingnft-app

2023-08-09T19:17:48Z

2

0

transformers

[ "transformers", "huggingnft", "nft", "huggan", "gan", "image", "images", "unconditional-image-generation", "dataset:huggingnft/milady", "license:mit", "endpoints_compatible", "region:us" ]

unconditional-image-generation

2023-08-09T19:17:21Z

--- tags: - huggingnft - nft - huggan - gan - image - images - unconditional-image-generation datasets: - huggingnft/milady license: mit --- # Hugging NFT: milady ## Disclaimer All rights belong to their owners. Models and datasets can be removed from the site at the request of the copyright holder. ## Model description LightWeight GAN model for unconditional generation. NFT collection available [here](https://opensea.io/collection/milady). Dataset is available [here](https://huggingface.co/datasets/huggingnft/milady). Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft). Project repository: [link](https://github.com/AlekseyKorshuk/huggingnft). [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingnft?style=social)](https://github.com/AlekseyKorshuk/huggingnft) ## Intended uses & limitations #### How to use Check project repository: [link](https://github.com/AlekseyKorshuk/huggingnft). #### Limitations and bias Check project repository: [link](https://github.com/AlekseyKorshuk/huggingnft). ## Training data Dataset is available [here](https://huggingface.co/datasets/huggingnft/milady). ## Training procedure Training script is available [here](https://github.com/AlekseyKorshuk/huggingnft). ## Generated Images Check results with Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft). ## About *Built by Aleksey Korshuk* [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingnft?style=social)](https://github.com/AlekseyKorshuk/huggingnft) ### BibTeX entry and citation info ```bibtex @InProceedings{huggingnft, author={Aleksey Korshuk} year=2022 } ```

HG7/ReQLoRA_GUD8

HG7

2023-08-09T19:04:43Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T19:04:35Z

--- 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

dfomin/Reinforce-1

dfomin

2023-08-09T18:51:39Z

0

null

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

reinforcement-learning

2023-08-09T18:51:29Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-1 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

cto-algo-huggingface/EternityRing

cto-algo-huggingface

2023-08-09T18:42:30Z

24

0

diffusers

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

text-to-image

2023-08-09T18:40:55Z

--- license: creativeml-openrail-m tags: - text-to-image --- ### eternity_ring on Stable Diffusion via Dreambooth #### model by cto-algo-huggingface This your the Stable Diffusion model fine-tuned the eternity_ring concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **<eternity_ring> jewellery** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept: ![image 0](https://huggingface.co/cto-algo-huggingface/eternity-ring/resolve/main/concept_images/1.jpeg) ![image 1](https://huggingface.co/cto-algo-huggingface/eternity-ring/resolve/main/concept_images/5.jpeg) ![image 2](https://huggingface.co/cto-algo-huggingface/eternity-ring/resolve/main/concept_images/0.jpeg) ![image 3](https://huggingface.co/cto-algo-huggingface/eternity-ring/resolve/main/concept_images/3.jpeg) ![image 4](https://huggingface.co/cto-algo-huggingface/eternity-ring/resolve/main/concept_images/2.jpeg) ![image 5](https://huggingface.co/cto-algo-huggingface/eternity-ring/resolve/main/concept_images/4.jpeg) ![image 6](https://huggingface.co/cto-algo-huggingface/eternity-ring/resolve/main/concept_images/6.jpeg)

stoyky/ppo-Huggy

stoyky

2023-08-09T18:40:35Z

0

ml-agents

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

reinforcement-learning

2023-08-09T18:40:27Z

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

AEJaspan/ppo-LunarLander-v2

AEJaspan

2023-08-09T18:37:08Z

4

0

stable-baselines3

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

reinforcement-learning

2023-08-09T18:36:49Z

--- 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: 279.39 +/- 20.53 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 ... ```

KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e5_s6789_v3_l5_v100

KingKazma

2023-08-09T18:36:20Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T18:36:19Z

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

KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e7_s6789_v3_l5_v50

KingKazma

2023-08-09T18:31:49Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-09T18:31:48Z

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

KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e3_s6789_v3_l5_v100

KingKazma

2023-08-09T18:22:28Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T18:22:26Z

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

KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e4_s6789_v3_l5_v50

KingKazma

2023-08-09T18:11:32Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T18:11:31Z

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

KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e1_s6789_v3_l5_v100

KingKazma

2023-08-09T18:08:35Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T18:08:34Z

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

Norod78/sdxl-BrainSlug-dreambooth

Norod78

2023-08-09T18:08:34Z

58

2

diffusers

[ "diffusers", "text-to-image", "lora", "autotrain", "en", "dataset:Norod78/BrainSlug-blip-captions-1024", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]

text-to-image

2023-08-09T17:10:00Z

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a brain slug tags: - text-to-image - diffusers - lora - autotrain widget: - text: photo of a brain slug enjoying a nice sunny day on the beach - text: photo of a brain slug attached to Snoop Doggs head - text: >- photo of a shocked old granny with a gooey (brain slug attached to her head), Very detailed, clean, high quality, sharp image - text: >- photo of a brain slug attacking the head of an anime girl, cartoon style, high quality datasets: - Norod78/BrainSlug-blip-captions-1024 inference: true language: - en --- # DreamBooth trained by AutoTrain Text enoder was not trained. # Trigger words Use "photo of a brain slug" / "brain slug" and etc # Examples photo of a brain slug enjoying a nice sunny day on the beach ![photo_of_a_brainslug_enjoying_a_nice_sunny_day_on_the_beach](https://huggingface.co/Norod78/sdxl-BrainSlug-dreambooth/resolve/main/Examples/i-42-photo_of_a_brainslug_enjoying_a_nice_sunny_day_on_the_beach-generated_image.jpg) photo of a shocked old granny with a gooey (brain slug attached to her head), Very detailed, clean, high quality, sharp image ![A_photo_of_a_shocked_old_granny](https://huggingface.co/Norod78/sdxl-BrainSlug-dreambooth/resolve/main/Examples/i-7777-A_photo_of_a_shocked_old_granny_with_a_gooey_(brain_slug_attached_to_her_head),_Very_detailed,_clean,_high_quality,_sharp_image,_Dave_Dorman-generated_image.jpg)

iampraveenvemula/lora-trained-xl-colab

iampraveenvemula

2023-08-09T18:06:47Z

1

diffusers

[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

2023-08-09T16:51:25Z

--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks dog tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - iampraveenvemula/lora-trained-xl-colab These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.

IAyoub/finetuning-bert-sentiment-reviews-2

IAyoub

2023-08-09T18:06:32Z

103

0

transformers

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

text-classification

2023-08-09T14:21:56Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-bert-sentiment-reviews-2 results: [] ---  # finetuning-bert-sentiment-reviews-2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2086 - Accuracy: 0.9308 - F1: 0.8368 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 0.01 | 10 | 0.6716 | 0.7463 | 0.2849 | | No log | 0.03 | 20 | 0.5789 | 0.7463 | 0.2849 | | No log | 0.04 | 30 | 0.4971 | 0.7788 | 0.3849 | | No log | 0.06 | 40 | 0.4298 | 0.8672 | 0.5506 | | No log | 0.07 | 50 | 0.3837 | 0.8794 | 0.5686 | | No log | 0.09 | 60 | 0.3481 | 0.8802 | 0.5672 | | No log | 0.1 | 70 | 0.3680 | 0.8757 | 0.5604 | | No log | 0.12 | 80 | 0.3259 | 0.8854 | 0.5736 | | No log | 0.13 | 90 | 0.3179 | 0.8854 | 0.5727 | | No log | 0.15 | 100 | 0.3306 | 0.8891 | 0.6295 | | No log | 0.16 | 110 | 0.3253 | 0.8894 | 0.6692 | | No log | 0.18 | 120 | 0.3041 | 0.9024 | 0.7285 | | No log | 0.19 | 130 | 0.2997 | 0.9068 | 0.7426 | | No log | 0.21 | 140 | 0.2881 | 0.9057 | 0.7434 | | No log | 0.22 | 150 | 0.2892 | 0.9094 | 0.7587 | | No log | 0.24 | 160 | 0.2771 | 0.9149 | 0.7801 | | No log | 0.25 | 170 | 0.2779 | 0.9135 | 0.7782 | | No log | 0.27 | 180 | 0.2992 | 0.9109 | 0.7720 | | No log | 0.28 | 190 | 0.2809 | 0.9083 | 0.7622 | | No log | 0.3 | 200 | 0.2636 | 0.9146 | 0.7680 | | No log | 0.31 | 210 | 0.3381 | 0.9079 | 0.7694 | | No log | 0.33 | 220 | 0.2661 | 0.9197 | 0.7858 | | No log | 0.34 | 230 | 0.3377 | 0.8854 | 0.7582 | | No log | 0.36 | 240 | 0.2614 | 0.9190 | 0.7881 | | No log | 0.37 | 250 | 0.2459 | 0.9264 | 0.7981 | | No log | 0.38 | 260 | 0.2490 | 0.9246 | 0.7934 | | No log | 0.4 | 270 | 0.2475 | 0.9197 | 0.7876 | | No log | 0.41 | 280 | 0.2648 | 0.9161 | 0.7840 | | No log | 0.43 | 290 | 0.2533 | 0.9249 | 0.8010 | | No log | 0.44 | 300 | 0.2446 | 0.9234 | 0.8067 | | No log | 0.46 | 310 | 0.2271 | 0.9260 | 0.8114 | | No log | 0.47 | 320 | 0.2219 | 0.9246 | 0.8211 | | No log | 0.49 | 330 | 0.2269 | 0.9320 | 0.8306 | | No log | 0.5 | 340 | 0.2276 | 0.9264 | 0.8219 | | No log | 0.52 | 350 | 0.2835 | 0.9201 | 0.7994 | | No log | 0.53 | 360 | 0.2787 | 0.9231 | 0.8029 | | No log | 0.55 | 370 | 0.2317 | 0.9301 | 0.8275 | | No log | 0.56 | 380 | 0.2502 | 0.9131 | 0.8076 | | No log | 0.58 | 390 | 0.2254 | 0.9294 | 0.8321 | | No log | 0.59 | 400 | 0.2066 | 0.9312 | 0.8215 | | No log | 0.61 | 410 | 0.2013 | 0.9342 | 0.8391 | | No log | 0.62 | 420 | 0.2295 | 0.9260 | 0.8279 | | No log | 0.64 | 430 | 0.2100 | 0.9338 | 0.8428 | | No log | 0.65 | 440 | 0.2129 | 0.9316 | 0.8297 | | No log | 0.67 | 450 | 0.2135 | 0.9327 | 0.8203 | | No log | 0.68 | 460 | 0.2681 | 0.9212 | 0.8028 | | No log | 0.7 | 470 | 0.2178 | 0.9320 | 0.8312 | | No log | 0.71 | 480 | 0.1999 | 0.9342 | 0.8321 | | No log | 0.72 | 490 | 0.2172 | 0.9305 | 0.8334 | | 0.2988 | 0.74 | 500 | 0.2086 | 0.9308 | 0.8368 | | 0.2988 | 0.75 | 510 | 0.2052 | 0.9342 | 0.8430 | | 0.2988 | 0.77 | 520 | 0.2111 | 0.9331 | 0.8333 | | 0.2988 | 0.78 | 530 | 0.2279 | 0.9327 | 0.8250 | | 0.2988 | 0.8 | 540 | 0.2361 | 0.9271 | 0.8164 | ### Framework versions - Transformers 4.29.2 - Pytorch 1.13.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

Phaaarus/QLoRA_replica_16rank_QKadap

Phaaarus

2023-08-09T18:02:31Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-09T18:02:03Z

--- 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: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0

KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e1_s6789_v3_l5_v50

KingKazma

2023-08-09T17:51:16Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T17:51:15Z

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

KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e0_s6789_v3_l5_v50

KingKazma

2023-08-09T17:44:30Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T17:44:29Z

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

rashmi035/wav2vec2-large-mms-1b-hindi_2-colab

rashmi035

2023-08-09T17:44:01Z

14

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/mms-1b-fl102", "base_model:finetune:facebook/mms-1b-fl102", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-01T18:01:18Z

--- license: cc-by-nc-4.0 base_model: facebook/mms-1b-fl102 tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-large-mms-1b-hindi_2-colab results: [] ---  # wav2vec2-large-mms-1b-hindi_2-colab This model is a fine-tuned version of [facebook/mms-1b-fl102](https://huggingface.co/facebook/mms-1b-fl102) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1619 - Wer: 0.9015 ## 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: 0.01 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 7.5635 | 0.02 | 20 | 7.4070 | 0.9985 | | 13.6122 | 0.04 | 40 | 14.5202 | 1.0 | | 10.4272 | 0.06 | 60 | 8.7994 | 1.5440 | | 8.1195 | 0.08 | 80 | 10.3713 | 1.0 | | 9.9347 | 0.1 | 100 | 7.1064 | 1.0 | | 5.752 | 0.12 | 120 | 5.6953 | 1.0 | | 5.1715 | 0.14 | 140 | 5.0103 | 1.0 | | 6.3111 | 0.15 | 160 | 4.6935 | 1.0 | | 4.4929 | 0.17 | 180 | 5.4670 | 1.0263 | | 6.038 | 0.19 | 200 | 5.6732 | 1.3148 | | 4.1732 | 0.21 | 220 | 4.2880 | 1.0015 | | 3.8954 | 0.23 | 240 | 4.3895 | 1.0 | | 3.9351 | 0.25 | 260 | 3.7766 | 1.0 | | 3.6591 | 0.27 | 280 | 3.7521 | 1.0 | | 3.6009 | 0.29 | 300 | 3.8260 | 1.0 | | 3.5822 | 0.31 | 320 | 3.5655 | 1.0 | | 3.5705 | 0.33 | 340 | 3.6623 | 1.0 | | 3.6825 | 0.35 | 360 | 3.5988 | 1.0 | | 3.5239 | 0.37 | 380 | 3.5307 | 1.0 | | 3.558 | 0.39 | 400 | 3.5847 | 1.0 | | 3.4658 | 0.41 | 420 | 3.4300 | 1.0 | | 3.4045 | 0.43 | 440 | 3.5261 | 1.0 | | 3.4564 | 0.44 | 460 | 3.4799 | 1.0 | | 3.4403 | 0.46 | 480 | 3.4126 | 1.0 | | 3.4733 | 0.48 | 500 | 3.5358 | 1.0 | | 3.445 | 0.5 | 520 | 3.3526 | 1.0 | | 3.4155 | 0.52 | 540 | 3.3508 | 1.0 | | 3.412 | 0.54 | 560 | 3.3205 | 1.0 | | 3.2547 | 0.56 | 580 | 3.3143 | 1.0 | | 3.2652 | 0.58 | 600 | 3.3057 | 1.0 | | 3.1801 | 0.6 | 620 | 3.2361 | 1.0 | | 3.2835 | 0.62 | 640 | 3.3567 | 1.0 | | 3.3545 | 0.64 | 660 | 3.2300 | 1.0 | | 3.1898 | 0.66 | 680 | 3.1771 | 1.0 | | 3.1109 | 0.68 | 700 | 3.3033 | 1.0 | | 3.1631 | 0.7 | 720 | 3.0177 | 0.9997 | | 3.0386 | 0.71 | 740 | 3.0339 | 0.9997 | | 3.074 | 0.73 | 760 | 3.0702 | 1.0 | | 2.8598 | 0.75 | 780 | 2.8458 | 1.0 | | 2.8116 | 0.77 | 800 | 2.9836 | 0.9995 | | 2.8086 | 0.79 | 820 | 2.5641 | 1.0 | | 2.6645 | 0.81 | 840 | 2.6182 | 1.0 | | 2.7035 | 0.83 | 860 | 2.5176 | 0.9995 | | 2.4736 | 0.85 | 880 | 2.3965 | 0.9995 | | 2.6259 | 0.87 | 900 | 2.5697 | 1.0 | | 2.44 | 0.89 | 920 | 2.3085 | 1.0 | | 2.22 | 0.91 | 940 | 2.1551 | 0.9997 | | 2.5394 | 0.93 | 960 | 2.1955 | 1.0 | | 2.1734 | 0.95 | 980 | 2.1015 | 1.0 | | 2.407 | 0.97 | 1000 | 2.3892 | 1.0 | | 2.1967 | 0.99 | 1020 | 1.9439 | 0.9943 | | 2.1704 | 1.0 | 1040 | 1.9236 | 0.9827 | | 1.9929 | 1.02 | 1060 | 1.9353 | 0.9964 | | 2.1652 | 1.04 | 1080 | 2.1551 | 0.9899 | | 2.003 | 1.06 | 1100 | 1.9230 | 0.9820 | | 2.0048 | 1.08 | 1120 | 1.9293 | 0.9869 | | 2.1665 | 1.1 | 1140 | 1.8845 | 0.9990 | | 1.8297 | 1.12 | 1160 | 1.7173 | 0.9866 | | 1.8388 | 1.14 | 1180 | 1.8550 | 0.9871 | | 1.8399 | 1.16 | 1200 | 1.7772 | 0.9789 | | 1.7256 | 1.18 | 1220 | 1.7840 | 0.9863 | | 2.0516 | 1.2 | 1240 | 1.7693 | 0.9520 | | 1.8014 | 1.22 | 1260 | 1.6744 | 0.9814 | | 1.8244 | 1.24 | 1280 | 1.6614 | 0.9907 | | 1.8233 | 1.26 | 1300 | 1.5975 | 0.9948 | | 1.6977 | 1.28 | 1320 | 1.5738 | 0.9874 | | 1.9592 | 1.29 | 1340 | 1.5922 | 0.9897 | | 1.6181 | 1.31 | 1360 | 1.4764 | 0.9626 | | 1.6739 | 1.33 | 1380 | 1.5381 | 0.9928 | | 1.6855 | 1.35 | 1400 | 1.4613 | 0.9410 | | 1.5535 | 1.37 | 1420 | 1.4878 | 0.9348 | | 1.7467 | 1.39 | 1440 | 1.6077 | 0.9618 | | 1.6744 | 1.41 | 1460 | 1.4419 | 0.9727 | | 1.6115 | 1.43 | 1480 | 1.6700 | 0.9379 | | 1.7357 | 1.45 | 1500 | 1.5228 | 0.9964 | | 1.7096 | 1.47 | 1520 | 1.4350 | 0.9611 | | 1.7402 | 1.49 | 1540 | 1.4351 | 0.9567 | | 1.4819 | 1.51 | 1560 | 1.4062 | 0.9727 | | 1.6863 | 1.53 | 1580 | 1.4908 | 0.9889 | | 1.5539 | 1.55 | 1600 | 1.4099 | 0.9827 | | 1.5733 | 1.57 | 1620 | 1.4508 | 0.9209 | | 1.7331 | 1.58 | 1640 | 1.3913 | 0.9755 | | 1.4361 | 1.6 | 1660 | 1.3525 | 0.9237 | | 1.4806 | 1.62 | 1680 | 1.3748 | 0.9557 | | 1.5834 | 1.64 | 1700 | 1.3428 | 0.9386 | | 1.4226 | 1.66 | 1720 | 1.2990 | 0.9523 | | 1.6159 | 1.68 | 1740 | 1.3351 | 0.9428 | | 1.4486 | 1.7 | 1760 | 1.2982 | 0.9276 | | 1.3682 | 1.72 | 1780 | 1.3810 | 0.9312 | | 1.3828 | 1.74 | 1800 | 1.2621 | 0.9242 | | 1.4604 | 1.76 | 1820 | 1.2883 | 0.9051 | | 1.4368 | 1.78 | 1840 | 1.2462 | 0.9191 | | 1.3652 | 1.8 | 1860 | 1.2544 | 0.8935 | | 1.4347 | 1.82 | 1880 | 1.2682 | 0.9185 | | 1.4109 | 1.84 | 1900 | 1.2385 | 0.8966 | | 1.251 | 1.86 | 1920 | 1.2293 | 0.9015 | | 1.4793 | 1.87 | 1940 | 1.2410 | 0.9075 | | 1.2481 | 1.89 | 1960 | 1.1916 | 0.9134 | | 1.2951 | 1.91 | 1980 | 1.2061 | 0.8891 | | 1.3724 | 1.93 | 2000 | 1.1730 | 0.9381 | | 1.3093 | 1.95 | 2020 | 1.1763 | 0.8951 | | 1.3305 | 1.97 | 2040 | 1.1709 | 0.9028 | | 1.3152 | 1.99 | 2060 | 1.1619 | 0.9015 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

papepipopu/q-FrozenLake-v1-4x4-noSlippery-course

papepipopu

2023-08-09T17:41:08Z

0

null

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

reinforcement-learning

2023-08-09T17:41:05Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery-course 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="papepipopu/q-FrozenLake-v1-4x4-noSlippery-course", 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"]) ```

KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e9_s6789_v3_l5_v100

KingKazma

2023-08-09T17:38:03Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T17:38:02Z

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

emptor/distilgender-es-2M

emptor

2023-08-09T17:34:13Z

1,110

0

transformers

[ "transformers", "pytorch", "safetensors", "distilbert", "text-classification", "es", "dataset:ittailup/issste", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-08-09T01:29:28Z

--- license: apache-2.0 datasets: - ittailup/issste language: - es metrics: - accuracy: 0.9951 widget: - text: AGATA - text: GABRIEL --- ## Model Card ### Overview This model card provides details about a trained model, its training process, and evaluation metrics. This information ensures transparency and assists users in understanding the model's performance and behavior. ### Training Details - **Training Epochs**: The model was trained for 2 epochs. - **Training Steps**: The model underwent 1856 training steps. - **Training Runtime**: The model's training runtime was approximately 2680.184 seconds. - **Training Speed**: The model trained at a rate of 0.692 steps per second and processed approximately 1417.813 samples per second. - **Learning Rate**: The learning rate during training was approximately 0.0000095905. - **Training Loss**: The average training loss recorded was approximately 0.0184, with a specific loss value of 0.023423514232553285. ### Evaluation Details - **Evaluation Loss**: The model achieved an evaluation loss of 0.017659155651926994. - **Evaluation Runtime**: The evaluation process took approximately 23.8414 seconds. - **Evaluation Speed**: The model was evaluated at a rate of 2.055 steps per second, processing approximately 4194.378 samples per second. ### Performance Metrics - **Accuracy**: The model achieved an accuracy of 0.9951 during evaluation. - **Precision**: The precision of the model is approximately 0.9957234121187588. - **Recall**: The model's recall is approximately 0.9956533216014078. - **F1-Score**: The F1-Score for the model is approximately 0.995688365626595.

cyriac880/dog

cyriac880

2023-08-09T17:29:51Z

7

0

diffusers

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

text-to-image

2023-08-09T17:17:29Z

--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### DOG Dreambooth model trained by cyriac880 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: VJCET294 Sample pictures of this concept: ![0](https://huggingface.co/cyriac880/dog/resolve/main/sample_images/xxx_(2).jpg) ![1](https://huggingface.co/cyriac880/dog/resolve/main/sample_images/xxx_(1).jpg)

KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e8_s6789_v3_l5_v100

KingKazma

2023-08-09T17:29:26Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-09T17:29:25Z

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

KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e9_s6789_v3_l5_v20

KingKazma

2023-08-09T17:22:43Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T17:22:42Z

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

alokedeep/distilbert-base-uncased-finetuned-emotion

alokedeep

2023-08-09T17:18:49Z

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

2023-08-09T13:50:58Z

--- 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: 0.9265 - name: F1 type: f1 value: 0.9265400264321207 ---  # 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: 0.2135 - Accuracy: 0.9265 - F1: 0.9265 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8263 | 1.0 | 250 | 0.3211 | 0.9035 | 0.9024 | | 0.2495 | 2.0 | 500 | 0.2135 | 0.9265 | 0.9265 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e8_s6789_v3_l5_v20

KingKazma

2023-08-09T17:16:04Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T17:16:03Z

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

Norod78/sd15-bender-lora

Norod78

2023-08-09T17:15:57Z

6

1

diffusers

[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "dataset:Norod78/bender-blip2-captions-512", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-06-14T08:25:16Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: A photo of bender tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora datasets: - Norod78/bender-blip2-captions-512 inference: true widget: - text: >- A picture of a a cute little bender working as a pokemon trainer - text: >- A picture of Godzilla as bender, Very detailed, clean, high quality, sharp image - text: A picture of bender - text: A photo of Bender rocking out on stage, shredding a guitar with sparks flying in the air. robot, reflective metal --- LoRA for generating images of Bender, the robot from futurama A diffusers version of [this model](https://civitai.com/models/85775/bender-lora) Make sure to include the word "Bender" in your prompt

KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e7_s6789_v3_l5_v20

KingKazma

2023-08-09T17:09:25Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T17:09:24Z

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

KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e5_s6789_v3_l5_v100

KingKazma

2023-08-09T17:03:39Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T17:03:38Z

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

KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e6_s6789_v3_l5_v20

KingKazma

2023-08-09T17:02:46Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T17:02:45Z

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

psychodoge/llama2-qlora-finetunined-friendchathinglish

psychodoge

2023-08-09T17:00:07Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T17:00:01Z

--- 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: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0

KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e5_s6789_v3_l5_v20

KingKazma

2023-08-09T16:56:07Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T16:56:06Z

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

KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e4_s6789_v3_l5_v100

KingKazma

2023-08-09T16:55:03Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T16:55:02Z

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

KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e4_s6789_v3_l5_v20

KingKazma

2023-08-09T16:49:28Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T16:49:27Z

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

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

scarlett623

2023-08-09T16:46:39Z

19

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

2023-08-09T03:52:42Z

--- 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-subset-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: 0.9394977168949772 ---  # wav2vec2-large-xlsr53-zh-cn-subset-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: 1.3992 - Wer: 0.9395 - Cer: 0.3184 ## 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: 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 | 33.6533 | 1.0 | 1.0 | | 70.5767 | 3.81 | 800 | 6.8140 | 1.0 | 1.0 | | 7.1379 | 5.71 | 1200 | 6.5163 | 1.0 | 1.0 | | 6.4771 | 7.62 | 1600 | 6.4602 | 1.0 | 1.0 | | 6.3627 | 9.52 | 2000 | 6.3406 | 1.0 | 0.9700 | | 6.3627 | 11.43 | 2400 | 6.1021 | 1.0 | 0.9678 | | 6.1201 | 13.33 | 2800 | 5.1523 | 1.0 | 0.8385 | | 5.3531 | 15.24 | 3200 | 4.2224 | 1.0 | 0.7084 | | 4.1733 | 17.14 | 3600 | 3.6981 | 1.0 | 0.6332 | | 3.5472 | 19.05 | 4000 | 3.2708 | 0.9994 | 0.5827 | | 3.5472 | 20.95 | 4400 | 2.9629 | 0.9989 | 0.5510 | | 3.0668 | 22.86 | 4800 | 2.7122 | 0.9943 | 0.5165 | | 2.7248 | 24.76 | 5200 | 2.5171 | 0.9914 | 0.4976 | | 2.4609 | 26.67 | 5600 | 2.3538 | 0.9897 | 0.4759 | | 2.2323 | 28.57 | 6000 | 2.2112 | 0.9874 | 0.4555 | | 2.2323 | 30.48 | 6400 | 2.0850 | 0.9834 | 0.4370 | | 2.0438 | 32.38 | 6800 | 1.9982 | 0.9806 | 0.4261 | | 1.8837 | 34.29 | 7200 | 1.9179 | 0.9766 | 0.4137 | | 1.7646 | 36.19 | 7600 | 1.8278 | 0.9766 | 0.4030 | | 1.6469 | 38.1 | 8000 | 1.7627 | 0.9755 | 0.3937 | | 1.6469 | 40.0 | 8400 | 1.7063 | 0.9709 | 0.3853 | | 1.5422 | 41.9 | 8800 | 1.6649 | 0.9663 | 0.3787 | | 1.4561 | 43.81 | 9200 | 1.6336 | 0.9697 | 0.3714 | | 1.3842 | 45.71 | 9600 | 1.5943 | 0.9606 | 0.3647 | | 1.3164 | 47.62 | 10000 | 1.5681 | 0.9669 | 0.3621 | | 1.3164 | 49.52 | 10400 | 1.5535 | 0.9600 | 0.3582 | | 1.2654 | 51.43 | 10800 | 1.5354 | 0.9538 | 0.3544 | | 1.2186 | 53.33 | 11200 | 1.5003 | 0.9555 | 0.3482 | | 1.1781 | 55.24 | 11600 | 1.4979 | 0.9572 | 0.3473 | | 1.1344 | 57.14 | 12000 | 1.4820 | 0.9549 | 0.3453 | | 1.1344 | 59.05 | 12400 | 1.4707 | 0.9509 | 0.3396 | | 1.0965 | 60.95 | 12800 | 1.4657 | 0.9509 | 0.3384 | | 1.0637 | 62.86 | 13200 | 1.4610 | 0.9509 | 0.3371 | | 1.0306 | 64.76 | 13600 | 1.4461 | 0.9509 | 0.3361 | | 1.0014 | 66.67 | 14000 | 1.4437 | 0.9503 | 0.3328 | | 1.0014 | 68.57 | 14400 | 1.4334 | 0.9463 | 0.3304 | | 0.9758 | 70.48 | 14800 | 1.4267 | 0.9429 | 0.3295 | | 0.9486 | 72.38 | 15200 | 1.4250 | 0.9469 | 0.3269 | | 0.933 | 74.29 | 15600 | 1.4214 | 0.9441 | 0.3273 | | 0.9121 | 76.19 | 16000 | 1.4161 | 0.9441 | 0.3267 | | 0.9121 | 78.1 | 16400 | 1.4137 | 0.9446 | 0.3268 | | 0.9001 | 80.0 | 16800 | 1.4216 | 0.9446 | 0.3253 | | 0.8789 | 81.9 | 17200 | 1.4164 | 0.9435 | 0.3264 | | 0.8659 | 83.81 | 17600 | 1.3996 | 0.9424 | 0.3216 | | 0.8471 | 85.71 | 18000 | 1.4079 | 0.9458 | 0.3226 | | 0.8471 | 87.62 | 18400 | 1.4042 | 0.9412 | 0.3214 | | 0.8387 | 89.52 | 18800 | 1.4073 | 0.9424 | 0.3214 | | 0.8299 | 91.43 | 19200 | 1.4005 | 0.9418 | 0.3192 | | 0.8257 | 93.33 | 19600 | 1.4040 | 0.9406 | 0.3200 | | 0.813 | 95.24 | 20000 | 1.4012 | 0.9412 | 0.3184 | | 0.813 | 97.14 | 20400 | 1.4011 | 0.9389 | 0.3183 | | 0.8062 | 99.05 | 20800 | 1.3992 | 0.9395 | 0.3184 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e2_s6789_v3_l5_v100

KingKazma

2023-08-09T16:37:52Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-09T16:37:51Z

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

KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e2_s6789_v3_l5_v20

KingKazma

2023-08-09T16:36:10Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T16:36:09Z

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

KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e1_s6789_v3_l5_v20

KingKazma

2023-08-09T16:29:31Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T16:29:30Z

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

KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e1_s6789_v3_l5_v100

KingKazma

2023-08-09T16:29:16Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-09T16:29:15Z

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

avurity/layoutlmv3-finetuned-wildreceipt

avurity

2023-08-09T16:24:08Z

76

0

transformers

[ "transformers", "pytorch", "tensorboard", "layoutlmv3", "token-classification", "generated_from_trainer", "dataset:wildreceipt", "base_model:microsoft/layoutlmv3-base", "base_model:finetune:microsoft/layoutlmv3-base", "license:cc-by-nc-sa-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-08-05T16:29:09Z

--- license: cc-by-nc-sa-4.0 base_model: microsoft/layoutlmv3-base tags: - generated_from_trainer datasets: - wildreceipt metrics: - precision - recall - f1 - accuracy model-index: - name: layoutlmv3-finetuned-wildreceipt results: - task: name: Token Classification type: token-classification dataset: name: wildreceipt type: wildreceipt config: WildReceipt split: test args: WildReceipt metrics: - name: Precision type: precision value: 0.8738394320043692 - name: Recall type: recall value: 0.88093599449415 - name: F1 type: f1 value: 0.8773733634930428 - name: Accuracy type: accuracy value: 0.9245552383044147 ---  # layoutlmv3-finetuned-wildreceipt This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the wildreceipt dataset. It achieves the following results on the evaluation set: - Loss: 0.3068 - Precision: 0.8738 - Recall: 0.8809 - F1: 0.8774 - Accuracy: 0.9246 ## 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: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 4000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 0.32 | 100 | 1.3498 | 0.6130 | 0.3126 | 0.4140 | 0.6742 | | No log | 0.63 | 200 | 0.8939 | 0.6665 | 0.5317 | 0.5915 | 0.7815 | | No log | 0.95 | 300 | 0.7159 | 0.7311 | 0.6425 | 0.6840 | 0.8161 | | No log | 1.26 | 400 | 0.5901 | 0.7554 | 0.6690 | 0.7095 | 0.8405 | | 1.0677 | 1.58 | 500 | 0.5263 | 0.7632 | 0.7232 | 0.7427 | 0.8578 | | 1.0677 | 1.89 | 600 | 0.4759 | 0.7871 | 0.7777 | 0.7824 | 0.8774 | | 1.0677 | 2.21 | 700 | 0.4299 | 0.8054 | 0.8070 | 0.8062 | 0.8890 | | 1.0677 | 2.52 | 800 | 0.4165 | 0.8064 | 0.8311 | 0.8185 | 0.8937 | | 1.0677 | 2.84 | 900 | 0.3845 | 0.8344 | 0.8300 | 0.8322 | 0.9005 | | 0.4267 | 3.15 | 1000 | 0.3540 | 0.8433 | 0.8318 | 0.8375 | 0.9056 | | 0.4267 | 3.47 | 1100 | 0.3429 | 0.8362 | 0.8540 | 0.8450 | 0.9086 | | 0.4267 | 3.79 | 1200 | 0.3274 | 0.8451 | 0.8545 | 0.8498 | 0.9105 | | 0.4267 | 4.1 | 1300 | 0.3433 | 0.8397 | 0.8535 | 0.8466 | 0.9092 | | 0.4267 | 4.42 | 1400 | 0.3181 | 0.8514 | 0.8604 | 0.8559 | 0.9154 | | 0.2869 | 4.73 | 1500 | 0.3191 | 0.8472 | 0.8637 | 0.8554 | 0.9129 | | 0.2869 | 5.05 | 1600 | 0.3128 | 0.8613 | 0.8658 | 0.8635 | 0.9182 | | 0.2869 | 5.36 | 1700 | 0.3121 | 0.8622 | 0.8695 | 0.8658 | 0.9182 | | 0.2869 | 5.68 | 1800 | 0.3230 | 0.8473 | 0.8661 | 0.8566 | 0.9140 | | 0.2869 | 5.99 | 1900 | 0.2986 | 0.8729 | 0.8633 | 0.8681 | 0.9209 | | 0.2134 | 6.31 | 2000 | 0.3032 | 0.8555 | 0.8694 | 0.8624 | 0.9169 | | 0.2134 | 6.62 | 2100 | 0.3056 | 0.8705 | 0.8710 | 0.8708 | 0.9220 | | 0.2134 | 6.94 | 2200 | 0.3122 | 0.8630 | 0.8790 | 0.8709 | 0.9217 | | 0.2134 | 7.26 | 2300 | 0.3047 | 0.8692 | 0.8778 | 0.8734 | 0.9215 | | 0.2134 | 7.57 | 2400 | 0.3103 | 0.8701 | 0.8780 | 0.8741 | 0.9225 | | 0.1661 | 7.89 | 2500 | 0.3080 | 0.8712 | 0.8787 | 0.8749 | 0.9226 | | 0.1661 | 8.2 | 2600 | 0.3011 | 0.8653 | 0.8834 | 0.8743 | 0.9236 | | 0.1661 | 8.52 | 2700 | 0.3034 | 0.8735 | 0.8798 | 0.8766 | 0.9247 | | 0.1661 | 8.83 | 2800 | 0.3054 | 0.8698 | 0.8793 | 0.8745 | 0.9238 | | 0.1661 | 9.15 | 2900 | 0.3105 | 0.8697 | 0.8812 | 0.8754 | 0.9237 | | 0.1415 | 9.46 | 3000 | 0.3068 | 0.8738 | 0.8809 | 0.8774 | 0.9246 | | 0.1415 | 9.78 | 3100 | 0.3086 | 0.8730 | 0.8793 | 0.8761 | 0.9229 | | 0.1415 | 10.09 | 3200 | 0.3013 | 0.8755 | 0.8830 | 0.8792 | 0.9256 | | 0.1415 | 10.41 | 3300 | 0.3107 | 0.8692 | 0.8815 | 0.8753 | 0.9241 | | 0.1415 | 10.73 | 3400 | 0.3073 | 0.8759 | 0.8794 | 0.8777 | 0.9261 | | 0.1239 | 11.04 | 3500 | 0.3109 | 0.8727 | 0.8819 | 0.8773 | 0.9253 | | 0.1239 | 11.36 | 3600 | 0.3124 | 0.8723 | 0.8790 | 0.8756 | 0.9243 | | 0.1239 | 11.67 | 3700 | 0.3171 | 0.8724 | 0.8805 | 0.8764 | 0.9241 | | 0.1239 | 11.99 | 3800 | 0.3081 | 0.8739 | 0.8804 | 0.8771 | 0.9254 | | 0.1239 | 12.3 | 3900 | 0.3095 | 0.8735 | 0.8798 | 0.8766 | 0.9254 | | 0.1106 | 12.62 | 4000 | 0.3094 | 0.8740 | 0.8796 | 0.8768 | 0.9254 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.0 - Datasets 2.14.3 - Tokenizers 0.13.3

KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e0_s6789_v3_l5_v20

KingKazma

2023-08-09T16:22:51Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T16:22:50Z

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

KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e0_s6789_v3_l5_v100

KingKazma

2023-08-09T16:20:40Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T16:20:39Z

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

kaoyer/pokemon-lora

kaoyer

2023-08-09T16:17:44Z

1

0

diffusers

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

text-to-image

2023-08-09T13:49:50Z

--- license: creativeml-openrail-m base_model: /root/autodl-fs/pre_trained_models/runwayml-stable-diffusion-v1-5/runwayml-stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - kaoyer/pokemon-lora These are LoRA adaption weights for /root/autodl-fs/pre_trained_models/runwayml-stable-diffusion-v1-5/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)

KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e-1_s6789_v3_l5_v20

KingKazma

2023-08-09T16:16:11Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T16:16:10Z

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

MarioNapoli/DynamicWav2Vec_TEST_9

MarioNapoli

2023-08-09T16:09:04Z

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_1_0", "base_model:facebook/wav2vec2-xls-r-300m", "base_model:finetune:facebook/wav2vec2-xls-r-300m", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-03T14:29:32Z

--- license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer datasets: - common_voice_1_0 model-index: - name: DynamicWav2Vec_TEST_9 results: [] ---  # DynamicWav2Vec_TEST_9 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_1_0 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e9_s6789_v3_l5_v20

KingKazma

2023-08-09T16:05:14Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T16:05:14Z

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

foilfoilfoil/cheesegulag3.5

foilfoilfoil

2023-08-09T16:04:54Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T16:04:02Z

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

KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e8_s6789_v3_l5_v50

KingKazma

2023-08-09T16:03:48Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T16:03:47Z

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

KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e7_s6789_v3_l5_v50

KingKazma

2023-08-09T15:56:16Z

1

0

peft

[ "peft", "region:us" ]

null

2023-08-09T15:56:15Z

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

adon81/bert-finetuned-fishing-NER

adon81

2023-08-09T15:48:12Z

108

0

transformers

[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "base_model:adon81/bert-finetuned-ner", "base_model:finetune:adon81/bert-finetuned-ner", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-08-09T13:13:46Z

--- license: apache-2.0 base_model: adon81/bert-finetuned-ner tags: - generated_from_trainer model-index: - name: bert-finetuned-fishing-NER results: [] ---  # bert-finetuned-fishing-NER This model is a fine-tuned version of [adon81/bert-finetuned-ner](https://huggingface.co/adon81/bert-finetuned-ner) 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: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 300000000000000000000000000000000 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

Shafaet02/bert-fine-tuned-cola

Shafaet02

2023-08-09T15:48:02Z

61

0

transformers

[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-08-09T08:59:17Z

--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_keras_callback model-index: - name: Shafaet02/bert-fine-tuned-cola results: [] ---  # Shafaet02/bert-fine-tuned-cola 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: 0.2831 - Validation Loss: 0.4311 - 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': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.4914 | 0.4282 | 0 | | 0.2831 | 0.4311 | 1 | ### Framework versions - Transformers 4.31.0 - TensorFlow 2.11.0 - Datasets 2.14.3 - Tokenizers 0.13.3

Francesco-A/bert-finetuned-ner

Francesco-A

2023-08-09T15:45:53Z

104

0

transformers

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

token-classification

2023-08-09T15:29:35Z

--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.9323631552836117 - name: Recall type: recall value: 0.9488387748232918 - name: F1 type: f1 value: 0.940528818083243 - name: Accuracy type: accuracy value: 0.9861217401542356 ---  # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0592 - Precision: 0.9324 - Recall: 0.9488 - F1: 0.9405 - Accuracy: 0.9861 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0774 | 1.0 | 1756 | 0.0764 | 0.9146 | 0.9337 | 0.9241 | 0.9802 | | 0.0394 | 2.0 | 3512 | 0.0554 | 0.9265 | 0.9483 | 0.9373 | 0.9860 | | 0.0261 | 3.0 | 5268 | 0.0592 | 0.9324 | 0.9488 | 0.9405 | 0.9861 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e6_s6789_v3_l5_v20

KingKazma

2023-08-09T15:44:09Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T15:44:08Z

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

mbueno/llama2-qlora-finetunined-french

mbueno

2023-08-09T15:40:36Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T15:40:28Z

--- 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

KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e5_s6789_v3_l5_v20

KingKazma

2023-08-09T15:37:07Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T15:37:06Z

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

Ripo-2007/dreambooth_alfonso

Ripo-2007

2023-08-09T15:32:17Z

4

1

diffusers

[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]

text-to-image

2023-08-09T13:35:48Z

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: alfonsoaraco tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Test enoder was not trained.

santiagotoso/ppo-LunarLander-v2

santiagotoso

2023-08-09T15:27:34Z

0

stable-baselines3

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

reinforcement-learning

2023-08-09T13:24:45Z

--- 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: 232.20 +/- 76.62 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 ... ```

murodbek/uzroberta-panx-uz

murodbek

2023-08-09T15:27:23Z

167

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "roberta", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-04-13T09:47:13Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: uzroberta-panx-uz results: [] ---  # uzroberta-panx-uz This model is a fine-tuned version of [rifkat/uztext-3Gb-BPE-Roberta](https://huggingface.co/rifkat/uztext-3Gb-BPE-Roberta) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1626 - F1: 0.9175 ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0515 | 1.0 | 150 | 0.1373 | 0.9141 | | 0.0415 | 2.0 | 300 | 0.1268 | 0.9194 | | 0.0101 | 3.0 | 450 | 0.1225 | 0.9416 | | 0.0038 | 4.0 | 600 | 0.1426 | 0.9353 | | 0.0004 | 5.0 | 750 | 0.1458 | 0.9320 | ### Framework versions - Transformers 4.27.3 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.12.1

Meohong/Dialect-Polyglot-12.8b-QLoRA

Meohong

2023-08-09T15:26:17Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T15:26:09Z

--- 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

felixshier/osc-01-bert-finetuned

felixshier

2023-08-09T15:24:55Z

61

0

transformers

[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-08-09T13:35:56Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_keras_callback model-index: - name: osc-01-bert-finetuned results: [] ---  # osc-01-bert-finetuned This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3193 - Validation Loss: 0.7572 - Train Precision: 0.6026 - Epoch: 6 ## 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 110, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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 Precision | Epoch | |:----------:|:---------------:|:---------------:|:-----:| | 0.6873 | 0.6937 | 0.5147 | 0 | | 0.6544 | 0.6854 | 0.5 | 1 | | 0.6127 | 0.7071 | 0.5242 | 2 | | 0.5651 | 0.6813 | 0.5591 | 3 | | 0.5015 | 0.7012 | 0.5747 | 4 | | 0.4006 | 0.7292 | 0.5882 | 5 | | 0.3193 | 0.7572 | 0.6026 | 6 | ### Framework versions - Transformers 4.31.0 - TensorFlow 2.13.0 - Datasets 2.14.4 - Tokenizers 0.13.3

felixshier/csc-01-bert-finetuned

felixshier

2023-08-09T15:24:52Z

62

0

transformers

[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-08-09T13:35:35Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_keras_callback model-index: - name: csc-01-bert-finetuned results: [] ---  # csc-01-bert-finetuned This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4789 - Validation Loss: 0.7231 - Train Precision: 0.6429 - Epoch: 5 ## 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 70, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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 Precision | Epoch | |:----------:|:---------------:|:---------------:|:-----:| | 0.7100 | 0.7421 | 0.0 | 0 | | 0.6764 | 0.6861 | 0.625 | 1 | | 0.6311 | 0.6838 | 0.5862 | 2 | | 0.5909 | 0.7072 | 0.6286 | 3 | | 0.5413 | 0.7504 | 0.6667 | 4 | | 0.4789 | 0.7231 | 0.6429 | 5 | ### Framework versions - Transformers 4.31.0 - TensorFlow 2.13.0 - Datasets 2.14.4 - Tokenizers 0.13.3

KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e3_s6789_v3_l5_v20

KingKazma

2023-08-09T15:23:02Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T15:23:01Z

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

jordyvl/vit-base_rvl-cdip_r2_32

jordyvl

2023-08-09T15:18:05Z

163

0

transformers

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

image-classification

2023-08-08T08:10:20Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-base_rvl-cdip_r2_32 results: [] ---  # vit-base_rvl-cdip_r2_32 This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6372 - Accuracy: 0.8985 - Brier Loss: 0.1792 - Nll: 1.1736 - F1 Micro: 0.8985 - F1 Macro: 0.8987 - Ece: 0.0847 - Aurc: 0.0201 ## 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: 96 - eval_batch_size: 96 - 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | 0.1647 | 1.0 | 3334 | 0.4024 | 0.8887 | 0.1682 | 1.2086 | 0.8887 | 0.8891 | 0.0457 | 0.0178 | | 0.1418 | 2.0 | 6668 | 0.4075 | 0.8941 | 0.1646 | 1.2066 | 0.8941 | 0.8942 | 0.0522 | 0.0177 | | 0.0989 | 3.0 | 10002 | 0.4409 | 0.8932 | 0.1690 | 1.1966 | 0.8932 | 0.8932 | 0.0647 | 0.0175 | | 0.0614 | 4.0 | 13336 | 0.4781 | 0.8944 | 0.1730 | 1.2083 | 0.8944 | 0.8951 | 0.0694 | 0.0181 | | 0.0392 | 5.0 | 16670 | 0.5329 | 0.8959 | 0.1761 | 1.1777 | 0.8959 | 0.8958 | 0.0776 | 0.0187 | | 0.0231 | 6.0 | 20004 | 0.5714 | 0.8957 | 0.1799 | 1.2083 | 0.8957 | 0.8958 | 0.0813 | 0.0198 | | 0.0126 | 7.0 | 23338 | 0.6002 | 0.8966 | 0.1802 | 1.1732 | 0.8966 | 0.8972 | 0.0839 | 0.0197 | | 0.0079 | 8.0 | 26672 | 0.6193 | 0.8984 | 0.1789 | 1.1849 | 0.8984 | 0.8985 | 0.0833 | 0.0200 | | 0.0049 | 9.0 | 30006 | 0.6333 | 0.8976 | 0.1798 | 1.1906 | 0.8976 | 0.8978 | 0.0851 | 0.0205 | | 0.0034 | 10.0 | 33340 | 0.6372 | 0.8985 | 0.1792 | 1.1736 | 0.8985 | 0.8987 | 0.0847 | 0.0201 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e2_s6789_v3_l5_v20

KingKazma

2023-08-09T15:16:00Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T15:15:59Z

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

imvladikon/alephbertgimmel_parashoot

imvladikon

2023-08-09T15:10:27Z

113

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "question-answering", "generated_from_trainer", "he", "dataset:imvladikon/parashoot", "base_model:imvladikon/alephbertgimmel-base-512", "base_model:finetune:imvladikon/alephbertgimmel-base-512", "endpoints_compatible", "region:us" ]

question-answering

2023-08-02T07:44:16Z

--- base_model: imvladikon/alephbertgimmel-base-512 tags: - generated_from_trainer datasets: - imvladikon/parashoot model-index: - name: alephbertgimmel_parashoot results: [] language: - he metrics: - f1 - exact_match pipeline_tag: question-answering ---  # alephbertgimmel_parashoot This model is a fine-tuned version of [imvladikon/alephbertgimmel-base-512](https://huggingface.co/imvladikon/alephbertgimmel-base-512) on the [imvladikon/parashoot](https://huggingface.co/datasets/imvladikon/parashoot) 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: 3e-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: 5.0 ### Training results ``` ***** predict metrics ***** predict_samples = 1102 test_exact_match = 27.7073 test_f1 = 51.787 test_runtime = 0:00:32.05 test_samples_per_second = 34.383 test_steps_per_second = 4.306 ``` ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.2 - Tokenizers 0.13.3

KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e1_s6789_v3_l5_v20

KingKazma

2023-08-09T15:08:58Z

0

peft

[ "peft", "region:us" ]

null

2023-08-09T15:08:56Z

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

KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e0_s6789_v3_l5_v50

KingKazma

2023-08-09T15:03:33Z

2

0

peft

[ "peft", "region:us" ]

null

2023-08-09T15:03:32Z

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

Cheetor1996/Efanatika_aku_no_onna_kanbu

Cheetor1996

2023-08-09T15:02:56Z

0

null

[ "art", "en", "license:cc-by-2.0", "region:us" ]

null

2023-08-09T15:00:15Z

--- license: cc-by-2.0 language: - en tags: - art --- **Efanatika** from **Aku no onna kanbu** - Trained with Anime (final-full-pruned) model. - Recommended LoRA weights: 0.7+ - Recommended LoRA weight blocks: ALL, MIDD, OUTD, and OUTALL - **Activation ta**g: *efanatika*, use with pink hair, long hair, very long hair, colored skin, blue skin, yellow eyes, colored sclera, and black sclera.

jcy204/cold_model2

jcy204

2023-08-09T15:02:39Z

62

0

transformers

[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-08-09T14:57:29Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: jcy204/cold_model2 results: [] ---  # jcy204/cold_model2 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: 0.3582 - Validation Loss: 0.6678 - Train Accuracy: 0.7477 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1545, '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 | |:----------:|:---------------:|:--------------:|:-----:| | 0.7779 | 0.6213 | 0.7392 | 0 | | 0.5323 | 0.6326 | 0.7315 | 1 | | 0.3582 | 0.6678 | 0.7477 | 2 | ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.4 - Tokenizers 0.13.3

leonard-pak/q-FrozenLake-v1-4x4-noSlippery

leonard-pak

2023-08-09T14:59:17Z

0

null

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

reinforcement-learning

2023-08-09T14:58:08Z

--- 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 model = load_from_hub(repo_id="leonard-pak/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"])

LarryAIDraw/ToukaLora-15

LarryAIDraw

2023-08-09T14:58:48Z

0

null

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

null

2023-08-09T14:39:49Z

--- license: creativeml-openrail-m --- https://civitai.com/models/125271/touka-kirishima-tokyo-ghoul-lora

LarryAIDraw/MiaChristoph-10

LarryAIDraw

2023-08-09T14:58:35Z

0

null

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

null

2023-08-09T14:39:26Z

--- license: creativeml-openrail-m --- https://civitai.com/models/124748/mia-christoph-tenpuru

LarryAIDraw/GirlsFrontlineAk12

LarryAIDraw

2023-08-09T14:58:21Z

0

null

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

null

2023-08-09T14:39:04Z

--- license: creativeml-openrail-m --- https://civitai.com/models/76960/ak-12-quiet-azure-girls-frontline

gsaivinay/Llama-2-7b-Chat-GPTQ

gsaivinay

2023-08-09T14:57:09Z

26

2

transformers

[ "transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-2", "en", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-18T19:21:58Z

--- language: - en license: other inference: true model_type: llama pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 --- # Meta's Llama 2 7b Chat GPTQ ## * Duplicated from TheBloke * These files are GPTQ model files for [Meta's Llama 2 7b Chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. ## Repositories available * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) ## Prompt template: Llama-2-Chat ``` System: You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. User: {prompt} Assistant: ``` ## Provided files Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. | Branch | Bits | Group Size | Act Order (desc_act) | File Size | ExLlama Compatible? | Made With | Description | | ------ | ---- | ---------- | -------------------- | --------- | ------------------- | --------- | ----------- | | main | 4 | 128 | False | 3.90 GB | True | AutoGPTQ | Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. | | gptq-4bit-32g-actorder_True | 4 | 32 | True | 4.28 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 32g gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. | | gptq-4bit-64g-actorder_True | 4 | 64 | True | 4.02 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 64g uses less VRAM than 32g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. | | gptq-4bit-128g-actorder_True | 4 | 128 | True | 3.90 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 128g uses even less VRAM, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. | ## How to download from branches - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Llama-2-7b-Chat-GPTQ:gptq-4bit-32g-actorder_True` - With Git, you can clone a branch with: ``` git clone --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Llama-2-7b-Chat-GPTQ` ``` - In Python Transformers code, the branch is the `revision` parameter; see below. ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/Llama-2-7b-Chat-GPTQ`. - To download from a specific branch, enter for example `TheBloke/Llama-2-7b-Chat-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done" 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `Llama-2-7b-Chat-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. * Note that you do not need to set GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started! ## How to use this GPTQ model from Python code First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) installed: `GITHUB_ACTIONS=true pip install auto-gptq` Then try the following example code: ```python from transformers import AutoTokenizer, pipeline, logging from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig model_name_or_path = "TheBloke/Llama-2-7b-Chat-GPTQ" model_basename = "gptq_model-4bit-128g" use_triton = False tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, model_basename=model_basename use_safetensors=True, trust_remote_code=True, device="cuda:0", use_triton=use_triton, quantize_config=None) """ To download from a specific branch, use the revision parameter, as in this example: model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, revision="gptq-4bit-32g-actorder_True", model_basename=model_basename, use_safetensors=True, trust_remote_code=True, device="cuda:0", quantize_config=None) """ prompt = "Tell me about AI" prompt_template=f'''System: You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. User: {prompt} Assistant: ''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline # Prevent printing spurious transformers error when using pipeline with AutoGPTQ logging.set_verbosity(logging.CRITICAL) print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, temperature=0.7, top_p=0.95, repetition_penalty=1.15 ) print(pipe(prompt_template)[0]['generated_text']) ``` ## Compatibility The files provided will work with AutoGPTQ (CUDA and Triton modes), GPTQ-for-LLaMa (only CUDA has been tested), and Occ4m's GPTQ-for-LLaMa fork. ExLlama works with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility. # Original model card: Meta's Llama 2 7b Chat # **Llama 2** Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details *Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.* Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. **Model Developers** Meta **Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. ||Training Data|Params|Content Length|GQA|Tokens|LR| |---|---|---|---|---|---|---| |Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10-4| |Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10-4| |Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10-4| *Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. **Model Dates** Llama 2 was trained between January 2023 and July 2023. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) ## Intended Use **Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212). **Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. ||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO2eq)| |---|---|---|---| |Llama 2 7B|184320|400|31.22| |Llama 2 13B|368640|400|62.44| |Llama 2 70B|1720320|400|291.42| |Total|3311616||539.00| **CO2 emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. |Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval| |---|---|---|---|---|---|---|---|---|---| |Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9| |Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9| |Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7| |Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6| |Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3| |Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1| |Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**| **Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. |||TruthfulQA|Toxigen| |---|---|---|---| |Llama 1|7B|27.42|23.00| |Llama 1|13B|41.74|23.08| |Llama 1|33B|44.19|22.57| |Llama 1|65B|48.71|21.77| |Llama 2|7B|33.29|**21.25**| |Llama 2|13B|41.86|26.10| |Llama 2|70B|**50.18**|24.60| **Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). |||TruthfulQA|Toxigen| |---|---|---|---| |Llama-2-Chat|7B|57.04|**0.00**| |Llama-2-Chat|13B|62.18|**0.00**| |Llama-2-Chat|70B|**64.14**|0.01| **Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index |Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf| |---|---|---|---|---| |7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)| |13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)| |70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|

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

liadraz

2023-08-09T14:54:50Z

0

null

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

reinforcement-learning

2023-08-09T14:54:46Z

--- 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="liadraz/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"]) ```

broAleks13/stablecode-completion-alpha-3b-4k

broAleks13

2023-08-09T14:49:26Z

0

null

[ "region:us" ]

null

2023-08-09T14:42:38Z

--- license: apache-2.0 --- stabilityai/stablecode-completion-alpha-3b-4k

twbrandon7/rl-course-unit3-dqn-SpaceInvadersNoFrameskip-v4

twbrandon7

2023-08-09T14:45:26Z

1

0

stable-baselines3

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

reinforcement-learning

2023-08-09T14:44:48Z

--- 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: 604.00 +/- 212.04 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 SB3: https://github.com/DLR-RM/stable-baselines3 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 twbrandon7 -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 twbrandon7 -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 twbrandon7 ``` ## 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'} ```