Datasets:
librarian-bots
/
model_cards_with_metadata

Dataset card Data Studio Files
xet
Community
2
modelId
stringlengths
5
139
author
stringlengths
2
42
last_modified
timestamp[us, tz=UTC]date
2020-02-15 11:33:14
2025-06-28 06:27:35
downloads
int64
0
223M
likes
int64
0
11.7k
library_name
stringclasses
500 values
tags
sequencelengths
1
4.05k
pipeline_tag
stringclasses
54 values
createdAt
timestamp[us, tz=UTC]date
2022-03-02 23:29:04
2025-06-28 06:24:42
card
stringlengths
11
1.01M
Realgon/N_bert_agnews_padding40model
Realgon
2023-12-27T17:03:28Z
5
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:ag_news", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-27T14:36:43Z
--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer datasets: - ag_news metrics: - accuracy model-index: - name: N_bert_agnews_padding40model results: - task: name: Text Classification type: text-classification dataset: name: ag_news type: ag_news config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.9473684210526315 --- <!-- 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. --> # N_bert_agnews_padding40model This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ag_news dataset. It achieves the following results on the evaluation set: - Loss: 0.5661 - Accuracy: 0.9474 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 0.1785 | 1.0 | 7500 | 0.1884 | 0.9421 | | 0.1379 | 2.0 | 15000 | 0.1990 | 0.9478 | | 0.1127 | 3.0 | 22500 | 0.2389 | 0.9408 | | 0.0846 | 4.0 | 30000 | 0.2528 | 0.9492 | | 0.0581 | 5.0 | 37500 | 0.3041 | 0.9436 | | 0.0456 | 6.0 | 45000 | 0.3415 | 0.9468 | | 0.0411 | 7.0 | 52500 | 0.4081 | 0.9430 | | 0.0239 | 8.0 | 60000 | 0.4415 | 0.9433 | | 0.0202 | 9.0 | 67500 | 0.4380 | 0.9404 | | 0.0126 | 10.0 | 75000 | 0.4637 | 0.9425 | | 0.0175 | 11.0 | 82500 | 0.4485 | 0.9455 | | 0.0126 | 12.0 | 90000 | 0.4761 | 0.9449 | | 0.0046 | 13.0 | 97500 | 0.5009 | 0.9455 | | 0.0038 | 14.0 | 105000 | 0.4784 | 0.9482 | | 0.0035 | 15.0 | 112500 | 0.5282 | 0.9451 | | 0.0046 | 16.0 | 120000 | 0.5256 | 0.9464 | | 0.0026 | 17.0 | 127500 | 0.5081 | 0.9501 | | 0.0008 | 18.0 | 135000 | 0.5543 | 0.9467 | | 0.0002 | 19.0 | 142500 | 0.5448 | 0.9488 | | 0.0016 | 20.0 | 150000 | 0.5661 | 0.9474 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
ullrichx/a2c-PandaReachDense-v3
ullrichx
2023-12-27T17:02:32Z
0
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-12-27T16:53:57Z
--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.27 +/- 0.10 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v3** This is a trained model of a **A2C** agent playing **PandaReachDense-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
tshirtstate/willemx_LoRA
tshirtstate
2023-12-27T16:56:25Z
0
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]
text-to-image
2023-12-27T16:56:23Z
--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: willemx license: openrail++ --- # SDXL LoRA DreamBooth - tshirtstate/willemx_LoRA <Gallery /> ## Model description These are tshirtstate/willemx_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use willemx to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](tshirtstate/willemx_LoRA/tree/main) them in the Files & versions tab.
ntc-ai/SDXL-LoRA-slider.hair-up
ntc-ai
2023-12-27T16:51:03Z
180
0
diffusers
[ "diffusers", "text-to-image", "stable-diffusion-xl", "lora", "template:sd-lora", "template:sdxl-lora", "sdxl-sliders", "ntcai.xyz-sliders", "concept", "en", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:mit", "region:us" ]
text-to-image
2023-12-27T16:51:00Z
--- language: - en thumbnail: "images/evaluate/hair up.../hair up_17_3.0.png" widget: - text: hair up output: url: images/hair up_17_3.0.png - text: hair up output: url: images/hair up_19_3.0.png - text: hair up output: url: images/hair up_20_3.0.png - text: hair up output: url: images/hair up_21_3.0.png - text: hair up output: url: images/hair up_22_3.0.png tags: - text-to-image - stable-diffusion-xl - lora - template:sd-lora - template:sdxl-lora - sdxl-sliders - ntcai.xyz-sliders - concept - diffusers license: "mit" inference: false instance_prompt: "hair up" base_model: "stabilityai/stable-diffusion-xl-base-1.0" --- # ntcai.xyz slider - hair up (SDXL LoRA) | Strength: -3 | Strength: 0 | Strength: 3 | | --- | --- | --- | | <img src="images/hair up_17_-3.0.png" width=256 height=256 /> | <img src="images/hair up_17_0.0.png" width=256 height=256 /> | <img src="images/hair up_17_3.0.png" width=256 height=256 /> | | <img src="images/hair up_19_-3.0.png" width=256 height=256 /> | <img src="images/hair up_19_0.0.png" width=256 height=256 /> | <img src="images/hair up_19_3.0.png" width=256 height=256 /> | | <img src="images/hair up_20_-3.0.png" width=256 height=256 /> | <img src="images/hair up_20_0.0.png" width=256 height=256 /> | <img src="images/hair up_20_3.0.png" width=256 height=256 /> | ## Download Weights for this model are available in Safetensors format. ## Trigger words You can apply this LoRA with trigger words for additional effect: ``` hair up ``` ## Use in diffusers ```python from diffusers import StableDiffusionXLPipeline from diffusers import EulerAncestralDiscreteScheduler import torch pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors") pipe.to("cuda") pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) # Load the LoRA pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.hair-up', weight_name='hair up.safetensors', adapter_name="hair up") # Activate the LoRA pipe.set_adapters(["hair up"], adapter_weights=[2.0]) prompt = "medieval rich kingpin sitting in a tavern, hair up" negative_prompt = "nsfw" width = 512 height = 512 num_inference_steps = 10 guidance_scale = 2 image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0] image.save('result.png') ``` ## Support the Patreon If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI). By joining our Patreon, you'll gain access to an ever-growing library of over 670+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities. Your support on Patreon will allow us to continue developing and refining new models. ## Other resources - [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs - [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
Rezakakooee/marian-finetuned-kde4-en-to-fr
Rezakakooee
2023-12-27T16:47:29Z
11
0
transformers
[ "transformers", "tensorboard", "safetensors", "marian", "text2text-generation", "translation", "generated_from_trainer", "base_model:Helsinki-NLP/opus-mt-en-fr", "base_model:finetune:Helsinki-NLP/opus-mt-en-fr", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
translation
2023-12-27T15:53:26Z
--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-fr tags: - translation - generated_from_trainer model-index: - name: marian-finetuned-kde4-en-to-fr 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. --> # marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 1.6964 - eval_bleu: 39.1660 - eval_runtime: 1604.2015 - eval_samples_per_second: 13.102 - eval_steps_per_second: 0.205 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - 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 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0
douglasadams11/roberta-large-ner-new
douglasadams11
2023-12-27T16:32:50Z
5
0
transformers
[ "transformers", "safetensors", "roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-large", "base_model:finetune:FacebookAI/roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-12-27T13:29:31Z
--- license: mit base_model: roberta-large tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: roberta-large-ner-new results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-large-ner-new This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1106 - Precision: 0.9670 - Recall: 0.9604 - F1: 0.9637 - Accuracy: 0.9600 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1241 | 0.71 | 5000 | 0.1161 | 0.9618 | 0.9505 | 0.9561 | 0.9521 | | 0.0993 | 1.42 | 10000 | 0.1132 | 0.9633 | 0.9568 | 0.9600 | 0.9562 | | 0.0812 | 2.13 | 15000 | 0.1223 | 0.9662 | 0.9574 | 0.9618 | 0.9580 | | 0.074 | 2.84 | 20000 | 0.1118 | 0.9661 | 0.9607 | 0.9634 | 0.9598 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0
jameswatanabegoogle2024/ppo-SnowballTarget
jameswatanabegoogle2024
2023-12-27T16:26:47Z
0
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]
reinforcement-learning
2023-12-27T16:26:44Z
--- 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: jameswatanabegoogle2024/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
Prezily/gpt2-trial-r1
Prezily
2023-12-27T16:25:09Z
5
0
transformers
[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-12-27T14:08:31Z
--- tags: - generated_from_trainer model-index: - name: gpt2-trial-r1 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-trial-r1 This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0
santiviquez/t5-small-finetuned-samsum-en
santiviquez
2023-12-27T16:17:25Z
24
0
transformers
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "summarization", "generated_from_trainer", "dataset:samsum", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
summarization
2022-06-07T15:52:00Z
--- license: apache-2.0 tags: - summarization - generated_from_trainer datasets: - samsum metrics: - rouge base_model: t5-small model-index: - name: t5-small-finetuned-samsum-en results: - task: type: text2text-generation name: Sequence-to-sequence Language Modeling dataset: name: samsum type: samsum args: samsum metrics: - type: rouge value: 44.3313 name: Rouge1 - task: type: summarization name: Summarization dataset: name: samsum type: samsum config: samsum split: test metrics: - type: rouge value: 40.0386 name: ROUGE-1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMmRlMjZmNjQyYWQ5MjcyM2M2MzUwMjk5ZTQxOTg3NzY1NjAxY2FkNzY5OGI2YjcxYTg1Y2M1Y2M2NDM2YmI1YSIsInZlcnNpb24iOjF9.xxrRepLefbFAUWkOJwOenMuwQ8g4i2QkEUgB_d1YsAv2aRRQd0vPfiGCMltGEtCxqrgQ6vmndOlkXIJhCPV9CQ - type: rouge value: 15.8501 name: ROUGE-2 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjQ4ZDQ0OTM2ZjI3NGExYWRjNWNjNTYwNjA0YWE0NWVkODJmODAwZTYzZjU3NzVhNjRiM2Y3ZDFhYjIwMTcxOSIsInZlcnNpb24iOjF9.UnymHQUy2s5P8yNUkFRhj6drPkKviYUNN2yB9E1KvYssNpRWnUbD5X_cVfYGWXVLPrtYe9dc-f7vSvm2Z1ZtDA - type: rouge value: 31.8084 name: ROUGE-L verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTllNjQ2MGRjMTJkNmI3OWI5MTNmNWJjNmUyMTU1ZjkxYzkyNDg4MWI2MGU1NWI5NmZhMTFjNjE4ZTI5M2MyMiIsInZlcnNpb24iOjF9.rVGbelDJoVmcTD6OOQ7O8C_4LhrMMuYUniY_hAmmgZ8kU_wgtApwi6Ms1sgzqtvbF0cDHaLxejE9XPZ8ZDZMAA - type: rouge value: 36.0888 name: ROUGE-LSUM verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNWQyNmZmMjFkZTY2MDhjZmIzZDBkM2ZkYzUxZTcxMTcwMDVjMDdiMzljMjU2NDA5OTUxZTEwYzQwZjg2NDJmMiIsInZlcnNpb24iOjF9.ZEBUBcPLCURLXPN5upXDHaIVu_ilUEyvZd81nnppZCWEuULyp30jcpmzLFb91v0WwRHMDPIjPl0hlckzq71ICw - type: loss value: 2.1917073726654053 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjA0MDk3MWZiMDgxMDlkZDFjY2UwODM0MTk4MmY2NzlkNThmYTA0ODk5MzgyZWQwYjVlZGFlZmJmNjA2NDA2ZSIsInZlcnNpb24iOjF9.Wc_5Wpf_Wa0Xm0A7w2EYnF1_eQ-2QU_v6eXr8SHveBszH5YhZBW6GS3yKslVVKKIaAGSGKtLIHzMW1H-NqqNDA - type: gen_len value: 18.1074 name: gen_len verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDFlMmU0MTAyMDM5M2UyZDA2N2U4MjQ3MjhjYjdkOGY1ODdlNDY1NWY3NTQ3MzBhOWE3OTk2ZGU3ZTYyNjU1ZCIsInZlcnNpb24iOjF9.Ob1cLE1iYpV00ae1RYRIUNZz7V-x8IYTcU6ofR5gf07PdRqfiOgZtpV0tN3yM0_nyAJI71J8fnC6yWq10Y0HBw --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-samsum-en This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.9335 - Rouge1: 44.3313 - Rouge2: 20.71 - Rougel: 37.221 - Rougelsum: 40.9603 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 1.4912 | 1.0 | 300 | 1.9043 | 44.1517 | 20.0186 | 36.6053 | 40.5164 | | 1.5055 | 2.0 | 600 | 1.8912 | 44.1473 | 20.4456 | 37.069 | 40.6714 | | 1.4852 | 3.0 | 900 | 1.8986 | 44.7536 | 20.8646 | 37.525 | 41.2189 | | 1.4539 | 4.0 | 1200 | 1.9136 | 44.2144 | 20.3446 | 37.1088 | 40.7581 | | 1.4262 | 5.0 | 1500 | 1.9215 | 44.2656 | 20.6044 | 37.3267 | 40.9469 | | 1.4118 | 6.0 | 1800 | 1.9247 | 43.8793 | 20.4663 | 37.0614 | 40.6065 | | 1.3987 | 7.0 | 2100 | 1.9256 | 43.9981 | 20.2703 | 36.7856 | 40.6354 | | 1.3822 | 8.0 | 2400 | 1.9316 | 43.9732 | 20.4559 | 36.8039 | 40.5784 | | 1.3773 | 9.0 | 2700 | 1.9314 | 44.3075 | 20.5435 | 37.0457 | 40.832 | | 1.3795 | 10.0 | 3000 | 1.9335 | 44.3313 | 20.71 | 37.221 | 40.9603 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
afrideva/MiniChat-2-3B-GGUF
afrideva
2023-12-27T16:11:06Z
33
1
transformers
[ "transformers", "gguf", "ggml", "quantized", "q2_k", "q3_k_m", "q4_k_m", "q5_k_m", "q6_k", "q8_0", "text-generation", "en", "zh", "arxiv:2311.07052", "arxiv:2310.05914", "arxiv:2305.18290", "base_model:GeneZC/MiniChat-2-3B", "base_model:quantized:GeneZC/MiniChat-2-3B", "license:apache-2.0", "region:us" ]
text-generation
2023-12-27T16:03:04Z
--- base_model: GeneZC/MiniChat-2-3B inference: false language: - en - zh library_name: transformers license: apache-2.0 model_creator: GeneZC model_name: MiniChat-2-3B pipeline_tag: text-generation quantized_by: afrideva tags: - gguf - ggml - quantized - q2_k - q3_k_m - q4_k_m - q5_k_m - q6_k - q8_0 widget: - text: "<s> [|User|] Hi \U0001F44B </s>[|Assistant|]" --- # GeneZC/MiniChat-2-3B-GGUF Quantized GGUF model files for [MiniChat-2-3B](https://huggingface.co/GeneZC/MiniChat-2-3B) from [GeneZC](https://huggingface.co/GeneZC) | Name | Quant method | Size | | ---- | ---- | ---- | | [minichat-2-3b.fp16.gguf](https://huggingface.co/afrideva/MiniChat-2-3B-GGUF/resolve/main/minichat-2-3b.fp16.gguf) | fp16 | 6.04 GB | | [minichat-2-3b.q2_k.gguf](https://huggingface.co/afrideva/MiniChat-2-3B-GGUF/resolve/main/minichat-2-3b.q2_k.gguf) | q2_k | 1.30 GB | | [minichat-2-3b.q3_k_m.gguf](https://huggingface.co/afrideva/MiniChat-2-3B-GGUF/resolve/main/minichat-2-3b.q3_k_m.gguf) | q3_k_m | 1.51 GB | | [minichat-2-3b.q4_k_m.gguf](https://huggingface.co/afrideva/MiniChat-2-3B-GGUF/resolve/main/minichat-2-3b.q4_k_m.gguf) | q4_k_m | 1.85 GB | | [minichat-2-3b.q5_k_m.gguf](https://huggingface.co/afrideva/MiniChat-2-3B-GGUF/resolve/main/minichat-2-3b.q5_k_m.gguf) | q5_k_m | 2.15 GB | | [minichat-2-3b.q6_k.gguf](https://huggingface.co/afrideva/MiniChat-2-3B-GGUF/resolve/main/minichat-2-3b.q6_k.gguf) | q6_k | 2.48 GB | | [minichat-2-3b.q8_0.gguf](https://huggingface.co/afrideva/MiniChat-2-3B-GGUF/resolve/main/minichat-2-3b.q8_0.gguf) | q8_0 | 3.21 GB | ## Original Model Card: ## MiniChat-2-3B 📑 [arXiv](https://arxiv.org/abs/2311.07052) | 👻 [GitHub](https://github.com/GeneZC/MiniMA) | 🤗 [HuggingFace-MiniMA](https://huggingface.co/GeneZC/MiniMA-3B) | 🤗 [HuggingFace-MiniChat](https://huggingface.co/GeneZC/MiniChat-3B) | 🤖 [ModelScope-MiniMA](https://modelscope.cn/models/GeneZC/MiniMA-3B) | 🤖 [ModelScope-MiniChat](https://modelscope.cn/models/GeneZC/MiniChat-3B) | 🤗 [HuggingFace-MiniChat-1.5](https://huggingface.co/GeneZC/MiniChat-1.5-3B) | 🤗 [HuggingFace-MiniMA-2](https://huggingface.co/GeneZC/MiniMA-2-3B) | 🤗 [HuggingFace-MiniChat-2](https://huggingface.co/GeneZC/MiniChat-2-3B) 🆕 **Updates from MiniChat-3B**: - better base model MiniMA-2-3B; - better data mixture; - use of [NEFTune](https://arxiv.org/abs/2310.05914); - use of [DPO](https://arxiv.org/abs/2305.18290). ❗ Must comply with LICENSE of LLaMA2 since it is derived from LLaMA2. A language model continued from MiniMA-3B and finetuned on both instruction and preference data. Surpassing Vicuna-7B and approximating LLaMA-2-Chat-7B on MT-Bench. <img src="./teaser_b.jpg" alt="teaser_b" width="687" /> **Standard Benchmarks** |Method|TFLOPs|MMLU (5-shot)|CEval (5-shot)|DROP (3-shot)|HumanEval (0-shot)|BBH (3-shot)|GSM8K (8-shot)| |--|--|--|--|--|--|--|--| |Mamba-2.8B|4.6E9|25.58|24.74|15.72|7.32|29.37|3.49| |ShearedLLaMA-2.7B|0.8E9|26.97|22.88|19.98|4.88|30.48|3.56| |BTLM-3B|11.3E9|27.20|26.00|17.84|10.98|30.87|4.55| |StableLM-3B|72.0E9|44.75|31.05|22.35|15.85|32.59|10.99| |Qwen-1.8B|23.8E9|44.05|54.75|12.97|14.02|30.80|22.97| |Phi-2-2.8B|159.9E9|56.74|34.03|30.74|46.95|44.13|55.42| |LLaMA-2-7B|84.0E9|46.00|34.40|31.57|12.80|32.02|14.10| || |MiniMA-3B|4.0E9|28.51|28.23|22.50|10.98|31.61|8.11| |MiniChat-3B|4.0E9|38.40|36.48|22.58|18.29|31.36|29.72| |MiniMA-2-3B|13.4E9|40.14|44.65|23.10|14.63|31.43|8.87| |MiniChat-2-3B|13.4E9|46.17|43.91|30.26|22.56|34.95|38.13| **Instruction-following Benchmarks** |Method|AlpacaEval|MT-Bench| |--|--|--| |GPT-4|95.28|9.18| |Zephyr-7B-Beta|90.60|7.34| |Phi-2-DPO|81.37|-| |StableLM Zephyr 3B|76.00|6.64| |Vicuna-7B|76.84|6.17| |LLaMA-2-Chat-7B|71.37|6.27| || |MiniChat-3B|48.82|-| |MiniChat-2-3B|77.30|6.23| The following is an example code snippet to use MiniChat-2-3B: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer from conversation import get_default_conv_template # MiniChat tokenizer = AutoTokenizer.from_pretrained("GeneZC/MiniChat-2-3B", use_fast=False) # GPU. model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-2-3B", use_cache=True, device_map="auto", torch_dtype=torch.float16).eval() # CPU. # model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-2-3B", use_cache=True, device_map="cpu", torch_dtype=torch.float16).eval() conv = get_default_conv_template("minichat") question = "Implement a program to find the common elements in two arrays without using any extra data structures." conv.append_message(conv.roles[0], question) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() input_ids = tokenizer([prompt]).input_ids output_ids = model.generate( torch.as_tensor(input_ids).cuda(), do_sample=True, temperature=0.7, max_new_tokens=1024, ) output_ids = output_ids[0][len(input_ids[0]):] output = tokenizer.decode(output_ids, skip_special_tokens=True).strip() # output: "def common_elements(arr1, arr2):\n if len(arr1) == 0:\n return []\n if len(arr2) == 0:\n return arr1\n\n common_elements = []\n for element in arr1:\n if element in arr2:\n common_elements.append(element)\n\n return common_elements" # Multiturn conversation could be realized by continuously appending questions to `conv`. ``` ## Bibtex ```bibtex @article{zhang2023law, title={Towards the Law of Capacity Gap in Distilling Language Models}, author={Zhang, Chen and Song, Dawei and Ye, Zheyu and Gao, Yan}, year={2023}, url={https://arxiv.org/abs/2311.07052} } ```
Weyaxi/MetaMath-NeuralHermes-2.5-Mistral-7B-Linear
Weyaxi
2023-12-27T16:02:09Z
1,537
1
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-12-05T10:59:54Z
--- license: apache-2.0 --- models: - model: meta-math/MetaMath-Mistral-7B parameters: weight: 0.5 - model: mlabonne/NeuralHermes-2.5-Mistral-7B parameters: weight: 0.3 merge_method: linear dtype: float16
sushistarlord/distilbert-base-uncased-finetuned-emotion-sushant
sushistarlord
2023-12-27T15:57:30Z
7
0
transformers
[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-27T15:06:09Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion-sushant results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9135 - name: F1 type: f1 value: 0.9128218997521944 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion-sushant This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2815 - Accuracy: 0.9135 - F1: 0.9128 ## 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: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 63 | 0.9420 | 0.6785 | 0.6074 | | No log | 2.0 | 126 | 0.4520 | 0.8705 | 0.8593 | | No log | 3.0 | 189 | 0.3137 | 0.9095 | 0.9084 | | 0.6765 | 4.0 | 252 | 0.2815 | 0.9135 | 0.9128 | ### Framework versions - Transformers 4.36.1 - Pytorch 2.1.0+cu121 - Datasets 2.14.4 - Tokenizers 0.15.0
Shaleen123/neural_medical_chat
Shaleen123
2023-12-27T15:52:09Z
1
0
peft
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Intel/neural-chat-7b-v3-3", "base_model:adapter:Intel/neural-chat-7b-v3-3", "region:us" ]
null
2023-12-27T15:08:11Z
--- library_name: peft base_model: Intel/neural-chat-7b-v3-3 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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 Dataset 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 Dataset 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] ### Framework versions - PEFT 0.7.1
bpugnaire/Reinforce-Pixelcopter-PLE-v0
bpugnaire
2023-12-27T15:30:16Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-12-27T15:29:40Z
--- 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: 38.60 +/- 29.75 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
we1kkk/llama2-hf-qlora-oasst1
we1kkk
2023-12-27T15:28:17Z
0
0
null
[ "safetensors", "conversational", "dataset:OpenAssistant/oasst1", "region:us" ]
text-generation
2023-07-21T02:18:15Z
--- datasets: - OpenAssistant/oasst1 pipeline_tag: conversational --- Lora weight for Llama2 trained on oasst1 dataset using 4bit quantazation QLoRA.
monsterapi/mistral_7b_DolphinCoder
monsterapi
2023-12-27T15:17:19Z
67
0
peft
[ "peft", "code", "instruct", "mistral", "dataset:cognitivecomputations/dolphin-coder", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]
null
2023-12-21T02:04:03Z
--- library_name: peft tags: - code - instruct - mistral datasets: - cognitivecomputations/dolphin-coder base_model: mistralai/Mistral-7B-v0.1 license: apache-2.0 --- ### Finetuning Overview: **Model Used:** mistralai/Mistral-7B-v0.1 **Dataset:** cognitivecomputations/dolphin-coder #### Dataset Insights: [Dolphin-Coder](https://huggingface.co/datasets/cognitivecomputations/dolphin-coder) dataset – a high-quality collection of 100,000+ coding questions and responses. It's perfect for supervised fine-tuning (SFT), and teaching language models to improve on coding-based tasks. #### Finetuning Details: With the utilization of [MonsterAPI](https://monsterapi.ai)'s [no-code LLM finetuner](https://monsterapi.ai/finetuning), this finetuning: - Was achieved with great cost-effectiveness. - Completed in a total duration of 15hr 36mins for 1 epochs using an A6000 48GB GPU. - Costed `$31.51` for the entire 1 epoch. #### Hyperparameters & Additional Details: - **Epochs:** 1 - **Cost Per Epoch:** $31.51 - **Model Path:** mistralai/Mistral-7B-v0.1 - **Learning Rate:** 0.0002 - **Data Split:** 100% train - **Gradient Accumulation Steps:** 128 - **lora r:** 32 - **lora alpha:** 64 ![Train Loss](https://cdn-uploads.huggingface.co/production/uploads/63ba46aa0a9866b28cb19a14/kUDqiPdErxwf8sU-lHwI1.png) --- license: apache-2.0
monsterapi/llama2_7b_DolphinCoder
monsterapi
2023-12-27T15:16:30Z
4
0
peft
[ "peft", "code", "instruct", "llama2", "dataset:cognitivecomputations/dolphin-coder", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:apache-2.0", "region:us" ]
null
2023-12-21T07:24:31Z
--- library_name: peft tags: - code - instruct - llama2 datasets: - cognitivecomputations/dolphin-coder base_model: meta-llama/Llama-2-7b-hf license: apache-2.0 --- ### Finetuning Overview: **Model Used:** meta-llama/Llama-2-7b-hf **Dataset:** cognitivecomputations/dolphin-coder #### Dataset Insights: [Dolphin-Coder](https://huggingface.co/datasets/cognitivecomputations/dolphin-coder) dataset – a high-quality collection of 100,000+ coding questions and responses. It's perfect for supervised fine-tuning (SFT), and teaching language models to improve on coding-based tasks. #### Finetuning Details: With the utilization of [MonsterAPI](https://monsterapi.ai)'s [no-code LLM finetuner](https://monsterapi.ai/finetuning), this finetuning: - Was achieved with great cost-effectiveness. - Completed in a total duration of 15hr 31mins for 1 epochs using an A6000 48GB GPU. - Costed `$30.64` for the entire 1 epoch. #### Hyperparameters & Additional Details: - **Epochs:** 1 - **Total Finetuning Cost:** $30.64 - **Model Path:** meta-llama/Llama-2-7b-hf - **Learning Rate:** 0.0002 - **Data Split:** 100% train - **Gradient Accumulation Steps:** 128 - **lora r:** 32 - **lora alpha:** 64 ![Train Loss](https://cdn-uploads.huggingface.co/production/uploads/63ba46aa0a9866b28cb19a14/WW95Ihn0urvg0aAYxeowZ.png) --- license: apache-2.0
Tobius/lugandawav2vec
Tobius
2023-12-27T15:13:14Z
9
0
transformers
[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "lg", "dataset:tericlabs", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-12-27T13:21:55Z
--- language: - lg license: apache-2.0 base_model: openai/whisper-small tags: - whisper-event - generated_from_trainer datasets: - tericlabs metrics: - wer model-index: - name: Whisper Small ganda results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Yogera data type: tericlabs config: lg split: test args: lg metrics: - name: Wer type: wer value: 54.276315789473685 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small ganda This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Yogera data dataset. It achieves the following results on the evaluation set: - Loss: 1.4937 - Wer: 54.2763 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.9882 | 26.0 | 500 | 1.4647 | 54.9342 | | 0.0026 | 52.0 | 1000 | 1.3967 | 60.8553 | | 0.0002 | 78.0 | 1500 | 1.4295 | 57.8947 | | 0.0001 | 105.0 | 2000 | 1.4494 | 58.2237 | | 0.0001 | 131.0 | 2500 | 1.4713 | 53.9474 | | 0.0001 | 157.0 | 3000 | 1.4835 | 54.2763 | | 0.0001 | 184.0 | 3500 | 1.4908 | 54.2763 | | 0.0001 | 210.0 | 4000 | 1.4937 | 54.2763 | ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0
judithrosell/SciBERT_CRAFT_NER_new
judithrosell
2023-12-27T15:01:14Z
5
0
transformers
[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:allenai/scibert_scivocab_uncased", "base_model:finetune:allenai/scibert_scivocab_uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-12-27T14:40:04Z
--- base_model: allenai/scibert_scivocab_uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: SciBERT_CRAFT_NER_new results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SciBERT_CRAFT_NER_new This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1199 - Precision: 0.9743 - Recall: 0.9761 - F1: 0.9752 - Accuracy: 0.9740 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1537 | 1.0 | 695 | 0.1140 | 0.9707 | 0.9727 | 0.9717 | 0.9704 | | 0.0452 | 2.0 | 1390 | 0.1128 | 0.9733 | 0.9750 | 0.9741 | 0.9731 | | 0.0185 | 3.0 | 2085 | 0.1199 | 0.9743 | 0.9761 | 0.9752 | 0.9740 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0
ccore/FT_512
ccore
2023-12-27T14:57:21Z
0
0
null
[ "license:mit", "region:us" ]
null
2023-12-27T14:25:40Z
--- license: mit --- # Conversational Language Model Interface using FASTTEXT This project provides a Command Line Interface (CLI) for interacting with a FastText language model, enabling users to generate text sequences based on their input. The script allows customization of parameters such as temperature, input text, top-k predictions, and model file path. ## Installation Before running the script, ensure you have Python installed on your system. Additionally, you'll need to install the FastText library: ## Colab [Google Colab Notebook](https://colab.research.google.com/drive/1jX1NShX7MzJnuL2whHNOA39Xu-meQ1ap?usp=sharing) ```bash pip install fasttext ``` ## Usage To use the script, you should first obtain or train a FastText model. Place the model file (usually with a `.bin` extension) in a known directory. The script can be executed with various command-line arguments to specify the behavior: ```python import argparse import fasttext import numpy as np def apply_repetition_penalty(labels, probabilities, used_labels, penalty_scale=1.9): """ Applies a repetition penalty to reduce the probability of already used labels. :param labels: List of possible labels. :param probabilities: Corresponding list of probabilities. :param used_labels: Set of labels that have already been used. :param penalty_scale: Scale of the penalty to be applied. :return: Adjusted probabilities. """ adjusted_probabilities = probabilities.copy() for i, label in enumerate(labels): if label in used_labels: adjusted_probabilities[i] /= penalty_scale # Normalize the probabilities to sum to 1 again adjusted_probabilities /= adjusted_probabilities.sum() return adjusted_probabilities def predict_sequence(model, text, sequence_length=20, temperature=.5, penalty_scale=1.9): """ Generates a sequence of labels using the FastText model with repetition penalty. :param model: Loaded FastText model. :param text: Initial text to start the prediction from. :param sequence_length: Desired length of the sequence. :param temperature: Temperature for sampling. :param penalty_scale: Scale of repetition penalty. :return: List of predicted labels. """ used_labels = set() sequence = [] for _ in range(sequence_length): # Predict the top k most probable labels labels, probabilities = model.predict(text, k=40) labels = [label.replace('__label__', '') for label in labels] probabilities = np.array(probabilities) # Adjust the probabilities with repetition penalty probabilities = apply_repetition_penalty(labels, probabilities, used_labels, penalty_scale) # Sampling according to the adjusted probabilities label_index = np.random.choice(range(len(labels)), p=probabilities) chosen_label = labels[label_index] # Add the chosen label to the sequence and to the set of used labels sequence.append(chosen_label) used_labels.add(chosen_label) # Update the text with the chosen label for the next prediction text += ' ' + chosen_label return sequence def generate_response(model, input_text, sequence_length=512, temperature=.5, penalty_scale=1.9): generated_sequence = predict_sequence(model, input_text, sequence_length, temperature, penalty_scale) return ' '.join(generated_sequence) def main(): parser = argparse.ArgumentParser(description="Run the language model with specified parameters.") parser.add_argument('-t', '--temperature', type=float, default=0.5, help='Temperature for sampling.') parser.add_argument('-f', '--file', type=str, help='File containing input text.') parser.add_argument('-p', '--text', type=str, help='Direct input text.') parser.add_argument('-n', '--length', type=int, default=50, help='length predictions to consider.') parser.add_argument('-m', '--model', type=str, required=True, help='Address of the FastText model file.') args = parser.parse_args() # Load the model model = fasttext.load_model(args.model) input_text = '' if args.file: with open(args.file, 'r') as file: input_text = file.read() elif args.text: input_text = args.text else: print("No input text provided. Please use -f to specify a file or -p for direct text input.") return # Generate and print the response response = generate_response(model, input_text + " [RESPONSE]", sequence_length=args.length, temperature=args.temperature) print("\nResponse:") print(response) if __name__ == "__main__": main() ``` ```bash python conversation_app.py -t TEMPERATURE -f FILE -p TEXT -k TOPK -m MODEL_PATH ``` - `-t TEMPERATURE` or `--temperature TEMPERATURE`: Sets the temperature for predictions. A higher temperature results in more diverse results. Default is 0.5. - `-f FILE` or `--file FILE`: Specifies a path to a file containing input text. The script will read this file and use its contents as input. - `-p TEXT` or `--text TEXT`: Directly provide the input text as a string. - `-n LENGTH` or `--length TOPK`: Determines the number of top predictions to consider for the model's output. Default is 50. - `-m MODEL_PATH` or `--model MODEL_PATH`: The path to the FastText model file (required). ### Example ```bash python conversation_app.py -t 0.7 -p "What is the future of AI?" -n 40 -m /path/to/model.bin ``` This command sets the temperature to 0.7, uses the provided question as input, considers the top 40 predictions, and specifies the model file path. ## Note - The script's output depends on the quality and training of the FastText model used. - Ensure the specified model file path and input file path (if used) are correct.
gms0817/mistral-ehrs-transcription-summary-v2
gms0817
2023-12-27T14:57:11Z
0
0
null
[ "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]
null
2023-12-27T14:05:23Z
--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - generated_from_trainer model-index: - name: mistral-ehrs-transcription-summary-v2 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. --> # mistral-ehrs-transcription-summary-v2 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6734 ## 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: 2.5e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.1798 | 0.17 | 25 | 0.9513 | | 0.8126 | 0.34 | 50 | 0.8061 | | 0.6553 | 0.51 | 75 | 0.7364 | | 0.5837 | 0.68 | 100 | 0.6550 | | 0.4432 | 0.85 | 125 | 0.6435 | | 0.4879 | 1.02 | 150 | 0.6329 | | 0.3297 | 1.19 | 175 | 0.6310 | | 0.2869 | 1.36 | 200 | 0.6310 | | 0.2945 | 1.53 | 225 | 0.6101 | | 0.221 | 1.7 | 250 | 0.6160 | | 0.2233 | 1.87 | 275 | 0.6117 | | 0.242 | 2.04 | 300 | 0.6295 | | 0.15 | 2.21 | 325 | 0.6303 | | 0.1496 | 2.38 | 350 | 0.6544 | | 0.206 | 2.55 | 375 | 0.6508 | | 0.1516 | 2.72 | 400 | 0.6466 | | 0.1409 | 2.89 | 425 | 0.6587 | | 0.1396 | 3.06 | 450 | 0.6656 | | 0.0848 | 3.23 | 475 | 0.6747 | | 0.1037 | 3.4 | 500 | 0.6734 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
udtsealusagi/kor-news-ner
udtsealusagi
2023-12-27T14:55:39Z
0
0
null
[ "ko", "region:us" ]
null
2023-12-27T13:28:02Z
--- language: - ko --- AI-HUB에서 제공하는 <낚시성 기사 탐지 데이터셋> 중 Part 1. 제목-본문 내용 불일치 자동생성 낚시성 텍스트에 대해 fine-tuning을 진행한 모델입니다. hugging face의 "noahkim/KoBigBird-KoBart-News-Summarization"을 fine-tuning 했습니다. Hyperparameters: learning rate : 2e-5 epochs : 10 batch size : 8 seed : 42
Sarthak279/Lyrics-Generation
Sarthak279
2023-12-27T14:55:37Z
10
1
transformers
[ "transformers", "tf", "gpt2", "text-generation", "generated_from_keras_callback", "base_model:openai-community/gpt2-medium", "base_model:finetune:openai-community/gpt2-medium", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2023-12-27T14:53:46Z
--- license: mit base_model: gpt2-medium tags: - generated_from_keras_callback model-index: - name: Lyrics-Generation 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. --> # Lyrics-Generation This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.2131 - Epoch: 4 ## 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': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -730, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Epoch | |:----------:|:-----:| | 3.2624 | 0 | | 2.9734 | 1 | | 2.7453 | 2 | | 2.4606 | 3 | | 2.2131 | 4 | ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Datasets 2.16.0 - Tokenizers 0.15.0
gehug/vit-base-patch16-224-finetuned-flower
gehug
2023-12-27T14:48:51Z
6
0
transformers
[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-12-27T14:38:17Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: vit-base-patch16-224-finetuned-flower results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-finetuned-flower This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 2.1.0+cu121 - Datasets 2.7.1 - Tokenizers 0.13.3
ThunpitchaPin/my_awesome_asr_mind_model
ThunpitchaPin
2023-12-27T14:47:47Z
3
0
transformers
[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-12-27T14:08:04Z
--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer model-index: - name: my_awesome_asr_mind_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_asr_mind_model This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0
anthony-eden/binary-cs-curriculum-classifier-v1
anthony-eden
2023-12-27T14:45:43Z
5
0
transformers
[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-25T22:15:54Z
--- license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: binary-cs-curriculum-classifier-v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # binary-cs-curriculum-classifier-v1 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0001 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 30 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.137 | 1.0 | 40 | 0.0012 | | 0.0004 | 2.0 | 80 | 0.0001 | | 0.0005 | 3.0 | 120 | 0.0001 | | 0.0002 | 4.0 | 160 | 0.0001 | | 0.0002 | 5.0 | 200 | 0.0001 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.0.post101 - Datasets 2.14.6 - Tokenizers 0.13.3
ostapeno/flan-library-for-neo-1B_evol_metav2_ai1_debug_ai1_debug
ostapeno
2023-12-27T14:42:12Z
0
0
null
[ "region:us" ]
null
2023-12-27T14:36:24Z
Number of experts present in the library: 1 | Expert Name | Base Model | Trained on | Adapter Type | | --- | --- | --- | --- | | ai2_arc_ARC_Challenge_1_0_0_v1 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ai2_arc_ARC_Challenge_1_0_0 | lora | Last updated on: 2023-12-27 14:42:11+00:00
stablediffusionapi/deliberate-v2
stablediffusionapi
2023-12-27T14:32:56Z
591
11
diffusers
[ "diffusers", "safetensors", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-03-27T14:45:13Z
--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # model_name API Inference ![generated from modelslab.com](https://cdn2.stablediffusionapi.com/generations/0-08c9563b-e380-45b6-834c-457316f5229a.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change **model_id** to "deliberate-v2" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs) Try model for free: [Generate Images](https://modelslab.com/models/deliberate-v2) Model link: [View model](https://modelslab.com/models/deliberate-v2) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "deliberate-v2", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**
ostapeno/flan-library-for-neo-1B_evol_metav2_ai1_debug
ostapeno
2023-12-27T14:24:32Z
0
0
null
[ "region:us" ]
null
2023-12-27T13:58:52Z
Number of experts present in the library: 1 | Expert Name | Base Model | Trained on | Adapter Type | | --- | --- | --- | --- | | ai2_arc_ARC_Challenge_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/adauni-v3-10k-flat/ai2_arc_ARC_Challenge_1_0_0 | lora | Last updated on: 2023-12-27 14:24:30+00:00
EExe/a2c-PandaReachDense-v2
EExe
2023-12-27T14:15:03Z
1
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "arxiv:2106.13687", "model-index", "region:us" ]
reinforcement-learning
2023-03-27T09:47:12Z
--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -1.48 +/- 0.45 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ``` Panda Gym environments: [arxiv.org/abs/2106.13687](https://arxiv.org/abs/2106.13687)
rabil/CodeNinja-1.0-OpenChat-7B-llamafile
rabil
2023-12-27T14:14:59Z
4
1
null
[ "llamafile", "region:us" ]
null
2023-12-27T14:13:16Z
## CodeNinja-1.0-OpenChat-7B-llamafile llamafile lets you distribute and run LLMs with a single file. [announcement blog post](https://hacks.mozilla.org/2023/11/introducing-llamafile/) #### Downloads - [codeninja-1.0-openchat-7b.Q4_K_M-server.llamafile](https://huggingface.co/rabil/CodeNinja-1.0-OpenChat-7B-llamafile/resolve/main/codeninja-1.0-openchat-7b.Q4_K_M-server.llamafile) This repository was created using the [llamafile-builder](https://github.com/rabilrbl/llamafile-builder)
jameswatanabegoogle2024/Reinforce-model-id1
jameswatanabegoogle2024
2023-12-27T14:11:53Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-12-27T14:11:27Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-model-id1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
rabil/mistral-ft-optimized-1218-llamafile
rabil
2023-12-27T14:09:43Z
8
0
null
[ "llamafile", "region:us" ]
null
2023-12-27T14:05:49Z
## mistral-ft-optimized-1218-llamafile llamafile lets you distribute and run LLMs with a single file. [announcement blog post](https://hacks.mozilla.org/2023/11/introducing-llamafile/) #### Downloads - [mistral-ft-optimized-1218.Q4_K_M-server.llamafile](https://huggingface.co/rabil/mistral-ft-optimized-1218-llamafile/resolve/main/mistral-ft-optimized-1218.Q4_K_M-server.llamafile) - [mistral-ft-optimized-1218.Q5_K_M-server.llamafile](https://huggingface.co/rabil/mistral-ft-optimized-1218-llamafile/resolve/main/mistral-ft-optimized-1218.Q5_K_M-server.llamafile) - [mistral-ft-optimized-1218.Q8_0-server.llamafile](https://huggingface.co/rabil/mistral-ft-optimized-1218-llamafile/resolve/main/mistral-ft-optimized-1218.Q8_0-server.llamafile) This repository was created using the [llamafile-builder](https://github.com/rabilrbl/llamafile-builder)
xyz2zyx/ppo-Huggy
xyz2zyx
2023-12-27T14:08:54Z
4
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-12-27T14:08:48Z
--- 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: xyz2zyx/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
ruizhaocv/MotionDirector
ruizhaocv
2023-12-27T14:01:54Z
0
8
null
[ "license:apache-2.0", "region:us" ]
null
2023-12-07T12:40:48Z
--- license: apache-2.0 --- git lfs clone https://huggingface.co/ruizhaocv/MotionDirector For more details, please refer to the github repo: https://github.com/showlab/MotionDirector
OpenNMT/Mistral-7B-v0.2-instruct-onmt-awq-gemv
OpenNMT
2023-12-27T13:55:49Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2023-11-29T17:22:20Z
--- license: apache-2.0 --- This is the OpenNMT-py converted version of Mistral 7b Instruct v0.1, 4-bit AWQ quantized. The safetensors file is 4.2GB hence runs smoothly on any RTX card. Command line to run is: ``` python onmt/bin/translate.py --config /pathto/mistral-instruct-inference-awq.yaml --src /pathto/input-vicuna.txt --output /pathto/mistral-output.txt ``` Where for instance, input-vicuna.txt contains: USER:｟newline｠Show me some attractions in Boston.｟newline｠｟newline｠ASSISTANT:｟newline｠ Output will be: ``` Boston is a great city with many attractions to visit. Here are some popular ones:｟newline｠｟newline｠1. The Freedom Trail - This is a 2.5-mile-long path through downtown Boston that passes by 16 historically significant sites related to the American Revolution.｟newline｠2. The Massachusetts State House - The iconic red brick building that serves as the seat of the Massachusetts government and is home to the Massachusetts Legislature and the Governor.｟newline｠3. The Boston Tea Party Ships and Museum - This museum tells the story of the Boston Tea Party and the events leading up to the American Revolution through interactive exhibits and live reenactments.｟newline｠4. The Paul Revere House - The oldest house in the United States, it was home to the famous silversmith and patriot Paul Revere.｟newline｠5. The USS Constitution - A historic warship that played a key role in the American Revolution, the USS Constitution is now a museum and a popular tourist attraction.｟newline｠6. The Bunker Hill Memorial Park - A beautiful park located on the site of the first military engagement of the American Revolution, the Battle of Bunker Hill.｟newline｠7. The Museum of Fine Arts, Boston - One of the largest ``` If you run with a batch size of 60 you can get a nice throughput even with GEMV: ``` [2023-12-27 14:54:47,967 INFO] Loading checkpoint from /mnt/InternalCrucial4/dataAI/mistral-7B/mistral-instruct-v0.2/mistral-instruct-v0.2-onmt-awq-gemv.pt [2023-12-27 14:54:48,063 INFO] awq_gemv compression of layer ['w_1', 'w_2', 'w_3', 'linear_values', 'linear_query', 'linear_keys', 'final_linear'] [2023-12-27 14:54:52,059 INFO] Loading data into the model step0 time: 1.2714881896972656 [2023-12-27 14:54:59,180 INFO] PRED SCORE: -0.2316, PRED PPL: 1.26 NB SENTENCES: 59 [2023-12-27 14:54:59,180 INFO] Total translation time (s): 6.1 [2023-12-27 14:54:59,180 INFO] Average translation time (ms): 103.5 [2023-12-27 14:54:59,180 INFO] Tokens per second: 2183.8 Time w/o python interpreter load/terminate: 11.222625255584717 ```
behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned_SystemError1.0_Seed105
behzadnet
2023-12-27T13:54:26Z
0
0
peft
[ "peft", "arxiv:1910.09700", "base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16", "base_model:adapter:Trelis/Llama-2-7b-chat-hf-sharded-bf16", "region:us" ]
null
2023-12-27T13:54:22Z
--- library_name: peft base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## 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] ## 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 0.7.0.dev0
behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters_SystemError1.0_Seed105
behzadnet
2023-12-27T13:54:14Z
1
0
peft
[ "peft", "arxiv:1910.09700", "base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16", "base_model:adapter:Trelis/Llama-2-7b-chat-hf-sharded-bf16", "region:us" ]
null
2023-12-27T13:54:06Z
--- library_name: peft base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## 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] ## 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 0.7.0.dev0 ## 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 0.7.0.dev0
Sloba/RRLearn-Taxi-v3-3
Sloba
2023-12-27T13:43:38Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-12-27T13:43:36Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: RRLearn-Taxi-v3-3 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="Sloba/RRLearn-Taxi-v3-3", 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"]) ```
am-infoweb/classification_test_27Dec
am-infoweb
2023-12-27T13:43:26Z
6
0
transformers
[ "transformers", "safetensors", "longformer", "text-classification", "generated_from_trainer", "base_model:allenai/longformer-base-4096", "base_model:finetune:allenai/longformer-base-4096", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-27T11:05:03Z
--- license: apache-2.0 base_model: allenai/longformer-base-4096 tags: - generated_from_trainer metrics: - accuracy model-index: - name: classification_test_14Dec 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. --> # classification_test_14Dec This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3279 - Accuracy: 0.9493 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.4495 | 1.0 | 5293 | 0.6105 | 0.8841 | | 0.2916 | 2.0 | 10586 | 0.4680 | 0.9294 | | 0.3098 | 3.0 | 15879 | 0.4065 | 0.9419 | | 0.2335 | 4.0 | 21172 | 0.3796 | 0.9430 | | 0.1565 | 5.0 | 26465 | 0.3467 | 0.9428 | | 0.1886 | 6.0 | 31758 | 0.3174 | 0.9487 | | 0.1042 | 7.0 | 37051 | 0.3279 | 0.9493 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
razeruwu/prigozhinJenya
razeruwu
2023-12-27T13:40:25Z
0
0
null
[ "license:other", "region:us" ]
null
2023-12-27T13:32:24Z
--- license: other license_name: evgeniy license_link: LICENSE ---
Sloba/RRLearn-Taxi-v3
Sloba
2023-12-27T13:34:29Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-12-27T13:34:27Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: RRLearn-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="Sloba/RRLearn-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"]) ```
renardkorzeniowski/distilbert-base-uncased-finetuned-cola
renardkorzeniowski
2023-12-27T13:29:32Z
5
0
transformers
[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-27T12:01:37Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-cola results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola 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: - eval_loss: 0.5964 - eval_matthews_correlation: 0.0 - eval_runtime: 36.4296 - eval_samples_per_second: 28.631 - eval_steps_per_second: 1.812 - epoch: 0.09 - step: 50 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0
DucPham1501/finetuning-sentiment-model-3000-samples
DucPham1501
2023-12-27T12:52:30Z
5
0
transformers
[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-27T12:45:19Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3393 - Accuracy: 0.8667 - F1: 0.8693 ## 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 4.36.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0
snoop088/testing_fine_tune_qa
snoop088
2023-12-27T12:44:33Z
3
0
peft
[ "peft", "safetensors", "generated_from_trainer", "base_model:bigscience/bloom-3b", "base_model:adapter:bigscience/bloom-3b", "license:bigscience-bloom-rail-1.0", "region:us" ]
null
2023-12-24T14:34:57Z
--- license: bigscience-bloom-rail-1.0 library_name: peft tags: - generated_from_trainer base_model: bigscience/bloom-3b model-index: - name: testing_fine_tune_qa 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. --> # testing_fine_tune_qa This model is a fine-tuned version of [bigscience/bloom-3b](https://huggingface.co/bigscience/bloom-3b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1709 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.9148 | 0.4 | 200 | 1.6602 | | 1.6726 | 0.8 | 400 | 1.3506 | | 1.0625 | 1.2 | 600 | 1.2383 | | 0.8001 | 1.6 | 800 | 1.1885 | | 0.3615 | 2.0 | 1000 | 1.1709 | ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0
LoneStriker/goliath-120b-2.9bpw-h6-exl2
LoneStriker
2023-12-27T12:41:28Z
5
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-12-27T12:23:59Z
--- license: llama2 language: - en pipeline_tag: conversational --- # Goliath 120B An auto-regressive causal LM created by combining 2x finetuned [Llama-2 70B](https://huggingface.co/meta-llama/llama-2-70b-hf) into one. Please check out the quantized formats provided by [@TheBloke](https:///huggingface.co/TheBloke) and [@Panchovix](https://huggingface.co/Panchovix): - [GGUF](https://huggingface.co/TheBloke/goliath-120b-GGUF) (llama.cpp) - [GPTQ](https://huggingface.co/TheBloke/goliath-120b-GPTQ) (KoboldAI, TGW, Aphrodite) - [AWQ](https://huggingface.co/TheBloke/goliath-120b-AWQ) (TGW, Aphrodite, vLLM) - [Exllamav2](https://huggingface.co/Panchovix/goliath-120b-exl2) (TGW, KoboldAI) # Prompting Format Both Vicuna and Alpaca will work, but due the initial and final layers belonging primarily to Xwin, I expect Vicuna to work the best. # Merge process The models used in the merge are [Xwin](https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1) and [Euryale](https://huggingface.co/Sao10K/Euryale-1.3-L2-70B). The layer ranges used are as follows: ```yaml - range 0, 16 Xwin - range 8, 24 Euryale - range 17, 32 Xwin - range 25, 40 Euryale - range 33, 48 Xwin - range 41, 56 Euryale - range 49, 64 Xwin - range 57, 72 Euryale - range 65, 80 Xwin ``` # Screenshots ![image/png](https://cdn-uploads.huggingface.co/production/uploads/635567189c72a7e742f1419c/Cat8_Rimaz6Ni7YhQiiGB.png) # Benchmarks Coming soon. # Acknowledgements Credits goes to [@chargoddard](https://huggingface.co/chargoddard) for developing the framework used to merge the model - [mergekit](https://github.com/cg123/mergekit). Special thanks to [@Undi95](https://huggingface.co/Undi95) for helping with the merge ratios.
Weiming1122/Reinforce-Pixelcopter-PLE-v0
Weiming1122
2023-12-27T12:36:53Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-12-27T05:45:39Z
--- 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: 32.90 +/- 26.62 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
BramVanroy/llama2-13b-ft-mc4_nl_cleaned_tiny
BramVanroy
2023-12-27T12:31:50Z
130
4
transformers
[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "lora", "adapters", "nl", "dataset:yhavinga/mc4_nl_cleaned", "arxiv:2312.12852", "base_model:meta-llama/Llama-2-13b-hf", "base_model:adapter:meta-llama/Llama-2-13b-hf", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-08-10T05:21:50Z
--- license: apache-2.0 base_model: meta-llama/Llama-2-13b-hf tags: - generated_from_trainer - llama - lora - adapters datasets: - yhavinga/mc4_nl_cleaned language: - nl model-index: - name: llama2-13b-ft-mc4_nl_cleaned_tiny results: [] --- # llama2-13b-ft-mc4_nl_cleaned_tiny This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) on the [yhavinga/mc4_nl_cleaned](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned/viewer/tiny/train) dataset (`tiny` partition) on a context of 4096 tokens. See the original [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) for more information, intended use, and biases. If you use this model or refer to it, please use the following citation: Vanroy, B. (2023). *Language Resources for Dutch Large Language Modelling*. [https://arxiv.org/abs/2312.12852](https://arxiv.org/abs/2312.12852) ```bibtext @article{vanroy2023language, title={Language Resources for {Dutch} Large Language Modelling}, author={Vanroy, Bram}, journal={arXiv preprint arXiv:2312.12852}, year={2023} } ``` ## Intended uses & limitations While Llama 2 already contains some proficiency in Dutch, this finetune is intended to improve the fluency of Dutch (not increase its knowledge). It is therefore intended as a generative model for Dutch language. The biases, shortcomings and intended uses are otherwise the same as those of the [original model]((https://huggingface.co/meta-llama/Llama-2-13b-hf)). The model can be used for generative tasks or finetuned further on other tasks such as summarization, adaptation, instruction or chat finetuning. ## Training and evaluation data Trained on the [yhavinga/mc4_nl_cleaned](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned/viewer/tiny/train) dataset (`tiny` partition) for one epoch. The canonical validation split was not used but instead 5% of `train` was used as validation. ## Training procedure Trained with LoRA targetting `["q_proj", "v_proj"]` in 4 bit and merged before upload. Trained with Flash Attention as borrowed from [here](https://github.com/philschmid/deep-learning-pytorch-huggingface/blob/main/training/utils/llama_patch.py). The adapters are in the `adapters` branch. Initial training investigation on the Tier-1 HPC of [Vlaams Supercomputer Centrum (VSC)](https://www.vscentrum.be/) and training on our own server of 4x 3090s. ### Training hyperparameters The following hyperparameters were used during training in the HPC investigation: - learning_rate: 0.0003 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - gradient_accumulation_steps: 6 - total_train_batch_size: 1152 - total_eval_batch_size: 192 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.8784 | 0.09 | 90 | 1.8820 | | 1.8344 | 0.19 | 180 | 1.8542 | | 1.8351 | 0.28 | 270 | 1.8355 | | 1.8206 | 0.37 | 360 | 1.8212 | | 1.8021 | 0.47 | 450 | 1.8088 | | 1.8102 | 0.56 | 540 | 1.7982 | | 1.7991 | 0.65 | 630 | 1.7890 | | 1.7788 | 0.74 | 720 | 1.7811 | | 1.7915 | 0.84 | 810 | 1.7742 | | 1.7715 | 0.93 | 900 | 1.7676 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3 # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_BramVanroy__llama2-13b-ft-mc4_nl_cleaned_tiny) | Metric | Value | |-----------------------|---------------------------| | Avg. | 46.81 | | ARC (25-shot) | 59.3 | | HellaSwag (10-shot) | 82.04 | | MMLU (5-shot) | 54.67 | | TruthfulQA (0-shot) | 38.03 | | Winogrande (5-shot) | 77.27 | | GSM8K (5-shot) | 10.31 | | DROP (3-shot) | 6.08 |
wenjun123/token_calssification_model
wenjun123
2023-12-27T12:30:18Z
3
0
transformers
[ "transformers", "tf", "bert", "token-classification", "generated_from_keras_callback", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-12-27T07:56:04Z
--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_keras_callback model-index: - name: wenjun123/token_calssification_model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # wenjun123/token_calssification_model This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0468 - Validation Loss: 0.0531 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2634, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1741 | 0.0639 | 0 | | 0.0468 | 0.0531 | 1 | ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Tokenizers 0.15.0
andrijdavid/Mistral-T5-7B-v1-GGUF
andrijdavid
2023-12-27T12:18:22Z
3
0
transformers
[ "transformers", "pytorch", "mistral", "text-generation", "GGUF", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-12-27T12:18:18Z
--- license: apache-2.0 tags: - GGUF quantized_by: andrijdavid --- # Mistral-T5-7B-v1-GGUF - Original model: [Mistral-T5-7B-v1](https://huggingface.co/ignos/Mistral-T5-7B-v1) <!-- description start --> ## Description This repo contains GGUF format model files for [Mistral-T5-7B-v1](https://huggingface.co/ignos/Mistral-T5-7B-v1). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration. * [Ollama](https://github.com/jmorganca/ollama) Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications​ * [KoboldCpp](https://github.com/LostRuins/koboldcpp), A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling. * [GPT4All](https://gpt4all.io), This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration. * [LM Studio](https://lmstudio.ai/) An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection. * [Faraday.dev](https://faraday.dev/), An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use. * [ctransformers](https://github.com/marella/ctransformers), A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server. * [localGPT](https://github.com/PromtEngineer/localGPT) An open-source initiative enabling private conversations with documents. <!-- README_GGUF.md-about-gguf end --> <!-- compatibility_gguf start --> ## Explanation of quantisation methods <details> <summary>Click to see details</summary> 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. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: andrijdavid/Mistral-T5-7B-v1-GGUF and below it, a specific filename to download, such as: Mistral-T5-7B-v1-f16.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download andrijdavid/Mistral-T5-7B-v1-GGUF Mistral-T5-7B-v1-f16.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download andrijdavid/Mistral-T5-7B-v1-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download andrijdavid/Mistral-T5-7B-v1-GGUF Mistral-T5-7B-v1-f16.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m Mistral-T5-7B-v1-f16.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./Mistral-T5-7B-v1-f16.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<PROMPT>", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./Mistral-T5-7B-v1-f16.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer end --> <!-- original-model-card start --> # Original model card: Mistral-T5-7B-v1 # Model Card for Model ID This model is a finetuning of [Toten5/Marcoroni-neural-chat-7B-v2](https://huggingface.co/Toten5/Marcoroni-neural-chat-7B-v2) ## Model Details ### Model Description - **Developed by:** Ignos - **Model type:** Mistral - **License:** Apache-2.0 ## Uses Model created to improve instructional behavior. ## Bias, Risks, and Limitations The same bias, risks and limitations from base models. ## Training Details ### Training Data - [tatsu-lab/alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) ### Training Procedure - Training with QLoRA approach and merging with base model. ### Results - Huggingface evaluation pending #### Summary ## Technical Specifications ### Model Architecture and Objective - Models based on Mistral Architecture ### Compute Infrastructure - Training on RunPod #### Hardware - 3 x RTX 4090 - 48 vCPU 377 GB RAM #### Software - Axolotl 0.3.0 ### Framework versions - PEFT 0.6.0 <!-- original-model-card end -->
judithrosell/PubMedBERT_CRAFT_NER_new
judithrosell
2023-12-27T12:14:13Z
6
0
transformers
[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext", "base_model:finetune:microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-12-27T11:58:23Z
--- license: mit base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: PubMedBERT_CRAFT_NER_new results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # PubMedBERT_CRAFT_NER_new This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1034 - Precision: 0.9811 - Recall: 0.9782 - F1: 0.9797 - Accuracy: 0.9751 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2176 | 1.0 | 695 | 0.1101 | 0.9780 | 0.9739 | 0.9759 | 0.9708 | | 0.0555 | 2.0 | 1390 | 0.1019 | 0.9800 | 0.9770 | 0.9785 | 0.9739 | | 0.0283 | 3.0 | 2085 | 0.1034 | 0.9811 | 0.9782 | 0.9797 | 0.9751 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0
trl-lib/OpenHermes-2-Mistral-7B-sigmoid-beta-0.7-steps-800
trl-lib
2023-12-27T12:11:05Z
0
0
peft
[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]
null
2023-12-27T12:10:50Z
--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-sigmoid-beta-0.7-steps-800 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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 Dataset 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 Dataset 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] ### Framework versions - PEFT 0.7.1
trl-lib/OpenHermes-2-Mistral-7B-sigmoid-beta-0.1-steps-800
trl-lib
2023-12-27T12:09:38Z
0
0
peft
[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]
null
2023-12-27T12:09:25Z
--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-sigmoid-beta-0.1-steps-800 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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 Dataset 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 Dataset 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] ### Framework versions - PEFT 0.7.1
trl-lib/OpenHermes-2-Mistral-7B-kto-beta-0.9-steps-800
trl-lib
2023-12-27T12:09:25Z
2
0
peft
[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]
null
2023-12-27T12:09:12Z
--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-kto-beta-0.9-steps-800 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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 Dataset 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 Dataset 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] ### Framework versions - PEFT 0.7.1
trl-lib/OpenHermes-2-Mistral-7B-kto-beta-0.8-steps-800
trl-lib
2023-12-27T12:09:09Z
1
0
peft
[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]
null
2023-12-27T12:08:55Z
--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-kto-beta-0.8-steps-800 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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 Dataset 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 Dataset 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] ### Framework versions - PEFT 0.7.1
trl-lib/OpenHermes-2-Mistral-7B-kto-beta-0.7-steps-800
trl-lib
2023-12-27T12:08:54Z
1
0
peft
[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]
null
2023-12-27T12:08:41Z
--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-kto-beta-0.7-steps-800 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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 Dataset 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 Dataset 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] ### Framework versions - PEFT 0.7.1
trl-lib/OpenHermes-2-Mistral-7B-kto-beta-0.4-steps-800
trl-lib
2023-12-27T12:07:47Z
1
0
peft
[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]
null
2023-12-27T12:07:34Z
--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-kto-beta-0.4-steps-800 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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 Dataset 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 Dataset 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] ### Framework versions - PEFT 0.7.1
ku-nlp/gpt2-large-japanese-char
ku-nlp
2023-12-27T12:07:30Z
63
5
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "ja", "dataset:wikipedia", "dataset:cc100", "dataset:oscar", "license:cc-by-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-12-27T11:18:45Z
--- language: ja license: cc-by-sa-4.0 library_name: transformers tags: - gpt2 datasets: - wikipedia - cc100 - oscar widget: - text: "<s>昨日私は京都で" --- # Model Card for Japanese character-level GPT-2 Large ## Model description This is a Japanese character-level GPT-2 Large (717M parameters) language model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the Japanese portion of OSCAR. ## How to use You can use this model directly with a pipeline for text generation. ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='ku-nlp/gpt2-large-japanese-char') >>> set_seed(5) >>> generator("<s>昨日私は京都で", max_length=30, do_sample=True, num_return_sequences=5) [{'generated_text': '<s>昨日私は京都で仕事だったのですが、帰りは車を信号で止めて、'}, {'generated_text': '<s>昨日私は京都で開かれた大阪市都市戦略会議に出席しました。そ'}, {'generated_text': '<s>昨日私は京都で行われました関西の教育者・学校事例が集まるイ'}, {'generated_text': '<s>昨日私は京都では初雪を見ました。朝は少しパッとしない天気で'}, {'generated_text': '<s>昨日私は京都でこみっくトレジャーさんの撮影を見学させていた'}] ``` You can also use this model to get the features of a given text. ## Vocabulary A character-level vocabulary of size 6K is used. To be precise, rare characters may be split into bytes because byte-level byte-pair encoding (BPE) is used. The BPE tokenizer was trained on a small subset of the training data. Since the data were converted into a one-character-per-line format, merge operations never go beyond character boundaries. Note that the tokenizer maps U+0020 to `[UNK]` because preprocessing eliminated whitespace characters (U+0020) from training data. Use U+3000 (Ideographic Space) instead. ## Training data We used the following corpora for pre-training: - Japanese Wikipedia (as of 20221020, 3.2GB, 27M sentences, 1.3M documents) - Japanese portion of CC-100 (85GB, 619M sentences, 66M documents) - Japanese portion of OSCAR (54GB, 326M sentences, 25M documents) Note that we filtered out documents annotated with "header", "footer", or "noisy" tags in OSCAR. Also note that Japanese Wikipedia was duplicated 10 times to make the total size of the corpus comparable to that of CC-100 and OSCAR. As a result, the total size of the training data is 171GB. ## Training procedure The training took about 8 months (with 7 interruptions) with a single NVIDIA A100 80GB GPU. The following hyperparameters were used during pre-training: - learning_rate: 2e-4 - per_device_train_batch_size: 6 - gradient_accumulation_steps: 98 - optimizer: AdamW with betas=(0.9, 0.999) and epsilon=1e-06 - weight_decay: 0.01 - lr_scheduler_type: linear - max_grad_norm: 1.0 - max_steps: 500,000 (but terminated at 186,000 steps ~= 2.0 epochs) - warmup_steps: 10,000 The eval loss was 1.309 while the eval accuracy was 0.6890. The evaluation set consists of 5,000 randomly sampled documents from each of the training corpora.
trl-lib/OpenHermes-2-Mistral-7B-kto-beta-0.2-steps-800
trl-lib
2023-12-27T12:07:04Z
3
0
peft
[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]
null
2023-12-27T12:06:46Z
--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-kto-beta-0.2-steps-800 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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 Dataset 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 Dataset 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] ### Framework versions - PEFT 0.7.1
trl-lib/OpenHermes-2-Mistral-7B-ipo-beta-0.9-steps-800
trl-lib
2023-12-27T12:06:31Z
2
0
peft
[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]
null
2023-12-27T12:06:17Z
--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-ipo-beta-0.9-steps-800 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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 Dataset 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 Dataset 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] ### Framework versions - PEFT 0.7.1
SamSJackson/Reinforce-CartPole-v1
SamSJackson
2023-12-27T12:06:28Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-12-27T12:06:18Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
trl-lib/OpenHermes-2-Mistral-7B-ipo-beta-0.8-steps-800
trl-lib
2023-12-27T12:06:17Z
0
0
peft
[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]
null
2023-12-27T12:06:04Z
--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-ipo-beta-0.8-steps-800 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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 Dataset 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 Dataset 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] ### Framework versions - PEFT 0.7.1
trl-lib/OpenHermes-2-Mistral-7B-ipo-beta-0.4-steps-800
trl-lib
2023-12-27T12:05:18Z
1
0
peft
[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]
null
2023-12-27T12:05:03Z
--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-ipo-beta-0.4-steps-800 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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 Dataset 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 Dataset 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] ### Framework versions - PEFT 0.7.1
trl-lib/OpenHermes-2-Mistral-7B-ipo-beta-0.3-steps-800
trl-lib
2023-12-27T12:05:03Z
3
0
peft
[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]
null
2023-12-27T12:04:42Z
--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-ipo-beta-0.3-steps-800 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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 Dataset 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 Dataset 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] ### Framework versions - PEFT 0.7.1
trl-lib/OpenHermes-2-Mistral-7B-ipo-beta-0.2-steps-800
trl-lib
2023-12-27T12:04:32Z
1
0
peft
[ "peft", "safetensors", "en", "arxiv:1910.09700", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]
null
2023-12-27T12:04:10Z
--- library_name: peft base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: OpenHermes-2-Mistral-7B-ipo-beta-0.2-steps-800 results: [] license: apache-2.0 language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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 Dataset 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 Dataset 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] ### Framework versions - PEFT 0.7.1
livingbox/vintage-model
livingbox
2023-12-27T12:02:48Z
0
1
diffusers
[ "diffusers", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-12-27T11:59:16Z
--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### Vintage-model Dreambooth model trained by livingbox 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:
kghanlon/distilbert-base-uncased-finetuned-MP-unannotated-half-frozen-v1-RILE-v1_fully_frozen
kghanlon
2023-12-27T12:01:05Z
6
0
transformers
[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:kghanlon/distilbert-base-uncased-finetuned-MP-unannotated-half-frozen-v1", "base_model:finetune:kghanlon/distilbert-base-uncased-finetuned-MP-unannotated-half-frozen-v1", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-27T11:39:22Z
--- base_model: kghanlon/distilbert-base-uncased-finetuned-MP-unannotated-half-frozen-v1 tags: - generated_from_trainer metrics: - accuracy - recall - f1 model-index: - name: distilbert-base-uncased-finetuned-MP-unannotated-half-frozen-v1-RILE-v1_fully_frozen results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-MP-unannotated-half-frozen-v1-RILE-v1_fully_frozen This model is a fine-tuned version of [kghanlon/distilbert-base-uncased-finetuned-MP-unannotated-half-frozen-v1](https://huggingface.co/kghanlon/distilbert-base-uncased-finetuned-MP-unannotated-half-frozen-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7869 - Accuracy: 0.6445 - Recall: 0.6445 - F1: 0.6397 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:| | 0.8369 | 1.0 | 15490 | 0.8333 | 0.6152 | 0.6152 | 0.6068 | | 0.8373 | 2.0 | 30980 | 0.8094 | 0.6324 | 0.6324 | 0.6311 | | 0.822 | 3.0 | 46470 | 0.8024 | 0.6361 | 0.6361 | 0.6279 | | 0.7985 | 4.0 | 61960 | 0.7927 | 0.6425 | 0.6425 | 0.6345 | | 0.7929 | 5.0 | 77450 | 0.7869 | 0.6445 | 0.6445 | 0.6397 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0
ytu-ce-cosmos/turkish-base-bert-uncased
ytu-ce-cosmos
2023-12-27T11:56:00Z
106
14
transformers
[ "transformers", "pytorch", "tf", "safetensors", "bert", "pretraining", "Turkish", "turkish", "fill-mask", "tr", "arxiv:2307.14134", "endpoints_compatible", "region:us" ]
fill-mask
2023-07-29T21:59:38Z
--- widget: - text: "gelirken bir litre [MASK] aldım." example_title: "Örnek 1" pipeline_tag: fill-mask tags: - Turkish - turkish language: - tr --- # turkish-base-bert-uncased This is a Turkish Base uncased BERT model. Since this model is uncased: it does not make a difference between turkish and Turkish. #### ⚠ Uncased use requires manual lowercase conversion **Don't** use the `do_lower_case = True` flag with the tokenizer. Instead, convert your text to lower case as follows: ```python text.replace("I", "ı").lower() ``` This is due to a [known issue](https://github.com/huggingface/transformers/issues/6680) with the tokenizer. Be aware that this model may exhibit biased predictions as it was trained primarily on crawled data, which inherently can contain various biases. Other relevant information can be found in the [paper](https://arxiv.org/abs/2307.14134). ## Example Usage ```python from transformers import AutoTokenizer, BertForMaskedLM from transformers import pipeline model = BertForMaskedLM.from_pretrained("ytu-ce-cosmos/turkish-base-bert-uncased") # or # model = BertForMaskedLM.from_pretrained("ytu-ce-cosmos/turkish-base-bert-uncased", from_tf = True) tokenizer = AutoTokenizer.from_pretrained("ytu-ce-cosmos/turkish-base-bert-uncased") unmasker = pipeline('fill-mask', model=model, tokenizer=tokenizer) unmasker("gelirken bir litre [MASK] aldım.") [{'score': 0.6248273253440857, 'token': 2417, 'token_str': 'su', 'sequence': 'gelirken bir litre su aldım.'}, {'score': 0.10369712114334106, 'token': 2168, 'token_str': 'daha', 'sequence': 'gelirken bir litre daha aldım.'}, {'score': 0.06832519918680191, 'token': 11818, 'token_str': 'benzin', 'sequence': 'gelirken bir litre benzin aldım.'}, {'score': 0.027739914134144783, 'token': 11973, 'token_str': 'bira', 'sequence': 'gelirken bir litre bira aldım.'}, {'score': 0.02571810781955719, 'token': 7279, 'token_str': 'alkol', 'sequence': 'gelirken bir litre alkol aldım.'}] ``` # Acknowledgments - Research supported with Cloud TPUs from [Google's TensorFlow Research Cloud](https://sites.research.google/trc/about/) (TFRC). Thanks for providing access to the TFRC ❤️ - Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage 🤗 # Citations ```bibtex @article{kesgin2023developing, title={Developing and Evaluating Tiny to Medium-Sized Turkish BERT Models}, author={Kesgin, Himmet Toprak and Yuce, Muzaffer Kaan and Amasyali, Mehmet Fatih}, journal={arXiv preprint arXiv:2307.14134}, year={2023} } ``` # License MIT
ytu-ce-cosmos/turkish-medium-bert-uncased
ytu-ce-cosmos
2023-12-27T11:55:38Z
16
6
transformers
[ "transformers", "pytorch", "tf", "safetensors", "bert", "pretraining", "Turkish", "turkish", "fill-mask", "tr", "arxiv:2307.14134", "endpoints_compatible", "region:us" ]
fill-mask
2023-07-29T17:11:57Z
--- widget: - text: "gelirken bir litre [MASK] aldım." example_title: "Örnek 1" pipeline_tag: fill-mask tags: - Turkish - turkish language: - tr --- # turkish-medium-bert-uncased This is a Turkish Medium uncased BERT model, developed to fill the gap for small-sized BERT models for Turkish. Since this model is uncased: it does not make a difference between turkish and Turkish. #### ⚠ Uncased use requires manual lowercase conversion **Don't** use the `do_lower_case = True` flag with the tokenizer. Instead, convert your text to lower case as follows: ```python text.replace("I", "ı").lower() ``` This is due to a [known issue](https://github.com/huggingface/transformers/issues/6680) with the tokenizer. Be aware that this model may exhibit biased predictions as it was trained primarily on crawled data, which inherently can contain various biases. Other relevant information can be found in the [paper](https://arxiv.org/abs/2307.14134). ## Example Usage ```python from transformers import AutoTokenizer, BertForMaskedLM from transformers import pipeline model = BertForMaskedLM.from_pretrained("ytu-ce-cosmos/turkish-medium-bert-uncased") # or # model = BertForMaskedLM.from_pretrained("ytu-ce-cosmos/turkish-medium-bert-uncased", from_tf = True) tokenizer = AutoTokenizer.from_pretrained("ytu-ce-cosmos/turkish-medium-bert-uncased") unmasker = pipeline('fill-mask', model=model, tokenizer=tokenizer) unmasker("gelirken bir litre [MASK] aldım.") [{'score': 0.6158884763717651, 'token': 11818, 'token_str': 'benzin', 'sequence': 'gelirken bir litre benzin aldım.'}, {'score': 0.1580735594034195, 'token': 2417, 'token_str': 'su', 'sequence': 'gelirken bir litre su aldım.'}, {'score': 0.07746931910514832, 'token': 29480, 'token_str': 'mazot', 'sequence': 'gelirken bir litre mazot aldım.'}, {'score': 0.0339476652443409, 'token': 4521, 'token_str': 'süt', 'sequence': 'gelirken bir litre süt aldım.'}, {'score': 0.021608062088489532, 'token': 7279, 'token_str': 'alkol', 'sequence': 'gelirken bir litre alkol aldım.'}] ``` # Acknowledgments - Research supported with Cloud TPUs from [Google's TensorFlow Research Cloud](https://sites.research.google/trc/about/) (TFRC). Thanks for providing access to the TFRC ❤️ - Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage 🤗 # Citations ```bibtex @article{kesgin2023developing, title={Developing and Evaluating Tiny to Medium-Sized Turkish BERT Models}, author={Kesgin, Himmet Toprak and Yuce, Muzaffer Kaan and Amasyali, Mehmet Fatih}, journal={arXiv preprint arXiv:2307.14134}, year={2023} } ``` # License MIT
ytu-ce-cosmos/turkish-small-bert-uncased
ytu-ce-cosmos
2023-12-27T11:54:55Z
22
5
transformers
[ "transformers", "pytorch", "tf", "safetensors", "bert", "pretraining", "Turkish", "turkish", "fill-mask", "tr", "arxiv:2307.14134", "endpoints_compatible", "region:us" ]
fill-mask
2023-07-29T17:02:37Z
--- widget: - text: "gelirken bir litre [MASK] aldım." example_title: "Örnek 1" pipeline_tag: fill-mask tags: - Turkish - turkish language: - tr --- # turkish-small-bert-uncased This is a Turkish Small uncased BERT model, developed to fill the gap for small-sized BERT models for Turkish. Since this model is uncased: it does not make a difference between turkish and Turkish. #### ⚠ Uncased use requires manual lowercase conversion **Don't** use the `do_lower_case = True` flag with the tokenizer. Instead, convert your text to lower case as follows: ```python text.replace("I", "ı").lower() ``` This is due to a [known issue](https://github.com/huggingface/transformers/issues/6680) with the tokenizer. Be aware that this model may exhibit biased predictions as it was trained primarily on crawled data, which inherently can contain various biases. Other relevant information can be found in the [paper](https://arxiv.org/abs/2307.14134). ## Example Usage ```python from transformers import AutoTokenizer, BertForMaskedLM from transformers import pipeline model = BertForMaskedLM.from_pretrained("ytu-ce-cosmos/turkish-small-bert-uncased") # or # model = BertForMaskedLM.from_pretrained("ytu-ce-cosmos/turkish-small-bert-uncased", from_tf = True) tokenizer = AutoTokenizer.from_pretrained("ytu-ce-cosmos/turkish-small-bert-uncased") unmasker = pipeline('fill-mask', model=model, tokenizer=tokenizer) unmasker("gelirken bir litre [MASK] aldım.") [{'score': 0.3692811131477356, 'token': 2417, 'token_str': 'su', 'sequence': 'gelirken bir litre su aldım.'}, {'score': 0.2551537752151489, 'token': 11818, 'token_str': 'benzin', 'sequence': 'gelirken bir litre benzin aldım.'}, {'score': 0.036265160888433456, 'token': 29480, 'token_str': 'mazot', 'sequence': 'gelirken bir litre mazot aldım.'}, {'score': 0.03350532799959183, 'token': 4521, 'token_str': 'süt', 'sequence': 'gelirken bir litre süt aldım.'}, {'score': 0.02558029256761074, 'token': 2168, 'token_str': 'daha', 'sequence': 'gelirken bir litre daha aldım.'}] ``` # Acknowledgments - Research supported with Cloud TPUs from [Google's TensorFlow Research Cloud](https://sites.research.google/trc/about/) (TFRC). Thanks for providing access to the TFRC ❤️ - Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage 🤗 # Citations ```bibtex @article{kesgin2023developing, title={Developing and Evaluating Tiny to Medium-Sized Turkish BERT Models}, author={Kesgin, Himmet Toprak and Yuce, Muzaffer Kaan and Amasyali, Mehmet Fatih}, journal={arXiv preprint arXiv:2307.14134}, year={2023} } ``` # License MIT
ytu-ce-cosmos/turkish-tiny-bert-uncased
ytu-ce-cosmos
2023-12-27T11:53:53Z
31
6
transformers
[ "transformers", "pytorch", "tf", "safetensors", "bert", "pretraining", "Turkish", "turkish", "fill-mask", "tr", "arxiv:2307.14134", "license:mit", "endpoints_compatible", "region:us" ]
fill-mask
2023-07-29T14:02:27Z
--- widget: - pipeline_tag: Fill-Mask - text: gelirken bir litre [MASK] aldım. example_title: ürün pipeline_tag: fill-mask tags: - Turkish - turkish license: mit language: - tr --- # turkish-tiny-bert-uncased This is a Turkish Tiny uncased BERT model, developed to fill the gap for small-sized BERT models for Turkish. Since this model is uncased: it does not make a difference between turkish and Turkish. #### ⚠ Uncased use requires manual lowercase conversion **Don't** use the `do_lower_case = True` flag with the tokenizer. Instead, convert your text to lower case as follows: ```python text.replace("I", "ı").lower() ``` This is due to a [known issue](https://github.com/huggingface/transformers/issues/6680) with the tokenizer. Be aware that this model may exhibit biased predictions as it was trained primarily on crawled data, which inherently can contain various biases. Other relevant information can be found in the [paper](https://arxiv.org/abs/2307.14134). ## Example Usage ```python from transformers import AutoTokenizer, BertForMaskedLM from transformers import pipeline model = BertForMaskedLM.from_pretrained("ytu-ce-cosmos/turkish-tiny-bert-uncased") # or # model = BertForMaskedLM.from_pretrained("ytu-ce-cosmos/turkish-tiny-bert-uncased", from_tf = True) tokenizer = AutoTokenizer.from_pretrained("ytu-ce-cosmos/turkish-tiny-bert-uncased") unmasker = pipeline('fill-mask', model=model, tokenizer=tokenizer) unmasker("gelirken bir litre [MASK] aldım.") # [{'score': 0.202457457780838, # 'token': 2417, # 'token_str': 'su', # 'sequence': 'gelirken bir litre su aldım.'}, # {'score': 0.09290537238121033, # 'token': 11818, # 'token_str': 'benzin', # 'sequence': 'gelirken bir litre benzin aldım.'}, # {'score': 0.07785643637180328, # 'token': 2026, # 'token_str': '##den', # 'sequence': 'gelirken bir litreden aldım.'}, # {'score': 0.06889808923006058, # 'token': 2299, # 'token_str': '##yi', # 'sequence': 'gelirken bir litreyi aldım.'}, # {'score': 0.03152570128440857, # 'token': 2647, # 'token_str': '##ye', # 'sequence': 'gelirken bir litreye aldım.'}] ``` # Acknowledgments - Research supported with Cloud TPUs from [Google's TensorFlow Research Cloud](https://sites.research.google/trc/about/) (TFRC). Thanks for providing access to the TFRC ❤️ - Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage 🤗 # Citations ```bibtex @article{kesgin2023developing, title={Developing and Evaluating Tiny to Medium-Sized Turkish BERT Models}, author={Kesgin, Himmet Toprak and Yuce, Muzaffer Kaan and Amasyali, Mehmet Fatih}, journal={arXiv preprint arXiv:2307.14134}, year={2023} } ``` # License MIT
tunarebus/indonesian-roberta-base-posp-tagger-finetuned-tweet_pemilu2024_2
tunarebus
2023-12-27T11:49:47Z
3
0
transformers
[ "transformers", "tf", "roberta", "fill-mask", "generated_from_keras_callback", "base_model:w11wo/indonesian-roberta-base-posp-tagger", "base_model:finetune:w11wo/indonesian-roberta-base-posp-tagger", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-12-27T11:27:59Z
--- license: mit base_model: w11wo/indonesian-roberta-base-posp-tagger tags: - generated_from_keras_callback model-index: - name: tunarebus/indonesian-roberta-base-posp-tagger-finetuned-tweet_pemilu2024_2 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. --> # tunarebus/indonesian-roberta-base-posp-tagger-finetuned-tweet_pemilu2024_2 This model is a fine-tuned version of [w11wo/indonesian-roberta-base-posp-tagger](https://huggingface.co/w11wo/indonesian-roberta-base-posp-tagger) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 6.7581 - Validation Loss: 6.4974 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -969, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 11.7200 | 11.3464 | 0 | | 10.7101 | 10.0138 | 1 | | 9.4860 | 9.0052 | 2 | | 8.6505 | 8.1202 | 3 | | 8.0255 | 7.6902 | 4 | | 7.6350 | 7.2934 | 5 | | 7.3084 | 7.0449 | 6 | | 7.0715 | 6.9226 | 7 | | 6.9394 | 6.6571 | 8 | | 6.7581 | 6.4974 | 9 | ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Datasets 2.16.0 - Tokenizers 0.15.0
mtileria00/sentence-bert-finetuned-doc-classification
mtileria00
2023-12-27T11:47:07Z
2
0
sentence-transformers
[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]
sentence-similarity
2023-12-27T11:40:16Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 570 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 4, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 228, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (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}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
DanielDsouza/ppo-LunarLander-v2_8
DanielDsouza
2023-12-27T11:46:40Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-12-27T11:46:33Z
--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -91.60 +/- 37.06 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'DanielDsouza/ppo-LunarLander-v2_8' 'batch_size': 512 'minibatch_size': 128} ```
adarsh2350/distilbert_uncased_imdb_pytorch
adarsh2350
2023-12-27T11:46:17Z
5
0
transformers
[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-27T10:54:07Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert_uncased_imdb_pytorch results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_uncased_imdb_pytorch This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2278 - Accuracy: 0.9323 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2261 | 1.0 | 1563 | 0.2104 | 0.9179 | | 0.1476 | 2.0 | 3126 | 0.2278 | 0.9323 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0
kghanlon/distilbert-base-uncased-RILE-v1_un_frozen
kghanlon
2023-12-27T11:36:31Z
5
0
transformers
[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-27T10:24:00Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - recall - f1 model-index: - name: distilbert-base-uncased-RILE-v1_un_frozen results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-RILE-v1_un_frozen This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4132 - Accuracy: 0.7341 - Recall: 0.7341 - F1: 0.7334 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:| | 0.7018 | 1.0 | 15490 | 0.6802 | 0.7181 | 0.7181 | 0.7150 | | 0.5938 | 2.0 | 30980 | 0.6689 | 0.7291 | 0.7291 | 0.7295 | | 0.4566 | 3.0 | 46470 | 0.7686 | 0.7319 | 0.7319 | 0.7324 | | 0.3122 | 4.0 | 61960 | 1.1036 | 0.7330 | 0.7330 | 0.7325 | | 0.2418 | 5.0 | 77450 | 1.4132 | 0.7341 | 0.7341 | 0.7334 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0
adityamavle/dqn-SpaceInvaders-v3
adityamavle
2023-12-27T11:19:28Z
1
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-12-27T11:19:11Z
--- 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: 92.00 +/- 32.57 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 adityamavle -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 adityamavle -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 adityamavle ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 1000000), ('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', 10000), ('n_timesteps', 100000), ('policy', 'CnnPolicy'), ('target_update_interval', 100), ('train_freq', 4), ('normalize', False)]) ```
tandevstag/vi-fin-news
tandevstag
2023-12-27T11:07:13Z
7
0
transformers
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:FPTAI/vibert-base-cased", "base_model:finetune:FPTAI/vibert-base-cased", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-27T09:35:28Z
--- base_model: FPTAI/vibert-base-cased tags: - generated_from_trainer metrics: - accuracy model-index: - name: vi-fin-news 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. --> # vi-fin-news This model is a fine-tuned version of [FPTAI/vibert-base-cased](https://huggingface.co/FPTAI/vibert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4509 - Accuracy: 0.9136 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1176 | 1.0 | 1150 | 0.3566 | 0.9181 | | 0.0582 | 2.0 | 2300 | 0.4509 | 0.9136 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.2 - Datasets 2.12.0 - Tokenizers 0.13.3
litagin/Style-Bert-VITS2-1.0-base
litagin
2023-12-27T11:05:43Z
0
8
null
[ "text-to-speech", "ja", "zh", "en", "region:us" ]
text-to-speech
2023-12-26T17:05:07Z
--- language: - ja - zh - en pipeline_tag: text-to-speech --- # Style-Bert-VITS2 base model The base model of [Style-Bert-VITS2](https://github.com/litagin02/Style-Bert-VITS2). - These models are essentially the safetensors versions of [Bert-VITS2](https://github.com/fishaudio/Bert-VITS2) 2.1 base models (emo-related key in G_0 deleted). - Hence all credits go to the original authors of Bert-VITS2 2.1 base models and [Fish Audio](https://github.com/fishaudio).
EleanorLin/bert-finetuned-squad
EleanorLin
2023-12-27T10:59:10Z
22
0
transformers
[ "transformers", "tensorboard", "safetensors", "bert", "question-answering", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]
question-answering
2023-12-26T18:31:41Z
--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer model-index: - name: bert-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0
renardkorzeniowski/wav2vec2-base-finetuned-ks
renardkorzeniowski
2023-12-27T10:55:08Z
5
0
transformers
[ "transformers", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:superb", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]
audio-classification
2023-12-27T10:47:31Z
--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - superb model-index: - name: wav2vec2-base-finetuned-ks 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-base-finetuned-ks This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset. It achieves the following results on the evaluation set: - eval_loss: 2.3769 - eval_accuracy: 0.6209 - eval_runtime: 91.7806 - eval_samples_per_second: 74.068 - eval_steps_per_second: 2.321 - epoch: 0.06 - step: 25 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu121 - Datasets 1.16.1 - Tokenizers 0.14.1
ntc-ai/SDXL-LoRA-slider.inflateable
ntc-ai
2023-12-27T10:50:37Z
12
0
diffusers
[ "diffusers", "text-to-image", "stable-diffusion-xl", "lora", "template:sd-lora", "template:sdxl-lora", "sdxl-sliders", "ntcai.xyz-sliders", "concept", "en", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:mit", "region:us" ]
text-to-image
2023-12-27T10:50:33Z
--- language: - en thumbnail: "images/evaluate/inflateable.../inflateable_17_3.0.png" widget: - text: inflateable output: url: images/inflateable_17_3.0.png - text: inflateable output: url: images/inflateable_19_3.0.png - text: inflateable output: url: images/inflateable_20_3.0.png - text: inflateable output: url: images/inflateable_21_3.0.png - text: inflateable output: url: images/inflateable_22_3.0.png tags: - text-to-image - stable-diffusion-xl - lora - template:sd-lora - template:sdxl-lora - sdxl-sliders - ntcai.xyz-sliders - concept - diffusers license: "mit" inference: false instance_prompt: "inflateable" base_model: "stabilityai/stable-diffusion-xl-base-1.0" --- # ntcai.xyz slider - inflateable (SDXL LoRA) | Strength: -3 | Strength: 0 | Strength: 3 | | --- | --- | --- | | <img src="images/inflateable_17_-3.0.png" width=256 height=256 /> | <img src="images/inflateable_17_0.0.png" width=256 height=256 /> | <img src="images/inflateable_17_3.0.png" width=256 height=256 /> | | <img src="images/inflateable_19_-3.0.png" width=256 height=256 /> | <img src="images/inflateable_19_0.0.png" width=256 height=256 /> | <img src="images/inflateable_19_3.0.png" width=256 height=256 /> | | <img src="images/inflateable_20_-3.0.png" width=256 height=256 /> | <img src="images/inflateable_20_0.0.png" width=256 height=256 /> | <img src="images/inflateable_20_3.0.png" width=256 height=256 /> | ## Download Weights for this model are available in Safetensors format. ## Trigger words You can apply this LoRA with trigger words for additional effect: ``` inflateable ``` ## Use in diffusers ```python from diffusers import StableDiffusionXLPipeline from diffusers import EulerAncestralDiscreteScheduler import torch pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors") pipe.to("cuda") pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) # Load the LoRA pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.inflateable', weight_name='inflateable.safetensors', adapter_name="inflateable") # Activate the LoRA pipe.set_adapters(["inflateable"], adapter_weights=[2.0]) prompt = "medieval rich kingpin sitting in a tavern, inflateable" negative_prompt = "nsfw" width = 512 height = 512 num_inference_steps = 10 guidance_scale = 2 image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0] image.save('result.png') ``` ## Support the Patreon If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI). By joining our Patreon, you'll gain access to an ever-growing library of over 660+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities. Your support on Patreon will allow us to continue developing and refining new models. ## Other resources - [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs - [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
DataVare/DataVare-PST-To-PDF-Converter
DataVare
2023-12-27T10:45:58Z
0
0
null
[ "region:us" ]
null
2023-12-27T10:43:36Z
Use the expert DataVare Outlook PST to PDF Converter Tool to export Outlook OST and PST files to Adobe PDF file format with a few clicks of the mouse. It converts PST file emails with attachments to PDF in bulk without creating any errors. All editions of MS Outlook, such as 2021, 2019, 2016, 2013, 2010, 2007, etc, are performed with it. This application works with all Windows and Adobe PDF versions. This software comes with complete features box as - an easy and quick GUI, fully tested by all online scanners and antiviruses, users can store the resultant file at their chosen location, a free demo is also offered, and 24x7 technical support is availiable to all the users for fix theirs problem and queries. Read More:- https://www.datavare.com/software/pst-to-pdf-converter-expert.html
judithrosell/BlueBERT_CRAFT_NER_new
judithrosell
2023-12-27T10:38:24Z
11
0
transformers
[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:bionlp/bluebert_pubmed_mimic_uncased_L-12_H-768_A-12", "base_model:finetune:bionlp/bluebert_pubmed_mimic_uncased_L-12_H-768_A-12", "license:cc0-1.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-12-27T10:22:28Z
--- license: cc0-1.0 base_model: bionlp/bluebert_pubmed_mimic_uncased_L-12_H-768_A-12 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: BlueBERT_CRAFT_NER_new results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BlueBERT_CRAFT_NER_new This model is a fine-tuned version of [bionlp/bluebert_pubmed_mimic_uncased_L-12_H-768_A-12](https://huggingface.co/bionlp/bluebert_pubmed_mimic_uncased_L-12_H-768_A-12) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1392 - Precision: 0.8229 - Recall: 0.7998 - F1: 0.8112 - Accuracy: 0.9659 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2722 | 1.0 | 695 | 0.1429 | 0.7839 | 0.7856 | 0.7847 | 0.9603 | | 0.0811 | 2.0 | 1390 | 0.1351 | 0.8229 | 0.7933 | 0.8078 | 0.9654 | | 0.0421 | 3.0 | 2085 | 0.1392 | 0.8229 | 0.7998 | 0.8112 | 0.9659 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0
stagvn/vi-fin-news
stagvn
2023-12-27T10:27:38Z
43
1
transformers
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "vi", "base_model:FPTAI/vibert-base-cased", "base_model:finetune:FPTAI/vibert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-22T03:08:39Z
--- base_model: FPTAI/vibert-base-cased tags: - generated_from_trainer metrics: - accuracy model-index: - name: vi-fin-news results: [] license: apache-2.0 language: - vi library_name: transformers pipeline_tag: text-classification --- <!-- 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. --> # vi-fin-news This model is a fine-tuned version of [FPTAI/vibert-base-cased](https://huggingface.co/FPTAI/vibert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4509 - Accuracy: 0.9136 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1176 | 1.0 | 1150 | 0.3566 | 0.9181 | | 0.0582 | 2.0 | 2300 | 0.4509 | 0.9136 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.2 - Datasets 2.12.0 - Tokenizers 0.13.3
lakshay/llama2-test
lakshay
2023-12-27T10:22:36Z
2
0
peft
[ "peft", "safetensors", "llama", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "region:us" ]
null
2023-11-22T06:20:13Z
--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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 Dataset 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 Dataset 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] ### Framework versions - PEFT 0.7.1
kghanlon/distilbert-base-uncased-RILE-v1_frozen_4
kghanlon
2023-12-27T10:22:29Z
5
0
transformers
[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-27T09:48:58Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - recall - f1 model-index: - name: distilbert-base-uncased-RILE-v1_frozen_4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-RILE-v1_frozen_4 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8650 - Accuracy: 0.7404 - Recall: 0.7404 - F1: 0.7399 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:| | 0.6951 | 1.0 | 15490 | 0.6812 | 0.7140 | 0.7140 | 0.7124 | | 0.6449 | 2.0 | 30980 | 0.6663 | 0.7282 | 0.7282 | 0.7292 | | 0.5592 | 3.0 | 46470 | 0.6709 | 0.7376 | 0.7376 | 0.7366 | | 0.4423 | 4.0 | 61960 | 0.7656 | 0.7389 | 0.7389 | 0.7376 | | 0.3611 | 5.0 | 77450 | 0.8650 | 0.7404 | 0.7404 | 0.7399 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0
freyagracia/indonesian-roberta-base-posp-tagger-finetuned-tweet_pemilu_postagger
freyagracia
2023-12-27T10:10:36Z
1
0
transformers
[ "transformers", "tf", "roberta", "fill-mask", "generated_from_keras_callback", "base_model:w11wo/indonesian-roberta-base-posp-tagger", "base_model:finetune:w11wo/indonesian-roberta-base-posp-tagger", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-12-27T10:00:23Z
--- license: mit base_model: w11wo/indonesian-roberta-base-posp-tagger tags: - generated_from_keras_callback model-index: - name: freyagracia/indonesian-roberta-base-posp-tagger-finetuned-tweet_pemilu_postagger 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. --> # freyagracia/indonesian-roberta-base-posp-tagger-finetuned-tweet_pemilu_postagger This model is a fine-tuned version of [w11wo/indonesian-roberta-base-posp-tagger](https://huggingface.co/w11wo/indonesian-roberta-base-posp-tagger) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 8.1066 - Validation Loss: 7.8001 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -969, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 11.8409 | 11.4612 | 0 | | 10.7746 | 10.0532 | 1 | | 9.5651 | 9.0262 | 2 | | 8.7226 | 8.2780 | 3 | | 8.1066 | 7.8001 | 4 | ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Datasets 2.16.0 - Tokenizers 0.15.0
li-jay-cs/gpt2-medium-supervised-summarize-checkpoint
li-jay-cs
2023-12-27T09:53:16Z
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2-medium", "base_model:finetune:openai-community/gpt2-medium", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-11-24T07:29:38Z
--- license: mit base_model: gpt2-medium tags: - generated_from_trainer metrics: - rouge model-index: - name: gpt2-medium-supervised-summarize-checkpoint 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-medium-supervised-summarize-checkpoint This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7422 - Rouge1: 0.6035 - Rouge2: 0.2047 - Rougel: 0.4141 - Rougelsum: 0.5319 ## 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: 50 - eval_batch_size: 50 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 1.859 | 0.21 | 500 | 1.8105 | 0.5966 | 0.1961 | 0.4025 | 0.5237 | | 1.852 | 0.43 | 1000 | 1.7900 | 0.5994 | 0.1981 | 0.4061 | 0.5271 | | 1.8189 | 0.64 | 1500 | 1.7764 | 0.6000 | 0.2005 | 0.4082 | 0.5276 | | 1.8191 | 0.86 | 2000 | 1.7695 | 0.6013 | 0.2009 | 0.4096 | 0.5290 | | 1.7969 | 1.07 | 2500 | 1.7617 | 0.6038 | 0.2020 | 0.4108 | 0.5311 | | 1.7967 | 1.28 | 3000 | 1.7578 | 0.6024 | 0.2024 | 0.4114 | 0.5304 | | 1.7813 | 1.5 | 3500 | 1.7520 | 0.6038 | 0.2039 | 0.4128 | 0.5320 | | 1.7704 | 1.71 | 4000 | 1.7480 | 0.6033 | 0.2045 | 0.4132 | 0.5310 | | 1.7852 | 1.93 | 4500 | 1.7422 | 0.6035 | 0.2047 | 0.4141 | 0.5319 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
nitinbhayana/Llama-2-7b-chat-hf-adapter-client-361
nitinbhayana
2023-12-27T09:49:39Z
1
0
peft
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "region:us" ]
null
2023-12-27T09:49:29Z
--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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 Dataset 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 Dataset 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] ### Framework versions - PEFT 0.7.2.dev0
MarkTsao/HW-01
MarkTsao
2023-12-27T09:48:52Z
5
0
transformers
[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-23T01:41:56Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: HW-01 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. --> # HW-01 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8505 - Matthews Correlation: 0.5410 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5194 | 1.0 | 535 | 0.4550 | 0.4394 | | 0.3454 | 2.0 | 1070 | 0.4657 | 0.5110 | | 0.2382 | 3.0 | 1605 | 0.6482 | 0.5196 | | 0.1609 | 4.0 | 2140 | 0.7522 | 0.5390 | | 0.1207 | 5.0 | 2675 | 0.8505 | 0.5410 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0
kghanlon/distilbert-base-uncased-RILE-v1_fully_frozen
kghanlon
2023-12-27T09:46:16Z
6
0
transformers
[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-27T09:24:40Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - recall - f1 model-index: - name: distilbert-base-uncased-RILE-v1_fully_frozen results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-RILE-v1_fully_frozen This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8010 - Accuracy: 0.6370 - Recall: 0.6370 - F1: 0.6323 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:| | 0.8514 | 1.0 | 15490 | 0.8465 | 0.6078 | 0.6078 | 0.5986 | | 0.8486 | 2.0 | 30980 | 0.8245 | 0.6248 | 0.6248 | 0.6247 | | 0.8332 | 3.0 | 46470 | 0.8174 | 0.6260 | 0.6260 | 0.6161 | | 0.8157 | 4.0 | 61960 | 0.8080 | 0.6317 | 0.6317 | 0.6222 | | 0.8163 | 5.0 | 77450 | 0.8010 | 0.6370 | 0.6370 | 0.6323 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0
MaziyarPanahi/vision_pro_dreambooth_project
MaziyarPanahi
2023-12-27T09:39:12Z
33
2
diffusers
[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "vision-pro", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]
text-to-image
2023-12-25T14:59:06Z
--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora - vision-pro widget: - text: photo of a sks Vision Pro output: url: images/Apple-WWCD23-Vision-Pro-with-battery-230605.jpg base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: null pipeline_tag: text-to-image --- # vision_pro_dreambooth_project <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/MaziyarPanahi/vision_pro_dreambooth_project/tree/main) them in the Files & versions tab.
Wolverine01/dqn-SpaceInvadersNoFrameskip-v4
Wolverine01
2023-12-27T09:31:18Z
4
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-12-27T09:30:40Z
--- 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: 680.50 +/- 327.38 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 Wolverine01 -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 Wolverine01 -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 Wolverine01 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
mesa44/SpaceInvadersNoFrameskip-v4
mesa44
2023-12-27T09:16:24Z
0
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-12-27T09:15:45Z
--- 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: 698.00 +/- 241.27 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 mesa44 -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 mesa44 -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 mesa44 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
tiennguyenbnbk/teacher-status-van-tiny-256-0
tiennguyenbnbk
2023-12-27T09:05:53Z
7
0
transformers
[ "transformers", "safetensors", "van", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:Visual-Attention-Network/van-tiny", "base_model:finetune:Visual-Attention-Network/van-tiny", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-12-27T08:01:39Z
--- license: apache-2.0 base_model: Visual-Attention-Network/van-tiny tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - recall - precision model-index: - name: teacher-status-van-tiny-256-0 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: 0.9777777777777777 - name: Recall type: recall value: 0.9893162393162394 - name: Precision type: precision value: 0.9788583509513742 --- <!-- 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. --> # teacher-status-van-tiny-256-0 This model is a fine-tuned version of [Visual-Attention-Network/van-tiny](https://huggingface.co/Visual-Attention-Network/van-tiny) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0672 - Accuracy: 0.9778 - F1 Score: 0.9841 - Recall: 0.9893 - Precision: 0.9789 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall | Precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:| | 0.6788 | 0.99 | 47 | 0.6437 | 0.6933 | 0.8189 | 1.0 | 0.6933 | | 0.463 | 2.0 | 95 | 0.3406 | 0.8756 | 0.9162 | 0.9808 | 0.8596 | | 0.3596 | 2.99 | 142 | 0.2072 | 0.9304 | 0.9504 | 0.9615 | 0.9395 | | 0.3505 | 4.0 | 190 | 0.1564 | 0.9526 | 0.9661 | 0.9744 | 0.9580 | | 0.2962 | 4.99 | 237 | 0.1262 | 0.9556 | 0.9681 | 0.9722 | 0.9640 | | 0.2762 | 6.0 | 285 | 0.1038 | 0.9644 | 0.9745 | 0.9808 | 0.9684 | | 0.2604 | 6.99 | 332 | 0.0932 | 0.9719 | 0.9798 | 0.9829 | 0.9766 | | 0.2427 | 8.0 | 380 | 0.0928 | 0.9719 | 0.9797 | 0.9786 | 0.9807 | | 0.2465 | 8.99 | 427 | 0.0898 | 0.9719 | 0.9797 | 0.9786 | 0.9807 | | 0.2519 | 10.0 | 475 | 0.0913 | 0.9689 | 0.9775 | 0.9765 | 0.9786 | | 0.2258 | 10.99 | 522 | 0.0847 | 0.9733 | 0.9809 | 0.9872 | 0.9747 | | 0.2184 | 12.0 | 570 | 0.0812 | 0.9793 | 0.9851 | 0.9893 | 0.9809 | | 0.2208 | 12.99 | 617 | 0.0693 | 0.9807 | 0.9861 | 0.9872 | 0.9851 | | 0.2201 | 14.0 | 665 | 0.0628 | 0.9763 | 0.9829 | 0.9850 | 0.9809 | | 0.2251 | 14.99 | 712 | 0.0811 | 0.9733 | 0.9810 | 0.9915 | 0.9707 | | 0.2135 | 16.0 | 760 | 0.0718 | 0.9763 | 0.9829 | 0.9850 | 0.9809 | | 0.1851 | 16.99 | 807 | 0.0791 | 0.9763 | 0.9830 | 0.9872 | 0.9788 | | 0.2152 | 18.0 | 855 | 0.0737 | 0.9748 | 0.9818 | 0.9808 | 0.9829 | | 0.1871 | 18.99 | 902 | 0.0814 | 0.9763 | 0.9830 | 0.9872 | 0.9788 | | 0.1714 | 20.0 | 950 | 0.0692 | 0.9763 | 0.9830 | 0.9893 | 0.9768 | | 0.188 | 20.99 | 997 | 0.0641 | 0.9778 | 0.9840 | 0.9850 | 0.9829 | | 0.191 | 22.0 | 1045 | 0.0644 | 0.9793 | 0.9851 | 0.9872 | 0.9830 | | 0.2025 | 22.99 | 1092 | 0.0675 | 0.9793 | 0.9850 | 0.9829 | 0.9871 | | 0.1753 | 24.0 | 1140 | 0.0655 | 0.9822 | 0.9872 | 0.9893 | 0.9851 | | 0.1857 | 24.99 | 1187 | 0.0731 | 0.9793 | 0.9851 | 0.9915 | 0.9789 | | 0.2007 | 26.0 | 1235 | 0.0677 | 0.9793 | 0.9851 | 0.9915 | 0.9789 | | 0.2086 | 26.99 | 1282 | 0.0640 | 0.9793 | 0.9851 | 0.9893 | 0.9809 | | 0.1666 | 28.0 | 1330 | 0.0712 | 0.9778 | 0.9841 | 0.9893 | 0.9789 | | 0.157 | 28.99 | 1377 | 0.0661 | 0.9807 | 0.9862 | 0.9893 | 0.9830 | | 0.1758 | 29.68 | 1410 | 0.0672 | 0.9778 | 0.9841 | 0.9893 | 0.9789 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0
xaviviro/llama-2-7b-chat-catala
xaviviro
2023-12-27T09:02:39Z
5
0
transformers
[ "transformers", "pytorch", "llama", "text-generation", "ca", "en", "dataset:xaviviro/oasst1_ca_threads", "base_model:NousResearch/Llama-2-7b-chat-hf", "base_model:finetune:NousResearch/Llama-2-7b-chat-hf", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-12-26T22:51:41Z
--- base_model: NousResearch/Llama-2-7b-chat-hf datasets: - xaviviro/oasst1_ca_threads language: - ca - en model_type: llama prompt_template: >- <s>[INST] <<SYS>> Ets un xatbot genèric que sempre respon en català. <</SYS>> {instruction} [/INST] license: apache-2.0 --- # llama-2-7b-chat-catala ## Prompt template ``` <s>[INST] <<SYS>> Ets un xatbot genèric que sempre respon en català. <</SYS>> {instruction} [/INST] ```
0x7o/fialka-7B-v2.1
0x7o
2023-12-27T08:57:25Z
8
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "ru", "dataset:0x7194633/fialka-v1-zephyr", "dataset:0x7194633/oasst2-best-ru", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-12-26T08:08:34Z
--- license: apache-2.0 datasets: - 0x7194633/fialka-v1-zephyr - 0x7194633/oasst2-best-ru language: - ru pipeline_tag: text-generation --- # Fialka v2.1 7B ![Violet](https://i.imgur.com/EDwW6t6.png) ## Description Fialka language models are trained to follow instructions and maintain communication in Russian. Version 2.1 is trained on a dataset of [instructions](0x7194633/fialka-v1-zephyr) and [dialogs](https://huggingface.co/datasets/0x7194633/oasst2-best-ru). **Check out our [new V3 model](https://huggingface.co/0x7194633/fialka-7B-v3), which generates Russian text more accurately and better.** ## Usage The model has a query format as in zephyr. ``` <|user|> Напиши код на python, который удалит файл `1.txt`.</s> <|assistant|> Для того чтобы удалить файл в Python необходимо использовать функцию os.remove(). Она принимает имя файла в качестве аргумента и выполняет операции удаления <...> ``` Check out the [space](https://huggingface.co/spaces/0x7194633/fialka) to use the model in UI without downloading.
Raihan004/Hierarchical_Agent_Action
Raihan004
2023-12-27T08:55:41Z
9
0
transformers
[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:image_folder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-12-02T08:24:08Z
--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - generated_from_trainer datasets: - image_folder metrics: - accuracy model-index: - name: Hierarchical_Agent_Action results: - task: name: Image Classification type: image-classification dataset: name: agent_action_class type: image_folder config: hierarchical-action-agent split: train args: hierarchical-action-agent metrics: - name: Accuracy type: accuracy value: 0.8402877697841726 --- <!-- 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. --> # Hierarchical_Agent_Action This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the agent_action_class dataset. It achieves the following results on the evaluation set: - Loss: 0.5942 - Accuracy: 0.8403 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.4407 | 0.81 | 100 | 2.2716 | 0.6058 | | 1.7756 | 1.61 | 200 | 1.6162 | 0.7065 | | 1.3948 | 2.42 | 300 | 1.2200 | 0.7698 | | 1.131 | 3.23 | 400 | 1.0012 | 0.7856 | | 0.9239 | 4.03 | 500 | 0.9055 | 0.7827 | | 0.8699 | 4.84 | 600 | 0.8103 | 0.7827 | | 0.6707 | 5.65 | 700 | 0.7610 | 0.7842 | | 0.6206 | 6.45 | 800 | 0.7312 | 0.7885 | | 0.5795 | 7.26 | 900 | 0.6989 | 0.8101 | | 0.4914 | 8.06 | 1000 | 0.7066 | 0.7813 | | 0.5087 | 8.87 | 1100 | 0.6398 | 0.8187 | | 0.4373 | 9.68 | 1200 | 0.6293 | 0.8043 | | 0.4365 | 10.48 | 1300 | 0.6726 | 0.7971 | | 0.4517 | 11.29 | 1400 | 0.6047 | 0.8245 | | 0.4114 | 12.1 | 1500 | 0.6088 | 0.8230 | | 0.426 | 12.9 | 1600 | 0.6165 | 0.8201 | | 0.3456 | 13.71 | 1700 | 0.6133 | 0.8259 | | 0.332 | 14.52 | 1800 | 0.6736 | 0.8201 | | 0.3646 | 15.32 | 1900 | 0.6406 | 0.8173 | | 0.3287 | 16.13 | 2000 | 0.6978 | 0.7971 | | 0.2793 | 16.94 | 2100 | 0.6433 | 0.8173 | | 0.2924 | 17.74 | 2200 | 0.6474 | 0.8144 | | 0.2605 | 18.55 | 2300 | 0.6279 | 0.8288 | | 0.2016 | 19.35 | 2400 | 0.6361 | 0.8216 | | 0.2524 | 20.16 | 2500 | 0.6394 | 0.8259 | | 0.2017 | 20.97 | 2600 | 0.6683 | 0.8158 | | 0.2082 | 21.77 | 2700 | 0.6389 | 0.8345 | | 0.2751 | 22.58 | 2800 | 0.6141 | 0.8374 | | 0.207 | 23.39 | 2900 | 0.6052 | 0.8259 | | 0.1791 | 24.19 | 3000 | 0.6332 | 0.8230 | | 0.1719 | 25.0 | 3100 | 0.5942 | 0.8403 | | 0.1685 | 25.81 | 3200 | 0.6121 | 0.8360 | | 0.1557 | 26.61 | 3300 | 0.6237 | 0.8345 | | 0.1694 | 27.42 | 3400 | 0.6372 | 0.8317 | | 0.1927 | 28.23 | 3500 | 0.6378 | 0.8273 | | 0.1375 | 29.03 | 3600 | 0.6258 | 0.8331 | | 0.1653 | 29.84 | 3700 | 0.6262 | 0.8331 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.14.1
AIYIYA/my_new_login4
AIYIYA
2023-12-27T08:54:13Z
1
0
transformers
[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "base_model:google-bert/bert-base-chinese", "base_model:finetune:google-bert/bert-base-chinese", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-12-27T08:41:03Z
--- base_model: bert-base-chinese tags: - generated_from_keras_callback model-index: - name: AIYIYA/my_new_login4 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. --> # AIYIYA/my_new_login4 This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3002 - Validation Loss: 0.3570 - Train Accuracy: 0.8732 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 35, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.5716 | 0.4986 | 0.7887 | 0 | | 0.3840 | 0.4054 | 0.8451 | 1 | | 0.3002 | 0.3570 | 0.8732 | 2 | ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Datasets 2.16.0 - Tokenizers 0.15.0