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 2025-06-29 00:46:34	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 502 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-29 00:44:25	card stringlengths 11 1.01M
ksaw39/ksawmegga	ksaw39	2023-07-27T22:11:13Z	0	0	keras	[ "keras", "reinforcement-learning", "en", "region:us" ]	reinforcement-learning	2023-07-27T22:08:14Z	--- language: - en metrics: - accuracy - code_eval library_name: keras pipeline_tag: reinforcement-learning ---
NasimB/aochildes-cbt-log-rarity	NasimB	2023-07-27T21:46:57Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-27T19:38:45Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: aochildes-cbt-log-rarity 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. --> # aochildes-cbt-log-rarity This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.1483 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.3649 \| 0.29 \| 500 \| 5.3433 \| \| 5.0506 \| 0.59 \| 1000 \| 4.9337 \| \| 4.7079 \| 0.88 \| 1500 \| 4.6957 \| \| 4.4512 \| 1.17 \| 2000 \| 4.5593 \| \| 4.3031 \| 1.47 \| 2500 \| 4.4458 \| \| 4.2085 \| 1.76 \| 3000 \| 4.3418 \| \| 4.0809 \| 2.05 \| 3500 \| 4.2739 \| \| 3.9047 \| 2.35 \| 4000 \| 4.2277 \| \| 3.8846 \| 2.64 \| 4500 \| 4.1774 \| \| 3.8392 \| 2.93 \| 5000 \| 4.1313 \| \| 3.6392 \| 3.23 \| 5500 \| 4.1305 \| \| 3.6016 \| 3.52 \| 6000 \| 4.1020 \| \| 3.5828 \| 3.81 \| 6500 \| 4.0709 \| \| 3.4733 \| 4.11 \| 7000 \| 4.0797 \| \| 3.3271 \| 4.4 \| 7500 \| 4.0758 \| \| 3.3228 \| 4.69 \| 8000 \| 4.0635 \| \| 3.3147 \| 4.99 \| 8500 \| 4.0528 \| \| 3.154 \| 5.28 \| 9000 \| 4.0692 \| \| 3.1461 \| 5.58 \| 9500 \| 4.0692 \| \| 3.1416 \| 5.87 \| 10000 \| 4.0684 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
jariasn/q-Taxi-v3	jariasn	2023-07-27T21:32:51Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-27T21:32:49Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="jariasn/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
sghirardelli/vit-base-patch16-224-rgbd1k2	sghirardelli	2023-07-27T21:26:49Z	65	0	transformers	[ "transformers", "tf", "tensorboard", "vit", "image-classification", "generated_from_keras_callback", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-21T21:15:59Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_keras_callback model-index: - name: sghirardelli/vit-base-patch16-224-rgbd1k2 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. --> # sghirardelli/vit-base-patch16-224-rgbd1k2 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.9711 - Train Accuracy: 0.4384 - Train Top-3-accuracy: 0.6297 - Validation Loss: 0.2537 - Validation Accuracy: 0.9323 - Validation Top-3-accuracy: 0.9940 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.002, 'decay_steps': 544, '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 \| Train Accuracy \| Train Top-3-accuracy \| Validation Loss \| Validation Accuracy \| Validation Top-3-accuracy \| Epoch \| \|:----------:\|:--------------:\|:--------------------:\|:---------------:\|:-------------------:\|:-------------------------:\|:-----:\| \| 1.9711 \| 0.4384 \| 0.6297 \| 0.2537 \| 0.9323 \| 0.9940 \| 0 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.1 - Tokenizers 0.13.3
vivianchen98/distilbert-base-uncased-finetuned-cola	vivianchen98	2023-07-27T21:06:15Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "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	2023-07-27T19:48:05Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5317477654019562 --- <!-- 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-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8526 - Matthews Correlation: 0.5317 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5224 \| 1.0 \| 535 \| 0.4742 \| 0.4397 \| \| 0.3484 \| 2.0 \| 1070 \| 0.5877 \| 0.4558 \| \| 0.2357 \| 3.0 \| 1605 \| 0.6307 \| 0.5301 \| \| 0.1668 \| 4.0 \| 2140 \| 0.7054 \| 0.5288 \| \| 0.1218 \| 5.0 \| 2675 \| 0.8526 \| 0.5317 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
EulerianKnight/LunarLander-v2-unit1	EulerianKnight	2023-07-27T21:00:43Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-27T21:00:27Z	--- 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: 236.62 +/- 48.16 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
jariasn/q-FrozenLake-v1-4x4-noSlippery	jariasn	2023-07-27T20:58:10Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-27T20:58:08Z	--- 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 ```python model = load_from_hub(repo_id="jariasn/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"]) ```
YoonSeul/LawBot-5.8B	YoonSeul	2023-07-27T20:40:46Z	4	0	peft	[ "peft", "region:us" ]	null	2023-07-26T07:47:12Z	--- library_name: peft --- <img src=https://github.com/taemin6697/Paper_Review/assets/96530685/54ecd6cf-8695-4caa-bdc8-fb85c9b7d70d style="max-width: 700px; width: 100%" /> ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0
dgergherherherhererher/dfsgs	dgergherherherhererher	2023-07-27T20:37:27Z	0	0	sklearn	[ "sklearn", "sports", "tabular-classification", "en", "dataset:knkarthick/dialogsum", "license:openrail", "region:us" ]	tabular-classification	2023-07-27T17:50:51Z	--- language: - en metrics: - accuracy library_name: sklearn pipeline_tag: tabular-classification license: openrail datasets: - knkarthick/dialogsum tags: - sports ---
NicolasDenier/speecht5-finetuned-voxpopuli-sl	NicolasDenier	2023-07-27T20:21:31Z	89	0	transformers	[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "sl", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "model-index", "endpoints_compatible", "region:us" ]	text-to-speech	2023-07-27T17:17:36Z	--- language: - sl license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer - text-to-speech datasets: - facebook/voxpopuli model-index: - name: speecht5-finetuned-voxpopuli-sl results: - task: name: Text to Speech type: text-to-speech dataset: name: Voxpopuli type: facebook/voxpopuli config: sl split: train args: all metrics: - name: Loss type: loss value: 0.4546 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # speecht5-finetuned-voxpopuli-sl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the Voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4546 ## 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: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.4942 \| 21.68 \| 1000 \| 0.4567 \| \| 0.4698 \| 43.36 \| 2000 \| 0.4544 \| \| 0.4615 \| 65.04 \| 3000 \| 0.4541 \| \| 0.462 \| 86.72 \| 4000 \| 0.4546 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
patonw/q-Taxi-v3	patonw	2023-07-27T20:16:02Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-27T20:16:00Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="patonw/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Ronan14232/Omar	Ronan14232	2023-07-27T20:12:29Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2023-07-27T20:12:29Z	--- license: bigscience-openrail-m ---
Jonathaniu/llama2-breast-cancer-13b-knowledge-epoch-8	Jonathaniu	2023-07-27T20:09:53Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-27T20:09:32Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False ### Framework versions - PEFT 0.4.0.dev0
ianvaz/llama2-qlora-finetunined-french	ianvaz	2023-07-27T20:00:58Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-27T20:00:54Z	--- 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
Za88yes/Ocha	Za88yes	2023-07-27T19:56:37Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-27T12:21:45Z	--- license: creativeml-openrail-m ---
grace-pro/three_class_5e-5_hausa	grace-pro	2023-07-27T19:48:09Z	117	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:Davlan/afro-xlmr-base", "base_model:finetune:Davlan/afro-xlmr-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-27T18:28:05Z	--- license: mit base_model: Davlan/afro-xlmr-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: three_class_5e-5_hausa 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. --> # three_class_5e-5_hausa This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2379 - Precision: 0.2316 - Recall: 0.1636 - F1: 0.1917 - Accuracy: 0.9392 ## 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: 8 - 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 \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.2129 \| 1.0 \| 1283 \| 0.2033 \| 0.2278 \| 0.0810 \| 0.1195 \| 0.9416 \| \| 0.1901 \| 2.0 \| 2566 \| 0.1988 \| 0.2444 \| 0.0890 \| 0.1305 \| 0.9429 \| \| 0.1657 \| 3.0 \| 3849 \| 0.2056 \| 0.2561 \| 0.1278 \| 0.1705 \| 0.9430 \| \| 0.139 \| 4.0 \| 5132 \| 0.2205 \| 0.2269 \| 0.1655 \| 0.1914 \| 0.9388 \| \| 0.1179 \| 5.0 \| 6415 \| 0.2379 \| 0.2316 \| 0.1636 \| 0.1917 \| 0.9392 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
asenella/MMVAEPlus_beta_10_scale_False_seed_3	asenella	2023-07-27T19:43:04Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-27T19:42:50Z	--- 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") ```
tommilyjones/swin-tiny-patch4-window7-224-cats_dogs	tommilyjones	2023-07-27T19:38:02Z	204	0	transformers	[ "transformers", "pytorch", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-27T19:31:44Z	--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-cats_dogs results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.9973147153598282 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-cats_dogs This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0126 - Accuracy: 0.9973 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0832 \| 0.98 \| 47 \| 0.0235 \| 0.9909 \| \| 0.0788 \| 1.99 \| 95 \| 0.0126 \| 0.9973 \| \| 0.0534 \| 2.95 \| 141 \| 0.0127 \| 0.9957 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
AdiOO7/llama-2-7B-finetuned	AdiOO7	2023-07-27T19:34:23Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-27T19:34:20Z	--- 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: bfloat16 ### Framework versions - PEFT 0.5.0.dev0
DunnBC22/mit-b0-Image_segmentation_Dominoes_v2	DunnBC22	2023-07-27T19:26:06Z	0	1	null	[ "pytorch", "tensorboard", "generated_from_trainer", "image-segmentation", "en", "dataset:adelavega/dominoes_raw", "license:other", "region:us" ]	image-segmentation	2023-07-26T21:13:35Z	--- license: other tags: - generated_from_trainer model-index: - name: mit-b0-Image_segmentation_Dominoes_v2 results: [] datasets: - adelavega/dominoes_raw language: - en metrics: - mean_iou pipeline_tag: image-segmentation --- # mit-b0-Image_segmentation_Dominoes_v2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0). It achieves the following results on the evaluation set: - Loss: 0.1149 - Mean Iou: 0.9198 - Mean Accuracy: 0.9515 - Overall Accuracy: 0.9778 - Per Category Iou: - Segment 0: 0.974110559111975 - Segment 1: 0.8655745252092782 - Per Category Accuracy - Segment 0: 0.9897833441005461 - Segment 1: 0.913253525550903 ## Model description For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Segmentation/Dominoes/Fine-Tuning%20-%20Dominoes%20-%20Image%20Segmentation%20with%20LoRA.ipynb ## Intended uses & limitations This model is intended to demonstrate my ability to solve a complex problem using technology. ## Training and evaluation data Dataset Source: https://huggingface.co/datasets/adelavega/dominoes_raw ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Per Category Iou Segment 0 \| Per Category Iou Segment 1 \| Per Category Accuracy Segment 0 \| Per Category Accuracy Segment 1\| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:-------------------:\|:---------------------:\|:-----------------:\| \| 0.0461 \| 1.0 \| 86 \| 0.1233 \| 0.9150 \| 0.9527 \| 0.9762 \| 0.9721967854031923 \| 0.8578619172251059 \| 0.9869082633464498 \| 0.9184139264010376 \| \| 0.0708 \| 2.0 \| 172 \| 0.1366 \| 0.9172 \| 0.9490 \| 0.9771 \| 0.9732821853093164 \| 0.8611008788165083 \| 0.9898473600751747 \| 0.9082362492748777 \| \| 0.048 \| 3.0 \| 258 \| 0.1260 \| 0.9199 \| 0.9534 \| 0.9777 \| 0.9740118174014271 \| 0.8658241844233872 \| 0.9888392553004053 \| 0.9179240730467295 \| \| 0.0535 \| 4.0 \| 344 \| 0.1184 \| 0.9200 \| 0.9520 \| 0.9778 \| 0.974142444792198 \| 0.8658711064023369 \| 0.9896291184589182 \| 0.9142864290038782 \| \| 0.0185 \| 5.0 \| 430 \| 0.1296 \| 0.9182 \| 0.9477 \| 0.9775 \| 0.9737715695013129 \| 0.8627108292167807 \| 0.9910418746696423 \| 0.904378218719681 \| \| 0.036 \| 6.0 \| 516 \| 0.1410 \| 0.9213 \| 0.9538 \| 0.9782 \| 0.9745002408443008 \| 0.8680673581922554 \| 0.9892677512186527 \| 0.9182967669045321 \| \| 0.0376 \| 7.0 \| 602 \| 0.1451 \| 0.9206 \| 0.9550 \| 0.9779 \| 0.9741455743906073 \| 0.8669703237367214 \| 0.9883004639689904 \| 0.9216576612178001 \| \| 0.0186 \| 8.0 \| 688 \| 0.1380 \| 0.9175 \| 0.9496 \| 0.9772 \| 0.9733616852468584 \| 0.8616466350192237 \| 0.9897043519116697 \| 0.9094762400541087 \| \| 0.0162 \| 9.0 \| 774 \| 0.1459 \| 0.9218 \| 0.9539 \| 0.9783 \| 0.9746840649852051 \| 0.8688930149000804 \| 0.989455276913138 \| 0.9182917005479264 \| \| 0.0169 \| 10.0 \| 860 \| 0.1467 \| 0.9191 \| 0.9502 \| 0.9776 \| 0.9739086600912814 \| 0.8642187978193332 \| 0.9901195747929759 \| 0.9102564589713776 \| \| 0.0102 \| 11.0 \| 946 \| 0.1549 \| 0.9191 \| 0.9524 \| 0.9775 \| 0.9737696499931041 \| 0.8644247331609153 \| 0.9889789745698009 \| 0.915789237032027 \| \| 0.0204 \| 12.0 \| 1032 \| 0.1502 \| 0.9215 \| 0.9527 \| 0.9783 \| 0.974639596078376 \| 0.8682964916021273 \| 0.989902977623774 \| 0.9155653673995151 \| \| 0.0268 \| 13.0 \| 1118 \| 0.1413 \| 0.9194 \| 0.9505 \| 0.9777 \| 0.9740020531855834 \| 0.8647199376136 \| 0.99011699066189 \| 0.9107963425971664 \| \| 0.0166 \| 14.0 \| 1204 \| 0.1584 \| 0.9173 \| 0.9518 \| 0.9770 \| 0.9731154475737929 \| 0.8614276032542578 \| 0.9884142831972749 \| 0.9152366875147241 \| \| 0.0159 \| 15.0 \| 1290 \| 0.1563 \| 0.9170 \| 0.9492 \| 0.9770 \| 0.9731832402253996 \| 0.8607442858381036 \| 0.9896456803899689 \| 0.9087960816798012 \| \| 0.0211 \| 16.0 \| 1376 \| 0.1435 \| 0.9150 \| 0.9481 \| 0.9764 \| 0.9725201360275898 \| 0.8574847000491036 \| 0.989323310037 \| 0.9068449010920532 \| \| 0.0128 \| 17.0 \| 1462 \| 0.1421 \| 0.9212 \| 0.9519 \| 0.9782 \| 0.9745789801464504 \| 0.8677394402794754 \| 0.9901920479238856 \| 0.9136255861141298 \| \| 0.0167 \| 18.0 \| 1548 \| 0.1558 \| 0.9217 \| 0.9532 \| 0.9783 \| 0.9746811993626879 \| 0.8686470009484697 \| 0.9897428202266988 \| 0.9166850322093621 \| \| 0.0201 \| 19.0 \| 1634 \| 0.1623 \| 0.9156 \| 0.9484 \| 0.9766 \| 0.9727184720007118 \| 0.8584339325695252 \| 0.9894484642039114 \| 0.9072695251050635 \| \| 0.0133 \| 20.0 \| 1720 \| 0.1573 \| 0.9189 \| 0.9505 \| 0.9776 \| 0.9738320500157303 \| 0.8640203613069115 \| 0.9898665061373113 \| 0.9112263496140702 \| \| 0.012 \| 21.0 \| 1806 \| 0.1631 \| 0.9165 \| 0.9472 \| 0.9769 \| 0.9731344243001482 \| 0.8597866189796295 \| 0.9904592118400188 \| 0.9040137576913626 \| \| 0.0148 \| 22.0 \| 1892 \| 0.1629 \| 0.9181 \| 0.9507 \| 0.9773 \| 0.9735162429121835 \| 0.8627239955489192 \| 0.9894034768309156 \| 0.9120129014770962 \| \| 0.0137 \| 23.0 \| 1978 \| 0.1701 \| 0.9136 \| 0.9484 \| 0.9760 \| 0.9719681843338751 \| 0.8552607882028388 \| 0.9885083690609032 \| 0.908250815050119 \| \| 0.0142 \| 24.0 \| 2064 \| 0.1646 \| 0.9146 \| 0.9488 \| 0.9763 \| 0.9723134197764093 \| 0.8568918401744342 \| 0.9887405884771245 \| 0.9089100747034281 \| \| 0.0156 \| 25.0 \| 2150 \| 0.1615 \| 0.9144 \| 0.9465 \| 0.9763 \| 0.9723929259786395 \| 0.856345354289624 \| 0.9898487696012216 \| 0.9032139066422469 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1 - Datasets 2.13.1 - Tokenizers 0.13.3
vlabs/falcon-7b-sentiment_V3	vlabs	2023-07-27T19:21:16Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-27T19:21:14Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
Isaacgv/distilhubert-finetuned-gtzan	Isaacgv	2023-07-27T19:20:19Z	19	0	transformers	[ "transformers", "pytorch", "tensorboard", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-26T12:47:08Z	--- license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.88 --- <!-- 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. --> # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.5655 - Accuracy: 0.88 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.3836 \| 0.98 \| 14 \| 0.5798 \| 0.82 \| \| 0.3357 \| 1.96 \| 28 \| 0.5655 \| 0.88 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
Varshitha/flan-t5-small-finetune-medicine-v3	Varshitha	2023-07-27T19:17:58Z	102	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "text2textgeneration", "generated_from_trainer", "base_model:google/flan-t5-small", "base_model:finetune:google/flan-t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-27T19:16:23Z	--- license: apache-2.0 base_model: google/flan-t5-small tags: - text2textgeneration - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-small-finetune-medicine-v3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-small-finetune-medicine-v3 This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8757 - Rouge1: 15.991 - Rouge2: 5.2469 - Rougel: 14.6278 - Rougelsum: 14.7076 ## 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: 5.6e-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: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\| \| No log \| 1.0 \| 5 \| 2.9996 \| 12.4808 \| 4.9536 \| 12.3712 \| 12.2123 \| \| No log \| 2.0 \| 10 \| 2.9550 \| 13.6471 \| 4.9536 \| 13.5051 \| 13.5488 \| \| No log \| 3.0 \| 15 \| 2.9224 \| 13.8077 \| 5.117 \| 13.7274 \| 13.753 \| \| No log \| 4.0 \| 20 \| 2.9050 \| 13.7861 \| 5.117 \| 13.6982 \| 13.7001 \| \| No log \| 5.0 \| 25 \| 2.8920 \| 14.668 \| 5.117 \| 14.4497 \| 14.4115 \| \| No log \| 6.0 \| 30 \| 2.8820 \| 14.9451 \| 5.2469 \| 14.5797 \| 14.6308 \| \| No log \| 7.0 \| 35 \| 2.8770 \| 15.991 \| 5.2469 \| 14.6278 \| 14.7076 \| \| No log \| 8.0 \| 40 \| 2.8757 \| 15.991 \| 5.2469 \| 14.6278 \| 14.7076 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
FFusion/FFusionXL-LoRa-SDXL-Potion-Art-Engine	FFusion	2023-07-27T19:11:33Z	16	5	diffusers	[ "diffusers", "safetensors", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "en", "doi:10.57967/hf/0918", "license:other", "region:us" ]	text-to-image	2023-07-23T20:39:23Z	--- license: other base_model: diffusers/FFusionXL-1-SDXL instance_prompt: a 3d potion vial tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true library_name: diffusers badges: - alt: Name url: >- https://img.shields.io/badge/Name-FFusion%20XL%20LoRA%20%F0%9F%A7%AA%EF%B8%8F%20%20Potion%20Art%20Engine-89CFF0 src: >- https://img.shields.io/badge/Name-FFusion%20XL%20LoRA%20%F0%9F%A7%AA%EF%B8%8F%20%20Potion%20Art%20Engine-89CFF0 - alt: LoRA Type url: https://img.shields.io/badge/LoRA%20Type-LyCORIS%2FLoKr%2C%20Prodigy-blue src: https://img.shields.io/badge/LoRA%20Type-LyCORIS%2FLoKr%2C%20Prodigy-blue - alt: Refiner Compatible url: https://img.shields.io/badge/%F0%9F%94%A5%20Refiner%20Compatible-Yes-success src: https://img.shields.io/badge/%F0%9F%94%A5%20Refiner%20Compatible-Yes-success - alt: CLIP Tested url: >- https://img.shields.io/badge/%F0%9F%92%BB%20CLIP--ViT%2FG%20and%20CLIP--ViT%2FL%20tested-Yes-success src: >- https://img.shields.io/badge/%F0%9F%92%BB%20CLIP--ViT%2FG%20and%20CLIP--ViT%2FL%20tested-Yes-success - alt: Trained Resolution url: >- https://img.shields.io/badge/Trained%20Resolution-1024%20x%201024%20pixels-yellow src: >- https://img.shields.io/badge/Trained%20Resolution-1024%20x%201024%20pixels-yellow - alt: Tested Resolution url: >- https://img.shields.io/badge/Tested%20Resolution-Up%20to%202800%20x%202800%20pixels-brightgreen src: >- https://img.shields.io/badge/Tested%20Resolution-Up%20to%202800%20x%202800%20pixels-brightgreen - alt: Tested on url: >- https://img.shields.io/badge/Tested%20on-SDXL%200.9%20%26%20FFXL%200.001-blue src: >- https://img.shields.io/badge/Tested%20on-SDXL%200.9%20%26%20FFXL%200.001-blue - alt: Hugging Face Model url: https://img.shields.io/badge/Hugging%20Face-FFusion--BaSE-blue src: https://img.shields.io/badge/Hugging%20Face-FFusion--BaSE-blue - alt: GitHub url: https://img.shields.io/badge/GitHub-1e--2-green src: https://img.shields.io/badge/GitHub-1e--2-green - alt: Facebook url: https://img.shields.io/badge/Facebook-FFusionAI-blue src: https://img.shields.io/badge/Facebook-FFusionAI-blue - alt: Civitai url: https://img.shields.io/badge/Civitai-FFusionAI-blue src: https://img.shields.io/badge/Civitai-FFusionAI-blue language: - en --- # FFusion XL LoRA 🧪 Potion Art Engine ![FFusionXL Potion Art Engine](./Image_0.png) <div style="display: flex; flex-wrap: wrap; gap: 2px;"> <img src="https://img.shields.io/badge/%F0%9F%94%A5%20Refiner%20Compatible-Yes-success"> <img src="https://img.shields.io/badge/%F0%9F%92%BB%20CLIP--ViT%2FG%20and%20CLIP--ViT%2FL%20tested-Yes-success"> <img src="https://img.shields.io/badge/LoRA%20Type-LyCORIS%2FLoKr%2C%20Prodigy-blue"> <img src="https://img.shields.io/badge/Tested%20on-SDXL%200.9%20%26%20FFXL%200.001-blue"> </div> [![Download](https://img.shields.io/badge/-Download%20Model-brightgreen?style=for-the-badge&logo=appveyor)](https://huggingface.co/FFusion/FFusionXL-LoRa-SDXL-Potion-Art-Engine/resolve/main/FFusionXL-LoRa-SDXL-Potion-Art-Engine.safetensors) [![Samples](https://img.shields.io/badge/-View%20Samples-blue?style=for-the-badge&logo=appveyor)](https://huggingface.co/FFusion/FFusionXL-LoRa-SDXL-Potion-Art-Engine/tree/main/Samples) The Potion Art Engine is an experimental version of a game asset art generator, specifically designed for creating potion vials. ## Specifications - Model Name: FFusion XL LoRA Potion Art Engine - LoRA Type: LyCORIS/LoKr, Prodigy - Trained Resolution: 1024 x 1024 pixels - Tested Resolution: Up to 2800 x 2800 pixels <div style="display: flex; flex-wrap: wrap; gap: 4px;"><img src="https://img.shields.io/badge/Trained%20Resolution-1024%20x%201024%20pixels-yellow"> <img src="https://img.shields.io/badge/Tested%20Resolution-Up%20to%202800%20x%202800%20pixels-brightgreen"></div> ## How can the Potion Art Engine help game developers? The Potion Art Engine is a powerful tool for game developers, especially those working on fantasy or RPG games where potions and vials are common game assets. Here are a few ways this tool can be beneficial: 1. Speed up the asset creation process: Creating game assets can be a time-consuming process, especially for indie developers or small teams. The Potion Art Engine can generate high-quality potion vials, significantly reducing the time and effort required to create these assets. 2. Create a variety of unique assets: The Potion Art Engine can generate a wide variety of potion vials, ensuring that each potion in your game can have a unique and distinct look. This can add to the depth and richness of your game world. 3. Experiment with different styles: The Potion Art Engine allows you to experiment with different styles and looks for your potions. This can be particularly useful in the early stages of game development when you are still defining the visual style of your game. 4. Reduce costs: By using the Potion Art Engine to generate game assets, you can significantly reduce the costs associated with asset creation. This can be particularly beneficial for indie developers or small teams with limited budgets. ## Limitations - The Potion Art Engine is designed to generate potion vials, and its performance may vary when used to generate other types of game assets. - The quality of the generated assets may vary depending on the specific parameters and settings used. ## Ethical Considerations As with any AI model, it is important to use the Potion Art Engine responsibly. Please ensure that the generated assets do not infringe on any copyrights or intellectual property rights. It is also important to ensure that the generated assets are appropriate and do not contain any offensive or harmful content. ## Citations If you use the Potion Art Engine in your project or research, please provide appropriate citations to acknowledge the model's contribution. ## Disclaimer ![FFusionXL Potion Art Engine](./Image_8.jpg) The Potion Art Engine is a powerful tool for generating game assets, but it is not perfect and may have limitations. Users are encouraged to test and validate the generated assets thoroughly before integrating them into their games. The developers of this model hold no responsibility for any consequences that may arise from its usage. <div style="display: flex; flex-wrap: wrap; gap: 2px;"> <a href="https://huggingface.co/FFusion/FFusion-BaSE" target="_new" rel="ugc"><img src="https://img.shields.io/badge/Hugging%20Face-FFusion--BaSE-blue" alt="Hugging Face Model"></a> <a href="https://github.com/1e-2" target="_new" rel="ugc"><img src="https://img.shields.io/badge/GitHub-1e--2-green" alt="GitHub"></a> <a href="https://www.facebook.com/FFusionAI/" target="_new" rel="ugc"><img src="https://img.shields.io/badge/Facebook-FFusionAI-blue" alt="Facebook"></a> <a href="https://civitai.com/models/82039/ffusion-ai-sd-21" target="_new" rel="ugc"><img src="https://img.shields.io/badge/Civitai-FFusionAI-blue" alt="Civitai"></a> </div> <div style="display: flex; flex-wrap: wrap; gap: 10px; align-items: center;"> <p>These are LoRA adaption weights for</p> <a href="https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9" target="_new" rel="ugc"><img src="https://img.shields.io/badge/stable--diffusion--xl--base--0.9-Model-purple" alt="stable-diffusion-xl-base-0.9"></a> <p>&</p> <a href="https://huggingface.co/FFusion/FFusionXL-09-SDXL" target="_new" rel="ugc"><img src="https://img.shields.io/badge/FFusionXL--09--SDXL-Model-pink" alt="FFusionXL-09-SDXL"></a> <p>The weights were trained using experimental</p> <a href="https://github.com/kohya-ss/sd-scripts" target="_new" rel="ugc"><img src="https://img.shields.io/badge/kohya--ss-sd--scripts-blue" alt="kohya-ss/sd-scripts build"></a> <p>build</p> </div> Attribution: "SDXL 0.9 is licensed under the SDXL Research License, Copyright (c) Stability AI Ltd. All Rights Reserved." ## License [SDXL 0.9 Research License](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9/blob/main/LICENSE.md)" ![FFusionXL Potion Art Engine](./Image_9.png) [![Email](https://img.shields.io/badge/Email-di%40ffusion.ai-blue?style=for-the-badge&logo=gmail)](mailto:[email protected])
NasimB/bnc-cbt-log-rarity	NasimB	2023-07-27T19:04:03Z	14	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-27T16:42:30Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: bnc-cbt-log-rarity 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. --> # bnc-cbt-log-rarity This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.1410 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.351 \| 0.29 \| 500 \| 5.3291 \| \| 5.0493 \| 0.59 \| 1000 \| 4.9419 \| \| 4.7234 \| 0.88 \| 1500 \| 4.7048 \| \| 4.4524 \| 1.17 \| 2000 \| 4.5595 \| \| 4.3123 \| 1.46 \| 2500 \| 4.4472 \| \| 4.2134 \| 1.76 \| 3000 \| 4.3517 \| \| 4.0971 \| 2.05 \| 3500 \| 4.2754 \| \| 3.9151 \| 2.34 \| 4000 \| 4.2320 \| \| 3.8812 \| 2.63 \| 4500 \| 4.1763 \| \| 3.8438 \| 2.93 \| 5000 \| 4.1267 \| \| 3.6488 \| 3.22 \| 5500 \| 4.1269 \| \| 3.6024 \| 3.51 \| 6000 \| 4.0958 \| \| 3.5864 \| 3.81 \| 6500 \| 4.0625 \| \| 3.4842 \| 4.1 \| 7000 \| 4.0646 \| \| 3.3367 \| 4.39 \| 7500 \| 4.0563 \| \| 3.3316 \| 4.68 \| 8000 \| 4.0432 \| \| 3.3157 \| 4.98 \| 8500 \| 4.0354 \| \| 3.1598 \| 5.27 \| 9000 \| 4.0473 \| \| 3.1514 \| 5.56 \| 9500 \| 4.0472 \| \| 3.1497 \| 5.85 \| 10000 \| 4.0465 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
JBJoyce/wav2vec2-large-robust-12-ft-emotion-msp-dim-finetuned-gtzan	JBJoyce	2023-07-27T19:03:49Z	167	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-10T02:29:17Z	--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: wav2vec2-large-robust-12-ft-emotion-msp-dim-finetuned-gtzan 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-robust-12-ft-emotion-msp-dim-finetuned-gtzan This model is a fine-tuned version of [audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim](https://huggingface.co/audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.7711 - Accuracy: 0.83 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.186 \| 1.0 \| 112 \| 2.1638 \| 0.3 \| \| 1.655 \| 2.0 \| 225 \| 1.7677 \| 0.48 \| \| 1.5148 \| 3.0 \| 337 \| 1.3746 \| 0.54 \| \| 1.2349 \| 4.0 \| 450 \| 1.1218 \| 0.64 \| \| 0.9702 \| 5.0 \| 562 \| 1.0244 \| 0.69 \| \| 0.9191 \| 6.0 \| 675 \| 0.9180 \| 0.75 \| \| 0.6891 \| 7.0 \| 787 \| 0.8959 \| 0.76 \| \| 0.628 \| 8.0 \| 900 \| 0.8084 \| 0.81 \| \| 0.7337 \| 9.0 \| 1012 \| 0.7742 \| 0.83 \| \| 0.5573 \| 9.96 \| 1120 \| 0.7711 \| 0.83 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
StofEzz/mascir_fr_wav2vec_test	StofEzz	2023-07-27T18:45:29Z	135	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/wav2vec2-large-xlsr-53", "base_model:finetune:facebook/wav2vec2-large-xlsr-53", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-07-27T15:22:36Z	--- license: apache-2.0 base_model: facebook/wav2vec2-large-xlsr-53 tags: - generated_from_trainer metrics: - wer model-index: - name: mascir_fr_wav2vec_test 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_fr_wav2vec_test 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: 0.0136 - Wer: 0.1612 ## 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: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 8.06 \| 250 \| 3.7503 \| 0.9919 \| \| 8.0637 \| 16.13 \| 500 \| 3.0132 \| 0.9919 \| \| 8.0637 \| 24.19 \| 750 \| 2.9734 \| 0.9919 \| \| 2.9339 \| 32.26 \| 1000 \| 2.0538 \| 0.9963 \| \| 2.9339 \| 40.32 \| 1250 \| 0.4530 \| 0.5406 \| \| 0.9878 \| 48.39 \| 1500 \| 0.1807 \| 0.3373 \| \| 0.9878 \| 56.45 \| 1750 \| 0.0814 \| 0.2436 \| \| 0.3416 \| 64.52 \| 2000 \| 0.0512 \| 0.2114 \| \| 0.3416 \| 72.58 \| 2250 \| 0.0292 \| 0.1823 \| \| 0.1952 \| 80.65 \| 2500 \| 0.0198 \| 0.1742 \| \| 0.1952 \| 88.71 \| 2750 \| 0.0158 \| 0.1631 \| \| 0.1476 \| 96.77 \| 3000 \| 0.0136 \| 0.1612 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
w601sxs/b1ade-1b-orca-chkpt-506k	w601sxs	2023-07-27T18:44:34Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-27T18:43:42Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
dariowsz/ppo-Pyramids	dariowsz	2023-07-27T18:29:47Z	5	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-07-27T18:28:33Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: dariowsz/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Leogrin/eleuther-pythia1b-hh-dpo	Leogrin	2023-07-27T18:21:11Z	168	1	transformers	[ "transformers", "pytorch", "gpt_neox", "text-generation", "causal-lm", "pythia", "en", "dataset:Anthropic/hh-rlhf", "arxiv:2305.18290", "arxiv:2101.00027", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-27T14:35:26Z	--- language: - en tags: - pytorch - causal-lm - pythia license: apache-2.0 datasets: - Anthropic/hh-rlhf --- # Infos Pythia-1b supervised finetuned with Anthropic-hh-rlhf dataset for 1 epoch (sft-model), before DPO [(paper)](https://arxiv.org/abs/2305.18290) with same dataset for 1 epoch. [wandb log](https://wandb.ai/pythia_dpo/Pythia_DPO_new/runs/jk09pzqb) See [Pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) for model details [(paper)](https://arxiv.org/abs/2101.00027). # Benchmark raw results: Results for the base model are taken from the [Pythia paper](https://arxiv.org/abs/2101.00027). ## Zero shot \| Task \| 1B_base \| 1B_sft \| 1B_dpo \| \|------------------\|----------------\|----------------\|-----------------\| \| Lambada (OpenAI) \| 0.562 ± 0.007 \| 0.563 ± 0.007 \| 0.5575 ± 0.0069 \| \| PIQA \| 0.707 ± 0.011 \| 0.711 ± 0.011 \| 0.7122 ± 0.0106 \| \| WinoGrande \| 0.537 ± 0.014 \| 0.534 ± 0.014 \| 0.5525 ± 0.0140 \| \| WSC \| 0.365 ± 0.047 \| 0.365 ± 0.047 \| 0.3654 ± 0.0474 \| \| ARC - Easy \| 0.569 ± 0.010 \| 0.583 ± 0.010 \| 0.5901 ± 0.0101 \| \| ARC - Challenge \| 0.244 ± 0.013 \| 0.248 ± 0.013 \| 0.2611 ± 0.0128 \| \| SciQ \| 0.840 ± 0.012 \| 0.847 ± 0.011 \| 0.8530 ± 0.0112 \| \| LogiQA \| 0.223 ± 0.016 \| N/A \| N/A \| ## Five shot \| Task \| 1B_base \| 1B_sft \| 1B_dpo \| \|------------------\|----------------\|----------------\|-----------------\| \| Lambada (OpenAI) \| 0.507 ± 0.007 \| 0.4722 ± 0.007 \| 0.4669 ± 0.0070 \| \| PIQA \| 0.705 ± 0.011 \| 0.7165 ± 0.0105\| 0.7138 ± 0.0105 \| \| WinoGrande \| 0.532 ± 0.014 \| 0.5343 ± 0.014 \| 0.5525 ± 0.0140 \| \| WSC \| 0.365 ± 0.047 \| 0.5000 ± 0.0493\| 0.5577 ± 0.0489 \| \| ARC - Easy \| 0.594 ± 0.010 \| 0.6010 ± 0.010 \| 0.6170 ± 0.0100 \| \| ARC - Challenge \| 0.259 ± 0.013 \| 0.2679 ± 0.0129\| 0.2833 ± 0.0132 \| \| SciQ \| 0.920 ± 0.009 \| 0.9100 ± 0.0091\| 0.9020 ± 0.0094 \| \| LogiQA \| 0.227 ± 0.016 \| N/A \| N/A \|
grace-pro/no-delete_5e-5_hausa	grace-pro	2023-07-27T18:18:51Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:Davlan/afro-xlmr-base", "base_model:finetune:Davlan/afro-xlmr-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-27T17:02:05Z	--- license: mit base_model: Davlan/afro-xlmr-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: no-delete_5e-5_hausa 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. --> # no-delete_5e-5_hausa This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1716 - Precision: 0.4009 - Recall: 0.2840 - F1: 0.3325 - Accuracy: 0.9559 ## 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: 8 - 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 \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1421 \| 1.0 \| 1283 \| 0.1347 \| 0.4610 \| 0.1779 \| 0.2567 \| 0.9594 \| \| 0.1234 \| 2.0 \| 2566 \| 0.1332 \| 0.4847 \| 0.1920 \| 0.2750 \| 0.9603 \| \| 0.1041 \| 3.0 \| 3849 \| 0.1412 \| 0.4581 \| 0.2305 \| 0.3067 \| 0.9595 \| \| 0.0822 \| 4.0 \| 5132 \| 0.1562 \| 0.3979 \| 0.2752 \| 0.3253 \| 0.9559 \| \| 0.0664 \| 5.0 \| 6415 \| 0.1716 \| 0.4009 \| 0.2840 \| 0.3325 \| 0.9559 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
Leogrin/eleuther-pythia1.4b-hh-dpo	Leogrin	2023-07-27T18:16:00Z	180	1	transformers	[ "transformers", "pytorch", "gpt_neox", "text-generation", "causal-lm", "pythia", "en", "dataset:Anthropic/hh-rlhf", "arxiv:2305.18290", "arxiv:2101.00027", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-27T15:07:41Z	--- language: - en tags: - pytorch - causal-lm - pythia license: apache-2.0 datasets: - Anthropic/hh-rlhf --- # Infos Pythia-1.4b supervised finetuned with Anthropic-hh-rlhf dataset for 1 epoch (sft-model), before DPO [(paper)](https://arxiv.org/abs/2305.18290) with same dataset for 1 epoch. [wandb log](https://wandb.ai/pythia_dpo/Pythia_DPO_new/runs/6yrtkj3s) See [Pythia-1.4b](https://huggingface.co/EleutherAI/pythia-1.4b) for model details [(paper)](https://arxiv.org/abs/2101.00027). # Benchmark raw results: Results for the base model are taken from the [Pythia paper](https://arxiv.org/abs/2101.00027). ## Zero shot \| Task \| 1.4B_base \| 1.4B_sft \| 1.4B_dpo \| \|------------------\|--------------:\|--------------:\|---------------:\| \| Lambada (OpenAI) \| 0.616 ± 0.007 \| 0.5977 ± 0.0068 \| 0.5948 ± 0.0068 \| \| PIQA \| 0.711 ± 0.011 \| 0.7133 ± 0.0106 \| 0.7165 ± 0.0105 \| \| WinoGrande \| 0.573 ± 0.014 \| 0.5793 ± 0.0139 \| 0.5746 ± 0.0139 \| \| WSC \| 0.365 ± 0.047 \| 0.3654 ± 0.0474 \| 0.3654 ± 0.0474 \| \| ARC - Easy \| 0.606 ± 0.010 \| 0.6098 ± 0.0100 \| 0.6199 ± 0.0100 \| \| ARC - Challenge \| 0.260 ± 0.013 \| 0.2696 ± 0.0130 \| 0.2884 ± 0.0132 \| \| SciQ \| 0.865 ± 0.011 \| 0.8540 ± 0.0112 \| 0.8550 ± 0.0111 \| \| LogiQA \| 0.210 ± 0.016 \| NA \| NA \| ## Five shot \| Task \| 1.4B_base \| 1.4B_sft \| 1.4B_dpo \| \|------------------\|----------------:\|----------------:\|----------------:\| \| Lambada (OpenAI) \| 0.578 ± 0.007 \| 0.5201 ± 0.007 \| 0.5247 ± 0.007 \| \| PIQA \| 0.705 ± 0.011 \| 0.7176 ± 0.0105\| 0.7209 ± 0.0105\| \| WinoGrande \| 0.580 ± 0.014 \| 0.5793 ± 0.0139\| 0.5746 ± 0.0139\| \| WSC \| 0.365 ± 0.047 \| 0.5288 ± 0.0492\| 0.5769 ± 0.0487\| \| ARC - Easy \| 0.643 ± 0.010 \| 0.6376 ± 0.0099\| 0.6561 ± 0.0097\| \| ARC - Challenge \| 0.290 ± 0.013 \| 0.2935 ± 0.0133\| 0.3166 ± 0.0136\| \| SciQ \| 0.92 ± 0.009 \| 0.9180 ± 0.0087\| 0.9150 ± 0.0088\| \| LogiQA \| 0.240 ± 0.017 \| N/A \| N/A \|
Khushnur/t5-base-end2end-questions-generation_squad_all_pcmq	Khushnur	2023-07-27T18:11:03Z	159	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-base", "base_model:finetune:google-t5/t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-27T15:33:55Z	--- license: apache-2.0 base_model: t5-base tags: - generated_from_trainer model-index: - name: t5-base-end2end-questions-generation_squad_all_pcmq results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-end2end-questions-generation_squad_all_pcmq This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5861 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.8599 \| 0.67 \| 100 \| 1.6726 \| \| 1.8315 \| 1.35 \| 200 \| 1.6141 \| \| 1.7564 \| 2.02 \| 300 \| 1.5942 \| \| 1.7153 \| 2.69 \| 400 \| 1.5861 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
BauyrjanQ/whisper-kk-sp2ner-b2-ms1000-b	BauyrjanQ	2023-07-27T18:07:56Z	75	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:BauyrjanQ/whisper-kk", "base_model:finetune:BauyrjanQ/whisper-kk", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-07-26T18:02:30Z	--- license: apache-2.0 base_model: BauyrjanQ/whisper-kk tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-kk-sp2ner-b4-ms1000-b 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-sp2ner-b4-ms1000-b This model is a fine-tuned version of [BauyrjanQ/whisper-kk](https://huggingface.co/BauyrjanQ/whisper-kk) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4320 - Wer: 95.6625 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 4 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 1.1132 \| 0.06 \| 1000 \| 0.4320 \| 95.6625 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
jordyvl/vit-base_rvl_cdip_crl	jordyvl	2023-07-27T18:00:23Z	167	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-26T16:38:18Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-base_rvl_cdip_crl 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_crl 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: 0.6238 - Accuracy: 0.8956 - Brier Loss: 0.1819 - Nll: 1.1791 - F1 Micro: 0.8957 - F1 Macro: 0.8958 - Ece: 0.0846 - Aurc: 0.0210 ## 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 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| 0.1844 \| 1.0 \| 1250 \| 0.4411 \| 0.8961 \| 0.1614 \| 1.1240 \| 0.8961 \| 0.8963 \| 0.0528 \| 0.0161 \| \| 0.1394 \| 2.0 \| 2500 \| 0.4830 \| 0.8927 \| 0.1716 \| 1.1324 \| 0.8927 \| 0.8927 \| 0.0646 \| 0.0175 \| \| 0.1 \| 3.0 \| 3750 \| 0.5257 \| 0.8911 \| 0.1791 \| 1.1569 \| 0.8911 \| 0.8912 \| 0.0737 \| 0.0187 \| \| 0.068 \| 4.0 \| 5000 \| 0.5497 \| 0.8913 \| 0.1806 \| 1.1705 \| 0.8913 \| 0.8913 \| 0.0770 \| 0.0192 \| \| 0.048 \| 5.0 \| 6250 \| 0.5762 \| 0.8915 \| 0.1834 \| 1.1906 \| 0.8915 \| 0.8914 \| 0.0808 \| 0.0195 \| \| 0.033 \| 6.0 \| 7500 \| 0.5877 \| 0.8936 \| 0.1822 \| 1.1690 \| 0.8936 \| 0.8938 \| 0.0817 \| 0.0196 \| \| 0.0231 \| 7.0 \| 8750 \| 0.6000 \| 0.8938 \| 0.1822 \| 1.1867 \| 0.8938 \| 0.8939 \| 0.0833 \| 0.0206 \| \| 0.0162 \| 8.0 \| 10000 \| 0.6187 \| 0.8948 \| 0.1834 \| 1.1827 \| 0.8948 \| 0.8949 \| 0.0841 \| 0.0208 \| \| 0.0123 \| 9.0 \| 11250 \| 0.6191 \| 0.8953 \| 0.1824 \| 1.1868 \| 0.8953 \| 0.8955 \| 0.0836 \| 0.0207 \| \| 0.0102 \| 10.0 \| 12500 \| 0.6238 \| 0.8956 \| 0.1819 \| 1.1791 \| 0.8957 \| 0.8958 \| 0.0846 \| 0.0210 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
snob/TagMyBookmark-KoAlpaca-QLoRA-v1.0-Finetune300	snob	2023-07-27T17:57:27Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-27T17:57:09Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
dariowsz/ppo-SnowballTarget	dariowsz	2023-07-27T17:45:56Z	7	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-07-27T17:45:49Z	--- 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: dariowsz/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
asenella/MMVAEPlus_beta_10_scale_False_seed_2	asenella	2023-07-27T17:21:14Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-27T17:21:01Z	--- 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") ```
asenella/MMVAEPlus_beta_5_scale_False_seed_2	asenella	2023-07-27T17:15:39Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-27T17:15:20Z	--- 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") ```
magnustragardh/poca-SoccerTwos	magnustragardh	2023-07-27T17:06:35Z	5	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-07-27T17:01:53Z	--- 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: magnustragardh/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
asenella/MMVAEPlus_beta_25_scale_False_seed_0	asenella	2023-07-27T17:03:43Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-27T17:03:30Z	--- 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") ```
asenella/MMVAEPlus_beta_10_scale_False_seed_0	asenella	2023-07-27T17:01:30Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-27T17:01:18Z	--- 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") ```
Pallisgaard/whisper-small-dv	Pallisgaard	2023-07-27T16:52:23Z	76	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dv", "dataset:mozilla-foundation/common_voice_13_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-07-27T15:24:33Z	--- language: - dv license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_13_0 metrics: - wer model-index: - name: Whisper Small Dv - Sanchit Gandhi results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 13 type: mozilla-foundation/common_voice_13_0 config: dv split: test args: dv metrics: - name: Wer type: wer value: 13.097680564732064 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Dv - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset. It achieves the following results on the evaluation set: - Loss: 0.1691 - Wer Ortho: 62.1144 - Wer: 13.0977 ## 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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:-------:\| \| 0.1237 \| 1.63 \| 500 \| 0.1691 \| 62.1144 \| 13.0977 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
augtoma/qCammel-70-x	augtoma	2023-07-27T16:47:02Z	1,686	27	transformers	[ "transformers", "pytorch", "llama", "text-generation", "llama-2", "qCammel-70", "en", "arxiv:2305.12031", "arxiv:2305.14314", "arxiv:2302.70971", "license:other", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-07-23T00:39:34Z	--- license: other language: - en pipeline_tag: text-generation inference: false tags: - pytorch - llama - llama-2 - qCammel-70 library_name: transformers --- # qCammel-70 qCammel-70 is a fine-tuned version of Llama-2 70B model, trained on a distilled dataset of 15,000 instructions using QLoRA. This model is optimized for academic medical knowledge and instruction-following capabilities. ## 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 their License before downloading this model . The fine-tuning process applied to qCammel-70 involves a distilled dataset of 15,000 instructions and is trained with QLoRA, Variations The original Llama 2 has parameter sizes of 7B, 13B, and 70B. This is the fine-tuned version of the 70B model. Input Models input text only. Output Models generate text only. Model Architecture qCammel-70 is based on the Llama 2 architecture, an auto-regressive language model that uses a decoder only transformer architecture. 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/) Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved Research Papers - [Clinical Camel: An Open-Source Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding](https://arxiv.org/abs/2305.12031) - [QLoRA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/abs/2305.14314) - [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.70971)
digitaljungle/ppo-LunarLander-v2	digitaljungle	2023-07-27T16:25:57Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-27T16:25:36Z	--- 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: 260.52 +/- 19.49 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
blackmount8/WizardLM-13B-V1.2-ct2-int8	blackmount8	2023-07-27T16:17:54Z	2	0	transformers	[ "transformers", "license:mit", "endpoints_compatible", "region:us" ]	null	2023-07-26T16:16:35Z	--- license: mit --- # blackmount8/WizardLM-13B-V1.2-ct2-int8 Int8 version of [WizardLM/WizardLM-13B-V1.2](https://huggingface.co/WizardLM/WizardLM-13B-V1.2), quantized using CTranslate2.
mdhafeez29/llama2-qlora-finetunined-french	mdhafeez29	2023-07-27T16:09:16Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-27T16:09:10Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
NasimB/aochildes-rarity-2	NasimB	2023-07-27T16:08:36Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-27T13:44:03Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: aochildes-rarity-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. --> # aochildes-rarity-2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.1181 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.351 \| 0.29 \| 500 \| 5.3358 \| \| 5.0412 \| 0.59 \| 1000 \| 4.9250 \| \| 4.7138 \| 0.88 \| 1500 \| 4.6868 \| \| 4.4435 \| 1.17 \| 2000 \| 4.5444 \| \| 4.3073 \| 1.47 \| 2500 \| 4.4317 \| \| 4.205 \| 1.76 \| 3000 \| 4.3274 \| \| 4.0796 \| 2.05 \| 3500 \| 4.2630 \| \| 3.8987 \| 2.35 \| 4000 \| 4.2145 \| \| 3.8749 \| 2.64 \| 4500 \| 4.1579 \| \| 3.8421 \| 2.93 \| 5000 \| 4.1113 \| \| 3.6388 \| 3.23 \| 5500 \| 4.1089 \| \| 3.5906 \| 3.52 \| 6000 \| 4.0804 \| \| 3.5776 \| 3.81 \| 6500 \| 4.0451 \| \| 3.4712 \| 4.11 \| 7000 \| 4.0519 \| \| 3.3209 \| 4.4 \| 7500 \| 4.0435 \| \| 3.3179 \| 4.69 \| 8000 \| 4.0297 \| \| 3.3071 \| 4.99 \| 8500 \| 4.0193 \| \| 3.1447 \| 5.28 \| 9000 \| 4.0337 \| \| 3.1394 \| 5.57 \| 9500 \| 4.0322 \| \| 3.1343 \| 5.87 \| 10000 \| 4.0318 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
Lazycuber/Pygnen-dolly-6B	Lazycuber	2023-07-27T16:08:06Z	6	0	transformers	[ "transformers", "pytorch", "gptj", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-07-25T14:12:36Z	--- license: apache-2.0 ---
yancongwen/chatglm2-medical-lora-20230727	yancongwen	2023-07-27T16:07:33Z	0	0	peft	[ "peft", "tensorboard", "region:us" ]	null	2023-07-27T16:06:20Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0
drradford/ppo-Huggy	drradford	2023-07-27T16:05:42Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-27T16:05:37Z	--- 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: drradford/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
WforGodot/add-lora-7b	WforGodot	2023-07-27T15:54:56Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-26T17:39:31Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0
digitaljungle/taxi_q_ueue	digitaljungle	2023-07-27T15:53:32Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-27T15:53:30Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi_q_ueue results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="digitaljungle/taxi_q_ueue", 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"]) ```
digitaljungle/q-FrozenLake-v1-4x4-noSlippery	digitaljungle	2023-07-27T15:52:16Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-27T15:52:12Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="digitaljungle/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"]) ```
aman38649/marian-finetuned-kde4-en-to-fr	aman38649	2023-07-27T15:50:48Z	60	0	transformers	[ "transformers", "tf", "marian", "text2text-generation", "generated_from_keras_callback", "base_model:Helsinki-NLP/opus-mt-en-fr", "base_model:finetune:Helsinki-NLP/opus-mt-en-fr", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-27T09:19:00Z	--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-fr tags: - generated_from_keras_callback model-index: - name: aman38649/marian-finetuned-kde4-en-to-fr 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. --> # aman38649/marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7983 - Validation Loss: 0.8210 - 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 17733, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 1.0611 \| 0.8791 \| 0 \| \| 0.7983 \| 0.8210 \| 1 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.0 - Tokenizers 0.13.3
timothytruong/my_awesome_billsum_model	timothytruong	2023-07-27T15:40:12Z	104	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:billsum", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-26T16:27:51Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - billsum metrics: - rouge model-index: - name: my_awesome_billsum_model results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: billsum type: billsum config: default split: ca_test args: default metrics: - name: Rouge1 type: rouge value: 0.1365 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5173 - Rouge1: 0.1365 - Rouge2: 0.0489 - Rougel: 0.1158 - Rougelsum: 0.1158 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 62 \| 2.8028 \| 0.1229 \| 0.0364 \| 0.1048 \| 0.1048 \| 19.0 \| \| No log \| 2.0 \| 124 \| 2.5974 \| 0.1324 \| 0.0467 \| 0.1121 \| 0.1122 \| 19.0 \| \| No log \| 3.0 \| 186 \| 2.5350 \| 0.1354 \| 0.0491 \| 0.1153 \| 0.1151 \| 19.0 \| \| No log \| 4.0 \| 248 \| 2.5173 \| 0.1365 \| 0.0489 \| 0.1158 \| 0.1158 \| 19.0 \| ### Framework versions - Transformers 4.31.0 - Pytorch 1.10.1+cpu - Datasets 2.13.1 - Tokenizers 0.13.3
mahmoudzamani/t5_recommendation_sports_equipment_english	mahmoudzamani	2023-07-27T15:30:14Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-27T15:18:57Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5_recommendation_sports_equipment_english results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5_recommendation_sports_equipment_english This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4517 - Rouge1: 57.9365 - Rouge2: 47.6190 - Rougel: 56.9841 - Rougelsum: 56.6667 - Gen Len: 3.9048 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 0.96 \| 6 \| 6.7882 \| 8.8278 \| 0.9524 \| 8.7668 \| 8.8278 \| 19.0 \| \| No log \| 1.96 \| 12 \| 2.3412 \| 18.0952 \| 0.0 \| 18.0952 \| 18.0952 \| 3.2381 \| \| No log \| 2.96 \| 18 \| 0.8550 \| 11.9048 \| 4.7619 \| 11.9048 \| 11.9048 \| 4.0 \| \| No log \| 3.96 \| 24 \| 0.7481 \| 32.3810 \| 4.7619 \| 32.0635 \| 32.0635 \| 3.9048 \| \| No log \| 4.96 \| 30 \| 0.7208 \| 21.2698 \| 4.7619 \| 20.7937 \| 20.7937 \| 3.6190 \| \| No log \| 5.96 \| 36 \| 0.6293 \| 31.7460 \| 23.8095 \| 31.7460 \| 31.7460 \| 3.6667 \| \| No log \| 6.96 \| 42 \| 0.6203 \| 43.6508 \| 33.3333 \| 43.4921 \| 42.6984 \| 3.9048 \| \| No log \| 7.96 \| 48 \| 0.6352 \| 48.4127 \| 33.3333 \| 46.8254 \| 46.8254 \| 3.8095 \| \| No log \| 8.96 \| 54 \| 0.5334 \| 53.2540 \| 42.8571 \| 52.3810 \| 52.0635 \| 3.9524 \| \| No log \| 9.96 \| 60 \| 0.4517 \| 57.9365 \| 47.6190 \| 56.9841 \| 56.6667 \| 3.9048 \| ### Framework versions - Transformers 4.26.0 - Pytorch 2.0.1+cu118 - Datasets 2.8.0 - Tokenizers 0.13.3
alexandremarie/Falcon7b-wiki2-fr	alexandremarie	2023-07-27T15:14:35Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-27T15:14:35Z	--- license: creativeml-openrail-m ---
royokong/prompteol-llama-7b	royokong	2023-07-27T15:07:54Z	3	0	peft	[ "peft", "region:us" ]	null	2023-07-27T15:06:19Z	--- 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: float32 ### Framework versions - PEFT 0.4.0.dev0
StofEzz/mascir_fr_hubert_test	StofEzz	2023-07-27T15:03:40Z	136	0	transformers	[ "transformers", "pytorch", "tensorboard", "hubert", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/hubert-large-ls960-ft", "base_model:finetune:facebook/hubert-large-ls960-ft", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-07-27T12:22:50Z	--- license: apache-2.0 base_model: facebook/hubert-large-ls960-ft tags: - generated_from_trainer metrics: - wer model-index: - name: mascir_fr_hubert_test 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_fr_hubert_test This model is a fine-tuned version of [facebook/hubert-large-ls960-ft](https://huggingface.co/facebook/hubert-large-ls960-ft) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0113 - Wer: 0.1680 ## 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: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 8.06 \| 250 \| 3.0885 \| 0.9919 \| \| 5.8634 \| 16.13 \| 500 \| 2.8476 \| 0.9919 \| \| 5.8634 \| 24.19 \| 750 \| 1.1091 \| 0.9461 \| \| 1.7302 \| 32.26 \| 1000 \| 0.4035 \| 0.6076 \| \| 1.7302 \| 40.32 \| 1250 \| 0.1643 \| 0.3980 \| \| 0.5446 \| 48.39 \| 1500 \| 0.0872 \| 0.2784 \| \| 0.5446 \| 56.45 \| 1750 \| 0.0464 \| 0.2257 \| \| 0.3144 \| 64.52 \| 2000 \| 0.0311 \| 0.2021 \| \| 0.3144 \| 72.58 \| 2250 \| 0.0213 \| 0.1891 \| \| 0.2224 \| 80.65 \| 2500 \| 0.0155 \| 0.1816 \| \| 0.2224 \| 88.71 \| 2750 \| 0.0132 \| 0.1699 \| \| 0.1871 \| 96.77 \| 3000 \| 0.0113 \| 0.1680 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
epsilonai/SargeRVB	epsilonai	2023-07-27T14:42:04Z	0	0	null	[ "rvb", "red vs blue", "music", "rvc", "text-to-speech", "en", "region:us" ]	text-to-speech	2023-07-27T14:38:06Z	--- language: - en pipeline_tag: text-to-speech tags: - rvb - red vs blue - music - rvc ---
nakcnx/wangchang-math-v2	nakcnx	2023-07-27T14:29:04Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-27T10:25:44Z	--- 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 ### Framework versions - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0
Varshitha/flan-t5-small-finetuned-medicine	Varshitha	2023-07-27T14:27:11Z	102	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "text2textgeneration", "generated_from_trainer", "base_model:google/flan-t5-small", "base_model:finetune:google/flan-t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-27T14:10:18Z	--- license: apache-2.0 base_model: google/flan-t5-small tags: - text2textgeneration - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-small-finetuned-medicine results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-small-finetuned-medicine This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9066 - Rouge1: 9.3596 - Rouge2: 2.6144 - Rougel: 8.94 - Rougelsum: 8.94 ## 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: 5.6e-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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\| \| 3.1417 \| 1.0 \| 5 \| 2.9168 \| 9.5238 \| 2.6144 \| 8.9947 \| 8.9947 \| \| 3.1069 \| 2.0 \| 10 \| 2.9066 \| 9.3596 \| 2.6144 \| 8.94 \| 8.94 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
Andreaa4/Llama-2-7b-chat-hf	Andreaa4	2023-07-27T14:14:13Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-27T14:09:28Z	--- 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: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0
sophy/qa_model	sophy	2023-07-27T14:05:19Z	0	0	transformers	[ "transformers", "question-answering", "dataset:squad", "license:openrail", "endpoints_compatible", "region:us" ]	question-answering	2023-07-22T22:02:20Z	--- license: openrail datasets: - squad pipeline_tag: question-answering library_name: transformers ---
reach-vb/musicgen-large-endpoint	reach-vb	2023-07-27T14:04:06Z	6	0	transformers	[ "transformers", "pytorch", "musicgen", "text-to-audio", "arxiv:2306.05284", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	text-to-audio	2023-07-27T11:46:07Z	--- inference: false tags: - musicgen license: cc-by-nc-4.0 duplicated_from: facebook/musicgen-large --- # MusicGen - Large - 3.3B MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts. It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. Unlike existing methods, like MusicLM, MusicGen doesn't require a self-supervised semantic representation, and it generates all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict them in parallel, thus having only 50 auto-regressive steps per second of audio. MusicGen was published in [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez. Four checkpoints are released: - [small](https://huggingface.co/facebook/musicgen-small) - [medium](https://huggingface.co/facebook/musicgen-medium) - [large (this checkpoint)](https://huggingface.co/facebook/musicgen-large) - [melody](https://huggingface.co/facebook/musicgen-melody) ## Example Try out MusicGen yourself! * Audiocraft Colab: <a target="_blank" href="https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> * Hugging Face Colab: <a target="_blank" href="https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/MusicGen.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> * Hugging Face Demo: <a target="_blank" href="https://huggingface.co/spaces/facebook/MusicGen"> <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/> </a> ## 🤗 Transformers Usage You can run MusicGen locally with the 🤗 Transformers library from version 4.31.0 onwards. 1. First install the 🤗 [Transformers library](https://github.com/huggingface/transformers) from main: ``` pip install git+https://github.com/huggingface/transformers.git ``` 2. Run the following Python code to generate text-conditional audio samples: ```py from transformers import AutoProcessor, MusicgenForConditionalGeneration processor = AutoProcessor.from_pretrained("facebook/musicgen-large") model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-large") inputs = processor( text=["80s pop track with bassy drums and synth", "90s rock song with loud guitars and heavy drums"], padding=True, return_tensors="pt", ) audio_values = model.generate(inputs, max_new_tokens=256) ``` 3. Listen to the audio samples either in an ipynb notebook: ```py from IPython.display import Audio sampling_rate = model.config.audio_encoder.sampling_rate Audio(audio_values[0].numpy(), rate=sampling_rate) ``` Or save them as a `.wav` file using a third-party library, e.g. `scipy`: ```py import scipy sampling_rate = model.config.audio_encoder.sampling_rate scipy.io.wavfile.write("musicgen_out.wav", rate=sampling_rate, data=audio_values[0, 0].numpy()) ``` For more details on using the MusicGen model for inference using the 🤗 Transformers library, refer to the [MusicGen docs](https://huggingface.co/docs/transformers/model_doc/musicgen). ## Audiocraft Usage You can also run MusicGen locally through the original [Audiocraft library]((https://github.com/facebookresearch/audiocraft): 1. First install the [`audiocraft` library](https://github.com/facebookresearch/audiocraft) ``` pip install git+https://github.com/facebookresearch/audiocraft.git ``` 2. Make sure to have [`ffmpeg`](https://ffmpeg.org/download.html) installed: ``` apt get install ffmpeg ``` 3. Run the following Python code: ```py from audiocraft.models import MusicGen from audiocraft.data.audio import audio_write model = MusicGen.get_pretrained("large") model.set_generation_params(duration=8) # generate 8 seconds. descriptions = ["happy rock", "energetic EDM"] wav = model.generate(descriptions) # generates 2 samples. for idx, one_wav in enumerate(wav): # Will save under {idx}.wav, with loudness normalization at -14 db LUFS. audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness") ``` ## Model details Organization developing the model: The FAIR team of Meta AI. Model date: MusicGen was trained between April 2023 and May 2023. Model version: This is the version 1 of the model. Model type: MusicGen consists of an EnCodec model for audio tokenization, an auto-regressive language model based on the transformer architecture for music modeling. The model comes in different sizes: 300M, 1.5B and 3.3B parameters ; and two variants: a model trained for text-to-music generation task and a model trained for melody-guided music generation. Paper or resources for more information: More information can be found in the paper [Simple and Controllable Music Generation][https://arxiv.org/abs/2306.05284]. Citation details: ``` @misc{copet2023simple, title={Simple and Controllable Music Generation}, author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez}, year={2023}, eprint={2306.05284}, archivePrefix={arXiv}, primaryClass={cs.SD} } ``` License Code is released under MIT, model weights are released under CC-BY-NC 4.0. Where to send questions or comments about the model: Questions and comments about MusicGen can be sent via the [Github repository](https://github.com/facebookresearch/audiocraft) of the project, or by opening an issue. ## Intended use Primary intended use: The primary use of MusicGen is research on AI-based music generation, including: - Research efforts, such as probing and better understanding the limitations of generative models to further improve the state of science - Generation of music guided by text or melody to understand current abilities of generative AI models by machine learning amateurs Primary intended users: The primary intended users of the model are researchers in audio, machine learning and artificial intelligence, as well as amateur seeking to better understand those models. Out-of-scope use cases The model should not be used on downstream applications without further risk evaluation and mitigation. The model should not be used to intentionally create or disseminate music pieces that create hostile or alienating environments for people. This includes generating music that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. ## Metrics Models performance measures: We used the following objective measure to evaluate the model on a standard music benchmark: - Frechet Audio Distance computed on features extracted from a pre-trained audio classifier (VGGish) - Kullback-Leibler Divergence on label distributions extracted from a pre-trained audio classifier (PaSST) - CLAP Score between audio embedding and text embedding extracted from a pre-trained CLAP model Additionally, we run qualitative studies with human participants, evaluating the performance of the model with the following axes: - Overall quality of the music samples; - Text relevance to the provided text input; - Adherence to the melody for melody-guided music generation. More details on performance measures and human studies can be found in the paper. Decision thresholds: Not applicable. ## Evaluation datasets The model was evaluated on the [MusicCaps benchmark](https://www.kaggle.com/datasets/googleai/musiccaps) and on an in-domain held-out evaluation set, with no artist overlap with the training set. ## Training datasets The model was trained using the following sources: the [Meta Music Initiative Sound Collection](https://www.fb.com/sound), [Shutterstock music collection](https://www.shutterstock.com/music) and the [Pond5 music collection](https://www.pond5.com/). See the paper for more details about the training set and corresponding preprocessing. ## Quantitative analysis More information can be found in the paper [Simple and Controllable Music Generation][arxiv], in the Experimental Setup section. ## Limitations and biases Data: The data sources used to train the model are created by music professionals and covered by legal agreements with the right holders. The model is trained on 20K hours of data, we believe that scaling the model on larger datasets can further improve the performance of the model. Mitigations: All vocals have been removed from the data source using a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs). The model is therefore not able to produce vocals. Limitations: - The model is not able to generate realistic vocals. - The model has been trained with English descriptions and will not perform as well in other languages. - The model does not perform equally well for all music styles and cultures. - The model sometimes generates end of songs, collapsing to silence. - It is sometimes difficult to assess what types of text descriptions provide the best generations. Prompt engineering may be required to obtain satisfying results. Biases: The source of data is potentially lacking diversity and all music cultures are not equally represented in the dataset. The model may not perform equally well on the wide variety of music genres that exists. The generated samples from the model will reflect the biases from the training data. Further work on this model should include methods for balanced and just representations of cultures, for example, by scaling the training data to be both diverse and inclusive. Risks and harms: Biases and limitations of the model may lead to generation of samples that may be considered as biased, inappropriate or offensive. We believe that providing the code to reproduce the research and train new models will allow to broaden the application to new and more representative data. Use cases:** Users must be aware of the biases, limitations and risks of the model. MusicGen is a model developed for artificial intelligence research on controllable music generation. As such, it should not be used for downstream applications without further investigation and mitigation of risks.
Chat-Error/deberta-xlarge-reward	Chat-Error	2023-07-27T14:02:34Z	0	1	null	[ "tensorboard", "region:us" ]	null	2023-07-27T00:26:56Z	This is Deberta V2 xlarge trained on my https://huggingface.co/datasets/nRuaif/RLHF-hh dataset, using trl.
SigSegev/t5-large_PREFIX_TUNING_SEQ2SEQ_v2	SigSegev	2023-07-27T13:41:44Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-27T13:41:29Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
luodian/Flamingo-Llama2-Chat7B-CC3M	luodian	2023-07-27T13:34:33Z	4	10	transformers	[ "transformers", "pytorch", "flamingo", "text2text-generation", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-26T01:22:21Z	--- license: mit --- TLDR: We trained a Flamingo with Llama2-Chat7B as LLM on CC3M in less than 5 hours using just 4 A100s. The model showed promising zero-shot captioning skills. High-quality captioning data really helps fast alignment. You could test it via following code. Be sure to visit [Otter](https://github.com/Luodian/Otter) to get necessary Flamingo/Otter models. ```python from flamingo.modeling_flamingo import FlamingoForConditionalGeneration flamingo_model = FlamingoForConditionalGeneration.from_pretrained("luodian/Flamingo-Llama2-Chat7B-CC3M", device_map=auto) prompt = "<image>an image of" simple_prompt = "<image>" ```
SaferChat/falcon-7b-test	SaferChat	2023-07-27T13:33:36Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-27T13:19:45Z	--- 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
winterbro/distilbert-base-uncased-finetuned-cola	winterbro	2023-07-27T13:15:59Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-27T11:28:54Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5425688103069501 --- <!-- 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-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5259 - Matthews Correlation: 0.5426 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5221 \| 1.0 \| 535 \| 0.5361 \| 0.4307 \| \| 0.3492 \| 2.0 \| 1070 \| 0.5128 \| 0.4921 \| \| 0.2382 \| 3.0 \| 1605 \| 0.5259 \| 0.5426 \| \| 0.1758 \| 4.0 \| 2140 \| 0.7495 \| 0.5301 \| \| 0.1251 \| 5.0 \| 2675 \| 0.7982 \| 0.5414 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
undrwolf/Pyramid	undrwolf	2023-07-27T13:14:28Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-07-27T13:10:10Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: undrwolf/Pyramid 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Carloswear/llama2-qlora-finetunined-french	Carloswear	2023-07-27T13:12:23Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-27T13:12:18Z	--- 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
xinyangli/woman_photo	xinyangli	2023-07-27T13:07:00Z	0	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-07-27T12:41:48Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of a sks person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - xinyangli/woman_photo These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of a sks person using [DreamBooth](https://dreambooth.github.io/). 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) LoRA for the text encoder was enabled: False.
ditwoo/distilhubert-finetuned-gtzan	ditwoo	2023-07-27T13:04:50Z	161	1	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-06-25T19:25:16Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan 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. --> # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.9570 - Accuracy: 0.86 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.1586 \| 1.0 \| 112 \| 2.0855 \| 0.45 \| \| 1.4771 \| 2.0 \| 225 \| 1.3396 \| 0.72 \| \| 1.181 \| 3.0 \| 337 \| 0.9735 \| 0.76 \| \| 0.8133 \| 4.0 \| 450 \| 0.8692 \| 0.76 \| \| 0.5397 \| 5.0 \| 562 \| 0.7118 \| 0.81 \| \| 0.3424 \| 6.0 \| 675 \| 0.6237 \| 0.81 \| \| 0.2717 \| 7.0 \| 787 \| 0.6551 \| 0.83 \| \| 0.2653 \| 8.0 \| 900 \| 0.6707 \| 0.83 \| \| 0.0503 \| 9.0 \| 1012 \| 0.7025 \| 0.84 \| \| 0.0168 \| 10.0 \| 1125 \| 0.7643 \| 0.87 \| \| 0.1125 \| 11.0 \| 1237 \| 0.8550 \| 0.86 \| \| 0.155 \| 12.0 \| 1350 \| 0.9796 \| 0.82 \| \| 0.005 \| 13.0 \| 1462 \| 0.9539 \| 0.86 \| \| 0.0038 \| 14.0 \| 1575 \| 0.9206 \| 0.86 \| \| 0.0035 \| 15.0 \| 1687 \| 0.8725 \| 0.88 \| \| 0.051 \| 16.0 \| 1800 \| 0.9980 \| 0.86 \| \| 0.003 \| 17.0 \| 1912 \| 0.9579 \| 0.86 \| \| 0.0025 \| 18.0 \| 2025 \| 0.9735 \| 0.86 \| \| 0.0023 \| 19.0 \| 2137 \| 0.9589 \| 0.86 \| \| 0.0022 \| 19.91 \| 2240 \| 0.9570 \| 0.86 \| ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0 - Datasets 2.13.1 - Tokenizers 0.13.3
aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaalan/sbert_large_nlu_ru	aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaalan	2023-07-27T13:03:18Z	46	0	sentence-transformers	[ "sentence-transformers", "pytorch", "jax", "bert", "PyTorch", "Transformers", "ru", "region:us" ]	null	2023-07-27T09:07:35Z	--- library_name: sentence-transformers language: - ru tags: - PyTorch - Transformers --- # BERT large model (uncased) for Sentence Embeddings in Russian language. The model is described [in this article](https://habr.com/ru/company/sberdevices/blog/527576/) For better quality, use mean token embeddings. ## Usage (HuggingFace Models Repository) You can use the model directly from the model repository to compute sentence embeddings: ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1) sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9) return sum_embeddings / sum_mask #Sentences we want sentence embeddings for sentences = ['Привет! Как твои дела?', 'А правда, что 42 твое любимое число?'] #Load AutoModel from huggingface model repository tokenizer = AutoTokenizer.from_pretrained("sberbank-ai/sbert_large_nlu_ru") model = AutoModel.from_pretrained("sberbank-ai/sbert_large_nlu_ru") #Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=24, return_tensors='pt') #Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) #Perform pooling. In this case, mean pooling sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) ``` # Authors - [SberDevices](https://sberdevices.ru/) Team. - Denis Antykhov: [Github](https://github.com/gaphex); - Aleksandr Abramov: [Github](https://github.com/Ab1992ao), [Kaggle Competitions Master](https://www.kaggle.com/andrilko)
asenella/ms_MMVAEPlus_beta_5_scale_True_seed_2	asenella	2023-07-27T12:44:38Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-27T12:44:36Z	--- 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") ```
apple/coreml-stable-diffusion-xl-base	apple	2023-07-27T12:41:14Z	22	67	null	[ "coreml", "text-to-image", "stable-diffusion", "core-ml", "arxiv:2307.01952", "arxiv:2211.01324", "arxiv:2108.01073", "arxiv:2112.10752", "license:openrail++", "region:us" ]	text-to-image	2023-07-26T14:44:27Z	--- license: openrail++ tags: - text-to-image - stable-diffusion - core-ml --- # SD-XL 1.0-base Model Card (Core ML) This model was generated by Hugging Face using [Apple’s repository](https://github.com/apple/ml-stable-diffusion) which has [ASCL](https://github.com/apple/ml-stable-diffusion/blob/main/LICENSE.md). This version contains Core ML weights with the `ORIGINAL` attention implementation, suitable for running on macOS GPUs. The Core ML weights are also distributed as a zip archive for use in the [Hugging Face demo app](https://github.com/huggingface/swift-coreml-diffusers) and other third party tools. The zip archive was created from the contents of the `original/compiled` folder in this repo. Please, refer to https://huggingface.co/blog/diffusers-coreml for details. The remaining contents of this model card were copied from the [original repo](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) ![row01](01.png) ## Model ![pipeline](pipeline.png) [SDXL](https://arxiv.org/abs/2307.01952) consists of an [ensemble of experts](https://arxiv.org/abs/2211.01324) pipeline for latent diffusion: In a first step, the base model is used to generate (noisy) latents, which are then further processed with a refinement model (available here: https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/) specialized for the final denoising steps. Note that the base model can be used as a standalone module. Alternatively, we can use a two-stage pipeline as follows: First, the base model is used to generate latents of the desired output size. In the second step, we use a specialized high-resolution model and apply a technique called SDEdit (https://arxiv.org/abs/2108.01073, also known as "img2img") to the latents generated in the first step, using the same prompt. This technique is slightly slower than the first one, as it requires more function evaluations. Source code is available at https://github.com/Stability-AI/generative-models . ### Model Description - Developed by: Stability AI - Model type: Diffusion-based text-to-image generative model - License: [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md) - Model Description: This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses two fixed, pretrained text encoders ([OpenCLIP-ViT/G](https://github.com/mlfoundations/open_clip) and [CLIP-ViT/L](https://github.com/openai/CLIP/tree/main)). - Resources for more information: Check out our [GitHub Repository](https://github.com/Stability-AI/generative-models) and the [SDXL report on arXiv](https://arxiv.org/abs/2307.01952). ### Model Sources For research purposes, we recommned our `generative-models` Github repository (https://github.com/Stability-AI/generative-models), which implements the most popoular diffusion frameworks (both training and inference) and for which new functionalities like distillation will be added over time. [Clipdrop](https://clipdrop.co/stable-diffusion) provides free SDXL inference. - Repository: https://github.com/Stability-AI/generative-models - Demo: https://clipdrop.co/stable-diffusion ## Evaluation ![comparison](comparison.png) The chart above evaluates user preference for SDXL (with and without refinement) over SDXL 0.9 and Stable Diffusion 1.5 and 2.1. The SDXL base model performs significantly better than the previous variants, and the model combined with the refinement module achieves the best overall performance. ### 🧨 Diffusers Make sure to upgrade diffusers to >= 0.18.0: ``` pip install diffusers --upgrade ``` In addition make sure to install `transformers`, `safetensors`, `accelerate` as well as the invisible watermark: ``` pip install invisible_watermark transformers accelerate safetensors ``` You can use the model then as follows ```py from diffusers import DiffusionPipeline import torch pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16") pipe.to("cuda") # if using torch < 2.0 # pipe.enable_xformers_memory_efficient_attention() prompt = "An astronaut riding a green horse" images = pipe(prompt=prompt).images[0] ``` When using `torch >= 2.0`, you can improve the inference speed by 20-30% with torch.compile. Simple wrap the unet with torch compile before running the pipeline: ```py pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) ``` If you are limited by GPU VRAM, you can enable cpu offloading by calling `pipe.enable_model_cpu_offload` instead of `.to("cuda")`: ```diff - pipe.to("cuda") + pipe.enable_model_cpu_offload() ``` ## Uses ### Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. Excluded uses are described below. ### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model struggles with more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The autoencoding part of the model is lossy. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
bwilkie/bwilkie-whisper-small-dv	bwilkie	2023-07-27T12:32:54Z	84	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-07-27T09:25:35Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: bwilkie-whisper-small-dv results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: all split: None metrics: - name: Wer type: wer value: 0.23270055113288426 --- <!-- 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. --> # bwilkie-whisper-small-dv This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.7358 - Wer Ortho: 0.2389 - Wer: 0.2327 ## 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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\| \| 0.0001 \| 17.86 \| 500 \| 0.7358 \| 0.2389 \| 0.2327 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
asenella/ms_MMVAEPlus_beta_25_scale_True_seed_1	asenella	2023-07-27T12:28:12Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-27T12:28:10Z	--- 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") ```
annishaa/my_awesome_eli5_clm-model-2	annishaa	2023-07-27T12:26:30Z	226	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-27T11:17:11Z	--- license: apache-2.0 base_model: distilgpt2 tags: - generated_from_trainer model-index: - name: my_awesome_eli5_clm-model-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. --> # my_awesome_eli5_clm-model-2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7158 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.7157 \| 1.0 \| 1128 \| 3.7215 \| \| 3.6465 \| 2.0 \| 2256 \| 3.7161 \| \| 3.623 \| 3.0 \| 3384 \| 3.7158 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
asenella/ms_MMVAEPlus_beta_5_scale_True_seed_0	asenella	2023-07-27T12:17:13Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-27T12:17:11Z	--- 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") ```
rehanhaider/DBSD-1.5-9-vectors-lr-5e-6	rehanhaider	2023-07-27T12:17:03Z	0	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:runwayml/stable-diffusion-v1-5", "base_model:finetune:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-27T11:59:35Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: in the style of wlat_mntn tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - rehanhaider/DBSD-1.5-9-vectors-lr-5e-6 This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on in the style of wlat_mntn using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
asenella/ms_MMVAEPlus_beta_10_scale_False_seed_3	asenella	2023-07-27T12:15:36Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-27T12:15:34Z	--- 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") ```
AnushaPalle/my_awesome_eli5_clm-model	AnushaPalle	2023-07-27T12:09:17Z	226	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-27T11:05:43Z	--- license: apache-2.0 base_model: distilgpt2 tags: - generated_from_trainer model-index: - name: my_awesome_eli5_clm-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7490 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.8717 \| 1.0 \| 1113 \| 3.7653 \| \| 3.7754 \| 2.0 \| 2226 \| 3.7524 \| \| 3.7318 \| 3.0 \| 3339 \| 3.7490 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
asenella/ms_MMVAEPlus_beta_5_scale_False_seed_1	asenella	2023-07-27T12:05:36Z	0	1	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-27T12:05:35Z	--- 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") ```
asenella/ms_MMVAEPlus_beta_25_scale_True_seed_0	asenella	2023-07-27T12:05:35Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-27T12:05:33Z	--- 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") ```
asenella/ms_MMVAEPlus_beta_25_scale_False_seed_2	asenella	2023-07-27T12:05:32Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-27T12:05:30Z	--- 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") ```
alesanm/blip-image-captioning-base-fashionimages-finetuned	alesanm	2023-07-27T12:05:03Z	140	1	transformers	[ "transformers", "pytorch", "blip", "image-text-to-text", "image-to-text", "dataset:alesanm/balenciaga_short_descriptions", "region:us" ]	image-to-text	2023-07-24T11:00:40Z	--- inference: False datasets: - alesanm/balenciaga_short_descriptions library_name: transformers pipeline_tag: image-to-text --- The BLIP model was trained on 141 photos of the Balenciaga fashion brand and descriptions produced by GPT3
asenella/ms_MMVAEPlus_beta_10_scale_True_seed_3	asenella	2023-07-27T12:00:00Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-27T11:59:58Z	--- 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") ```
YanJiangJerry/baselineberttweetlarge	YanJiangJerry	2023-07-27T11:59:35Z	113	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "base_model:vinai/bertweet-large", "base_model:finetune:vinai/bertweet-large", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-27T07:55:47Z	--- base_model: vinai/bertweet-large tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: baselineberttweetlarge 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. --> # baselineberttweetlarge This model is a fine-tuned version of [vinai/bertweet-large](https://huggingface.co/vinai/bertweet-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6168 - Accuracy: 0.6274 - F1: 0.0 - Precision: 0.0 - Recall: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---:\|:---------:\|:------:\| \| 0.6062 \| 1.0 \| 788 \| 0.6020 \| 0.6274 \| 0.0 \| 0.0 \| 0.0 \| \| 0.5852 \| 2.0 \| 1576 \| 0.6168 \| 0.6274 \| 0.0 \| 0.0 \| 0.0 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
asenella/ms_MMVAEPlus_beta_5_scale_False_seed_0	asenella	2023-07-27T11:59:17Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-27T11:59:15Z	--- 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") ```
dhinman/Reinforce-Pixelcopter-200000	dhinman	2023-07-27T11:58:35Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-27T11:58:23Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-200000 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 182.70 +/- 200.09 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
snob/TagMyBookmark-KoAlpaca-QLoRA-v1.0_ALLDATA	snob	2023-07-27T11:58:28Z	3	0	peft	[ "peft", "region:us" ]	null	2023-07-27T11:58:20Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
asenella/ms_MMVAEPlus_beta_5_scale_True_seed_3	asenella	2023-07-27T11:58:19Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-27T11:58:17Z	--- 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") ```
asenella/ms_MMVAEPlus_beta_10_scale_True_seed_0	asenella	2023-07-27T11:53:07Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-27T11:53:05Z	--- 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") ```
asenella/ms_MMVAEPlus_beta_25_scale_True_seed_2	asenella	2023-07-27T11:52:01Z	0	0	null	[ "multivae", "en", "license:apache-2.0", "region:us" ]	null	2023-07-27T11:52:00Z	--- 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") ```
Chat-Error/Kimiko_7B	Chat-Error	2023-07-27T11:50:53Z	0	15	null	[ "arxiv:1910.09700", "region:us" ]	null	2023-07-26T14:59:07Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Kimiko_7B <!-- Provide a quick summary of what the model is/does. --> This is my new Kimiko models, trained with LLaMA2 for...purpose ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: nRuaif - Model type: Decoder only - License: CC BY-NC-SA - Finetuned from model [optional]: LLaMA2 ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/OpenAccess-AI-Collective/axolotl [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> This model is trained on 3k examples of instructions dataset, high quality roleplay, for best result follow this format ``` <<HUMAN>> How to do abc <<AIBOT>> Here is how Or with system prompting for roleplay <<SYSTEM>> A's Persona: B's Persona: Scenario: Add some instruction here on how you want your RP to go. ``` ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> All bias of this model come from LLaMA2 with an exception of NSFW bias..... ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> 3000 examples from LIMAERP, LIMA and I sample 1000 good instruction from Airboro ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> Model is trained with 1 L4 from GCP costing a whooping 1.5USD #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> 3 epochs with 0.0002 lr, full 4096 ctx token, LoRA #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> It takes 8 hours to train this model with xformers enable [More Information Needed] [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: L4 with 12CPUs 48gb ram - Hours used: 8 - Cloud Provider: GCP - Compute Region: US - Carbon Emitted: 0.2KG
MheniDevs/Kinyarwanda	MheniDevs	2023-07-27T11:43:04Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-07-24T02:16:52Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-kinyarwanda 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-xls-r-300m-kinyarwanda 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 dataset. It achieves the following results on the evaluation set: - Loss: 0.3917 - Wer: 0.3246 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 9.0634 \| 0.12 \| 400 \| 3.0554 \| 1.0 \| \| 2.8009 \| 0.24 \| 800 \| 1.5927 \| 0.9554 \| \| 0.9022 \| 0.36 \| 1200 \| 0.7328 \| 0.6445 \| \| 0.6213 \| 0.48 \| 1600 \| 0.6138 \| 0.5510 \| \| 0.5299 \| 0.6 \| 2000 \| 0.6072 \| 0.5223 \| \| 0.4999 \| 0.72 \| 2400 \| 0.5449 \| 0.4969 \| \| 0.4731 \| 0.84 \| 2800 \| 0.5261 \| 0.4828 \| \| 0.458 \| 0.96 \| 3200 \| 0.5058 \| 0.4607 \| \| 0.4158 \| 1.09 \| 3600 \| 0.4892 \| 0.4463 \| \| 0.4037 \| 1.21 \| 4000 \| 0.4759 \| 0.4429 \| \| 0.4021 \| 1.33 \| 4400 \| 0.4615 \| 0.4330 \| \| 0.3934 \| 1.45 \| 4800 \| 0.4593 \| 0.4315 \| \| 0.3808 \| 1.57 \| 5200 \| 0.4736 \| 0.4344 \| \| 0.3838 \| 1.69 \| 5600 \| 0.4569 \| 0.4249 \| \| 0.3726 \| 1.81 \| 6000 \| 0.4473 \| 0.4140 \| \| 0.3623 \| 1.93 \| 6400 \| 0.4403 \| 0.4097 \| \| 0.3517 \| 2.05 \| 6800 \| 0.4389 \| 0.4061 \| \| 0.333 \| 2.17 \| 7200 \| 0.4383 \| 0.4104 \| \| 0.3354 \| 2.29 \| 7600 \| 0.4360 \| 0.3955 \| \| 0.3257 \| 2.41 \| 8000 \| 0.4226 \| 0.3942 \| \| 0.3275 \| 2.53 \| 8400 \| 0.4206 \| 0.4040 \| \| 0.3262 \| 2.65 \| 8800 \| 0.4172 \| 0.3875 \| \| 0.3206 \| 2.77 \| 9200 \| 0.4209 \| 0.3877 \| \| 0.323 \| 2.89 \| 9600 \| 0.4177 \| 0.3825 \| \| 0.3099 \| 3.01 \| 10000 \| 0.4101 \| 0.3691 \| \| 0.3008 \| 3.14 \| 10400 \| 0.4055 \| 0.3709 \| \| 0.2918 \| 3.26 \| 10800 \| 0.4085 \| 0.3800 \| \| 0.292 \| 3.38 \| 11200 \| 0.4089 \| 0.3713 \| \| 0.292 \| 3.5 \| 11600 \| 0.4092 \| 0.3730 \| \| 0.2785 \| 3.62 \| 12000 \| 0.4151 \| 0.3687 \| \| 0.2941 \| 3.74 \| 12400 \| 0.4004 \| 0.3639 \| \| 0.2838 \| 3.86 \| 12800 \| 0.4108 \| 0.3703 \| \| 0.2854 \| 3.98 \| 13200 \| 0.3911 \| 0.3596 \| \| 0.2683 \| 4.1 \| 13600 \| 0.3944 \| 0.3575 \| \| 0.2647 \| 4.22 \| 14000 \| 0.3836 \| 0.3538 \| \| 0.2704 \| 4.34 \| 14400 \| 0.4006 \| 0.3540 \| \| 0.2664 \| 4.46 \| 14800 \| 0.3974 \| 0.3553 \| \| 0.2662 \| 4.58 \| 15200 \| 0.3890 \| 0.3470 \| \| 0.2615 \| 4.7 \| 15600 \| 0.3856 \| 0.3507 \| \| 0.2553 \| 4.82 \| 16000 \| 0.3814 \| 0.3497 \| \| 0.2587 \| 4.94 \| 16400 \| 0.3837 \| 0.3440 \| \| 0.2522 \| 5.06 \| 16800 \| 0.3834 \| 0.3486 \| \| 0.2451 \| 5.19 \| 17200 \| 0.3897 \| 0.3414 \| \| 0.2423 \| 5.31 \| 17600 \| 0.3864 \| 0.3481 \| \| 0.2434 \| 5.43 \| 18000 \| 0.3808 \| 0.3416 \| \| 0.2525 \| 5.55 \| 18400 \| 0.3795 \| 0.3408 \| \| 0.2427 \| 5.67 \| 18800 \| 0.3841 \| 0.3411 \| \| 0.2411 \| 5.79 \| 19200 \| 0.3804 \| 0.3366 \| \| 0.2404 \| 5.91 \| 19600 \| 0.3800 \| 0.3328 \| \| 0.2372 \| 6.03 \| 20000 \| 0.3749 \| 0.3335 \| \| 0.2244 \| 6.15 \| 20400 \| 0.3820 \| 0.3327 \| \| 0.2381 \| 6.27 \| 20800 \| 0.3789 \| 0.3325 \| \| 0.2294 \| 6.39 \| 21200 \| 0.3867 \| 0.3298 \| \| 0.2378 \| 6.51 \| 21600 \| 0.3843 \| 0.3281 \| \| 0.2312 \| 6.63 \| 22000 \| 0.3813 \| 0.3277 \| \| 0.2411 \| 6.75 \| 22400 \| 0.3780 \| 0.3268 \| \| 0.2315 \| 6.87 \| 22800 \| 0.3790 \| 0.3280 \| \| 0.241 \| 6.99 \| 23200 \| 0.3776 \| 0.3281 \| \| 0.2313 \| 7.11 \| 23600 \| 0.3929 \| 0.3283 \| \| 0.2423 \| 7.24 \| 24000 \| 0.3905 \| 0.3280 \| \| 0.2337 \| 7.36 \| 24400 \| 0.3979 \| 0.3249 \| \| 0.2368 \| 7.48 \| 24800 \| 0.3980 \| 0.3257 \| \| 0.2409 \| 7.6 \| 25200 \| 0.3937 \| 0.3229 \| \| 0.2416 \| 7.72 \| 25600 \| 0.3867 \| 0.3237 \| \| 0.2364 \| 7.84 \| 26000 \| 0.3912 \| 0.3253 \| \| 0.234 \| 7.96 \| 26400 \| 0.3917 \| 0.3246 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
google/flan-t5-xxl	google	2023-07-27T11:42:14Z	724,370	1,229	transformers	[ "transformers", "pytorch", "tf", "jax", "safetensors", "t5", "text2text-generation", "en", "fr", "ro", "de", "multilingual", "dataset:svakulenk0/qrecc", "dataset:taskmaster2", "dataset:djaym7/wiki_dialog", "dataset:deepmind/code_contests", "dataset:lambada", "dataset:gsm8k", "dataset:aqua_rat", "dataset:esnli", "dataset:quasc", "dataset:qed", "arxiv:2210.11416", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-10-21T15:54:59Z	--- language: - en - fr - ro - de - multilingual widget: - text: "Translate to German: My name is Arthur" example_title: "Translation" - text: "Please answer to the following question. Who is going to be the next Ballon d'or?" example_title: "Question Answering" - text: "Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering." example_title: "Logical reasoning" - text: "Please answer the following question. What is the boiling point of Nitrogen?" example_title: "Scientific knowledge" - text: "Answer the following yes/no question. Can you write a whole Haiku in a single tweet?" example_title: "Yes/no question" - text: "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?" example_title: "Reasoning task" - text: "Q: ( False or not False or False ) is? A: Let's think step by step" example_title: "Boolean Expressions" - text: "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?" example_title: "Math reasoning" - text: "Premise: At my age you will probably have learnt one lesson. Hypothesis: It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?" example_title: "Premise and hypothesis" tags: - text2text-generation datasets: - svakulenk0/qrecc - taskmaster2 - djaym7/wiki_dialog - deepmind/code_contests - lambada - gsm8k - aqua_rat - esnli - quasc - qed license: apache-2.0 --- # Model Card for FLAN-T5 XXL <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/flan2_architecture.jpg" alt="drawing" width="600"/> # Table of Contents 0. [TL;DR](#TL;DR) 1. [Model Details](#model-details) 2. [Usage](#usage) 3. [Uses](#uses) 4. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 5. [Training Details](#training-details) 6. [Evaluation](#evaluation) 7. [Environmental Impact](#environmental-impact) 8. [Citation](#citation) # TL;DR If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages. As mentioned in the first few lines of the abstract : > Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints,1 which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models. Disclaimer: Content from this model card has been written by the Hugging Face team, and parts of it were copy pasted from the [T5 model card](https://huggingface.co/t5-large). # Model Details ## Model Description - Model type: Language model - Language(s) (NLP): English, German, French - License: Apache 2.0 - Related Models: [All FLAN-T5 Checkpoints](https://huggingface.co/models?search=flan-t5) - Original Checkpoints: [All Original FLAN-T5 Checkpoints](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-t5-checkpoints) - Resources for more information: - [Research paper](https://arxiv.org/pdf/2210.11416.pdf) - [GitHub Repo](https://github.com/google-research/t5x) - [Hugging Face FLAN-T5 Docs (Similar to T5) ](https://huggingface.co/docs/transformers/model_doc/t5) # Usage Find below some example scripts on how to use the model in `transformers`: ## Using the Pytorch model ### Running the model on a CPU <details> <summary> Click to expand </summary> ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xxl") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xxl") input_text = "translate English to German: How old are you?" input_ids = tokenizer(input_text, return_tensors="pt").input_ids outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> ### Running the model on a GPU <details> <summary> Click to expand </summary> ```python # pip install accelerate from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xxl") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xxl", device_map="auto") input_text = "translate English to German: How old are you?" input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> ### Running the model on a GPU using different precisions #### FP16 <details> <summary> Click to expand </summary> ```python # pip install accelerate import torch from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xxl") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xxl", device_map="auto", torch_dtype=torch.float16) input_text = "translate English to German: How old are you?" input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> #### INT8 <details> <summary> Click to expand </summary> ```python # pip install bitsandbytes accelerate from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xxl") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xxl", device_map="auto", load_in_8bit=True) input_text = "translate English to German: How old are you?" input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> # Uses ## Direct Use and Downstream Use The authors write in [the original paper's model card](https://arxiv.org/pdf/2210.11416.pdf) that: > The primary use is research on language models, including: research on zero-shot NLP tasks and in-context few-shot learning NLP tasks, such as reasoning, and question answering; advancing fairness and safety research, and understanding limitations of current large language models See the [research paper](https://arxiv.org/pdf/2210.11416.pdf) for further details. ## Out-of-Scope Use More information needed. # Bias, Risks, and Limitations The information below in this section are copied from the model's [official model card](https://arxiv.org/pdf/2210.11416.pdf): > Language models, including Flan-T5, can potentially be used for language generation in a harmful way, according to Rae et al. (2021). Flan-T5 should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application. ## Ethical considerations and risks > Flan-T5 is fine-tuned on a large corpus of text data that was not filtered for explicit content or assessed for existing biases. As a result the model itself is potentially vulnerable to generating equivalently inappropriate content or replicating inherent biases in the underlying data. ## Known Limitations > Flan-T5 has not been tested in real world applications. ## Sensitive Use: > Flan-T5 should not be applied for any unacceptable use cases, e.g., generation of abusive speech. # Training Details ## Training Data The model was trained on a mixture of tasks, that includes the tasks described in the table below (from the original paper, figure 2): ![table.png](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/flan_t5_tasks.png) ## Training Procedure According to the model card from the [original paper](https://arxiv.org/pdf/2210.11416.pdf): > These models are based on pretrained T5 (Raffel et al., 2020) and fine-tuned with instructions for better zero-shot and few-shot performance. There is one fine-tuned Flan model per T5 model size. The model has been trained on TPU v3 or TPU v4 pods, using [`t5x`](https://github.com/google-research/t5x) codebase together with [`jax`](https://github.com/google/jax). # Evaluation ## Testing Data, Factors & Metrics The authors evaluated the model on various tasks covering several languages (1836 in total). See the table below for some quantitative evaluation: ![image.png](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/flan_t5_evals_lang.png) For full details, please check the [research paper](https://arxiv.org/pdf/2210.11416.pdf). ## Results For full results for FLAN-T5-XXL, see the [research paper](https://arxiv.org/pdf/2210.11416.pdf), Table 3. # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: Google Cloud TPU Pods - TPU v3 or TPU v4 \| Number of chips ≥ 4. - Hours used: More information needed - Cloud Provider: GCP - Compute Region: More information needed - Carbon Emitted: More information needed # Citation BibTeX: ```bibtex @misc{https://doi.org/10.48550/arxiv.2210.11416, doi = {10.48550/ARXIV.2210.11416}, url = {https://arxiv.org/abs/2210.11416}, author = {Chung, Hyung Won and Hou, Le and Longpre, Shayne and Zoph, Barret and Tay, Yi and Fedus, William and Li, Eric and Wang, Xuezhi and Dehghani, Mostafa and Brahma, Siddhartha and Webson, Albert and Gu, Shixiang Shane and Dai, Zhuyun and Suzgun, Mirac and Chen, Xinyun and Chowdhery, Aakanksha and Narang, Sharan and Mishra, Gaurav and Yu, Adams and Zhao, Vincent and Huang, Yanping and Dai, Andrew and Yu, Hongkun and Petrov, Slav and Chi, Ed H. and Dean, Jeff and Devlin, Jacob and Roberts, Adam and Zhou, Denny and Le, Quoc V. and Wei, Jason}, keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Scaling Instruction-Finetuned Language Models}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

ksaw39/ksawmegga

ksaw39

2023-07-27T22:11:13Z

0

keras

[ "keras", "reinforcement-learning", "en", "region:us" ]

reinforcement-learning

2023-07-27T22:08:14Z

--- language: - en metrics: - accuracy - code_eval library_name: keras pipeline_tag: reinforcement-learning ---

NasimB/aochildes-cbt-log-rarity

NasimB

2023-07-27T21:46:57Z

5

0

transformers

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

text-generation

2023-07-27T19:38:45Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: aochildes-cbt-log-rarity results: [] ---  # aochildes-cbt-log-rarity This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.1483 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.3649 | 0.29 | 500 | 5.3433 | | 5.0506 | 0.59 | 1000 | 4.9337 | | 4.7079 | 0.88 | 1500 | 4.6957 | | 4.4512 | 1.17 | 2000 | 4.5593 | | 4.3031 | 1.47 | 2500 | 4.4458 | | 4.2085 | 1.76 | 3000 | 4.3418 | | 4.0809 | 2.05 | 3500 | 4.2739 | | 3.9047 | 2.35 | 4000 | 4.2277 | | 3.8846 | 2.64 | 4500 | 4.1774 | | 3.8392 | 2.93 | 5000 | 4.1313 | | 3.6392 | 3.23 | 5500 | 4.1305 | | 3.6016 | 3.52 | 6000 | 4.1020 | | 3.5828 | 3.81 | 6500 | 4.0709 | | 3.4733 | 4.11 | 7000 | 4.0797 | | 3.3271 | 4.4 | 7500 | 4.0758 | | 3.3228 | 4.69 | 8000 | 4.0635 | | 3.3147 | 4.99 | 8500 | 4.0528 | | 3.154 | 5.28 | 9000 | 4.0692 | | 3.1461 | 5.58 | 9500 | 4.0692 | | 3.1416 | 5.87 | 10000 | 4.0684 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

jariasn/q-Taxi-v3

jariasn

2023-07-27T21:32:51Z

0

null

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

reinforcement-learning

2023-07-27T21:32:49Z

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

sghirardelli/vit-base-patch16-224-rgbd1k2

sghirardelli

2023-07-27T21:26:49Z

65

0

transformers

[ "transformers", "tf", "tensorboard", "vit", "image-classification", "generated_from_keras_callback", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-21T21:15:59Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_keras_callback model-index: - name: sghirardelli/vit-base-patch16-224-rgbd1k2 results: [] ---  # sghirardelli/vit-base-patch16-224-rgbd1k2 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.9711 - Train Accuracy: 0.4384 - Train Top-3-accuracy: 0.6297 - Validation Loss: 0.2537 - Validation Accuracy: 0.9323 - Validation Top-3-accuracy: 0.9940 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.002, 'decay_steps': 544, '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 | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch | |:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:| | 1.9711 | 0.4384 | 0.6297 | 0.2537 | 0.9323 | 0.9940 | 0 | ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.1 - Tokenizers 0.13.3

vivianchen98/distilbert-base-uncased-finetuned-cola

vivianchen98

2023-07-27T21:06:15Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "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

2023-07-27T19:48:05Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5317477654019562 ---  # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8526 - Matthews Correlation: 0.5317 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5224 | 1.0 | 535 | 0.4742 | 0.4397 | | 0.3484 | 2.0 | 1070 | 0.5877 | 0.4558 | | 0.2357 | 3.0 | 1605 | 0.6307 | 0.5301 | | 0.1668 | 4.0 | 2140 | 0.7054 | 0.5288 | | 0.1218 | 5.0 | 2675 | 0.8526 | 0.5317 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

EulerianKnight/LunarLander-v2-unit1

EulerianKnight

2023-07-27T21:00:43Z

0

stable-baselines3

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

reinforcement-learning

2023-07-27T21:00:27Z

--- 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: 236.62 +/- 48.16 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

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

jariasn

2023-07-27T20:58:10Z

0

null

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

reinforcement-learning

2023-07-27T20:58:08Z

--- 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 ```python model = load_from_hub(repo_id="jariasn/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"]) ```

YoonSeul/LawBot-5.8B

YoonSeul

2023-07-27T20:40:46Z

4

0

peft

[ "peft", "region:us" ]

null

2023-07-26T07:47:12Z

--- library_name: peft --- <img src=https://github.com/taemin6697/Paper_Review/assets/96530685/54ecd6cf-8695-4caa-bdc8-fb85c9b7d70d style="max-width: 700px; width: 100%" /> ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0

dgergherherherhererher/dfsgs

dgergherherherhererher

2023-07-27T20:37:27Z

0

sklearn

[ "sklearn", "sports", "tabular-classification", "en", "dataset:knkarthick/dialogsum", "license:openrail", "region:us" ]

tabular-classification

2023-07-27T17:50:51Z

--- language: - en metrics: - accuracy library_name: sklearn pipeline_tag: tabular-classification license: openrail datasets: - knkarthick/dialogsum tags: - sports ---

NicolasDenier/speecht5-finetuned-voxpopuli-sl

NicolasDenier

2023-07-27T20:21:31Z

89

0

transformers

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

text-to-speech

2023-07-27T17:17:36Z

--- language: - sl license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer - text-to-speech datasets: - facebook/voxpopuli model-index: - name: speecht5-finetuned-voxpopuli-sl results: - task: name: Text to Speech type: text-to-speech dataset: name: Voxpopuli type: facebook/voxpopuli config: sl split: train args: all metrics: - name: Loss type: loss value: 0.4546 ---  # speecht5-finetuned-voxpopuli-sl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the Voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4546 ## 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: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.4942 | 21.68 | 1000 | 0.4567 | | 0.4698 | 43.36 | 2000 | 0.4544 | | 0.4615 | 65.04 | 3000 | 0.4541 | | 0.462 | 86.72 | 4000 | 0.4546 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

patonw/q-Taxi-v3

patonw

2023-07-27T20:16:02Z

0

null

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

reinforcement-learning

2023-07-27T20:16:00Z

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

Ronan14232/Omar

Ronan14232

2023-07-27T20:12:29Z

0

null

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

null

2023-07-27T20:12:29Z

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

Jonathaniu/llama2-breast-cancer-13b-knowledge-epoch-8

Jonathaniu

2023-07-27T20:09:53Z

0

peft

[ "peft", "region:us" ]

null

2023-07-27T20:09:32Z

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

ianvaz/llama2-qlora-finetunined-french

ianvaz

2023-07-27T20:00:58Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-27T20:00:54Z

--- 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

Za88yes/Ocha

Za88yes

2023-07-27T19:56:37Z

0

null

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

null

2023-07-27T12:21:45Z

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

grace-pro/three_class_5e-5_hausa

grace-pro

2023-07-27T19:48:09Z

117

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:Davlan/afro-xlmr-base", "base_model:finetune:Davlan/afro-xlmr-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-27T18:28:05Z

--- license: mit base_model: Davlan/afro-xlmr-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: three_class_5e-5_hausa results: [] ---  # three_class_5e-5_hausa This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2379 - Precision: 0.2316 - Recall: 0.1636 - F1: 0.1917 - Accuracy: 0.9392 ## 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: 8 - 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2129 | 1.0 | 1283 | 0.2033 | 0.2278 | 0.0810 | 0.1195 | 0.9416 | | 0.1901 | 2.0 | 2566 | 0.1988 | 0.2444 | 0.0890 | 0.1305 | 0.9429 | | 0.1657 | 3.0 | 3849 | 0.2056 | 0.2561 | 0.1278 | 0.1705 | 0.9430 | | 0.139 | 4.0 | 5132 | 0.2205 | 0.2269 | 0.1655 | 0.1914 | 0.9388 | | 0.1179 | 5.0 | 6415 | 0.2379 | 0.2316 | 0.1636 | 0.1917 | 0.9392 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

asenella/MMVAEPlus_beta_10_scale_False_seed_3

asenella

2023-07-27T19:43:04Z

0

null

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

null

2023-07-27T19:42:50Z

--- 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") ```

tommilyjones/swin-tiny-patch4-window7-224-cats_dogs

tommilyjones

2023-07-27T19:38:02Z

204

0

transformers

[ "transformers", "pytorch", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-27T19:31:44Z

--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-cats_dogs results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.9973147153598282 ---  # swin-tiny-patch4-window7-224-cats_dogs This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0126 - Accuracy: 0.9973 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0832 | 0.98 | 47 | 0.0235 | 0.9909 | | 0.0788 | 1.99 | 95 | 0.0126 | 0.9973 | | 0.0534 | 2.95 | 141 | 0.0127 | 0.9957 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

AdiOO7/llama-2-7B-finetuned

AdiOO7

2023-07-27T19:34:23Z

0

peft

[ "peft", "region:us" ]

null

2023-07-27T19:34:20Z

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

DunnBC22/mit-b0-Image_segmentation_Dominoes_v2

DunnBC22

2023-07-27T19:26:06Z

0

1

null

[ "pytorch", "tensorboard", "generated_from_trainer", "image-segmentation", "en", "dataset:adelavega/dominoes_raw", "license:other", "region:us" ]

image-segmentation

2023-07-26T21:13:35Z

--- license: other tags: - generated_from_trainer model-index: - name: mit-b0-Image_segmentation_Dominoes_v2 results: [] datasets: - adelavega/dominoes_raw language: - en metrics: - mean_iou pipeline_tag: image-segmentation --- # mit-b0-Image_segmentation_Dominoes_v2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0). It achieves the following results on the evaluation set: - Loss: 0.1149 - Mean Iou: 0.9198 - Mean Accuracy: 0.9515 - Overall Accuracy: 0.9778 - Per Category Iou: - Segment 0: 0.974110559111975 - Segment 1: 0.8655745252092782 - Per Category Accuracy - Segment 0: 0.9897833441005461 - Segment 1: 0.913253525550903 ## Model description For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Segmentation/Dominoes/Fine-Tuning%20-%20Dominoes%20-%20Image%20Segmentation%20with%20LoRA.ipynb ## Intended uses & limitations This model is intended to demonstrate my ability to solve a complex problem using technology. ## Training and evaluation data Dataset Source: https://huggingface.co/datasets/adelavega/dominoes_raw ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou Segment 0 | Per Category Iou Segment 1 | Per Category Accuracy Segment 0 | Per Category Accuracy Segment 1| |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------------:|:---------------------:|:-----------------:| | 0.0461 | 1.0 | 86 | 0.1233 | 0.9150 | 0.9527 | 0.9762 | 0.9721967854031923 | 0.8578619172251059 | 0.9869082633464498 | 0.9184139264010376 | | 0.0708 | 2.0 | 172 | 0.1366 | 0.9172 | 0.9490 | 0.9771 | 0.9732821853093164 | 0.8611008788165083 | 0.9898473600751747 | 0.9082362492748777 | | 0.048 | 3.0 | 258 | 0.1260 | 0.9199 | 0.9534 | 0.9777 | 0.9740118174014271 | 0.8658241844233872 | 0.9888392553004053 | 0.9179240730467295 | | 0.0535 | 4.0 | 344 | 0.1184 | 0.9200 | 0.9520 | 0.9778 | 0.974142444792198 | 0.8658711064023369 | 0.9896291184589182 | 0.9142864290038782 | | 0.0185 | 5.0 | 430 | 0.1296 | 0.9182 | 0.9477 | 0.9775 | 0.9737715695013129 | 0.8627108292167807 | 0.9910418746696423 | 0.904378218719681 | | 0.036 | 6.0 | 516 | 0.1410 | 0.9213 | 0.9538 | 0.9782 | 0.9745002408443008 | 0.8680673581922554 | 0.9892677512186527 | 0.9182967669045321 | | 0.0376 | 7.0 | 602 | 0.1451 | 0.9206 | 0.9550 | 0.9779 | 0.9741455743906073 | 0.8669703237367214 | 0.9883004639689904 | 0.9216576612178001 | | 0.0186 | 8.0 | 688 | 0.1380 | 0.9175 | 0.9496 | 0.9772 | 0.9733616852468584 | 0.8616466350192237 | 0.9897043519116697 | 0.9094762400541087 | | 0.0162 | 9.0 | 774 | 0.1459 | 0.9218 | 0.9539 | 0.9783 | 0.9746840649852051 | 0.8688930149000804 | 0.989455276913138 | 0.9182917005479264 | | 0.0169 | 10.0 | 860 | 0.1467 | 0.9191 | 0.9502 | 0.9776 | 0.9739086600912814 | 0.8642187978193332 | 0.9901195747929759 | 0.9102564589713776 | | 0.0102 | 11.0 | 946 | 0.1549 | 0.9191 | 0.9524 | 0.9775 | 0.9737696499931041 | 0.8644247331609153 | 0.9889789745698009 | 0.915789237032027 | | 0.0204 | 12.0 | 1032 | 0.1502 | 0.9215 | 0.9527 | 0.9783 | 0.974639596078376 | 0.8682964916021273 | 0.989902977623774 | 0.9155653673995151 | | 0.0268 | 13.0 | 1118 | 0.1413 | 0.9194 | 0.9505 | 0.9777 | 0.9740020531855834 | 0.8647199376136 | 0.99011699066189 | 0.9107963425971664 | | 0.0166 | 14.0 | 1204 | 0.1584 | 0.9173 | 0.9518 | 0.9770 | 0.9731154475737929 | 0.8614276032542578 | 0.9884142831972749 | 0.9152366875147241 | | 0.0159 | 15.0 | 1290 | 0.1563 | 0.9170 | 0.9492 | 0.9770 | 0.9731832402253996 | 0.8607442858381036 | 0.9896456803899689 | 0.9087960816798012 | | 0.0211 | 16.0 | 1376 | 0.1435 | 0.9150 | 0.9481 | 0.9764 | 0.9725201360275898 | 0.8574847000491036 | 0.989323310037 | 0.9068449010920532 | | 0.0128 | 17.0 | 1462 | 0.1421 | 0.9212 | 0.9519 | 0.9782 | 0.9745789801464504 | 0.8677394402794754 | 0.9901920479238856 | 0.9136255861141298 | | 0.0167 | 18.0 | 1548 | 0.1558 | 0.9217 | 0.9532 | 0.9783 | 0.9746811993626879 | 0.8686470009484697 | 0.9897428202266988 | 0.9166850322093621 | | 0.0201 | 19.0 | 1634 | 0.1623 | 0.9156 | 0.9484 | 0.9766 | 0.9727184720007118 | 0.8584339325695252 | 0.9894484642039114 | 0.9072695251050635 | | 0.0133 | 20.0 | 1720 | 0.1573 | 0.9189 | 0.9505 | 0.9776 | 0.9738320500157303 | 0.8640203613069115 | 0.9898665061373113 | 0.9112263496140702 | | 0.012 | 21.0 | 1806 | 0.1631 | 0.9165 | 0.9472 | 0.9769 | 0.9731344243001482 | 0.8597866189796295 | 0.9904592118400188 | 0.9040137576913626 | | 0.0148 | 22.0 | 1892 | 0.1629 | 0.9181 | 0.9507 | 0.9773 | 0.9735162429121835 | 0.8627239955489192 | 0.9894034768309156 | 0.9120129014770962 | | 0.0137 | 23.0 | 1978 | 0.1701 | 0.9136 | 0.9484 | 0.9760 | 0.9719681843338751 | 0.8552607882028388 | 0.9885083690609032 | 0.908250815050119 | | 0.0142 | 24.0 | 2064 | 0.1646 | 0.9146 | 0.9488 | 0.9763 | 0.9723134197764093 | 0.8568918401744342 | 0.9887405884771245 | 0.9089100747034281 | | 0.0156 | 25.0 | 2150 | 0.1615 | 0.9144 | 0.9465 | 0.9763 | 0.9723929259786395 | 0.856345354289624 | 0.9898487696012216 | 0.9032139066422469 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1 - Datasets 2.13.1 - Tokenizers 0.13.3

vlabs/falcon-7b-sentiment_V3

vlabs

2023-07-27T19:21:16Z

0

peft

[ "peft", "region:us" ]

null

2023-07-27T19:21:14Z

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

Isaacgv/distilhubert-finetuned-gtzan

Isaacgv

2023-07-27T19:20:19Z

19

0

transformers

[ "transformers", "pytorch", "tensorboard", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

audio-classification

2023-07-26T12:47:08Z

--- license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.88 ---  # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.5655 - Accuracy: 0.88 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3836 | 0.98 | 14 | 0.5798 | 0.82 | | 0.3357 | 1.96 | 28 | 0.5655 | 0.88 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

Varshitha/flan-t5-small-finetune-medicine-v3

Varshitha

2023-07-27T19:17:58Z

102

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "text2textgeneration", "generated_from_trainer", "base_model:google/flan-t5-small", "base_model:finetune:google/flan-t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-27T19:16:23Z

--- license: apache-2.0 base_model: google/flan-t5-small tags: - text2textgeneration - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-small-finetune-medicine-v3 results: [] ---  # flan-t5-small-finetune-medicine-v3 This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8757 - Rouge1: 15.991 - Rouge2: 5.2469 - Rougel: 14.6278 - Rougelsum: 14.7076 ## 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: 5.6e-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: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | No log | 1.0 | 5 | 2.9996 | 12.4808 | 4.9536 | 12.3712 | 12.2123 | | No log | 2.0 | 10 | 2.9550 | 13.6471 | 4.9536 | 13.5051 | 13.5488 | | No log | 3.0 | 15 | 2.9224 | 13.8077 | 5.117 | 13.7274 | 13.753 | | No log | 4.0 | 20 | 2.9050 | 13.7861 | 5.117 | 13.6982 | 13.7001 | | No log | 5.0 | 25 | 2.8920 | 14.668 | 5.117 | 14.4497 | 14.4115 | | No log | 6.0 | 30 | 2.8820 | 14.9451 | 5.2469 | 14.5797 | 14.6308 | | No log | 7.0 | 35 | 2.8770 | 15.991 | 5.2469 | 14.6278 | 14.7076 | | No log | 8.0 | 40 | 2.8757 | 15.991 | 5.2469 | 14.6278 | 14.7076 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

FFusion/FFusionXL-LoRa-SDXL-Potion-Art-Engine

FFusion

2023-07-27T19:11:33Z

16

5

diffusers

[ "diffusers", "safetensors", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "en", "doi:10.57967/hf/0918", "license:other", "region:us" ]

text-to-image

2023-07-23T20:39:23Z

--- license: other base_model: diffusers/FFusionXL-1-SDXL instance_prompt: a 3d potion vial tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true library_name: diffusers badges: - alt: Name url: >- https://img.shields.io/badge/Name-FFusion%20XL%20LoRA%20%F0%9F%A7%AA%EF%B8%8F%20%20Potion%20Art%20Engine-89CFF0 src: >- https://img.shields.io/badge/Name-FFusion%20XL%20LoRA%20%F0%9F%A7%AA%EF%B8%8F%20%20Potion%20Art%20Engine-89CFF0 - alt: LoRA Type url: https://img.shields.io/badge/LoRA%20Type-LyCORIS%2FLoKr%2C%20Prodigy-blue src: https://img.shields.io/badge/LoRA%20Type-LyCORIS%2FLoKr%2C%20Prodigy-blue - alt: Refiner Compatible url: https://img.shields.io/badge/%F0%9F%94%A5%20Refiner%20Compatible-Yes-success src: https://img.shields.io/badge/%F0%9F%94%A5%20Refiner%20Compatible-Yes-success - alt: CLIP Tested url: >- https://img.shields.io/badge/%F0%9F%92%BB%20CLIP--ViT%2FG%20and%20CLIP--ViT%2FL%20tested-Yes-success src: >- https://img.shields.io/badge/%F0%9F%92%BB%20CLIP--ViT%2FG%20and%20CLIP--ViT%2FL%20tested-Yes-success - alt: Trained Resolution url: >- https://img.shields.io/badge/Trained%20Resolution-1024%20x%201024%20pixels-yellow src: >- https://img.shields.io/badge/Trained%20Resolution-1024%20x%201024%20pixels-yellow - alt: Tested Resolution url: >- https://img.shields.io/badge/Tested%20Resolution-Up%20to%202800%20x%202800%20pixels-brightgreen src: >- https://img.shields.io/badge/Tested%20Resolution-Up%20to%202800%20x%202800%20pixels-brightgreen - alt: Tested on url: >- https://img.shields.io/badge/Tested%20on-SDXL%200.9%20%26%20FFXL%200.001-blue src: >- https://img.shields.io/badge/Tested%20on-SDXL%200.9%20%26%20FFXL%200.001-blue - alt: Hugging Face Model url: https://img.shields.io/badge/Hugging%20Face-FFusion--BaSE-blue src: https://img.shields.io/badge/Hugging%20Face-FFusion--BaSE-blue - alt: GitHub url: https://img.shields.io/badge/GitHub-1e--2-green src: https://img.shields.io/badge/GitHub-1e--2-green - alt: Facebook url: https://img.shields.io/badge/Facebook-FFusionAI-blue src: https://img.shields.io/badge/Facebook-FFusionAI-blue - alt: Civitai url: https://img.shields.io/badge/Civitai-FFusionAI-blue src: https://img.shields.io/badge/Civitai-FFusionAI-blue language: - en --- # FFusion XL LoRA 🧪 Potion Art Engine ![FFusionXL Potion Art Engine](./Image_0.png) <div style="display: flex; flex-wrap: wrap; gap: 2px;"> <img src="https://img.shields.io/badge/%F0%9F%94%A5%20Refiner%20Compatible-Yes-success"> <img src="https://img.shields.io/badge/%F0%9F%92%BB%20CLIP--ViT%2FG%20and%20CLIP--ViT%2FL%20tested-Yes-success"> <img src="https://img.shields.io/badge/LoRA%20Type-LyCORIS%2FLoKr%2C%20Prodigy-blue"> <img src="https://img.shields.io/badge/Tested%20on-SDXL%200.9%20%26%20FFXL%200.001-blue"> </div> [![Download](https://img.shields.io/badge/-Download%20Model-brightgreen?style=for-the-badge&logo=appveyor)](https://huggingface.co/FFusion/FFusionXL-LoRa-SDXL-Potion-Art-Engine/resolve/main/FFusionXL-LoRa-SDXL-Potion-Art-Engine.safetensors) [![Samples](https://img.shields.io/badge/-View%20Samples-blue?style=for-the-badge&logo=appveyor)](https://huggingface.co/FFusion/FFusionXL-LoRa-SDXL-Potion-Art-Engine/tree/main/Samples) The Potion Art Engine is an experimental version of a game asset art generator, specifically designed for creating potion vials. ## Specifications - **Model Name**: FFusion XL LoRA Potion Art Engine - **LoRA Type**: LyCORIS/LoKr, Prodigy - **Trained Resolution**: 1024 x 1024 pixels - **Tested Resolution**: Up to 2800 x 2800 pixels <div style="display: flex; flex-wrap: wrap; gap: 4px;"><img src="https://img.shields.io/badge/Trained%20Resolution-1024%20x%201024%20pixels-yellow"> <img src="https://img.shields.io/badge/Tested%20Resolution-Up%20to%202800%20x%202800%20pixels-brightgreen"></div> ## How can the Potion Art Engine help game developers? The Potion Art Engine is a powerful tool for game developers, especially those working on fantasy or RPG games where potions and vials are common game assets. Here are a few ways this tool can be beneficial: 1. **Speed up the asset creation process**: Creating game assets can be a time-consuming process, especially for indie developers or small teams. The Potion Art Engine can generate high-quality potion vials, significantly reducing the time and effort required to create these assets. 2. **Create a variety of unique assets**: The Potion Art Engine can generate a wide variety of potion vials, ensuring that each potion in your game can have a unique and distinct look. This can add to the depth and richness of your game world. 3. **Experiment with different styles**: The Potion Art Engine allows you to experiment with different styles and looks for your potions. This can be particularly useful in the early stages of game development when you are still defining the visual style of your game. 4. **Reduce costs**: By using the Potion Art Engine to generate game assets, you can significantly reduce the costs associated with asset creation. This can be particularly beneficial for indie developers or small teams with limited budgets. ## Limitations - The Potion Art Engine is designed to generate potion vials, and its performance may vary when used to generate other types of game assets. - The quality of the generated assets may vary depending on the specific parameters and settings used. ## Ethical Considerations As with any AI model, it is important to use the Potion Art Engine responsibly. Please ensure that the generated assets do not infringe on any copyrights or intellectual property rights. It is also important to ensure that the generated assets are appropriate and do not contain any offensive or harmful content. ## Citations If you use the Potion Art Engine in your project or research, please provide appropriate citations to acknowledge the model's contribution. ## Disclaimer ![FFusionXL Potion Art Engine](./Image_8.jpg) The Potion Art Engine is a powerful tool for generating game assets, but it is not perfect and may have limitations. Users are encouraged to test and validate the generated assets thoroughly before integrating them into their games. The developers of this model hold no responsibility for any consequences that may arise from its usage. <div style="display: flex; flex-wrap: wrap; gap: 2px;"> <a href="https://huggingface.co/FFusion/FFusion-BaSE" target="_new" rel="ugc"><img src="https://img.shields.io/badge/Hugging%20Face-FFusion--BaSE-blue" alt="Hugging Face Model"></a> <a href="https://github.com/1e-2" target="_new" rel="ugc"><img src="https://img.shields.io/badge/GitHub-1e--2-green" alt="GitHub"></a> <a href="https://www.facebook.com/FFusionAI/" target="_new" rel="ugc"><img src="https://img.shields.io/badge/Facebook-FFusionAI-blue" alt="Facebook"></a> <a href="https://civitai.com/models/82039/ffusion-ai-sd-21" target="_new" rel="ugc"><img src="https://img.shields.io/badge/Civitai-FFusionAI-blue" alt="Civitai"></a> </div> <div style="display: flex; flex-wrap: wrap; gap: 10px; align-items: center;"> <p>These are LoRA adaption weights for</p> <a href="https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9" target="_new" rel="ugc"><img src="https://img.shields.io/badge/stable--diffusion--xl--base--0.9-Model-purple" alt="stable-diffusion-xl-base-0.9"></a> <p>&</p> <a href="https://huggingface.co/FFusion/FFusionXL-09-SDXL" target="_new" rel="ugc"><img src="https://img.shields.io/badge/FFusionXL--09--SDXL-Model-pink" alt="FFusionXL-09-SDXL"></a> <p>The weights were trained using experimental</p> <a href="https://github.com/kohya-ss/sd-scripts" target="_new" rel="ugc"><img src="https://img.shields.io/badge/kohya--ss-sd--scripts-blue" alt="kohya-ss/sd-scripts build"></a> <p>build</p> </div> **Attribution:** "SDXL 0.9 is licensed under the SDXL Research License, Copyright (c) Stability AI Ltd. All Rights Reserved." ## License [SDXL 0.9 Research License](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9/blob/main/LICENSE.md)" ![FFusionXL Potion Art Engine](./Image_9.png) [![Email](https://img.shields.io/badge/Email-di%40ffusion.ai-blue?style=for-the-badge&logo=gmail)](mailto:[email protected])

NasimB/bnc-cbt-log-rarity

NasimB

2023-07-27T19:04:03Z

14

0

transformers

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

text-generation

2023-07-27T16:42:30Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: bnc-cbt-log-rarity results: [] ---  # bnc-cbt-log-rarity This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.1410 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.351 | 0.29 | 500 | 5.3291 | | 5.0493 | 0.59 | 1000 | 4.9419 | | 4.7234 | 0.88 | 1500 | 4.7048 | | 4.4524 | 1.17 | 2000 | 4.5595 | | 4.3123 | 1.46 | 2500 | 4.4472 | | 4.2134 | 1.76 | 3000 | 4.3517 | | 4.0971 | 2.05 | 3500 | 4.2754 | | 3.9151 | 2.34 | 4000 | 4.2320 | | 3.8812 | 2.63 | 4500 | 4.1763 | | 3.8438 | 2.93 | 5000 | 4.1267 | | 3.6488 | 3.22 | 5500 | 4.1269 | | 3.6024 | 3.51 | 6000 | 4.0958 | | 3.5864 | 3.81 | 6500 | 4.0625 | | 3.4842 | 4.1 | 7000 | 4.0646 | | 3.3367 | 4.39 | 7500 | 4.0563 | | 3.3316 | 4.68 | 8000 | 4.0432 | | 3.3157 | 4.98 | 8500 | 4.0354 | | 3.1598 | 5.27 | 9000 | 4.0473 | | 3.1514 | 5.56 | 9500 | 4.0472 | | 3.1497 | 5.85 | 10000 | 4.0465 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

JBJoyce/wav2vec2-large-robust-12-ft-emotion-msp-dim-finetuned-gtzan

JBJoyce

2023-07-27T19:03:49Z

167

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us" ]

audio-classification

2023-07-10T02:29:17Z

--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: wav2vec2-large-robust-12-ft-emotion-msp-dim-finetuned-gtzan results: [] ---  # wav2vec2-large-robust-12-ft-emotion-msp-dim-finetuned-gtzan This model is a fine-tuned version of [audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim](https://huggingface.co/audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.7711 - Accuracy: 0.83 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.186 | 1.0 | 112 | 2.1638 | 0.3 | | 1.655 | 2.0 | 225 | 1.7677 | 0.48 | | 1.5148 | 3.0 | 337 | 1.3746 | 0.54 | | 1.2349 | 4.0 | 450 | 1.1218 | 0.64 | | 0.9702 | 5.0 | 562 | 1.0244 | 0.69 | | 0.9191 | 6.0 | 675 | 0.9180 | 0.75 | | 0.6891 | 7.0 | 787 | 0.8959 | 0.76 | | 0.628 | 8.0 | 900 | 0.8084 | 0.81 | | 0.7337 | 9.0 | 1012 | 0.7742 | 0.83 | | 0.5573 | 9.96 | 1120 | 0.7711 | 0.83 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

StofEzz/mascir_fr_wav2vec_test

StofEzz

2023-07-27T18:45:29Z

135

0

transformers

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

automatic-speech-recognition

2023-07-27T15:22:36Z

--- license: apache-2.0 base_model: facebook/wav2vec2-large-xlsr-53 tags: - generated_from_trainer metrics: - wer model-index: - name: mascir_fr_wav2vec_test results: [] ---  # mascir_fr_wav2vec_test 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: 0.0136 - Wer: 0.1612 ## 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: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 8.06 | 250 | 3.7503 | 0.9919 | | 8.0637 | 16.13 | 500 | 3.0132 | 0.9919 | | 8.0637 | 24.19 | 750 | 2.9734 | 0.9919 | | 2.9339 | 32.26 | 1000 | 2.0538 | 0.9963 | | 2.9339 | 40.32 | 1250 | 0.4530 | 0.5406 | | 0.9878 | 48.39 | 1500 | 0.1807 | 0.3373 | | 0.9878 | 56.45 | 1750 | 0.0814 | 0.2436 | | 0.3416 | 64.52 | 2000 | 0.0512 | 0.2114 | | 0.3416 | 72.58 | 2250 | 0.0292 | 0.1823 | | 0.1952 | 80.65 | 2500 | 0.0198 | 0.1742 | | 0.1952 | 88.71 | 2750 | 0.0158 | 0.1631 | | 0.1476 | 96.77 | 3000 | 0.0136 | 0.1612 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

w601sxs/b1ade-1b-orca-chkpt-506k

w601sxs

2023-07-27T18:44:34Z

0

peft

[ "peft", "region:us" ]

null

2023-07-27T18:43:42Z

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

dariowsz/ppo-Pyramids

dariowsz

2023-07-27T18:29:47Z

5

0

ml-agents

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

reinforcement-learning

2023-07-27T18:28:33Z

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

Leogrin/eleuther-pythia1b-hh-dpo

Leogrin

2023-07-27T18:21:11Z

168

1

transformers

[ "transformers", "pytorch", "gpt_neox", "text-generation", "causal-lm", "pythia", "en", "dataset:Anthropic/hh-rlhf", "arxiv:2305.18290", "arxiv:2101.00027", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-27T14:35:26Z

--- language: - en tags: - pytorch - causal-lm - pythia license: apache-2.0 datasets: - Anthropic/hh-rlhf --- # Infos Pythia-1b supervised finetuned with Anthropic-hh-rlhf dataset for 1 epoch (sft-model), before DPO [(paper)](https://arxiv.org/abs/2305.18290) with same dataset for 1 epoch. [wandb log](https://wandb.ai/pythia_dpo/Pythia_DPO_new/runs/jk09pzqb) See [Pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) for model details [(paper)](https://arxiv.org/abs/2101.00027). # Benchmark raw results: Results for the base model are taken from the [Pythia paper](https://arxiv.org/abs/2101.00027). ## Zero shot | Task | 1B_base | 1B_sft | 1B_dpo | |------------------|----------------|----------------|-----------------| | Lambada (OpenAI) | 0.562 ± 0.007 | 0.563 ± 0.007 | 0.5575 ± 0.0069 | | PIQA | 0.707 ± 0.011 | 0.711 ± 0.011 | 0.7122 ± 0.0106 | | WinoGrande | 0.537 ± 0.014 | 0.534 ± 0.014 | 0.5525 ± 0.0140 | | WSC | 0.365 ± 0.047 | 0.365 ± 0.047 | 0.3654 ± 0.0474 | | ARC - Easy | 0.569 ± 0.010 | 0.583 ± 0.010 | 0.5901 ± 0.0101 | | ARC - Challenge | 0.244 ± 0.013 | 0.248 ± 0.013 | 0.2611 ± 0.0128 | | SciQ | 0.840 ± 0.012 | 0.847 ± 0.011 | 0.8530 ± 0.0112 | | LogiQA | 0.223 ± 0.016 | N/A | N/A | ## Five shot | Task | 1B_base | 1B_sft | 1B_dpo | |------------------|----------------|----------------|-----------------| | Lambada (OpenAI) | 0.507 ± 0.007 | 0.4722 ± 0.007 | 0.4669 ± 0.0070 | | PIQA | 0.705 ± 0.011 | 0.7165 ± 0.0105| 0.7138 ± 0.0105 | | WinoGrande | 0.532 ± 0.014 | 0.5343 ± 0.014 | 0.5525 ± 0.0140 | | WSC | 0.365 ± 0.047 | 0.5000 ± 0.0493| 0.5577 ± 0.0489 | | ARC - Easy | 0.594 ± 0.010 | 0.6010 ± 0.010 | 0.6170 ± 0.0100 | | ARC - Challenge | 0.259 ± 0.013 | 0.2679 ± 0.0129| 0.2833 ± 0.0132 | | SciQ | 0.920 ± 0.009 | 0.9100 ± 0.0091| 0.9020 ± 0.0094 | | LogiQA | 0.227 ± 0.016 | N/A | N/A |

grace-pro/no-delete_5e-5_hausa

grace-pro

2023-07-27T18:18:51Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:Davlan/afro-xlmr-base", "base_model:finetune:Davlan/afro-xlmr-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-27T17:02:05Z

--- license: mit base_model: Davlan/afro-xlmr-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: no-delete_5e-5_hausa results: [] ---  # no-delete_5e-5_hausa This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1716 - Precision: 0.4009 - Recall: 0.2840 - F1: 0.3325 - Accuracy: 0.9559 ## 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: 8 - 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1421 | 1.0 | 1283 | 0.1347 | 0.4610 | 0.1779 | 0.2567 | 0.9594 | | 0.1234 | 2.0 | 2566 | 0.1332 | 0.4847 | 0.1920 | 0.2750 | 0.9603 | | 0.1041 | 3.0 | 3849 | 0.1412 | 0.4581 | 0.2305 | 0.3067 | 0.9595 | | 0.0822 | 4.0 | 5132 | 0.1562 | 0.3979 | 0.2752 | 0.3253 | 0.9559 | | 0.0664 | 5.0 | 6415 | 0.1716 | 0.4009 | 0.2840 | 0.3325 | 0.9559 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

Leogrin/eleuther-pythia1.4b-hh-dpo

Leogrin

2023-07-27T18:16:00Z

180

1

transformers

[ "transformers", "pytorch", "gpt_neox", "text-generation", "causal-lm", "pythia", "en", "dataset:Anthropic/hh-rlhf", "arxiv:2305.18290", "arxiv:2101.00027", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-27T15:07:41Z

--- language: - en tags: - pytorch - causal-lm - pythia license: apache-2.0 datasets: - Anthropic/hh-rlhf --- # Infos Pythia-1.4b supervised finetuned with Anthropic-hh-rlhf dataset for 1 epoch (sft-model), before DPO [(paper)](https://arxiv.org/abs/2305.18290) with same dataset for 1 epoch. [wandb log](https://wandb.ai/pythia_dpo/Pythia_DPO_new/runs/6yrtkj3s) See [Pythia-1.4b](https://huggingface.co/EleutherAI/pythia-1.4b) for model details [(paper)](https://arxiv.org/abs/2101.00027). # Benchmark raw results: Results for the base model are taken from the [Pythia paper](https://arxiv.org/abs/2101.00027). ## Zero shot | Task | 1.4B_base | 1.4B_sft | 1.4B_dpo | |------------------|--------------:|--------------:|---------------:| | Lambada (OpenAI) | 0.616 ± 0.007 | 0.5977 ± 0.0068 | 0.5948 ± 0.0068 | | PIQA | 0.711 ± 0.011 | 0.7133 ± 0.0106 | 0.7165 ± 0.0105 | | WinoGrande | 0.573 ± 0.014 | 0.5793 ± 0.0139 | 0.5746 ± 0.0139 | | WSC | 0.365 ± 0.047 | 0.3654 ± 0.0474 | 0.3654 ± 0.0474 | | ARC - Easy | 0.606 ± 0.010 | 0.6098 ± 0.0100 | 0.6199 ± 0.0100 | | ARC - Challenge | 0.260 ± 0.013 | 0.2696 ± 0.0130 | 0.2884 ± 0.0132 | | SciQ | 0.865 ± 0.011 | 0.8540 ± 0.0112 | 0.8550 ± 0.0111 | | LogiQA | 0.210 ± 0.016 | NA | NA | ## Five shot | Task | 1.4B_base | 1.4B_sft | 1.4B_dpo | |------------------|----------------:|----------------:|----------------:| | Lambada (OpenAI) | 0.578 ± 0.007 | 0.5201 ± 0.007 | 0.5247 ± 0.007 | | PIQA | 0.705 ± 0.011 | 0.7176 ± 0.0105| 0.7209 ± 0.0105| | WinoGrande | 0.580 ± 0.014 | 0.5793 ± 0.0139| 0.5746 ± 0.0139| | WSC | 0.365 ± 0.047 | 0.5288 ± 0.0492| 0.5769 ± 0.0487| | ARC - Easy | 0.643 ± 0.010 | 0.6376 ± 0.0099| 0.6561 ± 0.0097| | ARC - Challenge | 0.290 ± 0.013 | 0.2935 ± 0.0133| 0.3166 ± 0.0136| | SciQ | 0.92 ± 0.009 | 0.9180 ± 0.0087| 0.9150 ± 0.0088| | LogiQA | 0.240 ± 0.017 | N/A | N/A |

Khushnur/t5-base-end2end-questions-generation_squad_all_pcmq

Khushnur

2023-07-27T18:11:03Z

159

0

transformers

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

text2text-generation

2023-07-27T15:33:55Z

--- license: apache-2.0 base_model: t5-base tags: - generated_from_trainer model-index: - name: t5-base-end2end-questions-generation_squad_all_pcmq results: [] ---  # t5-base-end2end-questions-generation_squad_all_pcmq This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5861 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - 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 | |:-------------:|:-----:|:----:|:---------------:| | 1.8599 | 0.67 | 100 | 1.6726 | | 1.8315 | 1.35 | 200 | 1.6141 | | 1.7564 | 2.02 | 300 | 1.5942 | | 1.7153 | 2.69 | 400 | 1.5861 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

BauyrjanQ/whisper-kk-sp2ner-b2-ms1000-b

BauyrjanQ

2023-07-27T18:07:56Z

75

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:BauyrjanQ/whisper-kk", "base_model:finetune:BauyrjanQ/whisper-kk", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-07-26T18:02:30Z

--- license: apache-2.0 base_model: BauyrjanQ/whisper-kk tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-kk-sp2ner-b4-ms1000-b results: [] ---  # whisper-kk-sp2ner-b4-ms1000-b This model is a fine-tuned version of [BauyrjanQ/whisper-kk](https://huggingface.co/BauyrjanQ/whisper-kk) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4320 - Wer: 95.6625 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 4 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 1.1132 | 0.06 | 1000 | 0.4320 | 95.6625 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

jordyvl/vit-base_rvl_cdip_crl

jordyvl

2023-07-27T18:00:23Z

167

0

transformers

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

image-classification

2023-07-26T16:38:18Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-base_rvl_cdip_crl results: [] ---  # vit-base_rvl_cdip_crl 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: 0.6238 - Accuracy: 0.8956 - Brier Loss: 0.1819 - Nll: 1.1791 - F1 Micro: 0.8957 - F1 Macro: 0.8958 - Ece: 0.0846 - Aurc: 0.0210 ## 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 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | 0.1844 | 1.0 | 1250 | 0.4411 | 0.8961 | 0.1614 | 1.1240 | 0.8961 | 0.8963 | 0.0528 | 0.0161 | | 0.1394 | 2.0 | 2500 | 0.4830 | 0.8927 | 0.1716 | 1.1324 | 0.8927 | 0.8927 | 0.0646 | 0.0175 | | 0.1 | 3.0 | 3750 | 0.5257 | 0.8911 | 0.1791 | 1.1569 | 0.8911 | 0.8912 | 0.0737 | 0.0187 | | 0.068 | 4.0 | 5000 | 0.5497 | 0.8913 | 0.1806 | 1.1705 | 0.8913 | 0.8913 | 0.0770 | 0.0192 | | 0.048 | 5.0 | 6250 | 0.5762 | 0.8915 | 0.1834 | 1.1906 | 0.8915 | 0.8914 | 0.0808 | 0.0195 | | 0.033 | 6.0 | 7500 | 0.5877 | 0.8936 | 0.1822 | 1.1690 | 0.8936 | 0.8938 | 0.0817 | 0.0196 | | 0.0231 | 7.0 | 8750 | 0.6000 | 0.8938 | 0.1822 | 1.1867 | 0.8938 | 0.8939 | 0.0833 | 0.0206 | | 0.0162 | 8.0 | 10000 | 0.6187 | 0.8948 | 0.1834 | 1.1827 | 0.8948 | 0.8949 | 0.0841 | 0.0208 | | 0.0123 | 9.0 | 11250 | 0.6191 | 0.8953 | 0.1824 | 1.1868 | 0.8953 | 0.8955 | 0.0836 | 0.0207 | | 0.0102 | 10.0 | 12500 | 0.6238 | 0.8956 | 0.1819 | 1.1791 | 0.8957 | 0.8958 | 0.0846 | 0.0210 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

snob/TagMyBookmark-KoAlpaca-QLoRA-v1.0-Finetune300

snob

2023-07-27T17:57:27Z

0

peft

[ "peft", "region:us" ]

null

2023-07-27T17:57:09Z

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

dariowsz/ppo-SnowballTarget

dariowsz

2023-07-27T17:45:56Z

7

0

ml-agents

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

reinforcement-learning

2023-07-27T17:45:49Z

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

asenella/MMVAEPlus_beta_10_scale_False_seed_2

asenella

2023-07-27T17:21:14Z

0

null

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

null

2023-07-27T17:21:01Z

--- 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") ```

asenella/MMVAEPlus_beta_5_scale_False_seed_2

asenella

2023-07-27T17:15:39Z

0

null

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

null

2023-07-27T17:15:20Z

--- 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") ```

magnustragardh/poca-SoccerTwos

magnustragardh

2023-07-27T17:06:35Z

5

0

ml-agents

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

reinforcement-learning

2023-07-27T17:01:53Z

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

asenella/MMVAEPlus_beta_25_scale_False_seed_0

asenella

2023-07-27T17:03:43Z

0

null

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

null

2023-07-27T17:03:30Z

--- 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") ```

asenella/MMVAEPlus_beta_10_scale_False_seed_0

asenella

2023-07-27T17:01:30Z

0

null

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

null

2023-07-27T17:01:18Z

--- 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") ```

Pallisgaard/whisper-small-dv

Pallisgaard

2023-07-27T16:52:23Z

76

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dv", "dataset:mozilla-foundation/common_voice_13_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-07-27T15:24:33Z

--- language: - dv license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_13_0 metrics: - wer model-index: - name: Whisper Small Dv - Sanchit Gandhi results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 13 type: mozilla-foundation/common_voice_13_0 config: dv split: test args: dv metrics: - name: Wer type: wer value: 13.097680564732064 ---  # Whisper Small Dv - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset. It achieves the following results on the evaluation set: - Loss: 0.1691 - Wer Ortho: 62.1144 - Wer: 13.0977 ## 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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:| | 0.1237 | 1.63 | 500 | 0.1691 | 62.1144 | 13.0977 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

augtoma/qCammel-70-x

augtoma

2023-07-27T16:47:02Z

1,686

27

transformers

[ "transformers", "pytorch", "llama", "text-generation", "llama-2", "qCammel-70", "en", "arxiv:2305.12031", "arxiv:2305.14314", "arxiv:2302.70971", "license:other", "autotrain_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-07-23T00:39:34Z

--- license: other language: - en pipeline_tag: text-generation inference: false tags: - pytorch - llama - llama-2 - qCammel-70 library_name: transformers --- # qCammel-70 qCammel-70 is a fine-tuned version of Llama-2 70B model, trained on a distilled dataset of 15,000 instructions using QLoRA. This model is optimized for academic medical knowledge and instruction-following capabilities. ## 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 their License before downloading this model .* The fine-tuning process applied to qCammel-70 involves a distilled dataset of 15,000 instructions and is trained with QLoRA, **Variations** The original Llama 2 has parameter sizes of 7B, 13B, and 70B. This is the fine-tuned version of the 70B model. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** qCammel-70 is based on the Llama 2 architecture, an auto-regressive language model that uses a decoder only transformer architecture. **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/) Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved **Research Papers** - [Clinical Camel: An Open-Source Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding](https://arxiv.org/abs/2305.12031) - [QLoRA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/abs/2305.14314) - [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.70971)

digitaljungle/ppo-LunarLander-v2

digitaljungle

2023-07-27T16:25:57Z

0

stable-baselines3

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

reinforcement-learning

2023-07-27T16:25:36Z

--- 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: 260.52 +/- 19.49 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

blackmount8/WizardLM-13B-V1.2-ct2-int8

blackmount8

2023-07-27T16:17:54Z

2

0

transformers

[ "transformers", "license:mit", "endpoints_compatible", "region:us" ]

null

2023-07-26T16:16:35Z

--- license: mit --- # blackmount8/WizardLM-13B-V1.2-ct2-int8 Int8 version of [WizardLM/WizardLM-13B-V1.2](https://huggingface.co/WizardLM/WizardLM-13B-V1.2), quantized using CTranslate2.

mdhafeez29/llama2-qlora-finetunined-french

mdhafeez29

2023-07-27T16:09:16Z

0

peft

[ "peft", "region:us" ]

null

2023-07-27T16:09:10Z

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

NasimB/aochildes-rarity-2

NasimB

2023-07-27T16:08:36Z

5

0

transformers

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

text-generation

2023-07-27T13:44:03Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: aochildes-rarity-2 results: [] ---  # aochildes-rarity-2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.1181 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.351 | 0.29 | 500 | 5.3358 | | 5.0412 | 0.59 | 1000 | 4.9250 | | 4.7138 | 0.88 | 1500 | 4.6868 | | 4.4435 | 1.17 | 2000 | 4.5444 | | 4.3073 | 1.47 | 2500 | 4.4317 | | 4.205 | 1.76 | 3000 | 4.3274 | | 4.0796 | 2.05 | 3500 | 4.2630 | | 3.8987 | 2.35 | 4000 | 4.2145 | | 3.8749 | 2.64 | 4500 | 4.1579 | | 3.8421 | 2.93 | 5000 | 4.1113 | | 3.6388 | 3.23 | 5500 | 4.1089 | | 3.5906 | 3.52 | 6000 | 4.0804 | | 3.5776 | 3.81 | 6500 | 4.0451 | | 3.4712 | 4.11 | 7000 | 4.0519 | | 3.3209 | 4.4 | 7500 | 4.0435 | | 3.3179 | 4.69 | 8000 | 4.0297 | | 3.3071 | 4.99 | 8500 | 4.0193 | | 3.1447 | 5.28 | 9000 | 4.0337 | | 3.1394 | 5.57 | 9500 | 4.0322 | | 3.1343 | 5.87 | 10000 | 4.0318 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

Lazycuber/Pygnen-dolly-6B

Lazycuber

2023-07-27T16:08:06Z

6

0

transformers

[ "transformers", "pytorch", "gptj", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-07-25T14:12:36Z

--- license: apache-2.0 ---

yancongwen/chatglm2-medical-lora-20230727

yancongwen

2023-07-27T16:07:33Z

0

peft

[ "peft", "tensorboard", "region:us" ]

null

2023-07-27T16:06:20Z

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

drradford/ppo-Huggy

drradford

2023-07-27T16:05:42Z

0

ml-agents

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

reinforcement-learning

2023-07-27T16:05:37Z

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

WforGodot/add-lora-7b

WforGodot

2023-07-27T15:54:56Z

0

peft

[ "peft", "region:us" ]

null

2023-07-26T17:39:31Z

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

digitaljungle/taxi_q_ueue

digitaljungle

2023-07-27T15:53:32Z

0

null

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

reinforcement-learning

2023-07-27T15:53:30Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi_q_ueue results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="digitaljungle/taxi_q_ueue", 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"]) ```

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

digitaljungle

2023-07-27T15:52:16Z

0

null

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

reinforcement-learning

2023-07-27T15:52:12Z

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

aman38649/marian-finetuned-kde4-en-to-fr

aman38649

2023-07-27T15:50:48Z

60

0

transformers

[ "transformers", "tf", "marian", "text2text-generation", "generated_from_keras_callback", "base_model:Helsinki-NLP/opus-mt-en-fr", "base_model:finetune:Helsinki-NLP/opus-mt-en-fr", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-27T09:19:00Z

--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-fr tags: - generated_from_keras_callback model-index: - name: aman38649/marian-finetuned-kde4-en-to-fr results: [] ---  # aman38649/marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7983 - Validation Loss: 0.8210 - 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 17733, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.0611 | 0.8791 | 0 | | 0.7983 | 0.8210 | 1 | ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.0 - Tokenizers 0.13.3

timothytruong/my_awesome_billsum_model

timothytruong

2023-07-27T15:40:12Z

104

0

transformers

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

text2text-generation

2023-07-26T16:27:51Z

--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - billsum metrics: - rouge model-index: - name: my_awesome_billsum_model results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: billsum type: billsum config: default split: ca_test args: default metrics: - name: Rouge1 type: rouge value: 0.1365 ---  # my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5173 - Rouge1: 0.1365 - Rouge2: 0.0489 - Rougel: 0.1158 - Rougelsum: 0.1158 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 62 | 2.8028 | 0.1229 | 0.0364 | 0.1048 | 0.1048 | 19.0 | | No log | 2.0 | 124 | 2.5974 | 0.1324 | 0.0467 | 0.1121 | 0.1122 | 19.0 | | No log | 3.0 | 186 | 2.5350 | 0.1354 | 0.0491 | 0.1153 | 0.1151 | 19.0 | | No log | 4.0 | 248 | 2.5173 | 0.1365 | 0.0489 | 0.1158 | 0.1158 | 19.0 | ### Framework versions - Transformers 4.31.0 - Pytorch 1.10.1+cpu - Datasets 2.13.1 - Tokenizers 0.13.3

mahmoudzamani/t5_recommendation_sports_equipment_english

mahmoudzamani

2023-07-27T15:30:14Z

107

0

transformers

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

text2text-generation

2023-07-27T15:18:57Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5_recommendation_sports_equipment_english results: [] ---  # t5_recommendation_sports_equipment_english This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4517 - Rouge1: 57.9365 - Rouge2: 47.6190 - Rougel: 56.9841 - Rougelsum: 56.6667 - Gen Len: 3.9048 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 0.96 | 6 | 6.7882 | 8.8278 | 0.9524 | 8.7668 | 8.8278 | 19.0 | | No log | 1.96 | 12 | 2.3412 | 18.0952 | 0.0 | 18.0952 | 18.0952 | 3.2381 | | No log | 2.96 | 18 | 0.8550 | 11.9048 | 4.7619 | 11.9048 | 11.9048 | 4.0 | | No log | 3.96 | 24 | 0.7481 | 32.3810 | 4.7619 | 32.0635 | 32.0635 | 3.9048 | | No log | 4.96 | 30 | 0.7208 | 21.2698 | 4.7619 | 20.7937 | 20.7937 | 3.6190 | | No log | 5.96 | 36 | 0.6293 | 31.7460 | 23.8095 | 31.7460 | 31.7460 | 3.6667 | | No log | 6.96 | 42 | 0.6203 | 43.6508 | 33.3333 | 43.4921 | 42.6984 | 3.9048 | | No log | 7.96 | 48 | 0.6352 | 48.4127 | 33.3333 | 46.8254 | 46.8254 | 3.8095 | | No log | 8.96 | 54 | 0.5334 | 53.2540 | 42.8571 | 52.3810 | 52.0635 | 3.9524 | | No log | 9.96 | 60 | 0.4517 | 57.9365 | 47.6190 | 56.9841 | 56.6667 | 3.9048 | ### Framework versions - Transformers 4.26.0 - Pytorch 2.0.1+cu118 - Datasets 2.8.0 - Tokenizers 0.13.3

alexandremarie/Falcon7b-wiki2-fr

alexandremarie

2023-07-27T15:14:35Z

0

null

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

null

2023-07-27T15:14:35Z

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

royokong/prompteol-llama-7b

royokong

2023-07-27T15:07:54Z

3

0

peft

[ "peft", "region:us" ]

null

2023-07-27T15:06:19Z

--- 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: float32 ### Framework versions - PEFT 0.4.0.dev0

StofEzz/mascir_fr_hubert_test

StofEzz

2023-07-27T15:03:40Z

136

0

transformers

[ "transformers", "pytorch", "tensorboard", "hubert", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/hubert-large-ls960-ft", "base_model:finetune:facebook/hubert-large-ls960-ft", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-07-27T12:22:50Z

--- license: apache-2.0 base_model: facebook/hubert-large-ls960-ft tags: - generated_from_trainer metrics: - wer model-index: - name: mascir_fr_hubert_test results: [] ---  # mascir_fr_hubert_test This model is a fine-tuned version of [facebook/hubert-large-ls960-ft](https://huggingface.co/facebook/hubert-large-ls960-ft) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0113 - Wer: 0.1680 ## 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: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 8.06 | 250 | 3.0885 | 0.9919 | | 5.8634 | 16.13 | 500 | 2.8476 | 0.9919 | | 5.8634 | 24.19 | 750 | 1.1091 | 0.9461 | | 1.7302 | 32.26 | 1000 | 0.4035 | 0.6076 | | 1.7302 | 40.32 | 1250 | 0.1643 | 0.3980 | | 0.5446 | 48.39 | 1500 | 0.0872 | 0.2784 | | 0.5446 | 56.45 | 1750 | 0.0464 | 0.2257 | | 0.3144 | 64.52 | 2000 | 0.0311 | 0.2021 | | 0.3144 | 72.58 | 2250 | 0.0213 | 0.1891 | | 0.2224 | 80.65 | 2500 | 0.0155 | 0.1816 | | 0.2224 | 88.71 | 2750 | 0.0132 | 0.1699 | | 0.1871 | 96.77 | 3000 | 0.0113 | 0.1680 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

epsilonai/SargeRVB

epsilonai

2023-07-27T14:42:04Z

0

null

[ "rvb", "red vs blue", "music", "rvc", "text-to-speech", "en", "region:us" ]

text-to-speech

2023-07-27T14:38:06Z

--- language: - en pipeline_tag: text-to-speech tags: - rvb - red vs blue - music - rvc ---

nakcnx/wangchang-math-v2

nakcnx

2023-07-27T14:29:04Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-27T10:25:44Z

--- 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 ### Framework versions - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0

Varshitha/flan-t5-small-finetuned-medicine

Varshitha

2023-07-27T14:27:11Z

102

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "text2textgeneration", "generated_from_trainer", "base_model:google/flan-t5-small", "base_model:finetune:google/flan-t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-07-27T14:10:18Z

--- license: apache-2.0 base_model: google/flan-t5-small tags: - text2textgeneration - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-small-finetuned-medicine results: [] ---  # flan-t5-small-finetuned-medicine This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9066 - Rouge1: 9.3596 - Rouge2: 2.6144 - Rougel: 8.94 - Rougelsum: 8.94 ## 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: 5.6e-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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 3.1417 | 1.0 | 5 | 2.9168 | 9.5238 | 2.6144 | 8.9947 | 8.9947 | | 3.1069 | 2.0 | 10 | 2.9066 | 9.3596 | 2.6144 | 8.94 | 8.94 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

Andreaa4/Llama-2-7b-chat-hf

Andreaa4

2023-07-27T14:14:13Z

0

peft

[ "peft", "region:us" ]

null

2023-07-27T14:09:28Z

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

sophy/qa_model

sophy

2023-07-27T14:05:19Z

0

transformers

[ "transformers", "question-answering", "dataset:squad", "license:openrail", "endpoints_compatible", "region:us" ]

question-answering

2023-07-22T22:02:20Z

--- license: openrail datasets: - squad pipeline_tag: question-answering library_name: transformers ---

reach-vb/musicgen-large-endpoint

reach-vb

2023-07-27T14:04:06Z

6

0

transformers

[ "transformers", "pytorch", "musicgen", "text-to-audio", "arxiv:2306.05284", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

text-to-audio

2023-07-27T11:46:07Z

--- inference: false tags: - musicgen license: cc-by-nc-4.0 duplicated_from: facebook/musicgen-large --- # MusicGen - Large - 3.3B MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts. It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. Unlike existing methods, like MusicLM, MusicGen doesn't require a self-supervised semantic representation, and it generates all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict them in parallel, thus having only 50 auto-regressive steps per second of audio. MusicGen was published in [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by *Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez*. Four checkpoints are released: - [small](https://huggingface.co/facebook/musicgen-small) - [medium](https://huggingface.co/facebook/musicgen-medium) - [**large** (this checkpoint)](https://huggingface.co/facebook/musicgen-large) - [melody](https://huggingface.co/facebook/musicgen-melody) ## Example Try out MusicGen yourself! * Audiocraft Colab: <a target="_blank" href="https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> * Hugging Face Colab: <a target="_blank" href="https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/MusicGen.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> * Hugging Face Demo: <a target="_blank" href="https://huggingface.co/spaces/facebook/MusicGen"> <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/> </a> ## 🤗 Transformers Usage You can run MusicGen locally with the 🤗 Transformers library from version 4.31.0 onwards. 1. First install the 🤗 [Transformers library](https://github.com/huggingface/transformers) from main: ``` pip install git+https://github.com/huggingface/transformers.git ``` 2. Run the following Python code to generate text-conditional audio samples: ```py from transformers import AutoProcessor, MusicgenForConditionalGeneration processor = AutoProcessor.from_pretrained("facebook/musicgen-large") model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-large") inputs = processor( text=["80s pop track with bassy drums and synth", "90s rock song with loud guitars and heavy drums"], padding=True, return_tensors="pt", ) audio_values = model.generate(**inputs, max_new_tokens=256) ``` 3. Listen to the audio samples either in an ipynb notebook: ```py from IPython.display import Audio sampling_rate = model.config.audio_encoder.sampling_rate Audio(audio_values[0].numpy(), rate=sampling_rate) ``` Or save them as a `.wav` file using a third-party library, e.g. `scipy`: ```py import scipy sampling_rate = model.config.audio_encoder.sampling_rate scipy.io.wavfile.write("musicgen_out.wav", rate=sampling_rate, data=audio_values[0, 0].numpy()) ``` For more details on using the MusicGen model for inference using the 🤗 Transformers library, refer to the [MusicGen docs](https://huggingface.co/docs/transformers/model_doc/musicgen). ## Audiocraft Usage You can also run MusicGen locally through the original [Audiocraft library]((https://github.com/facebookresearch/audiocraft): 1. First install the [`audiocraft` library](https://github.com/facebookresearch/audiocraft) ``` pip install git+https://github.com/facebookresearch/audiocraft.git ``` 2. Make sure to have [`ffmpeg`](https://ffmpeg.org/download.html) installed: ``` apt get install ffmpeg ``` 3. Run the following Python code: ```py from audiocraft.models import MusicGen from audiocraft.data.audio import audio_write model = MusicGen.get_pretrained("large") model.set_generation_params(duration=8) # generate 8 seconds. descriptions = ["happy rock", "energetic EDM"] wav = model.generate(descriptions) # generates 2 samples. for idx, one_wav in enumerate(wav): # Will save under {idx}.wav, with loudness normalization at -14 db LUFS. audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness") ``` ## Model details **Organization developing the model:** The FAIR team of Meta AI. **Model date:** MusicGen was trained between April 2023 and May 2023. **Model version:** This is the version 1 of the model. **Model type:** MusicGen consists of an EnCodec model for audio tokenization, an auto-regressive language model based on the transformer architecture for music modeling. The model comes in different sizes: 300M, 1.5B and 3.3B parameters ; and two variants: a model trained for text-to-music generation task and a model trained for melody-guided music generation. **Paper or resources for more information:** More information can be found in the paper [Simple and Controllable Music Generation][https://arxiv.org/abs/2306.05284]. **Citation details**: ``` @misc{copet2023simple, title={Simple and Controllable Music Generation}, author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez}, year={2023}, eprint={2306.05284}, archivePrefix={arXiv}, primaryClass={cs.SD} } ``` **License** Code is released under MIT, model weights are released under CC-BY-NC 4.0. **Where to send questions or comments about the model:** Questions and comments about MusicGen can be sent via the [Github repository](https://github.com/facebookresearch/audiocraft) of the project, or by opening an issue. ## Intended use **Primary intended use:** The primary use of MusicGen is research on AI-based music generation, including: - Research efforts, such as probing and better understanding the limitations of generative models to further improve the state of science - Generation of music guided by text or melody to understand current abilities of generative AI models by machine learning amateurs **Primary intended users:** The primary intended users of the model are researchers in audio, machine learning and artificial intelligence, as well as amateur seeking to better understand those models. **Out-of-scope use cases** The model should not be used on downstream applications without further risk evaluation and mitigation. The model should not be used to intentionally create or disseminate music pieces that create hostile or alienating environments for people. This includes generating music that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. ## Metrics **Models performance measures:** We used the following objective measure to evaluate the model on a standard music benchmark: - Frechet Audio Distance computed on features extracted from a pre-trained audio classifier (VGGish) - Kullback-Leibler Divergence on label distributions extracted from a pre-trained audio classifier (PaSST) - CLAP Score between audio embedding and text embedding extracted from a pre-trained CLAP model Additionally, we run qualitative studies with human participants, evaluating the performance of the model with the following axes: - Overall quality of the music samples; - Text relevance to the provided text input; - Adherence to the melody for melody-guided music generation. More details on performance measures and human studies can be found in the paper. **Decision thresholds:** Not applicable. ## Evaluation datasets The model was evaluated on the [MusicCaps benchmark](https://www.kaggle.com/datasets/googleai/musiccaps) and on an in-domain held-out evaluation set, with no artist overlap with the training set. ## Training datasets The model was trained using the following sources: the [Meta Music Initiative Sound Collection](https://www.fb.com/sound), [Shutterstock music collection](https://www.shutterstock.com/music) and the [Pond5 music collection](https://www.pond5.com/). See the paper for more details about the training set and corresponding preprocessing. ## Quantitative analysis More information can be found in the paper [Simple and Controllable Music Generation][arxiv], in the Experimental Setup section. ## Limitations and biases **Data:** The data sources used to train the model are created by music professionals and covered by legal agreements with the right holders. The model is trained on 20K hours of data, we believe that scaling the model on larger datasets can further improve the performance of the model. **Mitigations:** All vocals have been removed from the data source using a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs). The model is therefore not able to produce vocals. **Limitations:** - The model is not able to generate realistic vocals. - The model has been trained with English descriptions and will not perform as well in other languages. - The model does not perform equally well for all music styles and cultures. - The model sometimes generates end of songs, collapsing to silence. - It is sometimes difficult to assess what types of text descriptions provide the best generations. Prompt engineering may be required to obtain satisfying results. **Biases:** The source of data is potentially lacking diversity and all music cultures are not equally represented in the dataset. The model may not perform equally well on the wide variety of music genres that exists. The generated samples from the model will reflect the biases from the training data. Further work on this model should include methods for balanced and just representations of cultures, for example, by scaling the training data to be both diverse and inclusive. **Risks and harms:** Biases and limitations of the model may lead to generation of samples that may be considered as biased, inappropriate or offensive. We believe that providing the code to reproduce the research and train new models will allow to broaden the application to new and more representative data. **Use cases:** Users must be aware of the biases, limitations and risks of the model. MusicGen is a model developed for artificial intelligence research on controllable music generation. As such, it should not be used for downstream applications without further investigation and mitigation of risks.

Chat-Error/deberta-xlarge-reward

Chat-Error

2023-07-27T14:02:34Z

0

1

null

[ "tensorboard", "region:us" ]

null

2023-07-27T00:26:56Z

This is Deberta V2 xlarge trained on my https://huggingface.co/datasets/nRuaif/RLHF-hh dataset, using trl.

SigSegev/t5-large_PREFIX_TUNING_SEQ2SEQ_v2

SigSegev

2023-07-27T13:41:44Z

0

peft

[ "peft", "region:us" ]

null

2023-07-27T13:41:29Z

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

luodian/Flamingo-Llama2-Chat7B-CC3M

luodian

2023-07-27T13:34:33Z

4

10

transformers

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

text2text-generation

2023-07-26T01:22:21Z

--- license: mit --- **TLDR**: We trained a Flamingo with Llama2-Chat7B as LLM on CC3M in less than 5 hours using just 4 A100s. The model showed promising zero-shot captioning skills. High-quality captioning data really helps fast alignment. You could test it via following code. Be sure to visit [Otter](https://github.com/Luodian/Otter) to get necessary Flamingo/Otter models. ```python from flamingo.modeling_flamingo import FlamingoForConditionalGeneration flamingo_model = FlamingoForConditionalGeneration.from_pretrained("luodian/Flamingo-Llama2-Chat7B-CC3M", device_map=auto) prompt = "<image>an image of" simple_prompt = "<image>" ```

SaferChat/falcon-7b-test

SaferChat

2023-07-27T13:33:36Z

0

peft

[ "peft", "region:us" ]

null

2023-07-27T13:19:45Z

--- 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

winterbro/distilbert-base-uncased-finetuned-cola

winterbro

2023-07-27T13:15:59Z

107

0

transformers

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

text-classification

2023-07-27T11:28:54Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5425688103069501 ---  # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5259 - Matthews Correlation: 0.5426 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5221 | 1.0 | 535 | 0.5361 | 0.4307 | | 0.3492 | 2.0 | 1070 | 0.5128 | 0.4921 | | 0.2382 | 3.0 | 1605 | 0.5259 | 0.5426 | | 0.1758 | 4.0 | 2140 | 0.7495 | 0.5301 | | 0.1251 | 5.0 | 2675 | 0.7982 | 0.5414 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

undrwolf/Pyramid

undrwolf

2023-07-27T13:14:28Z

1

0

ml-agents

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

reinforcement-learning

2023-07-27T13:10:10Z

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

Carloswear/llama2-qlora-finetunined-french

Carloswear

2023-07-27T13:12:23Z

0

peft

[ "peft", "region:us" ]

null

2023-07-27T13:12:18Z

--- 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

xinyangli/woman_photo

xinyangli

2023-07-27T13:07:00Z

0

diffusers

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

text-to-image

2023-07-27T12:41:48Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of a sks person tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - xinyangli/woman_photo These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of a sks person using [DreamBooth](https://dreambooth.github.io/). 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) LoRA for the text encoder was enabled: False.

ditwoo/distilhubert-finetuned-gtzan

ditwoo

2023-07-27T13:04:50Z

161

1

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "license:apache-2.0", "endpoints_compatible", "region:us" ]

audio-classification

2023-06-25T19:25:16Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan results: [] ---  # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.9570 - Accuracy: 0.86 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.1586 | 1.0 | 112 | 2.0855 | 0.45 | | 1.4771 | 2.0 | 225 | 1.3396 | 0.72 | | 1.181 | 3.0 | 337 | 0.9735 | 0.76 | | 0.8133 | 4.0 | 450 | 0.8692 | 0.76 | | 0.5397 | 5.0 | 562 | 0.7118 | 0.81 | | 0.3424 | 6.0 | 675 | 0.6237 | 0.81 | | 0.2717 | 7.0 | 787 | 0.6551 | 0.83 | | 0.2653 | 8.0 | 900 | 0.6707 | 0.83 | | 0.0503 | 9.0 | 1012 | 0.7025 | 0.84 | | 0.0168 | 10.0 | 1125 | 0.7643 | 0.87 | | 0.1125 | 11.0 | 1237 | 0.8550 | 0.86 | | 0.155 | 12.0 | 1350 | 0.9796 | 0.82 | | 0.005 | 13.0 | 1462 | 0.9539 | 0.86 | | 0.0038 | 14.0 | 1575 | 0.9206 | 0.86 | | 0.0035 | 15.0 | 1687 | 0.8725 | 0.88 | | 0.051 | 16.0 | 1800 | 0.9980 | 0.86 | | 0.003 | 17.0 | 1912 | 0.9579 | 0.86 | | 0.0025 | 18.0 | 2025 | 0.9735 | 0.86 | | 0.0023 | 19.0 | 2137 | 0.9589 | 0.86 | | 0.0022 | 19.91 | 2240 | 0.9570 | 0.86 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0 - Datasets 2.13.1 - Tokenizers 0.13.3

aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaalan/sbert_large_nlu_ru

aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaalan

2023-07-27T13:03:18Z

46

0

sentence-transformers

[ "sentence-transformers", "pytorch", "jax", "bert", "PyTorch", "Transformers", "ru", "region:us" ]

null

2023-07-27T09:07:35Z

--- library_name: sentence-transformers language: - ru tags: - PyTorch - Transformers --- # BERT large model (uncased) for Sentence Embeddings in Russian language. The model is described [in this article](https://habr.com/ru/company/sberdevices/blog/527576/) For better quality, use mean token embeddings. ## Usage (HuggingFace Models Repository) You can use the model directly from the model repository to compute sentence embeddings: ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1) sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9) return sum_embeddings / sum_mask #Sentences we want sentence embeddings for sentences = ['Привет! Как твои дела?', 'А правда, что 42 твое любимое число?'] #Load AutoModel from huggingface model repository tokenizer = AutoTokenizer.from_pretrained("sberbank-ai/sbert_large_nlu_ru") model = AutoModel.from_pretrained("sberbank-ai/sbert_large_nlu_ru") #Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=24, return_tensors='pt') #Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) #Perform pooling. In this case, mean pooling sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) ``` # Authors - [SberDevices](https://sberdevices.ru/) Team. - Denis Antykhov: [Github](https://github.com/gaphex); - Aleksandr Abramov: [Github](https://github.com/Ab1992ao), [Kaggle Competitions Master](https://www.kaggle.com/andrilko)

asenella/ms_MMVAEPlus_beta_5_scale_True_seed_2

asenella

2023-07-27T12:44:38Z

0

null

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

null

2023-07-27T12:44:36Z

--- 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") ```

apple/coreml-stable-diffusion-xl-base

apple

2023-07-27T12:41:14Z

22

67

null

[ "coreml", "text-to-image", "stable-diffusion", "core-ml", "arxiv:2307.01952", "arxiv:2211.01324", "arxiv:2108.01073", "arxiv:2112.10752", "license:openrail++", "region:us" ]

text-to-image

2023-07-26T14:44:27Z

--- license: openrail++ tags: - text-to-image - stable-diffusion - core-ml --- # SD-XL 1.0-base Model Card (Core ML) This model was generated by Hugging Face using [Apple’s repository](https://github.com/apple/ml-stable-diffusion) which has [ASCL](https://github.com/apple/ml-stable-diffusion/blob/main/LICENSE.md). This version contains Core ML weights with the `ORIGINAL` attention implementation, suitable for running on macOS GPUs. The Core ML weights are also distributed as a zip archive for use in the [Hugging Face demo app](https://github.com/huggingface/swift-coreml-diffusers) and other third party tools. The zip archive was created from the contents of the `original/compiled` folder in this repo. Please, refer to https://huggingface.co/blog/diffusers-coreml for details. The remaining contents of this model card were copied from the [original repo](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) ![row01](01.png) ## Model ![pipeline](pipeline.png) [SDXL](https://arxiv.org/abs/2307.01952) consists of an [ensemble of experts](https://arxiv.org/abs/2211.01324) pipeline for latent diffusion: In a first step, the base model is used to generate (noisy) latents, which are then further processed with a refinement model (available here: https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/) specialized for the final denoising steps. Note that the base model can be used as a standalone module. Alternatively, we can use a two-stage pipeline as follows: First, the base model is used to generate latents of the desired output size. In the second step, we use a specialized high-resolution model and apply a technique called SDEdit (https://arxiv.org/abs/2108.01073, also known as "img2img") to the latents generated in the first step, using the same prompt. This technique is slightly slower than the first one, as it requires more function evaluations. Source code is available at https://github.com/Stability-AI/generative-models . ### Model Description - **Developed by:** Stability AI - **Model type:** Diffusion-based text-to-image generative model - **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md) - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses two fixed, pretrained text encoders ([OpenCLIP-ViT/G](https://github.com/mlfoundations/open_clip) and [CLIP-ViT/L](https://github.com/openai/CLIP/tree/main)). - **Resources for more information:** Check out our [GitHub Repository](https://github.com/Stability-AI/generative-models) and the [SDXL report on arXiv](https://arxiv.org/abs/2307.01952). ### Model Sources For research purposes, we recommned our `generative-models` Github repository (https://github.com/Stability-AI/generative-models), which implements the most popoular diffusion frameworks (both training and inference) and for which new functionalities like distillation will be added over time. [Clipdrop](https://clipdrop.co/stable-diffusion) provides free SDXL inference. - **Repository:** https://github.com/Stability-AI/generative-models - **Demo:** https://clipdrop.co/stable-diffusion ## Evaluation ![comparison](comparison.png) The chart above evaluates user preference for SDXL (with and without refinement) over SDXL 0.9 and Stable Diffusion 1.5 and 2.1. The SDXL base model performs significantly better than the previous variants, and the model combined with the refinement module achieves the best overall performance. ### 🧨 Diffusers Make sure to upgrade diffusers to >= 0.18.0: ``` pip install diffusers --upgrade ``` In addition make sure to install `transformers`, `safetensors`, `accelerate` as well as the invisible watermark: ``` pip install invisible_watermark transformers accelerate safetensors ``` You can use the model then as follows ```py from diffusers import DiffusionPipeline import torch pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16") pipe.to("cuda") # if using torch < 2.0 # pipe.enable_xformers_memory_efficient_attention() prompt = "An astronaut riding a green horse" images = pipe(prompt=prompt).images[0] ``` When using `torch >= 2.0`, you can improve the inference speed by 20-30% with torch.compile. Simple wrap the unet with torch compile before running the pipeline: ```py pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) ``` If you are limited by GPU VRAM, you can enable *cpu offloading* by calling `pipe.enable_model_cpu_offload` instead of `.to("cuda")`: ```diff - pipe.to("cuda") + pipe.enable_model_cpu_offload() ``` ## Uses ### Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. Excluded uses are described below. ### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model struggles with more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The autoencoding part of the model is lossy. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.

bwilkie/bwilkie-whisper-small-dv

bwilkie

2023-07-27T12:32:54Z

84

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-07-27T09:25:35Z

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: bwilkie-whisper-small-dv results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: all split: None metrics: - name: Wer type: wer value: 0.23270055113288426 ---  # bwilkie-whisper-small-dv This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.7358 - Wer Ortho: 0.2389 - Wer: 0.2327 ## 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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:| | 0.0001 | 17.86 | 500 | 0.7358 | 0.2389 | 0.2327 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

asenella/ms_MMVAEPlus_beta_25_scale_True_seed_1

asenella

2023-07-27T12:28:12Z

0

null

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

null

2023-07-27T12:28:10Z

--- 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") ```

annishaa/my_awesome_eli5_clm-model-2

annishaa

2023-07-27T12:26:30Z

226

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-27T11:17:11Z

--- license: apache-2.0 base_model: distilgpt2 tags: - generated_from_trainer model-index: - name: my_awesome_eli5_clm-model-2 results: [] ---  # my_awesome_eli5_clm-model-2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7158 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7157 | 1.0 | 1128 | 3.7215 | | 3.6465 | 2.0 | 2256 | 3.7161 | | 3.623 | 3.0 | 3384 | 3.7158 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

asenella/ms_MMVAEPlus_beta_5_scale_True_seed_0

asenella

2023-07-27T12:17:13Z

0

null

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

null

2023-07-27T12:17:11Z

--- 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") ```

rehanhaider/DBSD-1.5-9-vectors-lr-5e-6

rehanhaider

2023-07-27T12:17:03Z

0

diffusers

[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:runwayml/stable-diffusion-v1-5", "base_model:finetune:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-07-27T11:59:35Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: in the style of wlat_mntn tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - rehanhaider/DBSD-1.5-9-vectors-lr-5e-6 This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on in the style of wlat_mntn using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.

asenella/ms_MMVAEPlus_beta_10_scale_False_seed_3

asenella

2023-07-27T12:15:36Z

0

null

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

null

2023-07-27T12:15:34Z

--- 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") ```

AnushaPalle/my_awesome_eli5_clm-model

AnushaPalle

2023-07-27T12:09:17Z

226

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-27T11:05:43Z

--- license: apache-2.0 base_model: distilgpt2 tags: - generated_from_trainer model-index: - name: my_awesome_eli5_clm-model results: [] ---  # my_awesome_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7490 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.8717 | 1.0 | 1113 | 3.7653 | | 3.7754 | 2.0 | 2226 | 3.7524 | | 3.7318 | 3.0 | 3339 | 3.7490 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

asenella/ms_MMVAEPlus_beta_5_scale_False_seed_1

asenella

2023-07-27T12:05:36Z

0

1

null

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

null

2023-07-27T12:05:35Z

--- 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") ```

asenella/ms_MMVAEPlus_beta_25_scale_True_seed_0

asenella

2023-07-27T12:05:35Z

0

null

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

null

2023-07-27T12:05:33Z

--- 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") ```

asenella/ms_MMVAEPlus_beta_25_scale_False_seed_2

asenella

2023-07-27T12:05:32Z

0

null

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

null

2023-07-27T12:05:30Z

--- 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") ```

alesanm/blip-image-captioning-base-fashionimages-finetuned

alesanm

2023-07-27T12:05:03Z

140

1

transformers

[ "transformers", "pytorch", "blip", "image-text-to-text", "image-to-text", "dataset:alesanm/balenciaga_short_descriptions", "region:us" ]

image-to-text

2023-07-24T11:00:40Z

--- inference: False datasets: - alesanm/balenciaga_short_descriptions library_name: transformers pipeline_tag: image-to-text --- The BLIP model was trained on 141 photos of the Balenciaga fashion brand and descriptions produced by GPT3

asenella/ms_MMVAEPlus_beta_10_scale_True_seed_3

asenella

2023-07-27T12:00:00Z

0

null

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

null

2023-07-27T11:59:58Z

--- 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") ```

YanJiangJerry/baselineberttweetlarge

YanJiangJerry

2023-07-27T11:59:35Z

113

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "base_model:vinai/bertweet-large", "base_model:finetune:vinai/bertweet-large", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-27T07:55:47Z

--- base_model: vinai/bertweet-large tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: baselineberttweetlarge results: [] ---  # baselineberttweetlarge This model is a fine-tuned version of [vinai/bertweet-large](https://huggingface.co/vinai/bertweet-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6168 - Accuracy: 0.6274 - F1: 0.0 - Precision: 0.0 - Recall: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|:---------:|:------:| | 0.6062 | 1.0 | 788 | 0.6020 | 0.6274 | 0.0 | 0.0 | 0.0 | | 0.5852 | 2.0 | 1576 | 0.6168 | 0.6274 | 0.0 | 0.0 | 0.0 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

asenella/ms_MMVAEPlus_beta_5_scale_False_seed_0

asenella

2023-07-27T11:59:17Z

0

null

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

null

2023-07-27T11:59:15Z

--- 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") ```

dhinman/Reinforce-Pixelcopter-200000

dhinman

2023-07-27T11:58:35Z

0

null

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

reinforcement-learning

2023-07-27T11:58:23Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-200000 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 182.70 +/- 200.09 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

snob/TagMyBookmark-KoAlpaca-QLoRA-v1.0_ALLDATA

snob

2023-07-27T11:58:28Z

3

0

peft

[ "peft", "region:us" ]

null

2023-07-27T11:58:20Z

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

asenella/ms_MMVAEPlus_beta_5_scale_True_seed_3

asenella

2023-07-27T11:58:19Z

0

null

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

null

2023-07-27T11:58:17Z

--- 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") ```

asenella/ms_MMVAEPlus_beta_10_scale_True_seed_0

asenella

2023-07-27T11:53:07Z

0

null

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

null

2023-07-27T11:53:05Z

--- 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") ```

asenella/ms_MMVAEPlus_beta_25_scale_True_seed_2

asenella

2023-07-27T11:52:01Z

0

null

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

null

2023-07-27T11:52:00Z

--- 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") ```

Chat-Error/Kimiko_7B

Chat-Error

2023-07-27T11:50:53Z

0

15

null

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

null

2023-07-26T14:59:07Z

--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Kimiko_7B  This is my new Kimiko models, trained with LLaMA2 for...purpose ## Model Details ### Model Description  - **Developed by:** nRuaif - **Model type:** Decoder only - **License:** CC BY-NC-SA - **Finetuned from model [optional]:** LLaMA2 ### Model Sources [optional]  - **Repository:** https://github.com/OpenAccess-AI-Collective/axolotl [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) ## Uses  ### Direct Use  This model is trained on 3k examples of instructions dataset, high quality roleplay, for best result follow this format ``` <<HUMAN>> How to do abc <<AIBOT>> Here is how Or with system prompting for roleplay <<SYSTEM>> A's Persona: B's Persona: Scenario: Add some instruction here on how you want your RP to go. ``` ## Bias, Risks, and Limitations  All bias of this model come from LLaMA2 with an exception of NSFW bias..... ## Training Details ### Training Data  3000 examples from LIMAERP, LIMA and I sample 1000 good instruction from Airboro ### Training Procedure  Model is trained with 1 L4 from GCP costing a whooping 1.5USD #### Training Hyperparameters - **Training regime:** [More Information Needed]  3 epochs with 0.0002 lr, full 4096 ctx token, LoRA #### Speeds, Sizes, Times [optional]  It takes 8 hours to train this model with xformers enable [More Information Needed] [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** L4 with 12CPUs 48gb ram - **Hours used:** 8 - **Cloud Provider:** GCP - **Compute Region:** US - **Carbon Emitted:** 0.2KG

MheniDevs/Kinyarwanda

MheniDevs

2023-07-27T11:43:04Z

105

0

transformers

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

automatic-speech-recognition

2023-07-24T02:16:52Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-kinyarwanda results: [] ---  # wav2vec2-large-xls-r-300m-kinyarwanda 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 dataset. It achieves the following results on the evaluation set: - Loss: 0.3917 - Wer: 0.3246 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 9.0634 | 0.12 | 400 | 3.0554 | 1.0 | | 2.8009 | 0.24 | 800 | 1.5927 | 0.9554 | | 0.9022 | 0.36 | 1200 | 0.7328 | 0.6445 | | 0.6213 | 0.48 | 1600 | 0.6138 | 0.5510 | | 0.5299 | 0.6 | 2000 | 0.6072 | 0.5223 | | 0.4999 | 0.72 | 2400 | 0.5449 | 0.4969 | | 0.4731 | 0.84 | 2800 | 0.5261 | 0.4828 | | 0.458 | 0.96 | 3200 | 0.5058 | 0.4607 | | 0.4158 | 1.09 | 3600 | 0.4892 | 0.4463 | | 0.4037 | 1.21 | 4000 | 0.4759 | 0.4429 | | 0.4021 | 1.33 | 4400 | 0.4615 | 0.4330 | | 0.3934 | 1.45 | 4800 | 0.4593 | 0.4315 | | 0.3808 | 1.57 | 5200 | 0.4736 | 0.4344 | | 0.3838 | 1.69 | 5600 | 0.4569 | 0.4249 | | 0.3726 | 1.81 | 6000 | 0.4473 | 0.4140 | | 0.3623 | 1.93 | 6400 | 0.4403 | 0.4097 | | 0.3517 | 2.05 | 6800 | 0.4389 | 0.4061 | | 0.333 | 2.17 | 7200 | 0.4383 | 0.4104 | | 0.3354 | 2.29 | 7600 | 0.4360 | 0.3955 | | 0.3257 | 2.41 | 8000 | 0.4226 | 0.3942 | | 0.3275 | 2.53 | 8400 | 0.4206 | 0.4040 | | 0.3262 | 2.65 | 8800 | 0.4172 | 0.3875 | | 0.3206 | 2.77 | 9200 | 0.4209 | 0.3877 | | 0.323 | 2.89 | 9600 | 0.4177 | 0.3825 | | 0.3099 | 3.01 | 10000 | 0.4101 | 0.3691 | | 0.3008 | 3.14 | 10400 | 0.4055 | 0.3709 | | 0.2918 | 3.26 | 10800 | 0.4085 | 0.3800 | | 0.292 | 3.38 | 11200 | 0.4089 | 0.3713 | | 0.292 | 3.5 | 11600 | 0.4092 | 0.3730 | | 0.2785 | 3.62 | 12000 | 0.4151 | 0.3687 | | 0.2941 | 3.74 | 12400 | 0.4004 | 0.3639 | | 0.2838 | 3.86 | 12800 | 0.4108 | 0.3703 | | 0.2854 | 3.98 | 13200 | 0.3911 | 0.3596 | | 0.2683 | 4.1 | 13600 | 0.3944 | 0.3575 | | 0.2647 | 4.22 | 14000 | 0.3836 | 0.3538 | | 0.2704 | 4.34 | 14400 | 0.4006 | 0.3540 | | 0.2664 | 4.46 | 14800 | 0.3974 | 0.3553 | | 0.2662 | 4.58 | 15200 | 0.3890 | 0.3470 | | 0.2615 | 4.7 | 15600 | 0.3856 | 0.3507 | | 0.2553 | 4.82 | 16000 | 0.3814 | 0.3497 | | 0.2587 | 4.94 | 16400 | 0.3837 | 0.3440 | | 0.2522 | 5.06 | 16800 | 0.3834 | 0.3486 | | 0.2451 | 5.19 | 17200 | 0.3897 | 0.3414 | | 0.2423 | 5.31 | 17600 | 0.3864 | 0.3481 | | 0.2434 | 5.43 | 18000 | 0.3808 | 0.3416 | | 0.2525 | 5.55 | 18400 | 0.3795 | 0.3408 | | 0.2427 | 5.67 | 18800 | 0.3841 | 0.3411 | | 0.2411 | 5.79 | 19200 | 0.3804 | 0.3366 | | 0.2404 | 5.91 | 19600 | 0.3800 | 0.3328 | | 0.2372 | 6.03 | 20000 | 0.3749 | 0.3335 | | 0.2244 | 6.15 | 20400 | 0.3820 | 0.3327 | | 0.2381 | 6.27 | 20800 | 0.3789 | 0.3325 | | 0.2294 | 6.39 | 21200 | 0.3867 | 0.3298 | | 0.2378 | 6.51 | 21600 | 0.3843 | 0.3281 | | 0.2312 | 6.63 | 22000 | 0.3813 | 0.3277 | | 0.2411 | 6.75 | 22400 | 0.3780 | 0.3268 | | 0.2315 | 6.87 | 22800 | 0.3790 | 0.3280 | | 0.241 | 6.99 | 23200 | 0.3776 | 0.3281 | | 0.2313 | 7.11 | 23600 | 0.3929 | 0.3283 | | 0.2423 | 7.24 | 24000 | 0.3905 | 0.3280 | | 0.2337 | 7.36 | 24400 | 0.3979 | 0.3249 | | 0.2368 | 7.48 | 24800 | 0.3980 | 0.3257 | | 0.2409 | 7.6 | 25200 | 0.3937 | 0.3229 | | 0.2416 | 7.72 | 25600 | 0.3867 | 0.3237 | | 0.2364 | 7.84 | 26000 | 0.3912 | 0.3253 | | 0.234 | 7.96 | 26400 | 0.3917 | 0.3246 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

google/flan-t5-xxl

google

2023-07-27T11:42:14Z

724,370

1,229

transformers

[ "transformers", "pytorch", "tf", "jax", "safetensors", "t5", "text2text-generation", "en", "fr", "ro", "de", "multilingual", "dataset:svakulenk0/qrecc", "dataset:taskmaster2", "dataset:djaym7/wiki_dialog", "dataset:deepmind/code_contests", "dataset:lambada", "dataset:gsm8k", "dataset:aqua_rat", "dataset:esnli", "dataset:quasc", "dataset:qed", "arxiv:2210.11416", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-10-21T15:54:59Z

--- language: - en - fr - ro - de - multilingual widget: - text: "Translate to German: My name is Arthur" example_title: "Translation" - text: "Please answer to the following question. Who is going to be the next Ballon d'or?" example_title: "Question Answering" - text: "Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering." example_title: "Logical reasoning" - text: "Please answer the following question. What is the boiling point of Nitrogen?" example_title: "Scientific knowledge" - text: "Answer the following yes/no question. Can you write a whole Haiku in a single tweet?" example_title: "Yes/no question" - text: "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?" example_title: "Reasoning task" - text: "Q: ( False or not False or False ) is? A: Let's think step by step" example_title: "Boolean Expressions" - text: "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?" example_title: "Math reasoning" - text: "Premise: At my age you will probably have learnt one lesson. Hypothesis: It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?" example_title: "Premise and hypothesis" tags: - text2text-generation datasets: - svakulenk0/qrecc - taskmaster2 - djaym7/wiki_dialog - deepmind/code_contests - lambada - gsm8k - aqua_rat - esnli - quasc - qed license: apache-2.0 --- # Model Card for FLAN-T5 XXL <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/flan2_architecture.jpg" alt="drawing" width="600"/> # Table of Contents 0. [TL;DR](#TL;DR) 1. [Model Details](#model-details) 2. [Usage](#usage) 3. [Uses](#uses) 4. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 5. [Training Details](#training-details) 6. [Evaluation](#evaluation) 7. [Environmental Impact](#environmental-impact) 8. [Citation](#citation) # TL;DR If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages. As mentioned in the first few lines of the abstract : > Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints,1 which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models. **Disclaimer**: Content from **this** model card has been written by the Hugging Face team, and parts of it were copy pasted from the [T5 model card](https://huggingface.co/t5-large). # Model Details ## Model Description - **Model type:** Language model - **Language(s) (NLP):** English, German, French - **License:** Apache 2.0 - **Related Models:** [All FLAN-T5 Checkpoints](https://huggingface.co/models?search=flan-t5) - **Original Checkpoints:** [All Original FLAN-T5 Checkpoints](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-t5-checkpoints) - **Resources for more information:** - [Research paper](https://arxiv.org/pdf/2210.11416.pdf) - [GitHub Repo](https://github.com/google-research/t5x) - [Hugging Face FLAN-T5 Docs (Similar to T5) ](https://huggingface.co/docs/transformers/model_doc/t5) # Usage Find below some example scripts on how to use the model in `transformers`: ## Using the Pytorch model ### Running the model on a CPU <details> <summary> Click to expand </summary> ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xxl") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xxl") input_text = "translate English to German: How old are you?" input_ids = tokenizer(input_text, return_tensors="pt").input_ids outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> ### Running the model on a GPU <details> <summary> Click to expand </summary> ```python # pip install accelerate from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xxl") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xxl", device_map="auto") input_text = "translate English to German: How old are you?" input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> ### Running the model on a GPU using different precisions #### FP16 <details> <summary> Click to expand </summary> ```python # pip install accelerate import torch from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xxl") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xxl", device_map="auto", torch_dtype=torch.float16) input_text = "translate English to German: How old are you?" input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> #### INT8 <details> <summary> Click to expand </summary> ```python # pip install bitsandbytes accelerate from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xxl") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xxl", device_map="auto", load_in_8bit=True) input_text = "translate English to German: How old are you?" input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> # Uses ## Direct Use and Downstream Use The authors write in [the original paper's model card](https://arxiv.org/pdf/2210.11416.pdf) that: > The primary use is research on language models, including: research on zero-shot NLP tasks and in-context few-shot learning NLP tasks, such as reasoning, and question answering; advancing fairness and safety research, and understanding limitations of current large language models See the [research paper](https://arxiv.org/pdf/2210.11416.pdf) for further details. ## Out-of-Scope Use More information needed. # Bias, Risks, and Limitations The information below in this section are copied from the model's [official model card](https://arxiv.org/pdf/2210.11416.pdf): > Language models, including Flan-T5, can potentially be used for language generation in a harmful way, according to Rae et al. (2021). Flan-T5 should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application. ## Ethical considerations and risks > Flan-T5 is fine-tuned on a large corpus of text data that was not filtered for explicit content or assessed for existing biases. As a result the model itself is potentially vulnerable to generating equivalently inappropriate content or replicating inherent biases in the underlying data. ## Known Limitations > Flan-T5 has not been tested in real world applications. ## Sensitive Use: > Flan-T5 should not be applied for any unacceptable use cases, e.g., generation of abusive speech. # Training Details ## Training Data The model was trained on a mixture of tasks, that includes the tasks described in the table below (from the original paper, figure 2): ![table.png](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/flan_t5_tasks.png) ## Training Procedure According to the model card from the [original paper](https://arxiv.org/pdf/2210.11416.pdf): > These models are based on pretrained T5 (Raffel et al., 2020) and fine-tuned with instructions for better zero-shot and few-shot performance. There is one fine-tuned Flan model per T5 model size. The model has been trained on TPU v3 or TPU v4 pods, using [`t5x`](https://github.com/google-research/t5x) codebase together with [`jax`](https://github.com/google/jax). # Evaluation ## Testing Data, Factors & Metrics The authors evaluated the model on various tasks covering several languages (1836 in total). See the table below for some quantitative evaluation: ![image.png](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/flan_t5_evals_lang.png) For full details, please check the [research paper](https://arxiv.org/pdf/2210.11416.pdf). ## Results For full results for FLAN-T5-XXL, see the [research paper](https://arxiv.org/pdf/2210.11416.pdf), Table 3. # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** Google Cloud TPU Pods - TPU v3 or TPU v4 | Number of chips ≥ 4. - **Hours used:** More information needed - **Cloud Provider:** GCP - **Compute Region:** More information needed - **Carbon Emitted:** More information needed # Citation **BibTeX:** ```bibtex @misc{https://doi.org/10.48550/arxiv.2210.11416, doi = {10.48550/ARXIV.2210.11416}, url = {https://arxiv.org/abs/2210.11416}, author = {Chung, Hyung Won and Hou, Le and Longpre, Shayne and Zoph, Barret and Tay, Yi and Fedus, William and Li, Eric and Wang, Xuezhi and Dehghani, Mostafa and Brahma, Siddhartha and Webson, Albert and Gu, Shixiang Shane and Dai, Zhuyun and Suzgun, Mirac and Chen, Xinyun and Chowdhery, Aakanksha and Narang, Sharan and Mishra, Gaurav and Yu, Adams and Zhao, Vincent and Huang, Yanping and Dai, Andrew and Yu, Hongkun and Petrov, Slav and Chi, Ed H. and Dean, Jeff and Devlin, Jacob and Roberts, Adam and Zhou, Denny and Le, Quoc V. and Wei, Jason}, keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Scaling Instruction-Finetuned Language Models}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```