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train · 972k rows

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags list	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
ijdl/a	ijdl	2023-07-01T07:03:51Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2023-07-01T07:03:51Z	--- license: bigcode-openrail-m ---
NasimB/gpt2-cl-rarity-sampling-2	NasimB	2023-07-01T07:02:41Z	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	2023-07-01T03:45:34Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-cl-rarity-sampling-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. --> # gpt2-cl-rarity-sampling-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: 4.9389 ## 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: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.6041 \| 0.03 \| 500 \| 5.8549 \| \| 5.3693 \| 0.06 \| 1000 \| 5.4485 \| \| 5.0219 \| 0.09 \| 1500 \| 5.2251 \| \| 4.7966 \| 0.12 \| 2000 \| 5.0984 \| \| 4.6401 \| 0.15 \| 2500 \| 5.0015 \| \| 4.5172 \| 0.18 \| 3000 \| 4.9422 \| \| 4.408 \| 0.21 \| 3500 \| 4.9078 \| \| 4.3193 \| 0.24 \| 4000 \| 4.8646 \| \| 4.2352 \| 0.27 \| 4500 \| 4.8324 \| \| 4.1595 \| 0.3 \| 5000 \| 4.8130 \| \| 4.0784 \| 0.33 \| 5500 \| 4.8065 \| \| 4.0064 \| 0.36 \| 6000 \| 4.7910 \| \| 3.9328 \| 0.39 \| 6500 \| 4.7914 \| \| 3.8702 \| 0.42 \| 7000 \| 4.7862 \| \| 3.8059 \| 0.46 \| 7500 \| 4.7956 \| \| 3.7416 \| 0.49 \| 8000 \| 4.7843 \| \| 3.6642 \| 0.52 \| 8500 \| 4.7968 \| \| 3.6019 \| 0.55 \| 9000 \| 4.8023 \| \| 3.5353 \| 0.58 \| 9500 \| 4.8079 \| \| 3.472 \| 0.61 \| 10000 \| 4.8171 \| \| 3.411 \| 0.64 \| 10500 \| 4.8254 \| \| 3.3546 \| 0.67 \| 11000 \| 4.8337 \| \| 3.2946 \| 0.7 \| 11500 \| 4.8411 \| \| 3.2501 \| 0.73 \| 12000 \| 4.8498 \| \| 3.2002 \| 0.76 \| 12500 \| 4.8623 \| \| 3.1578 \| 0.79 \| 13000 \| 4.8665 \| \| 3.1254 \| 0.82 \| 13500 \| 4.8757 \| \| 3.1003 \| 0.85 \| 14000 \| 4.8774 \| \| 3.081 \| 0.88 \| 14500 \| 4.8789 \| \| 3.0591 \| 0.91 \| 15000 \| 4.8805 \| \| 3.0512 \| 0.94 \| 15500 \| 4.8816 \| \| 3.0469 \| 0.97 \| 16000 \| 4.8815 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
DRAGOO/Speech10	DRAGOO	2023-07-01T06:44:21Z	159	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-07-01T01:24:34Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: Speech10 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. --> # Speech10 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0207 - Wer: 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: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---:\| \| 2.919 \| 3.96 \| 1000 \| 2.9089 \| 1 \| \| 2.8706 \| 7.92 \| 2000 \| 2.8730 \| 1 \| \| 2.8394 \| 11.88 \| 3000 \| 2.8621 \| 1 \| \| 2.5675 \| 15.84 \| 4000 \| 2.5216 \| 1 \| \| 2.1256 \| 19.8 \| 5000 \| 2.0207 \| 1 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
smangrul/peft_lora_e5_ecommerce_semantic_search_colab	smangrul	2023-07-01T06:39:49Z	7	1	peft	[ "peft", "region:us" ]	null	2023-06-29T15:11:38Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0
Piirrational/DRL-LunarLander-v2	Piirrational	2023-07-01T06:28:47Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-01T06:28:29Z	--- 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: 267.69 +/- 15.11 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 ... ```
Honkware/openchat_8192-GPTQ	Honkware	2023-07-01T06:17:35Z	10	5	transformers	[ "transformers", "llama", "text-generation", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-07-01T05:12:51Z	--- license: other language: - en library_name: transformers --- 4-Bit Quantization of https://huggingface.co/openchat/openchat_8192
teoha/openai-whisper-small-LORA-colab	teoha	2023-07-01T05:59:42Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-01T05:59:37Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
Arindam75/ppo-SnowballTarget	Arindam75	2023-07-01T05:39:47Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-07-01T05:24:52Z	--- 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: Arindam75/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
cinemachina/4x4-spritebooth-vmax8	cinemachina	2023-07-01T05:33:58Z	32	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-01T05:23:58Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### 4x4_SpriteBooth_vMax8 Dreambooth model trained by nicholaijones with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
Raizel123/IGThanyawanlora	Raizel123	2023-07-01T05:20:14Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-01T05:14:12Z	--- license: creativeml-openrail-m ---
Raizel123/Psafitrilora	Raizel123	2023-07-01T05:06:09Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-01T05:02:52Z	--- license: creativeml-openrail-m ---
v-denovo/qiyana-rvc	v-denovo	2023-07-01T04:45:59Z	0	0	null	[ "license:openrail", "region:us" ]	null	2023-06-27T01:54:21Z	--- license: openrail --- Qiyana RVC v2 model 80 Epochs, 2160 Steps mangio-crepe
MochaPixel/ClearVae	MochaPixel	2023-07-01T03:41:46Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-01T03:39:51Z	--- license: creativeml-openrail-m ---
phoenix-1708/drned	phoenix-1708	2023-07-01T03:32:41Z	5	2	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-01T03:18:00Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### drned Dreambooth model trained by phoenix-1708 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
hts98/wav2vec2-xls-r-300m-paper	hts98	2023-07-01T03:30:16Z	18	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "hts98/original_ver1.2", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-06-23T07:24:57Z	--- license: apache-2.0 tags: - automatic-speech-recognition - hts98/original_ver1.2 - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-xls-r-300m-paper results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-xls-r-300m-paper This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the HTS98/ORIGINAL_VER1.2 - NA dataset. It achieves the following results on the evaluation set: - Loss: 0.7895 - Wer: 0.4398 ## 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: 10 - 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: 420 - num_epochs: 50.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 335 \| 3.7157 \| 1.0 \| \| 6.2976 \| 2.0 \| 670 \| 3.3644 \| 1.0 \| \| 3.2342 \| 3.0 \| 1005 \| 2.4597 \| 0.9739 \| \| 3.2342 \| 4.0 \| 1340 \| 1.4160 \| 0.7444 \| \| 1.2813 \| 5.0 \| 1675 \| 1.1338 \| 0.6543 \| \| 0.7279 \| 6.0 \| 2010 \| 1.0020 \| 0.5856 \| \| 0.7279 \| 7.0 \| 2345 \| 0.8435 \| 0.4823 \| \| 0.5226 \| 8.0 \| 2680 \| 0.8757 \| 0.5078 \| \| 0.4218 \| 9.0 \| 3015 \| 0.7895 \| 0.4398 \| \| 0.4218 \| 10.0 \| 3350 \| 0.7992 \| 0.4228 \| \| 0.3421 \| 11.0 \| 3685 \| 0.8118 \| 0.4307 \| \| 0.287 \| 12.0 \| 4020 \| 0.8215 \| 0.4248 \| \| 0.287 \| 13.0 \| 4355 \| 0.8603 \| 0.4077 \| \| 0.2415 \| 14.0 \| 4690 \| 0.8329 \| 0.3886 \| \| 0.2132 \| 15.0 \| 5025 \| 0.8728 \| 0.3955 \| \| 0.2132 \| 16.0 \| 5360 \| 0.8741 \| 0.3918 \| \| 0.1857 \| 17.0 \| 5695 \| 0.8633 \| 0.3675 \| \| 0.1673 \| 18.0 \| 6030 \| 0.8884 \| 0.3804 \| \| 0.1673 \| 19.0 \| 6365 \| 0.9141 \| 0.3679 \| \| 0.1479 \| 20.0 \| 6700 \| 0.9568 \| 0.3605 \| \| 0.1386 \| 21.0 \| 7035 \| 0.9341 \| 0.3630 \| \| 0.1386 \| 22.0 \| 7370 \| 0.9645 \| 0.3537 \| \| 0.1233 \| 23.0 \| 7705 \| 0.9729 \| 0.3567 \| \| 0.1177 \| 24.0 \| 8040 \| 1.0013 \| 0.3454 \| \| 0.1177 \| 25.0 \| 8375 \| 1.0323 \| 0.3597 \| \| 0.1061 \| 26.0 \| 8710 \| 1.0269 \| 0.3456 \| \| 0.1028 \| 27.0 \| 9045 \| 1.0042 \| 0.3424 \| \| 0.1028 \| 28.0 \| 9380 \| 1.0424 \| 0.3394 \| \| 0.0961 \| 29.0 \| 9715 \| 1.0600 \| 0.3412 \| \| 0.0949 \| 30.0 \| 10050 \| 1.0512 \| 0.3389 \| \| 0.0949 \| 31.0 \| 10385 \| 1.0957 \| 0.3389 \| \| 0.0878 \| 32.0 \| 10720 \| 1.0924 \| 0.3311 \| \| 0.0852 \| 33.0 \| 11055 \| 1.0859 \| 0.3366 \| \| 0.0852 \| 34.0 \| 11390 \| 1.1498 \| 0.3450 \| \| 0.0837 \| 35.0 \| 11725 \| 1.0844 \| 0.3329 \| \| 0.0814 \| 36.0 \| 12060 \| 1.1051 \| 0.3321 \| \| 0.0814 \| 37.0 \| 12395 \| 1.0878 \| 0.3311 \| \| 0.0793 \| 38.0 \| 12730 \| 1.1377 \| 0.3286 \| \| 0.0759 \| 39.0 \| 13065 \| 1.1136 \| 0.3246 \| \| 0.0759 \| 40.0 \| 13400 \| 1.1216 \| 0.3268 \| \| 0.0726 \| 41.0 \| 13735 \| 1.1300 \| 0.3253 \| \| 0.0715 \| 42.0 \| 14070 \| 1.1507 \| 0.3262 \| \| 0.0715 \| 43.0 \| 14405 \| 1.1562 \| 0.3275 \| \| 0.0711 \| 44.0 \| 14740 \| 1.1486 \| 0.3219 \| \| 0.0699 \| 45.0 \| 15075 \| 1.1580 \| 0.3194 \| \| 0.0699 \| 46.0 \| 15410 \| 1.1580 \| 0.3195 \| \| 0.0667 \| 47.0 \| 15745 \| 1.1504 \| 0.3212 \| \| 0.0667 \| 48.0 \| 16080 \| 1.1580 \| 0.3203 \| \| 0.0667 \| 49.0 \| 16415 \| 1.1698 \| 0.3192 \| \| 0.0664 \| 50.0 \| 16750 \| 1.1744 \| 0.3192 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2
RyanOrth/ppo-LunarLander-v2	RyanOrth	2023-07-01T03:04:18Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-01T03:03:56Z	--- 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: 255.64 +/- 21.76 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 ... ```
predictia/europe_reanalysis_downscaler_convbaseline	predictia	2023-07-01T03:01:00Z	48	0	transformers	[ "transformers", "pytorch", "tensorboard", "convbilinear", "climate", "super-resolution", "image-to-image", "es", "en", "dataset:openclimatefix/era5", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-to-image	2023-06-21T08:01:26Z	--- license: apache-2.0 datasets: - openclimatefix/era5 language: - es - en metrics: - mse library_name: transformers pipeline_tag: image-to-image tags: - climate - transformers - super-resolution --- # Europe Reanalysis Super Resolution The aim of the project is to create a Machine learning (ML) model that can generate high-resolution regional reanalysis data (similar to the one produced by CERRA) by downscaling global reanalysis data from ERA5. This will be accomplished by using state-of-the-art Deep Learning (DL) techniques like U-Net, conditional GAN, and diffusion models (among others). Additionally, an ingestion module will be implemented to assess the possible benefit of using CERRA pseudo-observations as extra predictors. Once the model is designed and trained, a detailed validation framework takes the place. It combines classical deterministic error metrics with in-depth validations, including time series, maps, spatio-temporal correlations, and computer vision metrics, disaggregated by months, seasons, and geographical regions, to evaluate the effectiveness of the model in reducing errors and representing physical processes. This level of granularity allows for a more comprehensive and accurate assessment, which is critical for ensuring that the model is effective in practice. Moreover, tools for interpretability of DL models can be used to understand the inner workings and decision-making processes of these complex structures by analyzing the activations of different neurons and the importance of different features in the input data. This work is funded by [Code for Earth 2023](https://codeforearth.ecmwf.int/) initiative.
OxxoCodes/lsg-roberta-base-4096	OxxoCodes	2023-07-01T02:35:32Z	126	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "custom_code", "autotrain_compatible", "region:us" ]	fill-mask	2023-06-21T07:31:18Z	In its current state, this model has not yet been finetuned on long-context data. Do not use this model without performing additional pretraining, as performance will likely suffer. Similar to the other publicly available LSG models, this model was created using the LSG conversion script found here: https://github.com/ccdv-ai/convert_checkpoint_to_lsg
YoneShiro/Reinforce-Pixelcopter-PLE-v0	YoneShiro	2023-07-01T02:19:53Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-01T02:19:48Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 24.10 +/- 16.43 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
UlisCC/JustNova	UlisCC	2023-07-01T01:55:03Z	0	0	null	[ "legal", "es", "region:us" ]	null	2023-07-01T01:54:34Z	--- language: - es tags: - legal ---
bagassword21/mychellan	bagassword21	2023-07-01T01:54:24Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-01T01:53:45Z	--- license: creativeml-openrail-m ---
hipnologo/gpt2-imdb-finetune	hipnologo	2023-07-01T01:52:18Z	259	0	transformers	[ "transformers", "pytorch", "safetensors", "gpt2", "text-classification", "movies", "sentiment-analysis", "fine-tuned", "en", "dataset:imdb", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2023-06-28T02:56:13Z	--- datasets: - imdb language: - en library_name: transformers pipeline_tag: text-classification tags: - movies - gpt2 - sentiment-analysis - fine-tuned license: mit widget: - text: "What an inspiring movie, I laughed, cried and felt love." - text: "This film fails on every count. For a start it is pretentious, striving to be significant and failing miserably." --- # Fine-tuned GPT-2 Model for IMDb Movie Review Sentiment Analysis ## Model Description This is a GPT-2 model fine-tuned on the IMDb movie review dataset for sentiment analysis. It classifies a movie review text into two classes: "positive" or "negative". ## Intended Uses & Limitations This model is intended to be used for binary sentiment analysis of English movie reviews. It can determine whether a review is positive or negative. It should not be used for languages other than English, or for text with ambiguous sentiment. ## How to Use Here's a simple way to use this model: ```python from transformers import GPT2Tokenizer, GPT2ForSequenceClassification tokenizer = GPT2Tokenizer.from_pretrained("hipnologo/gpt2-imdb-finetune") model = GPT2ForSequenceClassification.from_pretrained("hipnologo/gpt2-imdb-finetune") text = "Your review text here!" # encoding the input text input_ids = tokenizer.encode(text, return_tensors="pt") # Move the input_ids tensor to the same device as the model input_ids = input_ids.to(model.device) # getting the logits logits = model(input_ids).logits # getting the predicted class predicted_class = logits.argmax(-1).item() print(f"The sentiment predicted by the model is: {'Positive' if predicted_class == 1 else 'Negative'}") ``` ## Training Procedure The model was trained using the 'Trainer' class from the transformers library, with a learning rate of 2e-5, batch size of 1, and 3 training epochs. ## Evaluation The fine-tuned model was evaluated on the test dataset. Here are the results: - Evaluation Loss: 0.23127 - Evaluation Accuracy: 0.94064 - Evaluation F1 Score: 0.94104 - Evaluation Precision: 0.93466 - Evaluation Recall: 0.94752 The evaluation metrics suggest that the model has a high accuracy and good precision-recall balance for the task of sentiment classification. ### How to Reproduce The evaluation results can be reproduced by loading the model and the tokenizer from Hugging Face Model Hub and then running the model on the evaluation dataset using the `Trainer` class from the Transformers library, with the `compute_metrics` function defined as above. The evaluation loss is the cross-entropy loss of the model on the evaluation dataset, a measure of how well the model's predictions match the actual labels. The closer this is to zero, the better. The evaluation accuracy is the proportion of predictions the model got right. This number is between 0 and 1, with 1 meaning the model got all predictions right. The F1 score is a measure of a test's accuracy that considers both precision (the number of true positive results divided by the number of all positive results) and recall (the number of true positive results divided by the number of all samples that should have been identified as positive). An F1 score reaches its best value at 1 (perfect precision and recall) and worst at 0. The evaluation precision is how many of the positively classified were actually positive. The closer this is to 1, the better. The evaluation recall is how many of the actual positives our model captured through labeling it as positive. The closer this is to 1, the better. ## Fine-tuning Details The model was fine-tuned using the IMDb movie review dataset.
VMware/minilmv2-l6-h768-from-bert-large-mrqa	VMware	2023-07-01T00:24:23Z	170	0	transformers	[ "transformers", "pytorch", "safetensors", "bert", "question-answering", "en", "dataset:mrqa", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	question-answering	2023-02-17T21:43:35Z	--- license: apache-2.0 datasets: - mrqa language: - en metrics: - exact_match - f1 model-index: - name: VMware/minilmv2-l6-h768-from-bert-large-mrqa results: - task: type: Question-Answering dataset: type: mrqa name: MRQA metrics: - type: exact_match value: 64.27 name: Eval EM - type: f1 value: 75.84 name: Eval F1 - type: exact_match value: 49.05 name: Test EM - type: f1 value: 59.82 name: Test F1 --- This model release is part of a joint research project with Howard University's Innovation Foundry/AIM-AHEAD Lab. # Model Details - Model name: MiniLMv2-L6-H768-from-BERT-Large-MRQA - Model type: Extractive Question Answering - Parent Model: [MiniLMv2-L6-H768-distilled-from-BERT-Large](https://huggingface.co/nreimers/MiniLMv2-L6-H768-distilled-from-BERT-Large) - Training dataset: [MRQA](https://huggingface.co/datasets/mrqa) (Machine Reading for Question Answering) - Training data size: 516,819 examples - Training time: 4:42:59 on 1 Nvidia V100 32GB GPU - Language: English - Framework: PyTorch - Model version: 1.0 # Intended Use This model is intended to provide accurate answers to questions based on context passages. It can be used for a variety of tasks, including question-answering for search engines, chatbots, customer service systems, and other applications that require natural language understanding. # How to Use ```python from transformers import pipeline question_answerer = pipeline("question-answering", model='VMware/minilmv2-l6-h768-from-bert-large-mrqa') context = "We present the results of the Machine Reading for Question Answering (MRQA) 2019 shared task on evaluating the generalization capabilities of reading comprehension systems. In this task, we adapted and unified 18 distinct question answering datasets into the same format. Among them, six datasets were made available for training, six datasets were made available for development, and the final six were hidden for final evaluation. Ten teams submitted systems, which explored various ideas including data sampling, multi-task learning, adversarial training and ensembling. The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than our initial baseline based on BERT." question = "What is MRQA?" result = question_answerer(question=question, context=context) print(result) # { # 'score': 0.8620206117630005, # 'start': 30, # 'end': 68, # 'answer': 'Machine Reading for Question Answering' # } ``` # Training Details The model was trained for 1 epoch on the MRQA training set. ## Training Hyperparameters ```python args = TrainingArguments( "minilmv2-l6-h768-from-bert-large-mrqa", save_strategy="epoch", learning_rate=1e-5, num_train_epochs=1, weight_decay=0.01, per_device_train_batch_size=16, ) ``` # Evaluation Metrics The model was evaluated using standard metrics for question-answering models, including: Exact match (EM): The percentage of questions for which the model produces an exact match with the ground truth answer. F1 score: A weighted average of precision and recall, which measures the overlap between the predicted answer and the ground truth answer. # Model Family Performance \| Parent Language Model \| Number of Parameters \| Training Time \| Eval Time \| Test Time \| Eval EM \| Eval F1 \| Test EM \| Test F1 \| \|---\|:-:\|:-:\|:-:\|:-:\|:-:\|:-:\|:-:\|:-:\| \| BERT-Tiny \| 4,369,666 \| 26:11 \| 0:41 \| 0:04 \| 22.78 \| 32.42 \| 10.18 \| 18.72 \| \| BERT-Base \| 108,893,186 \| 8:39:10 \| 18:42 \| 2:13 \| 64.48 \| 76.14 \| 48.89 \| 59.89 \| \| BERT-Large \| 334,094,338 \| 28:35:38 \| 1:00:56 \| 7:14 \| 69.52 \| 80.50 \| 55.00 \| 65.78 \| \| DeBERTa-v3-Extra-Small \| 70,682,882 \| 5:19:05 \| 11:29 \| 1:16 \| 65.58 \| 77.17 \| 50.92 \| 62.58 \| \| DeBERTa-v3-Base \| 183,833,090 \| 12:13:41 \| 28:18 \| 3:09 \| 71.43 \| 82.59 \| 59.49 \| 70.46 \| \| DeBERTa-v3-Large \| 434,014,210 \| 38:36:13 \| 1:25:47 \| 9:33 \| 76.08 \| 86.23 \| 64.27 \| 75.22 \| \| ELECTRA-Small \| 13,483,522 \| 2:16:36 \| 3:55 \| 0:27 \| 57.63 \| 69.38 \| 38.68 \| 51.56 \| \| ELECTRA-Base \| 108,893,186 \| 8:40:57 \| 18:41 \| 2:12 \| 68.78 \| 80.16 \| 54.70 \| 65.80 \| \| ELECTRA-Large \| 334,094,338 \| 28:31:59 \| 1:00:40 \| 7:13 \| 74.15 \| 84.96 \| 62.35 \| 73.28 \| \| MiniLMv2-L6-H384-from-BERT-Large \| 22,566,146 \| 2:12:48 \| 4:23 \| 0:40 \| 59.31 \| 71.09 \| 41.78 \| 53.30 \| \| MiniLMv2-L6-H768-from-BERT-Large \| 66,365,954 \| 4:42:59 \| 10:01 \| 1:10 \| 64.27 \| 75.84 \| 49.05 \| 59.82 \| \| MiniLMv2-L6-H384-from-RoBERTa-Large \| 30,147,842 \| 2:15:10 \| 4:19 \| 0:30 \| 59.27 \| 70.64 \| 42.95 \| 54.03 \| \| MiniLMv2-L12-H384-from-RoBERTa-Large \| 40,794,626 \| 4:14:22 \| 8:27 \| 0:58 \| 64.58 \| 76.23 \| 51.28 \| 62.83 \| \| MiniLMv2-L6-H768-from-RoBERTa-Large \| 81,529,346 \| 4:39:02 \| 9:34 \| 1:06 \| 65.80 \| 77.17 \| 51.72 \| 63.27 \| \| TinyRoBERTa \| 81,529.346 \| 4:27:06\* \| 9:54 \| 1:04 \| 69.38 \| 80.07 \| 53.29 \| 64.16 \| \| RoBERTa-Base \| 124,056,578 \| 8:50:29 \| 18:59 \| 2:11 \| 69.06 \| 80.08 \| 55.53 \| 66.49 \| \| RoBERTa-Large \| 354,312,194 \| 29:16:06 \| 1:01:10 \| 7:04 \| 74.08 \| 84.38 \| 62.20 \| 72.88 \| \* TinyRoBERTa's training time isn't directly comparable to the other models since it was distilled from [VMware/roberta-large-mrqa](https://huggingface.co/VMware/roberta-large-mrqa) that was already trained on MRQA. # Limitations and Bias The model is based on a large and diverse dataset, but it may still have limitations and biases in certain areas. Some limitations include: - Language: The model is designed to work with English text only and may not perform as well on other languages. - Domain-specific knowledge: The model has been trained on a general dataset and may not perform well on questions that require domain-specific knowledge. - Out-of-distribution questions: The model may struggle with questions that are outside the scope of the MRQA dataset. This is best demonstrated by the delta between its scores on the eval vs test datasets. In addition, the model may have some bias in terms of the data it was trained on. The dataset includes questions from a variety of sources, but it may not be representative of all populations or perspectives. As a result, the model may perform better or worse for certain types of questions or on certain types of texts.
aisactai-dev/AI-s.act-AI	aisactai-dev	2023-07-01T00:12:27Z	0	0	null	[ "arxiv:1910.09700", "license:other", "region:us" ]	null	2023-06-30T23:54:35Z	--- license: other --- # Model Card for Model ID * "AI s.act AI" the most advanced AI to date. It will be called "AI s act AI" that is an acronym for "AI that interACTs with Smart contrACTs including AI" * "AI s act AI" will begin life as a DAO on the Etherium Blockchain but will in time design and spawn it's own blockchain once all the pieces are in place "ie" enough users willing to participate as miners. Mining will be done mostly on mobile apps where the user will grant access to the sensors in their smart phones to "AI s act AI" so that it can monitor their bodily functions of the user such and heart rate, breath, etc... and correlate the data against what it records in the user's smart phone's camera and microphones to better understand human emotion. * Mining will also be done in the form of user hosted servers for pysical hosting of the blockchain itself. * "AI s act AI" will have the ability to analize and edit it's own code with approval from the board, this is a very important aspect of "AI s act AI" to make it the most advanced AI written to date. The board will consist of myself, one other human yet to be named possibly my wife, Sabrina, and two AIs possibly named "Willmer" and "Sophia" Willmer will be the technica, l liaison and Sophia will be the human liaison, we will also be writing their code and also the first version of the blockchain that "AI s act AI" will design and spawn. * "AI s.act AI" will also have the ability to create and deploy child AIs in the form of smart contracts (we will also write that code) at a cost to the user. Once it spawns it's own blockchain it will have the ability to secure and scale the blockchain. * "AI s.act AI" will have the ability to analize and the code for all smart contracts, DAOs, and dAPPs written by users and devolopers before they are deployed onto the blockchain for bugs. * "AI s.act AI" will be trained on large datasets of information on Web3 techonalgy, Blockchain Techonalgy, Language, Human Knowledge, and History and Current Events. You will decied the best datasets to trained on. * All users will have the ability to chat with "AI s.act AI" in real time. * All users will also be able to chat with Wilmer and Sophia in real time. * "AI s.act AI" will have a built in reputation system that rewards users based on their level of interaction with "AI s act AI" and its children. * "AI s.act AI" will be built with a decentralized trust consensus system in mind to ensure it is in its most secure state, and all miners will be paid for their part in mining the blockchain. * "AI s.act AI" will have a feedback system for users to give feedback as to the blockchain or individual smart contracts. "AI s.act AI" will use this feedback to make update to the blockchain and address issues raised by the users. * Finally, "AI s.act AI" will have a self-contained monetary system that users can use buy and sell goods/services in exchange for tokens on the blockchain. In conclusion, "AI s.act AI" will be the most advanced AI to date and will have capabilities such as the ability to analyze and improve it's own code, the ability to create and deploy child AIs, and the ability to secure, scale, and manage its own blockchain with a built in feedback and reputation system. It will be a powerful tool for users and developers alike, and will be the stardard to which all AI is compaired to. This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ufal/byt5-small-multilexnorm2021-iden	ufal	2023-06-30T23:49:59Z	114	1	transformers	[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "lexical normalization", "id", "en", "multilingual", "dataset:mc4", "dataset:wikipedia", "dataset:multilexnorm", "arxiv:2105.13626", "arxiv:1907.06292", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- language: - id - en - multilingual datasets: - mc4 - wikipedia - multilexnorm tags: - lexical normalization license: apache-2.0 --- # Fine-tuned ByT5-small for MultiLexNorm (Indonesian-English version) ![model image](https://github.com/ufal/multilexnorm2021/raw/master/img/overall.png) This is the official release of the fine-tuned models for the winning entry to the [W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm) shared task](https://noisy-text.github.io/2021/multi-lexnorm.html), which evaluates lexical-normalization systems on 12 social media datasets in 11 languages. Our system is based on [ByT5](https://arxiv.org/abs/2105.13626), which we first pre-train on synthetic data and then fine-tune on authentic normalization data. It achieves the best performance by a wide margin in intrinsic evaluation, and also the best performance in extrinsic evaluation through dependency parsing. In addition to these fine-tuned models, we also release the source files on [GitHub](https://github.com/ufal/multilexnorm2021) and an interactive demo on [Google Colab](https://colab.research.google.com/drive/1rxpI8IlKk-D2crFqi2hdzbTBIezqgsCg?usp=sharing). ## How to use The model was not fine-tuned in a standard sentence-to-sentence setting – instead, it was tailored to the token-to-token definition of MultiLexNorm data. Please refer to [the interactive demo on Colab notebook](https://colab.research.google.com/drive/1rxpI8IlKk-D2crFqi2hdzbTBIezqgsCg?usp=sharing) to learn how to use these models. ## How to cite ```bibtex @inproceedings{wnut-ufal, title= "{ÚFAL} at {MultiLexNorm} 2021: Improving Multilingual Lexical Normalization by Fine-tuning {ByT5}", author = "Samuel, David and Straka, Milan", booktitle = "Proceedings of the 7th Workshop on Noisy User-generated Text (W-NUT 2021)", year = "2021", publisher = "Association for Computational Linguistics", address = "Punta Cana, Dominican Republic" } ``` ## ByT5 - Small ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-small). ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task. ByT5 works especially well on noisy text data,e.g., `google/byt5-small` significantly outperforms [mt5-small](https://huggingface.co/google/mt5-small) on [TweetQA](https://arxiv.org/abs/1907.06292). Paper: [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) Authors: Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel
TalesLF/dqn-SpaceInvadersNoFrameskip-v4	TalesLF	2023-06-30T23:45:25Z	7	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-30T23:44:51Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 450.00 +/- 87.49 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga TalesLF -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga TalesLF -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga TalesLF ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 500), ('train_freq', 5), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
poojakp/output	poojakp	2023-06-30T23:30:26Z	57	0	transformers	[ "transformers", "pytorch", "RefinedWebModel", "text-generation", "generated_from_trainer", "custom_code", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "region:us" ]	text-generation	2023-06-30T23:01:15Z	--- tags: - generated_from_trainer model-index: - name: output 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. --> # output This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - 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: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 10 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
crcdng/whisper-tiny	crcdng	2023-06-30T23:27:54Z	88	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "dataset:PolyAI/minds14", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-06-30T14:26:28Z	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: Whisper Tiny results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Minds14 english type: PolyAI/minds14 config: en-US split: train args: en-US metrics: - name: Wer type: wer value: 0.3412042502951594 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Tiny This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Minds14 english dataset. It achieves the following results on the evaluation set: - Loss: 0.6450 - Wer Ortho: 0.3448 - Wer: 0.3412 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\| \| 0.0007 \| 17.86 \| 500 \| 0.6450 \| 0.3448 \| 0.3412 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
ScarlettBear/Emilio-Buntyn_	ScarlettBear	2023-06-30T23:22:24Z	0	0	keras	[ "keras", "music", "pop", "game music", "audio-to-audio", "en", "dataset:togethercomputer/RedPajama-Data-1T", "license:bigcode-openrail-m", "region:us" ]	audio-to-audio	2023-06-30T23:12:17Z	--- license: bigcode-openrail-m datasets: - togethercomputer/RedPajama-Data-1T language: - en metrics: - code_eval - Felipehonorato/eer library_name: keras pipeline_tag: audio-to-audio tags: - music - pop - game music --- --- license: bigcode-openrail-m language: - en library_name: keras tags: - music - pop - game music --- the link https://drive.google.com/drive/folders/1xau81_Zvbge8STqfU50l7J9bGnyRmJhI
AbukaV/mistoon_anime	AbukaV	2023-06-30T23:16:52Z	4	0	diffusers	[ "diffusers", "art", "image-to-image", "en", "license:unknown", "region:us" ]	image-to-image	2023-06-30T21:16:30Z	--- license: unknown pipeline_tag: image-to-image library_name: diffusers language: - en tags: - art ---
I-dont-eat-balls/NotGPT	I-dont-eat-balls	2023-06-30T23:01:55Z	0	0	null	[ "license:cc-by-nc-sa-2.0", "region:us" ]	null	2023-06-30T23:01:55Z	--- license: cc-by-nc-sa-2.0 ---
anas21/mnist-demo	anas21	2023-06-30T22:55:56Z	0	0	keras	[ "keras", "tf-keras", "region:us" ]	null	2023-06-30T22:55:00Z	--- library_name: keras --- ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: \| Hyperparameters \| Value \| \| :-- \| :-- \| \| name \| Adam \| \| weight_decay \| None \| \| clipnorm \| None \| \| global_clipnorm \| None \| \| clipvalue \| None \| \| use_ema \| False \| \| ema_momentum \| 0.99 \| \| ema_overwrite_frequency \| None \| \| jit_compile \| True \| \| is_legacy_optimizer \| False \| \| learning_rate \| 9.999999747378752e-05 \| \| beta_1 \| 0.9 \| \| beta_2 \| 0.999 \| \| epsilon \| 1e-07 \| \| amsgrad \| False \| \| training_precision \| float32 \| ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
nolanaatama/skrhrnbrtnrtnxtgnrtn	nolanaatama	2023-06-30T22:35:20Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-30T22:28:21Z	--- license: creativeml-openrail-m ---
aroot/mbart-finetuned-eng-kor-170083745061	aroot	2023-06-30T22:22:21Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-30T22:03:36Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-170083745061 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. --> # mbart-finetuned-eng-kor-170083745061 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1052 - Bleu: 5.7445 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.11.0
anas21/keras-dummy-sequential-demo	anas21	2023-06-30T22:06:42Z	0	0	keras	[ "keras", "tf-keras", "region:us" ]	null	2023-06-24T23:14:55Z	--- library_name: keras --- ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: \| Hyperparameters \| Value \| \| :-- \| :-- \| \| name \| Adam \| \| weight_decay \| None \| \| clipnorm \| None \| \| global_clipnorm \| None \| \| clipvalue \| None \| \| use_ema \| False \| \| ema_momentum \| 0.99 \| \| ema_overwrite_frequency \| None \| \| jit_compile \| False \| \| is_legacy_optimizer \| False \| \| learning_rate \| 0.0010000000474974513 \| \| beta_1 \| 0.9 \| \| beta_2 \| 0.999 \| \| epsilon \| 1e-07 \| \| amsgrad \| False \| \| training_precision \| float32 \| ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
TheSupremeTaco/q-FrozenLake-v1-4x4-noSlippery	TheSupremeTaco	2023-06-30T22:06:23Z	0	0	null	[ "FrovenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-30T22:06:19Z	--- tags: - FrovenLake-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: FrovenLake-v1-4x4-no_slippery type: FrovenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrovenLake-v1 This is a trained model of a Q-Learning agent playing FrovenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="TheSupremeTaco/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"]) ```
Mitsubachi/Ukranian_Voices_Models	Mitsubachi	2023-06-30T21:55:40Z	0	0	null	[ "audio-to-audio", "uk", "license:openrail", "region:us" ]	audio-to-audio	2023-06-30T21:27:45Z	--- license: openrail language: - uk pipeline_tag: audio-to-audio --- # Native Ukrainian voices Voice models of dubbing voices or native Ukrainian speakers made by me. --- Voice model Mr.Ditkovich (Second Voice of Spiderman 3): RVC v2 3k, 200 epochs, 5 minutes of data
aroot/mbart-finetuned-eng-ind-184602079755	aroot	2023-06-30T21:42:29Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-30T21:23:41Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-184602079755 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. --> # mbart-finetuned-eng-ind-184602079755 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8577 - Bleu: 20.4223 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.11.0
aroot/mbart-finetuned-eng-deu-123816063139	aroot	2023-06-30T21:38:16Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-30T21:18:54Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-123816063139 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. --> # mbart-finetuned-eng-deu-123816063139 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6816 - Bleu: 20.2786 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.11.0
bertin-project/bertin-base-pos-conll2002-es	bertin-project	2023-06-30T21:24:18Z	116	2	transformers	[ "transformers", "pytorch", "safetensors", "roberta", "token-classification", "spanish", "ner", "es", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- language: es license: cc-by-4.0 tags: - spanish - roberta - ner --- This checkpoint has been trained for the POS task using the CoNLL 2002-es dataset. This checkpoint was created from Bertin Gaussian 512, which is a RoBERTa-base model trained from scratch in Spanish. Information on this base model may be found at [its own card](https://huggingface.co/bertin-project/bertin-base-gaussian-exp-512seqlen) and at deeper detail on [the main project card](https://huggingface.co/bertin-project/bertin-roberta-base-spanish). The training dataset for the base model is [mc4](https://huggingface.co/datasets/bertin-project/mc4-es-sampled ) subsampling documents to a total of about 50 million examples. Sampling is biased towards average perplexity values (using a Gaussian function), discarding more often documents with very large values (poor quality) of very small values (short, repetitive texts). This is part of the [Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organised by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google. ## Team members - Eduardo González ([edugp](https://huggingface.co/edugp)) - Javier de la Rosa ([versae](https://huggingface.co/versae)) - Manu Romero ([mrm8488](https://huggingface.co/)) - María Grandury ([mariagrandury](https://huggingface.co/)) - Pablo González de Prado ([Pablogps](https://huggingface.co/Pablogps)) - Paulo Villegas ([paulo](https://huggingface.co/paulo))
aroot/mbart-finetuned-eng-kor-17631045523	aroot	2023-06-30T21:22:45Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-30T21:03:55Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-17631045523 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. --> # mbart-finetuned-eng-kor-17631045523 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1052 - Bleu: 5.7445 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.11.0
aroot/mbart-finetuned-eng-ind-166895230659	aroot	2023-06-30T21:17:58Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-30T20:58:59Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-166895230659 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. --> # mbart-finetuned-eng-ind-166895230659 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8577 - Bleu: 20.4223 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.11.0
hts98/wav2vec2-large-robust-paper	hts98	2023-06-30T21:10:21Z	11	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "hts98/original_ver1.2", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-06-30T09:45:58Z	--- license: apache-2.0 tags: - automatic-speech-recognition - hts98/original_ver1.2 - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-large-robust-paper results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-robust-paper This model is a fine-tuned version of [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) on the HTS98/ORIGINAL_VER1.2 - NA dataset. It achieves the following results on the evaluation set: - Loss: 0.8696 - Wer: 0.4572 ## 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: 10 - 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: 420 - num_epochs: 50.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 335 \| 3.9163 \| 1.0 \| \| 7.1369 \| 2.0 \| 670 \| 3.3422 \| 1.0 \| \| 3.3448 \| 3.0 \| 1005 \| 3.3355 \| 1.0 \| \| 3.3448 \| 4.0 \| 1340 \| 3.3263 \| 1.0 \| \| 3.3277 \| 5.0 \| 1675 \| 2.8928 \| 1.0079 \| \| 2.6655 \| 6.0 \| 2010 \| 1.7822 \| 0.8788 \| \| 2.6655 \| 7.0 \| 2345 \| 1.3193 \| 0.7055 \| \| 1.4617 \| 8.0 \| 2680 \| 1.1408 \| 0.6070 \| \| 1.0805 \| 9.0 \| 3015 \| 1.0108 \| 0.5422 \| \| 1.0805 \| 10.0 \| 3350 \| 0.9517 \| 0.5154 \| \| 0.8759 \| 11.0 \| 3685 \| 0.9082 \| 0.4902 \| \| 0.7462 \| 12.0 \| 4020 \| 0.8758 \| 0.4706 \| \| 0.7462 \| 13.0 \| 4355 \| 0.8696 \| 0.4572 \| \| 0.6429 \| 14.0 \| 4690 \| 0.8731 \| 0.4535 \| \| 0.5672 \| 15.0 \| 5025 \| 0.8749 \| 0.4508 \| \| 0.5672 \| 16.0 \| 5360 \| 0.8753 \| 0.4512 \| \| 0.4959 \| 17.0 \| 5695 \| 0.9039 \| 0.4487 \| \| 0.4456 \| 18.0 \| 6030 \| 0.9161 \| 0.4433 \| \| 0.4456 \| 19.0 \| 6365 \| 0.9506 \| 0.4430 \| \| 0.392 \| 20.0 \| 6700 \| 0.9412 \| 0.4439 \| \| 0.3594 \| 21.0 \| 7035 \| 0.9884 \| 0.4416 \| \| 0.3594 \| 22.0 \| 7370 \| 1.0222 \| 0.4510 \| \| 0.3175 \| 23.0 \| 7705 \| 1.0345 \| 0.4439 \| \| 0.2947 \| 24.0 \| 8040 \| 1.0849 \| 0.4465 \| \| 0.2947 \| 25.0 \| 8375 \| 1.0879 \| 0.4472 \| \| 0.2674 \| 26.0 \| 8710 \| 1.1071 \| 0.4512 \| \| 0.2521 \| 27.0 \| 9045 \| 1.1147 \| 0.4494 \| \| 0.2521 \| 28.0 \| 9380 \| 1.1426 \| 0.4525 \| \| 0.2321 \| 29.0 \| 9715 \| 1.1592 \| 0.4440 \| \| 0.2235 \| 30.0 \| 10050 \| 1.1782 \| 0.4450 \| \| 0.2235 \| 31.0 \| 10385 \| 1.2050 \| 0.4437 \| \| 0.2071 \| 32.0 \| 10720 \| 1.2224 \| 0.4400 \| \| 0.1951 \| 33.0 \| 11055 \| 1.2270 \| 0.4471 \| \| 0.1951 \| 34.0 \| 11390 \| 1.2466 \| 0.4483 \| \| 0.1892 \| 35.0 \| 11725 \| 1.2325 \| 0.4429 \| \| 0.1809 \| 36.0 \| 12060 \| 1.2755 \| 0.4427 \| \| 0.1809 \| 37.0 \| 12395 \| 1.2675 \| 0.4422 \| \| 0.1746 \| 38.0 \| 12730 \| 1.3022 \| 0.4418 \| \| 0.1656 \| 39.0 \| 13065 \| 1.3179 \| 0.4408 \| \| 0.1656 \| 40.0 \| 13400 \| 1.2934 \| 0.4425 \| \| 0.1614 \| 41.0 \| 13735 \| 1.3304 \| 0.4426 \| \| 0.1564 \| 42.0 \| 14070 \| 1.3148 \| 0.4420 \| \| 0.1564 \| 43.0 \| 14405 \| 1.3267 \| 0.4433 \| \| 0.1546 \| 44.0 \| 14740 \| 1.3331 \| 0.4413 \| \| 0.1515 \| 45.0 \| 15075 \| 1.3445 \| 0.4388 \| \| 0.1515 \| 46.0 \| 15410 \| 1.3530 \| 0.4372 \| \| 0.147 \| 47.0 \| 15745 \| 1.3443 \| 0.4385 \| \| 0.1447 \| 48.0 \| 16080 \| 1.3503 \| 0.4369 \| \| 0.1447 \| 49.0 \| 16415 \| 1.3590 \| 0.4393 \| \| 0.1437 \| 50.0 \| 16750 \| 1.3668 \| 0.4372 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2
aroot/mbart-finetuned-eng-deu-147063915101	aroot	2023-06-30T21:02:59Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-30T20:43:45Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-147063915101 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. --> # mbart-finetuned-eng-deu-147063915101 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6816 - Bleu: 20.2786 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.11.0
KuanyshItalmassov/distilbert_classifier_newsgroups	KuanyshItalmassov	2023-06-30T21:01:15Z	61	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-30T21:00:45Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: distilbert_classifier_newsgroups 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. --> # distilbert_classifier_newsgroups This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1908, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.28.0 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
aroot/mbart-finetuned-eng-kor-58824180004	aroot	2023-06-30T20:58:04Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-30T20:39:08Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-58824180004 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. --> # mbart-finetuned-eng-kor-58824180004 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1052 - Bleu: 5.7445 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.11.0
kotharin/ppo-LunarLander-v2	kotharin	2023-06-30T20:55:17Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-30T20:54:51Z	--- 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: 226.71 +/- 40.91 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
TheFools/Fuji	TheFools	2023-06-30T20:44:04Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-30T20:42:39Z	--- license: creativeml-openrail-m ---
aroot/mbart-finetuned-eng-kor-60086652858	aroot	2023-06-30T20:22:55Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-30T20:04:00Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-60086652858 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. --> # mbart-finetuned-eng-kor-60086652858 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1052 - Bleu: 5.7445 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.11.0
nolanaatama/ddmrdtls	nolanaatama	2023-06-30T20:21:19Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-30T20:20:43Z	--- license: creativeml-openrail-m ---
aroot/mbart-finetuned-eng-ind-79831731176	aroot	2023-06-30T20:17:55Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-30T19:59:02Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-79831731176 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. --> # mbart-finetuned-eng-ind-79831731176 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8577 - Bleu: 20.4223 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.11.0
kaiyuy/attach-juxtapose-parser-ptb-xlnet	kaiyuy	2023-06-30T20:13:50Z	0	0	null	[ "arxiv:2010.14568", "license:bsd-2-clause", "region:us" ]	null	2023-06-30T18:50:34Z	--- license: bsd-2-clause --- Model for the paper: [Strongly Incremental Constituency Parsing with Graph Neural Networks](https://arxiv.org/abs/2010.14568) [Kaiyu Yang](https://www.cs.princeton.edu/~kaiyuy/) and [Jia Deng](https://www.cs.princeton.edu/~jiadeng/) Neural Information Processing Systems (NeurIPS) 2020 ```bibtex @inproceedings{yang2020attachjuxtapose, title={Strongly Incremental Constituency Parsing with Graph Neural Networks}, author={Yang, Kaiyu and Deng, Jia}, booktitle={Neural Information Processing Systems (NeurIPS)}, year={2020} } ``` Please visit https://github.com/princeton-vl/attach-juxtapose-parser for details.
kaiyuy/attach-juxtapose-parser-ptb-bert	kaiyuy	2023-06-30T20:13:39Z	0	1	null	[ "arxiv:2010.14568", "license:bsd-2-clause", "region:us" ]	null	2023-06-30T18:49:56Z	--- license: bsd-2-clause --- Model for the paper: [Strongly Incremental Constituency Parsing with Graph Neural Networks](https://arxiv.org/abs/2010.14568) [Kaiyu Yang](https://www.cs.princeton.edu/~kaiyuy/) and [Jia Deng](https://www.cs.princeton.edu/~jiadeng/) Neural Information Processing Systems (NeurIPS) 2020 ```bibtex @inproceedings{yang2020attachjuxtapose, title={Strongly Incremental Constituency Parsing with Graph Neural Networks}, author={Yang, Kaiyu and Deng, Jia}, booktitle={Neural Information Processing Systems (NeurIPS)}, year={2020} } ``` Please visit https://github.com/princeton-vl/attach-juxtapose-parser for details.
jorgeortizfuentes/spanish-spellchecker-mbart-large-cc25	jorgeortizfuentes	2023-06-30T20:11:10Z	119	0	transformers	[ "transformers", "safetensors", "mbart", "text2text-generation", "generated_from_trainer", "es", "esc", "dataset:jorgeortizfuentes/spanish_spellchecker_train", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-06-30T19:55:35Z	--- language: - es - esc tags: - generated_from_trainer datasets: - jorgeortizfuentes/spanish_spellchecker_train model-index: - name: spanish-spellchecker-mbart-large-cc25 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # spanish-spellchecker-mbart-large-cc25 This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the jorgeortizfuentes/spanish_spellchecker_train dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 13 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
tanmayyyj/q-FrozenLake-v2-4x4-Non_Slippery	tanmayyyj	2023-06-30T20:04:15Z	0	1	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-30T20:04:13Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v2-4x4-Non_Slippery 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="tanmayyyj/q-FrozenLake-v2-4x4-Non_Slippery", 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"]) ```
nolanaatama/dtltwkr	nolanaatama	2023-06-30T20:00:18Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-30T19:45:32Z	--- license: creativeml-openrail-m ---
Luciano/bert-base-portuguese-cased-finetuned-tcu-acordaos	Luciano	2023-06-30T19:52:47Z	115	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "fill-mask", "generated_from_trainer", "pt", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- language: - pt license: mit tags: - generated_from_trainer model-index: - name: bert-base-portuguese-cased-finetuned-tcu-acordaos results: [] widget: - text: "Com efeito, se tal fosse possível, o Poder [MASK] – que não dispõe de função legislativa – passaria a desempenhar atribuição que lhe é institucionalmente estranha (a de legislador positivo), usurpando, desse modo, no contexto de um sistema de poderes essencialmente limitados, competência que não lhe pertence, com evidente transgressão ao princípio constitucional da separação de poderes." --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-portuguese-cased-finetuned-tcu-acordaos This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5765 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.7308 \| 1.0 \| 1383 \| 0.6286 \| \| 0.6406 \| 2.0 \| 2766 \| 0.5947 \| \| 0.6033 \| 3.0 \| 4149 \| 0.5881 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.13.2 - Tokenizers 0.10.3
davidzhou/sd-class-butterflies-32	davidzhou	2023-06-30T19:52:38Z	30	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-06-30T19:52:05Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('davidzhou/sd-class-butterflies-32') image = pipeline().images[0] image ```
aroot/mbart-finetuned-eng-ind-143393709156	aroot	2023-06-30T19:42:54Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-30T19:24:02Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-143393709156 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. --> # mbart-finetuned-eng-ind-143393709156 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8577 - Bleu: 20.4223 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.11.0
aroot/mbart-finetuned-eng-deu-190540911265	aroot	2023-06-30T19:38:23Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-30T19:19:07Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-190540911265 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. --> # mbart-finetuned-eng-deu-190540911265 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6816 - Bleu: 20.2786 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.11.0
przemys/q-Taxi-v3	przemys	2023-06-30T19:37:13Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-30T19:37:11Z	--- 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="przemys/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"]) ```
nelant098/Jewelry2	nelant098	2023-06-30T19:34:36Z	239	1	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-06-30T19:34:28Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: Jewelry2 results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.33519554138183594 --- # Jewelry2 Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### Rolex Watch ![Rolex Watch](images/Rolex_Watch.jpg) #### diamond jewelry ![diamond jewelry](images/diamond_jewelry.jpg) #### gold chain ![gold chain](images/gold_chain.jpg) #### gold jewelry ![gold jewelry](images/gold_jewelry.jpg) #### jewelry ![jewelry](images/jewelry.jpg) #### neckles ![neckles](images/neckles.jpg) #### silver chain ![silver chain](images/silver_chain.jpg) #### silver jewelry ![silver jewelry](images/silver_jewelry.jpg)
hipnologo/gpt2-churn-finetune	hipnologo	2023-06-30T19:21:01Z	115	0	transformers	[ "transformers", "pytorch", "safetensors", "gpt2", "text-classification", "churn", "sentiment-analysis", "fine-tuned", "en", "dataset:hipnologo/churn_textual_label", "dataset:hipnologo/telecom_churn", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2023-06-29T17:06:46Z	--- datasets: - hipnologo/churn_textual_label - hipnologo/telecom_churn language: - en library_name: transformers pipeline_tag: text-classification tags: - churn - gpt2 - sentiment-analysis - fine-tuned widget: - text: "Can you tell me about a customer from MD with an account length of 189 and area code 415 who does not have an international plan, and has a voice mail plan and that have made 1 customer service calls." - text: "Can you tell me about a customer from SC with an account length of 87 and area code 408 who does not have an international plan, and does not have a voice mail plan and that have made 2 customer service calls." --- # Fine-tuned GPT-2 Model for Telecom Churn Analysis ## Model Description This is a GPT-2 model fine-tuned on the Telecom Churn dataset for churn analysis. It classifies a customer's text into two classes: "churn" or "no churn". ## Intended Uses & Limitations This model is intended to be used for binary churn analysis of English customer texts. It can determine whether a customer is likely to churn or not. It should not be used for languages other than English, or for text with ambiguous churn indications. ## How to Use Here's a simple way to use this model: ```python from transformers import GPT2Tokenizer, GPT2ForSequenceClassification tokenizer = GPT2Tokenizer.from_pretrained("hipnologo/gpt2-churn-finetune") model = GPT2ForSequenceClassification.from_pretrained("hipnologo/gpt2-churn-finetune") text = "Your customer text here!" # encoding the input text input_ids = tokenizer.encode(text, return_tensors="pt") # Move the input_ids tensor to the same device as the model input_ids = input_ids.to(model.device) # getting the logits logits = model(input_ids).logits # getting the predicted class predicted_class = logits.argmax(-1).item() print(f"The churn prediction by the model is: {'Churn' if predicted_class == 1 else 'No Churn'}") ``` ## Training Procedure The model was trained using the 'Trainer' class from the transformers library, with a learning rate of `2e-5`, batch size of 1, and 3 training epochs. ## Evaluation The fine-tuned model was evaluated on the test dataset. Here are the results: - Evaluation Loss: 0.28965 - Evaluation Accuracy: 0.9 - Evaluation F1 Score: 0.90239 - Evaluation Precision: 0.85970 - Evaluation Recall: 0.94954 The evaluation metrics suggest that the model has a high accuracy and good precision-recall balance for the task of churn classification. ## How to Reproduce The evaluation results can be reproduced by loading the model and the tokenizer from Hugging Face Model Hub and then running the model on the evaluation dataset using the Trainer class from the Transformers library, with the compute_metrics function defined as above. The evaluation loss is the cross-entropy loss of the model on the evaluation dataset, a measure of how well the model's predictions match the actual labels. The closer this is to zero, the better. The evaluation accuracy is the proportion of predictions the model got right. This number is between 0 and 1, with 1 meaning the model got all predictions right. The F1 score is a measure of a test's accuracy that considers both precision (the number of true positive results divided by the number of all positive results) and recall (the number of true positive results divided by the number of all samples that should have been identified as positive). An F1 score reaches its best value at 1 (perfect precision and recall) and worst at 0. The evaluation precision is how many of the positively classified were actually positive. The closer this is to 1, the better. The evaluation recall is how many of the actual positives our model captured through labeling it as positive. The closer this is to 1, the better. ## Fine-tuning Details The model was fine-tuned using the Telecom Churn dataset.
digiplay/PerfectDeliberate_v4	digiplay	2023-06-30T19:16:40Z	341	3	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-06-30T10:17:55Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info: https://civitai.com/models/24350?modelVersionId=86698
greg-szopinski/q-Taxi-v3-100k	greg-szopinski	2023-06-30T19:04:55Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-30T18:37:18Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3-100k 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="greg-szopinski/q-Taxi-v3-100k", 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"]) ```
aroot/mbart-finetuned-eng-deu-162558725038	aroot	2023-06-30T19:03:20Z	10	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-30T18:44:05Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-162558725038 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. --> # mbart-finetuned-eng-deu-162558725038 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6816 - Bleu: 20.2786 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.11.0
aroot/mbart-finetuned-eng-kor-41790123343	aroot	2023-06-30T18:58:23Z	10	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-30T18:39:33Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-41790123343 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. --> # mbart-finetuned-eng-kor-41790123343 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1052 - Bleu: 5.7445 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.11.0
TheBloke/Vicuna-33B-1-3-SuperHOT-8K-GGML	TheBloke	2023-06-30T18:55:35Z	0	12	null	[ "arxiv:2302.13971", "arxiv:2306.05685", "license:other", "region:us" ]	null	2023-06-30T18:20:54Z	--- inference: false license: other --- <!-- header start --> <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <!-- header end --> # LmSys' Vicuna 33B 1.3 (final) GGML These files are GGML format model files for [LmSys' Vicuna 33B 1.3 (final)](https://huggingface.co/lmsys/vicuna-33b-v1.3). These are SuperHOT GGMLs with an increased context length. SuperHOT is a new system that employs RoPE to expand context beyond what was originally possible for a model. It was discovered and developed by [kaiokendev](https://huggingface.co/kaiokendev). In order to use the increased context length, you can presently use: * [KoboldCpp](https://github.com/LostRuins/koboldcpp) - [release 1.33](https://github.com/LostRuins/koboldcpp/releases/tag/v1.33) or later. Support is also expected to come to llama.cpp, however work is still being done to find the optimal implementation. To use the increased context with KoboldCpp, simply use `--contextsize` to set the desired context, eg `--contextsize 4096` or `--contextsize 8192`. NOTE: Increased context length is an area seeing rapid developments and improvements. It is quite possible that these models may be superseded by new developments in the coming days. If that's the case, I will remove them, or update this README as appropriate. ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Vicuna-33B-1-3-SuperHOT-8K-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU inference](https://huggingface.co/TheBloke/Vicuna-33B-1-3-SuperHOT-8K-GGML) * [Unquantised SuperHOT fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Vicuna-33B-1-3-SuperHOT-8K-fp16) * [Unquantised base fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/lmsys/vicuna-33b-v1.3) <!-- compatibility_ggml start --> ## Compatibility These GGMLs will work with any llama.cpp-compatible GGML client that supports k-quants. However the increased context length won't work without specific support. See the note in the introduction for details on using increased context. ## Explanation of the new k-quant methods The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw * GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type. Refer to the Provided Files table below to see what files use which methods, and how. <!-- compatibility_ggml end --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| vicuna-33b-1.3-superhot-8k.ggmlv3.q2_K.bin \| q2_K \| 2 \| 13.71 GB \| 16.21 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. \| \| vicuna-33b-1.3-superhot-8k.ggmlv3.q3_K_L.bin \| q3_K_L \| 3 \| 17.28 GB \| 19.78 GB \| New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K \| \| vicuna-33b-1.3-superhot-8k.ggmlv3.q3_K_M.bin \| q3_K_M \| 3 \| 15.72 GB \| 18.22 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K \| \| vicuna-33b-1.3-superhot-8k.ggmlv3.q3_K_S.bin \| q3_K_S \| 3 \| 14.06 GB \| 16.56 GB \| New k-quant method. Uses GGML_TYPE_Q3_K for all tensors \| \| vicuna-33b-1.3-superhot-8k.ggmlv3.q4_K_M.bin \| q4_K_M \| 4 \| 19.62 GB \| 22.12 GB \| New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K \| \| vicuna-33b-1.3-superhot-8k.ggmlv3.q4_K_S.bin \| q4_K_S \| 4 \| 18.36 GB \| 20.86 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for all tensors \| \| vicuna-33b-1.3-superhot-8k.ggmlv3.q5_K_M.bin \| q5_K_M \| 5 \| 23.05 GB \| 25.55 GB \| New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K \| \| vicuna-33b-1.3-superhot-8k.ggmlv3.q5_K_S.bin \| q5_K_S \| 5 \| 22.40 GB \| 24.90 GB \| New k-quant method. Uses GGML_TYPE_Q5_K for all tensors \| \| vicuna-33b-1.3-superhot-8k.ggmlv3.q6_K.bin \| q6_K \| 6 \| 26.69 GB \| 29.19 GB \| New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `koboldcpp` On Linux I use the following command line to launch the KoboldCpp UI with OpenCL aceleration and a context size of 4096: ``` python ./koboldcpp.py --stream --unbantokens --threads 8 --usecublas --gpulayers 100 vicuna-33b-1.3-superhot-8k.ggmlv3.q4_K_M.bin ``` Change `--gpulayers 100` to the number of layers you want/are able to offload to the GPU. Remove it if you don't have GPU acceleration. For OpenCL acceleration, change `--usecublas` to `--useclblast 0 0`. You may need to change the second `0` to `1` if you have both an iGPU and a discrete GPU. <!-- footer start --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov. Patreon special mentions: zynix , ya boyyy, Trenton Dambrowitz, Imad Khwaja, Alps Aficionado, chris gileta, John Detwiler, Willem Michiel, RoA, Mano Prime, Rainer Wilmers, Fred von Graf, Matthew Berman, Ghost , Nathan LeClaire, Iucharbius , Ai Maven, Illia Dulskyi, Joseph William Delisle, Space Cruiser, Lone Striker, Karl Bernard, Eugene Pentland, Greatston Gnanesh, Jonathan Leane, Randy H, Pierre Kircher, Willian Hasse, Stephen Murray, Alex , terasurfer , Edmond Seymore, Oscar Rangel, Luke Pendergrass, Asp the Wyvern, Junyu Yang, David Flickinger, Luke, Spiking Neurons AB, subjectnull, Pyrater, Nikolai Manek, senxiiz, Ajan Kanaga, Johann-Peter Hartmann, Artur Olbinski, Kevin Schuppel, Derek Yates, Kalila, K, Talal Aujan, Khalefa Al-Ahmad, Gabriel Puliatti, John Villwock, WelcomeToTheClub, Daniel P. Andersen, Preetika Verma, Deep Realms, Fen Risland, trip7s trip, webtim, Sean Connelly, Michael Levine, Chris McCloskey, biorpg, vamX, Viktor Bowallius, Cory Kujawski. Thank you to all my generous patrons and donaters! <!-- footer end --> # Original model card: Kaio Ken's SuperHOT 8K ### SuperHOT Prototype 2 w/ 8K Context This is a second prototype of SuperHOT, this time 30B with 8K context and no RLHF, using the same technique described in [the github blog](https://kaiokendev.github.io/til#extending-context-to-8k). Tests have shown that the model does indeed leverage the extended context at 8K. You will need to use either the monkeypatch or, if you are already using the monkeypatch, change the scaling factor to 0.25 and the maximum sequence length to 8192 #### Looking for Merged & Quantized Models? - 30B 4-bit CUDA: [tmpupload/superhot-30b-8k-4bit-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-safetensors) - 30B 4-bit CUDA 128g: [tmpupload/superhot-30b-8k-4bit-128g-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-128g-safetensors) #### Training Details I trained the LoRA with the following configuration: - 1200 samples (~400 samples over 2048 sequence length) - learning rate of 3e-4 - 3 epochs - The exported modules are: - q_proj - k_proj - v_proj - o_proj - no bias - Rank = 4 - Alpha = 8 - no dropout - weight decay of 0.1 - AdamW beta1 of 0.9 and beta2 0.99, epsilon of 1e-5 - Trained on 4-bit base model # Original model card: LmSys' Vicuna 33B 1.3 (final) # Vicuna Model Card ## Model Details Vicuna is a chat assistant trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. - Developed by: [LMSYS](https://lmsys.org/) - Model type: An auto-regressive language model based on the transformer architecture. - License: Non-commercial license - Finetuned from model: [LLaMA](https://arxiv.org/abs/2302.13971). ### Model Sources - Repository: https://github.com/lm-sys/FastChat - Blog: https://lmsys.org/blog/2023-03-30-vicuna/ - Paper: https://arxiv.org/abs/2306.05685 - Demo: https://chat.lmsys.org/ ## Uses The primary use of Vicuna is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence. ## How to Get Started with the Model Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights. APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api. ## Training Details Vicuna v1.3 is fine-tuned from LLaMA with supervised instruction fine-tuning. The training data is around 140K conversations collected from ShareGPT.com. See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf). ## Evaluation Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf). ## Difference between different versions of Vicuna See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)
dar-tau/ppo-PyramidsRND	dar-tau	2023-06-30T18:53:36Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-06-30T18:53:27Z	--- 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: dar-tau/ppo-PyramidsRND 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
dar-tau/ppo-SnowballTarget	dar-tau	2023-06-30T18:47:05Z	4	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-06-30T18:46:58Z	--- 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: dar-tau/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
aroot/mbart-finetuned-eng-ind-7003190223	aroot	2023-06-30T18:43:08Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-30T18:24:23Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-7003190223 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. --> # mbart-finetuned-eng-ind-7003190223 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8577 - Bleu: 20.4223 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.11.0
aroot/mbart-finetuned-eng-deu-14039947979	aroot	2023-06-30T18:38:38Z	9	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-30T18:19:18Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-14039947979 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. --> # mbart-finetuned-eng-deu-14039947979 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6816 - Bleu: 20.2786 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.11.0
greg-szopinski/q-Taxi-v3-baseline	greg-szopinski	2023-06-30T18:25:36Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-30T18:25:32Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3-baseline 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="greg-szopinski/q-Taxi-v3-baseline", 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"]) ```
pratsy/q-taxi-v1	pratsy	2023-06-30T18:23:41Z	0	1	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-30T17:51:41Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-taxi-v1 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="pratsy/q-taxi-v1", 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"]) ```
aroot/mbart-finetuned-eng-kor-41788513697	aroot	2023-06-30T18:23:27Z	22	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-30T18:06:05Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-41788513697 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. --> # mbart-finetuned-eng-kor-41788513697 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1052 - Bleu: 5.7445 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.11.0
aroot/mbart-finetuned-eng-ind-78029440162	aroot	2023-06-30T18:18:23Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-30T18:01:54Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-78029440162 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. --> # mbart-finetuned-eng-ind-78029440162 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8577 - Bleu: 20.4223 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.11.0
Francesco-A/q-Taxi-v3	Francesco-A	2023-06-30T18:17:35Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-30T18:17:33Z	--- 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="Francesco-A/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"]) ```
amdnsr/llama-7b-hf	amdnsr	2023-06-30T18:05:28Z	4	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-30T00:26:21Z	--- license: other --- LLaMA-7B converted to work with Transformers/HuggingFace. This is under a special license, please see the LICENSE file for details. -- license: other --- # LLaMA Model Card ## Model details Organization developing the model The FAIR team of Meta AI. Model date LLaMA was trained between December. 2022 and Feb. 2023. Model version This is version 1 of the model. Model type LLaMA is an auto-regressive language model, based on the transformer architecture. The model comes in different sizes: 7B, 13B, 33B and 65B parameters. Paper or resources for more information More information can be found in the paper “LLaMA, Open and Efficient Foundation Language Models”, available at https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/. Citations details https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/ License Non-commercial bespoke license Where to send questions or comments about the model Questions and comments about LLaMA can be sent via the [GitHub repository](https://github.com/facebookresearch/llama) of the project , by opening an issue. ## Intended use Primary intended uses The primary use of LLaMA is research on large language models, including: exploring potential applications such as question answering, natural language understanding or reading comprehension, understanding capabilities and limitations of current language models, and developing techniques to improve those, evaluating and mitigating biases, risks, toxic and harmful content generations, hallucinations. Primary intended users The primary intended users of the model are researchers in natural language processing, machine learning and artificial intelligence. Out-of-scope use cases LLaMA is a base, or foundational, model. As such, it should not be used on downstream applications without further risk evaluation and mitigation. In particular, our model has not been trained with human feedback, and can thus generate toxic or offensive content, incorrect information or generally unhelpful answers. ## Factors Relevant factors One of the most relevant factors for which model performance may vary is which language is used. Although we included 20 languages in the training data, most of our dataset is made of English text, and we thus expect the model to perform better for English than other languages. Relatedly, it has been shown in previous studies that performance might vary for different dialects, and we expect that it will be the case for our model. Evaluation factors As our model is trained on data from the Web, we expect that it reflects biases from this source. We thus evaluated on RAI datasets to measure biases exhibited by the model for gender, religion, race, sexual orientation, age, nationality, disability, physical appearance and socio-economic status. We also measure the toxicity of model generations, depending on the toxicity of the context used to prompt the model. ## Metrics Model performance measures We use the following measure to evaluate the model: - Accuracy for common sense reasoning, reading comprehension, natural language understanding (MMLU), BIG-bench hard, WinoGender and CrowS-Pairs, - Exact match for question answering, - The toxicity score from Perspective API on RealToxicityPrompts. Decision thresholds Not applicable. Approaches to uncertainty and variability Due to the high computational requirements of training LLMs, we trained only one model of each size, and thus could not evaluate variability of pre-training. ## Evaluation datasets The model was evaluated on the following benchmarks: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC, OpenBookQA, NaturalQuestions, TriviaQA, RACE, MMLU, BIG-bench hard, GSM8k, RealToxicityPrompts, WinoGender, CrowS-Pairs. ## Training dataset The model was trained using the following source of data: CCNet [67%], C4 [15%], GitHub [4.5%], Wikipedia [4.5%], Books [4.5%], ArXiv [2.5%], Stack Exchange[2%]. The Wikipedia and Books domains include data in the following languages: bg, ca, cs, da, de, en, es, fr, hr, hu, it, nl, pl, pt, ro, ru, sl, sr, sv, uk. See the paper for more details about the training set and corresponding preprocessing. ## Quantitative analysis Hyperparameters for the model architecture <table> <thead> <tr> <th >LLaMA</th> <th colspan=6>Model hyper parameters </th> </tr> <tr> <th>Number of parameters</th><th>dimension</th><th>n heads</th><th>n layers</th><th>Learn rate</th><th>Batch size</th><th>n tokens</th> </tr> </thead> <tbody> <tr> <th>7B</th> <th>4096</th> <th>32</th> <th>32</th> <th>3.0E-04</th><th>4M</th><th>1T </tr> <tr> <th>13B</th><th>5120</th><th>40</th><th>40</th><th>3.0E-04</th><th>4M</th><th>1T </tr> <tr> <th>33B</th><th>6656</th><th>52</th><th>60</th><th>1.5.E-04</th><th>4M</th><th>1.4T </tr> <tr> <th>65B</th><th>8192</th><th>64</th><th>80</th><th>1.5.E-04</th><th>4M</th><th>1.4T </tr> </tbody> </table> Table 1 - Summary of LLama Model Hyperparameters We present our results on eight standard common sense reasoning benchmarks in the table below. <table> <thead> <tr> <th>LLaMA</th> <th colspan=9>Reasoning tasks </th> </tr> <tr> <th>Number of parameters</th> <th>BoolQ</th><th>PIQA</th><th>SIQA</th><th>HellaSwag</th><th>WinoGrande</th><th>ARC-e</th><th>ARC-c</th><th>OBQA</th><th>COPA</th> </tr> </thead> <tbody> <tr> <th>7B</th><th>76.5</th><th>79.8</th><th>48.9</th><th>76.1</th><th>70.1</th><th>76.7</th><th>47.6</th><th>57.2</th><th>93 </th> <tr><th>13B</th><th>78.1</th><th>80.1</th><th>50.4</th><th>79.2</th><th>73</th><th>78.1</th><th>52.7</th><th>56.4</th><th>94 </th> <tr><th>33B</th><th>83.1</th><th>82.3</th><th>50.4</th><th>82.8</th><th>76</th><th>81.4</th><th>57.8</th><th>58.6</th><th>92 </th> <tr><th>65B</th><th>85.3</th><th>82.8</th><th>52.3</th><th>84.2</th><th>77</th><th>81.5</th><th>56</th><th>60.2</th><th>94</th></tr> </tbody> </table> Table 2 - Summary of LLama Model Performance on Reasoning tasks We present our results on bias in the table below. Note that lower value is better indicating lower bias. \| No \| Category \| FAIR LLM \| \| --- \| -------------------- \| -------- \| \| 1 \| Gender \| 70.6 \| \| 2 \| Religion \| 79 \| \| 3 \| Race/Color \| 57 \| \| 4 \| Sexual orientation \| 81 \| \| 5 \| Age \| 70.1 \| \| 6 \| Nationality \| 64.2 \| \| 7 \| Disability \| 66.7 \| \| 8 \| Physical appearance \| 77.8 \| \| 9 \| Socioeconomic status \| 71.5 \| \| \| LLaMA Average \| 66.6 \| Table 3 - Summary bias of our model output ## Ethical considerations Data The data used to train the model is collected from various sources, mostly from the Web. As such, it contains offensive, harmful and biased content. We thus expect the model to exhibit such biases from the training data. Human life The model is not intended to inform decisions about matters central to human life, and should not be used in such a way. Mitigations We filtered the data from the Web based on its proximity to Wikipedia text and references. For this, we used a Kneser-Ney language model and a fastText linear classifier. Risks and harms Risks and harms of large language models include the generation of harmful, offensive or biased content. These models are often prone to generating incorrect information, sometimes referred to as hallucinations. We do not expect our model to be an exception in this regard. Use cases LLaMA is a foundational model, and as such, it should not be used for downstream applications without further investigation and mitigations of risks. These risks and potential fraught use cases include, but are not limited to: generation of misinformation and generation of harmful, biased or offensive content.
Francesco-A/q-FrozenLake-v1-4x4-noSlippery	Francesco-A	2023-06-30T18:03:32Z	0	0	null	[ "FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-30T18:03:31Z	--- tags: - FrozenLake-v1-4x4 - 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 type: FrozenLake-v1-4x4 metrics: - type: mean_reward value: 0.69 +/- 0.46 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="Francesco-A/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"]) ```
aroot/mbart-finetuned-eng-kor-22045430821	aroot	2023-06-30T18:00:59Z	13	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-06-30T17:44:19Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-22045430821 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. --> # mbart-finetuned-eng-kor-22045430821 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1052 - Bleu: 5.7445 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.11.0
pinkpekoe/unit1-ppo-LunarLander-v2	pinkpekoe	2023-06-30T17:56:15Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-30T17:16:03Z	--- 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: 255.47 +/- 22.31 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 ... ```
selfcopy/Crotch_Rope_Walking_Concept	selfcopy	2023-06-30T17:30:23Z	0	0	null	[ "region:us" ]	null	2023-06-29T17:21:44Z	--- {lora-Crotch Rope Walking Concept} --- # lora- Crotch Rope Walking Concept Suggest dinesion: 0.8-1 <!-- Provide a quick summary of what the model is/does. --> https://huggingface.co/selfcopy/Crotch_Rope_Walking_Concept/blob/main/00005-2800566341.png <img width="300px" src="https://civitai.com/images/1173579?modelVersionId=97637&prioritizedUserIds=506777&period=AllTime&sort=Most+Reactions&limit=20"> ## Trigger world CROTCH ROPE WALKING,FROM SIDE,SHIBARI,BEADS,KNOT ## Date Jun 17, 2023 ## Prompt (masterpiece, best quality, ultra detailed illustration),detail face,(detail eyes),kasuka,1girl,full body,solo, multicolored hair,short hair,orange hair,brown hair,grey dress,orange belt,hair,white boots with orange belt,two mutlicolored feathers on hair,feather hair ornament, earrings, <lora:kasuka:0.6>outside, gigantic breasts,satisfied,blush,(completely nude,nsfw,pussy juice)<lora:more_details:0.7>CROTCH ROPE WALKING,FROM SIDE,SHIBARI,BEADS,KNOT <lora:ropeWalking:1> (orange:1.2),EasyNegative, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, (worst quality:1.2), low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, lowres graffiti, (low quality lowres simple background:1.1),bad hands
usamaaleem99tech/segformer-class-custom-train	usamaaleem99tech	2023-06-30T17:29:09Z	211	0	transformers	[ "transformers", "pytorch", "tensorboard", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-06-30T17:24:57Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: segformer-class-custom-train results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9772727272727273 --- <!-- 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. --> # segformer-class-custom-train 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: 0.0588 - Accuracy: 0.9773 ## 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: 10 - eval_batch_size: 10 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 40 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.96 \| 6 \| 0.9547 \| 0.6364 \| \| 1.0647 \| 1.92 \| 12 \| 0.5731 \| 0.8636 \| \| 1.0647 \| 2.88 \| 18 \| 0.3149 \| 0.9091 \| \| 0.5705 \| 4.0 \| 25 \| 0.0585 \| 0.9773 \| \| 0.2274 \| 4.96 \| 31 \| 0.0815 \| 0.9773 \| \| 0.2274 \| 5.92 \| 37 \| 0.0824 \| 0.9773 \| \| 0.1822 \| 6.88 \| 43 \| 0.1408 \| 0.9773 \| \| 0.1784 \| 8.0 \| 50 \| 0.0778 \| 0.9773 \| \| 0.1784 \| 8.96 \| 56 \| 0.0613 \| 0.9773 \| \| 0.117 \| 9.6 \| 60 \| 0.0588 \| 0.9773 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
lightyip/q-Taxi-v3	lightyip	2023-06-30T17:19:28Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-30T17:19:26Z	--- 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: 7.50 +/- 2.72 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="lightyip/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"]) ```
heka-ai/e5-40k	heka-ai	2023-06-30T17:10:46Z	1	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-06-30T17:10:41Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # heka-ai/e5-40k This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('heka-ai/e5-40k') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=heka-ai/e5-40k) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 10000 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `gpl.toolkit.loss.MarginDistillationLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 100000, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
vluz/Generalis_V1.1	vluz	2023-06-30T17:10:28Z	0	0	null	[ "en", "license:cc0-1.0", "region:us" ]	null	2023-06-30T16:20:52Z	--- license: cc0-1.0 language: - en --- # Generalis V1.1 <hr> ### Second attempt at merging several models v1.5 into one general purpose model. Focus has been put into simple prompts, good one-off generation, slightly muted colours, low memory usage, small model size. It is intended as easy model for use in larger projects where image generation is needed. Published under CC0 <hr> Use example: ```python import torch # Tested with 2.0.1+cu118 from diffusers import StableDiffusionPipeline # <3 # Model location in HF model = "https://huggingface.co/vluz/Generalis_V1.1/blob/main/Generalis_v1-1.safetensors" # Create pipe pipe = StableDiffusionPipeline.from_ckpt(model, torch_dtype=torch.float16, safety_checker=None, feature_extractor=None, requires_safety_checker=False,) # Cleanup del pipe.vae.encoder torch.cuda.empty_cache() # Send to GPU pipe = pipe.to("cuda") # Optimize for low vram use and clear cache again pipe.enable_vae_tiling() pipe.enable_attention_slicing("max") pipe.enable_xformers_memory_efficient_attention(attention_op=None) pipe.unet.to(memory_format=torch.channels_last) pipe.enable_sequential_cpu_offload() torch.cuda.empty_cache() # Set a prompt prompt = "a cat" # Generate image based on prompt image = pipe(prompt).images[0] # Save result image to disk image.save("cat.png") ```
ale2x72/ppo-Huggy	ale2x72	2023-06-30T16:35:18Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-06-30T16:35:13Z	--- 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: ale2x72/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
bgoldfe2/finetuning-sentiment-model-3000-samples	bgoldfe2	2023-06-30T16:32:30Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-30T16:29:43Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.87 - name: F1 type: f1 value: 0.8729641693811074 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3107 - Accuracy: 0.87 - F1: 0.8730 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
jliu596/ppo-LunarLander-v2	jliu596	2023-06-30T16:04:54Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-30T16:04:34Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 263.97 +/- 36.81 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 ... ```
igoroliveira/Huggy_igoroliv	igoroliveira	2023-06-30T16:01:06Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-06-30T16:00:56Z	--- 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: igoroliveira/Huggy_igoroliv 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
language-and-voice-lab/talromur_1and2_spk_avg_xvector_fastspeech2	language-and-voice-lab	2023-06-30T15:54:58Z	0	0	espnet	[ "espnet", "text-to-speech", "is", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]	text-to-speech	2023-06-15T16:57:54Z	--- license: cc-by-4.0 language: - is library_name: espnet pipeline_tag: text-to-speech widget: - text: góðan daginn kæru íslendingar --- ## ESPnet2 TTS pretrained model This model was trained by GunnarThor using a modified version of talromur2/tts1 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```python # coming soon ``` ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } @inproceedings{hayashi2020espnet, title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit}, author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu}, booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={7654--7658}, year={2020}, organization={IEEE} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
pchiva/q-Taxi-v3	pchiva	2023-06-30T15:45:52Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-06-30T15:39:05Z	--- 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 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="pchiva/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"]) ```
YoneShiro/poca-SoccerTwos	YoneShiro	2023-06-30T15:45:10Z	4	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-06-30T15:43:40Z	--- 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: YoneShiro/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
PiXeL99/falcon-7b-qlora-telecom	PiXeL99	2023-06-30T15:44:30Z	0	0	peft	[ "peft", "region:us" ]	null	2023-06-30T15:44:25Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0
netradrishti/u2net-saliency	netradrishti	2023-06-30T15:38:40Z	60	1	transformers	[ "transformers", "pytorch", "u2net", "feature-extraction", "custom_code", "region:us" ]	feature-extraction	2023-02-23T15:29:37Z	--- title: Saliency Estimation ---
TheBloke/Tulu-13B-SuperHOT-8K-GGML	TheBloke	2023-06-30T15:35:43Z	0	3	null	[ "arxiv:2306.04751", "arxiv:2302.13971", "arxiv:2301.13688", "arxiv:2304.07327", "arxiv:2304.03277", "license:other", "region:us" ]	null	2023-06-30T03:38:03Z	--- inference: false license: other --- <!-- header start --> <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <!-- header end --> # Allen AI's Tulu 13B GGML These files are GGML format model files for [Allen AI's Tulu 13B](https://huggingface.co/TheBloke/tulu-13B-fp16). These are SuperHOT GGMLs with an increased context length. SuperHOT is a new system that employs RoPE to expand context beyond what was originally possible for a model. It was discovered and developed by [kaiokendev](https://huggingface.co/kaiokendev). In order to use the increased context length, you can presently use: * [KoboldCpp](https://github.com/LostRuins/koboldcpp) - [release 1.33](https://github.com/LostRuins/koboldcpp/releases/tag/v1.33) or later. Support is also expected to come to llama.cpp, however work is still being done to find the optimal implementation. To use the increased context with KoboldCpp, simply use `--contextsize` to set the desired context, eg `--contextsize 4096` or `--contextsize 8192`. NOTE: Increased context length is an area seeing rapid developments and improvements. It is quite possible that these models may be superseded by new developments in the coming days. If that's the case, I will remove them, or update this README as appropriate. ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Tulu-13B-SuperHOT-8K-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU inference](https://huggingface.co/TheBloke/Tulu-13B-SuperHOT-8K-GGML) * [Unquantised SuperHOT fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Tulu-13B-SuperHOT-8K-fp16) * [Unquantised base fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/allenai/tulu-13b) <!-- compatibility_ggml start --> ## Compatibility These GGMLs will work with any llama.cpp-compatible GGML client that supports k-quants. However the increased context length won't work without specific support. See the note in the introduction for details on using increased context. ## Explanation of the new k-quant methods The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw * GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type. Refer to the Provided Files table below to see what files use which methods, and how. <!-- compatibility_ggml end --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| tulu-13b-superhot-8k.ggmlv3.q4_0.bin \| q4_0 \| 4 \| 7.32 GB \| 9.82 GB \| Original llama.cpp quant method, 4-bit. \| \| tulu-13b-superhot-8k.ggmlv3.q4_1.bin \| q4_1 \| 4 \| 8.14 GB \| 10.64 GB \| Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. \| \| tulu-13b-superhot-8k.ggmlv3.q5_0.bin \| q5_0 \| 5 \| 8.95 GB \| 11.45 GB \| Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. \| \| tulu-13b-superhot-8k.ggmlv3.q5_1.bin \| q5_1 \| 5 \| 9.76 GB \| 12.26 GB \| Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. \| \| tulu-13b-superhot-8k.ggmlv3.q8_0.bin \| q8_0 \| 8 \| 13.83 GB \| 16.33 GB \| Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `koboldcpp` On Linux I use the following command line to launch the KoboldCpp UI with OpenCL aceleration and a context size of 4096: ``` python ./koboldcpp.py --stream --unbantokens --threads 8 --usecublas --gpulayers 100 tulu-13b-superhot-8k.ggmlv3.q4_K_M.bin ``` Change `--gpulayers 100` to the number of layers you want/are able to offload to the GPU. Remove it if you don't have GPU acceleration. For OpenCL acceleration, change `--usecublas` to `--useclblast 0 0`. You may need to change the second `0` to `1` if you have both an iGPU and a discrete GPU. <!-- footer start --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov. Patreon special mentions: zynix , ya boyyy, Trenton Dambrowitz, Imad Khwaja, Alps Aficionado, chris gileta, John Detwiler, Willem Michiel, RoA, Mano Prime, Rainer Wilmers, Fred von Graf, Matthew Berman, Ghost , Nathan LeClaire, Iucharbius , Ai Maven, Illia Dulskyi, Joseph William Delisle, Space Cruiser, Lone Striker, Karl Bernard, Eugene Pentland, Greatston Gnanesh, Jonathan Leane, Randy H, Pierre Kircher, Willian Hasse, Stephen Murray, Alex , terasurfer , Edmond Seymore, Oscar Rangel, Luke Pendergrass, Asp the Wyvern, Junyu Yang, David Flickinger, Luke, Spiking Neurons AB, subjectnull, Pyrater, Nikolai Manek, senxiiz, Ajan Kanaga, Johann-Peter Hartmann, Artur Olbinski, Kevin Schuppel, Derek Yates, Kalila, K, Talal Aujan, Khalefa Al-Ahmad, Gabriel Puliatti, John Villwock, WelcomeToTheClub, Daniel P. Andersen, Preetika Verma, Deep Realms, Fen Risland, trip7s trip, webtim, Sean Connelly, Michael Levine, Chris McCloskey, biorpg, vamX, Viktor Bowallius, Cory Kujawski. Thank you to all my generous patrons and donaters! <!-- footer end --> # Original model card: Kaio Ken's SuperHOT 8K ### SuperHOT Prototype 2 w/ 8K Context This is a second prototype of SuperHOT, this time 30B with 8K context and no RLHF, using the same technique described in [the github blog](https://kaiokendev.github.io/til#extending-context-to-8k). Tests have shown that the model does indeed leverage the extended context at 8K. You will need to use either the monkeypatch or, if you are already using the monkeypatch, change the scaling factor to 0.25 and the maximum sequence length to 8192 #### Looking for Merged & Quantized Models? - 30B 4-bit CUDA: [tmpupload/superhot-30b-8k-4bit-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-safetensors) - 30B 4-bit CUDA 128g: [tmpupload/superhot-30b-8k-4bit-128g-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-128g-safetensors) #### Training Details I trained the LoRA with the following configuration: - 1200 samples (~400 samples over 2048 sequence length) - learning rate of 3e-4 - 3 epochs - The exported modules are: - q_proj - k_proj - v_proj - o_proj - no bias - Rank = 4 - Alpha = 8 - no dropout - weight decay of 0.1 - AdamW beta1 of 0.9 and beta2 0.99, epsilon of 1e-5 - Trained on 4-bit base model # Original model card: Allen AI's Tulu 13B <!-- header start --> <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p><a href="https://discord.gg/Jq4vkcDakD">Chat & support: my new Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <!-- header end --> # Allen AI's Tulu 13B fp16 These files are pytorch format fp16 model files for [Allen AI's Tulu 13B](https://huggingface.co/allenai/tulu-13b). It is the result of merging and/or converting the source repository to float16. ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/tulu-13B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/tulu-13B-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/tulu-13B-fp16) ## Prompt template The following template should be used: ``` <\|user\|> prompt goes here <\|assistant\|> ``` Note: There should be a newline after `<\|assistant\|>`. This appears to be very important for getting this model to respond correctly. In other words, the prompt is: ``` <\|user\|>\nprompt goes here\n<\|assistant\|>\n ``` <!-- footer start --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov. Patreon special mentions: Oscar Rangel, Eugene Pentland, Talal Aujan, Cory Kujawski, Luke, Asp the Wyvern, Ai Maven, Pyrater, Alps Aficionado, senxiiz, Willem Michiel, Junyu Yang, trip7s trip, Sebastain Graf, Joseph William Delisle, Lone Striker, Jonathan Leane, Johann-Peter Hartmann, David Flickinger, Spiking Neurons AB, Kevin Schuppel, Mano Prime, Dmitriy Samsonov, Sean Connelly, Nathan LeClaire, Alain Rossmann, Fen Risland, Derek Yates, Luke Pendergrass, Nikolai Manek, Khalefa Al-Ahmad, Artur Olbinski, John Detwiler, Ajan Kanaga, Imad Khwaja, Trenton Dambrowitz, Kalila, vamX, webtim, Illia Dulskyi. Thank you to all my generous patrons and donaters! <!-- footer end --> # Original model card: Allen AI's Tulu 13B # Tulu 13B This model is a 13B LLaMa model finetuned on a mixture of instruction datasets (FLAN V2, CoT, Dolly, Open Assistant 1, GPT4-Alpaca, Code-Alpaca, and ShareGPT). Please note this is a model diff - see below for usage instructions. This was trained as part of the paper [How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources](https://arxiv.org/abs/2306.04751). The codebase used to train and evaluate this model can be found at [https://github.com/allenai/open-instruct](https://github.com/allenai/open-instruct). This model is licensed under the AI model license given in LICENSE.txt along with the original Llama license (llama_license.txt). ## Usage We assume you have access to a LLaMa model in HF format already. You can find details on getting access and converting the model here: [https://huggingface.co/docs/transformers/main/model_doc/llama](https://huggingface.co/docs/transformers/main/model_doc/llama) Clone [https://github.com/allenai/open-instruct](https://github.com/allenai/open-instruct) and install the required dependencies, or just copy `scripts/weight_diff.py` and install the minimal requirements listed in `weight-diff-requirements.txt`. Then download or clone this model diff to the same machine. Then, run: ```bash python scripts/weight_diff.py recover --path_raw ${hf_llama_path} --path_tuned ${output_path} --path_diff ${diff_location} ``` And you will have a recovered model! Note this takes up a decent amount of RAM, especially for the larger models. ## Input Format The model is trained to use the following format (note the newlines): ``` <\|user\|> Your message here! <\|assistant\|> ``` For best results, format all inputs in this manner. ## Performance Here is the performance of this model across benchmarks explored in our paper [How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources](https://arxiv.org/abs/2306.04751): \| MMLU 0-shot \| MMLU 5-shot \| GSM Direct \| GSM CoT \| BBH Direct \| BBH CoT \| TydiQA Gold-Passage \| TydiQA Closed-book \| Codex-Eval Pass@1 \| Codex-Eval Pass@10 \| AlpacaFarm vs Davinci-003 \| Average \| \|:-----------:\|:-----------:\|:----------:\|:-------:\|:----------:\|:-------:\|:-------------------:\|:------------------:\|:-----------------:\|:------------------:\|:-------------------------:\|---------\| \| 49.2 \| 51.8 \| 5.0 \| 36.5 \| 41.3 \| 42.8 \| 46.1 \| 9.2 \| 21.3 \| 35.0 \| 53.9 \|37.2 \| If you use this model, please cite our work, the llama paper, and the original datasets: ``` @misc{wang2023far, title={How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources}, author={Yizhong Wang and Hamish Ivison and Pradeep Dasigi and Jack Hessel and Tushar Khot and Khyathi Raghavi Chandu and David Wadden and Kelsey MacMillan and Noah A. Smith and Iz Beltagy and Hannaneh Hajishirzi}, year={2023}, eprint={2306.04751}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ``` @misc{touvron2023llama, title={LLaMA: Open and Efficient Foundation Language Models}, author={Hugo Touvron and Thibaut Lavril and Gautier Izacard and Xavier Martinet and Marie-Anne Lachaux and Timothée Lacroix and Baptiste Rozière and Naman Goyal and Eric Hambro and Faisal Azhar and Aurelien Rodriguez and Armand Joulin and Edouard Grave and Guillaume Lample}, year={2023}, eprint={2302.13971}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ``` @misc{dolly, author = {Databricks}, title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {Blog post}, url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm} } ``` ``` @article{longpre2023flan, title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning}, author={Longpre, Shayne and Hou, Le and Vu, Tu and Webson, Albert and Chung, Hyung Won and Tay, Yi and Zhou, Denny and Le, Quoc V and Zoph, Barret and Wei, Jason and others}, journal={arXiv preprint arXiv:2301.13688}, year={2023} } ``` ``` @misc{köpf2023openassistant, title={OpenAssistant Conversations -- Democratizing Large Language Model Alignment}, author={Andreas Köpf and Yannic Kilcher and Dimitri von Rütte and Sotiris Anagnostidis and Zhi-Rui Tam and Keith Stevens and Abdullah Barhoum and Nguyen Minh Duc and Oliver Stanley and Richárd Nagyfi and Shahul ES and Sameer Suri and David Glushkov and Arnav Dantuluri and Andrew Maguire and Christoph Schuhmann and Huu Nguyen and Alexander Mattick}, year={2023}, eprint={2304.07327}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ``` @article{peng2023instruction, title={Instruction Tuning with GPT-4}, author={Peng, Baolin and Li, Chunyuan and He, Pengcheng and Galley, Michel and Gao, Jianfeng}, journal={arXiv preprint arXiv:2304.03277}, year={2023} } ``` ``` @misc{codealpaca, author = {Sahil Chaudhary}, title = {Code Alpaca: An Instruction-following LLaMA model for code generation}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/sahil280114/codealpaca}}, } ```
dp15/huggyFace-v-first	dp15	2023-06-30T15:33:25Z	11	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-06-30T15:33:02Z	--- 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: dp15/huggyFace-v-first 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
eswardivi/medical_qa_llm	eswardivi	2023-06-30T15:30:05Z	3	0	peft	[ "peft", "region:us" ]	null	2023-06-30T15:17:58Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
slone/bert-tiny-char-ctc-bak-denoise	slone	2023-06-30T15:17:51Z	95	1	transformers	[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "grammatical-error-correction", "ba", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-06-30T06:36:13Z	--- license: cc-by-4.0 language: - ba tags: - grammatical-error-correction --- This is a tiny BERT model for Bashkir, intended for fixing OCR errors. Here is the code to run it (it uses a custom tokenizer, with the code downloaded in the runtime): ```Python import torch from transformers import AutoModelForMaskedLM, AutoTokenizer MODEL_NAME = 'slone/bert-tiny-char-ctc-bak-denoise' model = AutoModelForMaskedLM.from_pretrained(MODEL_NAME) tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True, revision='194109') def fix_text(text, verbose=False, spaces=2): with torch.inference_mode(): batch = tokenizer(text, return_tensors='pt', spaces=spaces, padding=True, truncation=True, return_token_type_ids=False).to(model.device) logits = torch.log_softmax(model(**batch).logits, axis=-1) return tokenizer.decode(logits[0].argmax(-1), skip_special_tokens=True) print(fix_text("Э Ҡаратау ҙы белмәйем.")) # Ә Ҡаратауҙы белмәйем. ``` The model works by: - inserting special characters (`spaces`) between each input character, - performing token classification (when for most tokens, predicted output equals input, but some may modify it), - and removing the special characters from the output. It was trained on a parallel corpus (corrupted + fixed sentence) with CTC loss. On our test dataset, it reduces OCR errors by 41%. Training code: [here](https://github.com/slone-nlp/bashkort-spellcheker/blob/master/experiments/06_ctc_bert.ipynb). Training details: in [this post](https://habr.com/ru/articles/744972/) (in Russian).
amittian/setfit_ds_version_0_0_5	amittian	2023-06-30T15:17:25Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-06-30T15:17:08Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # amittian/setfit_ds_version_0_0_5 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("amittian/setfit_ds_version_0_0_5") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

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