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text-to-image | diffusers |
# Fanta
Samples and prompts:
(Click for larger)
Top left: Elvis's Daughters. Movie still. Pretty CUTE LITTLE Girl with sister playing with miniature toy city, bokeh. DETAILED vintage colors photography brown EYES, sitting on a box of pepsis, gorgeous detailed Ponytail, cocacola can Magazine ad, iconic, 1935, sharp focus. Illustration By KlaysMoji and leyendecker and artgerm and Dave Rapoza
Top right: cinematic 1980 movie still, handsome VALENTINE MAN with pretty woman with cleavage, classroom, school Uniforms, blackboard. Pinup. He wears a backpack, bokeh
Bottom left: analog style 70s color photograph of young kiefer sutherland as jack bauer, 24 behind the scenes
Bottom right: absurdres, adorable cute harley quinn, at night, dark alley, moon, :) red ponytail, blonde ponytail, in matte black hardsuit, military, roughed up, bat, city fog,
FantasticMix v65 (my favorite) by michin mixed with artistic models to... well, make its outputs more artistic!
Original page: https://civitai.com/models/22402?modelVersionId=77784
# FantasticDreams
Same models, different mix. This model goes hard! Perhaps too hard... | {"language": ["en"], "license": "other", "tags": ["Base Model", "Photorealistic", "Beautiful", "art", "artistic", "michin", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers"]} | Yntec/Fanta | null | [
"diffusers",
"safetensors",
"Base Model",
"Photorealistic",
"Beautiful",
"art",
"artistic",
"michin",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"en",
"license:other",
"endpoints_compatible",
"has_space",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null | 2024-04-20T21:02:09+00:00 | [] | [
"en"
] | TAGS
#diffusers #safetensors #Base Model #Photorealistic #Beautiful #art #artistic #michin #stable-diffusion #stable-diffusion-diffusers #text-to-image #en #license-other #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us
|
# Fanta
Samples and prompts:
!Free online ai image generator fanta
(Click for larger)
Top left: Elvis's Daughters. Movie still. Pretty CUTE LITTLE Girl with sister playing with miniature toy city, bokeh. DETAILED vintage colors photography brown EYES, sitting on a box of pepsis, gorgeous detailed Ponytail, cocacola can Magazine ad, iconic, 1935, sharp focus. Illustration By KlaysMoji and leyendecker and artgerm and Dave Rapoza
Top right: cinematic 1980 movie still, handsome VALENTINE MAN with pretty woman with cleavage, classroom, school Uniforms, blackboard. Pinup. He wears a backpack, bokeh
Bottom left: analog style 70s color photograph of young kiefer sutherland as jack bauer, 24 behind the scenes
Bottom right: absurdres, adorable cute harley quinn, at night, dark alley, moon, :) red ponytail, blonde ponytail, in matte black hardsuit, military, roughed up, bat, city fog,
FantasticMix v65 (my favorite) by michin mixed with artistic models to... well, make its outputs more artistic!
Original page: URL
# FantasticDreams
Same models, different mix. This model goes hard! Perhaps too hard... | [
"# Fanta\n\nSamples and prompts:\n\n!Free online ai image generator fanta\n\n(Click for larger)\n\nTop left: Elvis's Daughters. Movie still. Pretty CUTE LITTLE Girl with sister playing with miniature toy city, bokeh. DETAILED vintage colors photography brown EYES, sitting on a box of pepsis, gorgeous detailed Ponytail, cocacola can Magazine ad, iconic, 1935, sharp focus. Illustration By KlaysMoji and leyendecker and artgerm and Dave Rapoza\n\nTop right: cinematic 1980 movie still, handsome VALENTINE MAN with pretty woman with cleavage, classroom, school Uniforms, blackboard. Pinup. He wears a backpack, bokeh\n\nBottom left: analog style 70s color photograph of young kiefer sutherland as jack bauer, 24 behind the scenes\n\nBottom right: absurdres, adorable cute harley quinn, at night, dark alley, moon, :) red ponytail, blonde ponytail, in matte black hardsuit, military, roughed up, bat, city fog,\n\nFantasticMix v65 (my favorite) by michin mixed with artistic models to... well, make its outputs more artistic!\n\nOriginal page: URL",
"# FantasticDreams\n\nSame models, different mix. This model goes hard! Perhaps too hard..."
] | [
"TAGS\n#diffusers #safetensors #Base Model #Photorealistic #Beautiful #art #artistic #michin #stable-diffusion #stable-diffusion-diffusers #text-to-image #en #license-other #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us \n",
"# Fanta\n\nSamples and prompts:\n\n!Free online ai image generator fanta\n\n(Click for larger)\n\nTop left: Elvis's Daughters. Movie still. Pretty CUTE LITTLE Girl with sister playing with miniature toy city, bokeh. DETAILED vintage colors photography brown EYES, sitting on a box of pepsis, gorgeous detailed Ponytail, cocacola can Magazine ad, iconic, 1935, sharp focus. Illustration By KlaysMoji and leyendecker and artgerm and Dave Rapoza\n\nTop right: cinematic 1980 movie still, handsome VALENTINE MAN with pretty woman with cleavage, classroom, school Uniforms, blackboard. Pinup. He wears a backpack, bokeh\n\nBottom left: analog style 70s color photograph of young kiefer sutherland as jack bauer, 24 behind the scenes\n\nBottom right: absurdres, adorable cute harley quinn, at night, dark alley, moon, :) red ponytail, blonde ponytail, in matte black hardsuit, military, roughed up, bat, city fog,\n\nFantasticMix v65 (my favorite) by michin mixed with artistic models to... well, make its outputs more artistic!\n\nOriginal page: URL",
"# FantasticDreams\n\nSame models, different mix. This model goes hard! Perhaps too hard..."
] |
text2text-generation | transformers |
<!-- 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. -->
# CS505_COQE_viT5_train_Instruction0_SOAPL_h2
This model is a fine-tuned version of [ThuyNT/CS505_COQE_viT5_train_Instruction0_SOAPL](https://huggingface.co/ThuyNT/CS505_COQE_viT5_train_Instruction0_SOAPL) 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: 5e-05
- train_batch_size: 8
- 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "ThuyNT/CS505_COQE_viT5_train_Instruction0_SOAPL", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_SOAPL_h2", "results": []}]} | ThuyNT/CS505_COQE_viT5_train_Instruction0_SOAPL_h2 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:ThuyNT/CS505_COQE_viT5_train_Instruction0_SOAPL",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T21:03:48+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-ThuyNT/CS505_COQE_viT5_train_Instruction0_SOAPL #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# CS505_COQE_viT5_train_Instruction0_SOAPL_h2
This model is a fine-tuned version of ThuyNT/CS505_COQE_viT5_train_Instruction0_SOAPL 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: 5e-05
- train_batch_size: 8
- 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
"# CS505_COQE_viT5_train_Instruction0_SOAPL_h2\n\nThis model is a fine-tuned version of ThuyNT/CS505_COQE_viT5_train_Instruction0_SOAPL on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-ThuyNT/CS505_COQE_viT5_train_Instruction0_SOAPL #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# CS505_COQE_viT5_train_Instruction0_SOAPL_h2\n\nThis model is a fine-tuned version of ThuyNT/CS505_COQE_viT5_train_Instruction0_SOAPL on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
null | adapter-transformers |
# Adapter `BigTMiami/m_imdb_par_bn_v_4_pretrain_adapter` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/imdb_TAPT_pretraining_dataset](https://huggingface.co/datasets/BigTMiami/imdb_TAPT_pretraining_dataset/) dataset and includes a prediction head for masked lm.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/m_imdb_par_bn_v_4_pretrain_adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["adapter-transformers", "roberta"], "datasets": ["BigTMiami/imdb_TAPT_pretraining_dataset"]} | BigTMiami/m_imdb_par_bn_v_4_pretrain_adapter | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/imdb_TAPT_pretraining_dataset",
"region:us"
] | null | 2024-04-20T21:07:11+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/imdb_TAPT_pretraining_dataset #region-us
|
# Adapter 'BigTMiami/m_imdb_par_bn_v_4_pretrain_adapter' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/imdb_TAPT_pretraining_dataset dataset and includes a prediction head for masked lm.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/m_imdb_par_bn_v_4_pretrain_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/imdb_TAPT_pretraining_dataset dataset and includes a prediction head for masked lm.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/imdb_TAPT_pretraining_dataset #region-us \n",
"# Adapter 'BigTMiami/m_imdb_par_bn_v_4_pretrain_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/imdb_TAPT_pretraining_dataset dataset and includes a prediction head for masked lm.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
text-generation | transformers |
<!-- 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. -->
# 0.0_ablation_5e7lr_4iters_256batch_iter_2
This model is a fine-tuned version of [ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_iter_1](https://huggingface.co/ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_iter_1) on the ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_dataset 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_dataset"], "base_model": "ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_iter_1", "model-index": [{"name": "0.0_ablation_5e7lr_4iters_256batch_iter_2", "results": []}]} | ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_iter_2 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_dataset",
"base_model:ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_iter_1",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T21:07:33+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #alignment-handbook #generated_from_trainer #trl #dpo #conversational #dataset-ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_dataset #base_model-ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_iter_1 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# 0.0_ablation_5e7lr_4iters_256batch_iter_2
This model is a fine-tuned version of ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_iter_1 on the ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_dataset 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| [
"# 0.0_ablation_5e7lr_4iters_256batch_iter_2\n\nThis model is a fine-tuned version of ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_iter_1 on the ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_dataset dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #alignment-handbook #generated_from_trainer #trl #dpo #conversational #dataset-ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_dataset #base_model-ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_iter_1 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# 0.0_ablation_5e7lr_4iters_256batch_iter_2\n\nThis model is a fine-tuned version of ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_iter_1 on the ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_dataset dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2"
] |
text-generation | transformers | # Locutusque/llama-3-neural-chat-v1-8b AWQ
- Model creator: [Locutusque](https://huggingface.co/Locutusque)
- Original model: [llama-3-neural-chat-v1-8b](https://huggingface.co/Locutusque/llama-3-neural-chat-v1-8b)
## Model Summary
I fine-tuned llama-3 8B on an approach similar to Intel's neural chat language model. I have slightly modified the data sources so it is stronger in coding, math, and writing. I use both SFT and DPO.
This model has great performance in writing and coding.
## Training Data
- Open-Orca/SlimOrca-Dedup
- jondurbin/airoboros-3.2
- microsoft/orca-math-word-problems-200k
- m-a-p/Code-Feedback
- MaziyarPanahi/WizardLM_evol_instruct_V2_196k
- mlabonne/orpo-dpo-mix-40k | {"license": "other", "library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible"], "datasets": ["mlabonne/orpo-dpo-mix-40k", "Open-Orca/SlimOrca-Dedup", "jondurbin/airoboros-3.2", "microsoft/orca-math-word-problems-200k", "m-a-p/Code-Feedback", "MaziyarPanahi/WizardLM_evol_instruct_V2_196k"], "base_model": "meta-llama/Meta-Llama-3-8B", "pipeline_tag": "text-generation", "inference": false, "quantized_by": "Suparious"} | solidrust/llama-3-neural-chat-v1-8b-AWQ | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible",
"conversational",
"dataset:mlabonne/orpo-dpo-mix-40k",
"dataset:Open-Orca/SlimOrca-Dedup",
"dataset:jondurbin/airoboros-3.2",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:m-a-p/Code-Feedback",
"dataset:MaziyarPanahi/WizardLM_evol_instruct_V2_196k",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T21:09:19+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #4-bit #AWQ #autotrain_compatible #endpoints_compatible #conversational #dataset-mlabonne/orpo-dpo-mix-40k #dataset-Open-Orca/SlimOrca-Dedup #dataset-jondurbin/airoboros-3.2 #dataset-microsoft/orca-math-word-problems-200k #dataset-m-a-p/Code-Feedback #dataset-MaziyarPanahi/WizardLM_evol_instruct_V2_196k #base_model-meta-llama/Meta-Llama-3-8B #license-other #text-generation-inference #region-us
| # Locutusque/llama-3-neural-chat-v1-8b AWQ
- Model creator: Locutusque
- Original model: llama-3-neural-chat-v1-8b
!image/png
## Model Summary
I fine-tuned llama-3 8B on an approach similar to Intel's neural chat language model. I have slightly modified the data sources so it is stronger in coding, math, and writing. I use both SFT and DPO.
This model has great performance in writing and coding.
## Training Data
- Open-Orca/SlimOrca-Dedup
- jondurbin/airoboros-3.2
- microsoft/orca-math-word-problems-200k
- m-a-p/Code-Feedback
- MaziyarPanahi/WizardLM_evol_instruct_V2_196k
- mlabonne/orpo-dpo-mix-40k | [
"# Locutusque/llama-3-neural-chat-v1-8b AWQ\n\n- Model creator: Locutusque\n- Original model: llama-3-neural-chat-v1-8b\n\n!image/png",
"## Model Summary\n\nI fine-tuned llama-3 8B on an approach similar to Intel's neural chat language model. I have slightly modified the data sources so it is stronger in coding, math, and writing. I use both SFT and DPO.\n\nThis model has great performance in writing and coding.",
"## Training Data\n- Open-Orca/SlimOrca-Dedup\n- jondurbin/airoboros-3.2\n- microsoft/orca-math-word-problems-200k\n- m-a-p/Code-Feedback\n- MaziyarPanahi/WizardLM_evol_instruct_V2_196k\n- mlabonne/orpo-dpo-mix-40k"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #4-bit #AWQ #autotrain_compatible #endpoints_compatible #conversational #dataset-mlabonne/orpo-dpo-mix-40k #dataset-Open-Orca/SlimOrca-Dedup #dataset-jondurbin/airoboros-3.2 #dataset-microsoft/orca-math-word-problems-200k #dataset-m-a-p/Code-Feedback #dataset-MaziyarPanahi/WizardLM_evol_instruct_V2_196k #base_model-meta-llama/Meta-Llama-3-8B #license-other #text-generation-inference #region-us \n",
"# Locutusque/llama-3-neural-chat-v1-8b AWQ\n\n- Model creator: Locutusque\n- Original model: llama-3-neural-chat-v1-8b\n\n!image/png",
"## Model Summary\n\nI fine-tuned llama-3 8B on an approach similar to Intel's neural chat language model. I have slightly modified the data sources so it is stronger in coding, math, and writing. I use both SFT and DPO.\n\nThis model has great performance in writing and coding.",
"## Training Data\n- Open-Orca/SlimOrca-Dedup\n- jondurbin/airoboros-3.2\n- microsoft/orca-math-word-problems-200k\n- m-a-p/Code-Feedback\n- MaziyarPanahi/WizardLM_evol_instruct_V2_196k\n- mlabonne/orpo-dpo-mix-40k"
] |
null | peft |
<!-- 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. -->
# Falcon-Summarization
This model is a fine-tuned version of [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) on the scitldr dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5006
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4431 | 0.2 | 200 | 2.5148 |
| 2.376 | 0.4 | 400 | 2.5112 |
| 2.4269 | 0.6 | 600 | 2.4901 |
| 2.368 | 0.8 | 800 | 2.4763 |
| 2.372 | 1.0 | 1000 | 2.4684 |
| 2.0904 | 1.2 | 1200 | 2.5018 |
| 2.099 | 1.41 | 1400 | 2.5137 |
| 2.0482 | 1.61 | 1600 | 2.4993 |
| 1.9974 | 1.81 | 1800 | 2.5006 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "datasets": ["scitldr"], "base_model": "tiiuae/falcon-7b-instruct", "model-index": [{"name": "Falcon-Summarization", "results": []}]} | pkbiswas/Falcon-7b-Summarization-QLoRa | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"dataset:scitldr",
"base_model:tiiuae/falcon-7b-instruct",
"license:apache-2.0",
"region:us"
] | null | 2024-04-20T21:10:02+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #generated_from_trainer #dataset-scitldr #base_model-tiiuae/falcon-7b-instruct #license-apache-2.0 #region-us
| Falcon-Summarization
====================
This model is a fine-tuned version of tiiuae/falcon-7b-instruct on the scitldr dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5006
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0002
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2
* num\_epochs: 2
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* PEFT 0.10.0
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #dataset-scitldr #base_model-tiiuae/falcon-7b-instruct #license-apache-2.0 #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] |
text-generation | transformers | To use with [LocalAI](https://localai.io) this is an example model definition:
```
name: WizardLM2
backend: transformers
parameters:
model: fakezeta/Not-WizardLM-2-7B-ov-int4
context_size: 8192
threads: 6
f16: true
type: OVModelForCausalLM
prompt_cache_path: "cache"
prompt_cache_all: true
template:
use_tokenizer_template: true
``` | {"license": "apache-2.0"} | fakezeta/Not-WizardLM-2-7B-ov-int4 | null | [
"transformers",
"openvino",
"mistral",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T21:10:21+00:00 | [] | [] | TAGS
#transformers #openvino #mistral #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| To use with LocalAI this is an example model definition:
| [] | [
"TAGS\n#transformers #openvino #mistral #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
| {"library_name": "transformers", "tags": []} | kakinola23/Meta-Llama-3-8B-bnb-4bit-new | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-20T21:11:31+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
reinforcement-learning | stable-baselines3 |
# **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
...
```
| {"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": "-0.21 +/- 0.14", "name": "mean_reward", "verified": false}]}]}]} | amine-01/a2c-PandaReachDense-v3 | null | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-04-20T21:15:21+00:00 | [] | [] | TAGS
#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing PandaReachDense-v3
This is a trained model of a A2C agent playing PandaReachDense-v3
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] |
text2text-generation | transformers |
<!-- 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. -->
# CS505_COQE_viT5_train_Instruction0_ASPOL_h2
This model is a fine-tuned version of [ThuyNT/CS505_COQE_viT5_train_Instruction0_ASPOL](https://huggingface.co/ThuyNT/CS505_COQE_viT5_train_Instruction0_ASPOL) 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: 5e-05
- train_batch_size: 8
- 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "ThuyNT/CS505_COQE_viT5_train_Instruction0_ASPOL", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_ASPOL_h2", "results": []}]} | ThuyNT/CS505_COQE_viT5_train_Instruction0_ASPOL_h2 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:ThuyNT/CS505_COQE_viT5_train_Instruction0_ASPOL",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T21:16:33+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-ThuyNT/CS505_COQE_viT5_train_Instruction0_ASPOL #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# CS505_COQE_viT5_train_Instruction0_ASPOL_h2
This model is a fine-tuned version of ThuyNT/CS505_COQE_viT5_train_Instruction0_ASPOL 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: 5e-05
- train_batch_size: 8
- 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
"# CS505_COQE_viT5_train_Instruction0_ASPOL_h2\n\nThis model is a fine-tuned version of ThuyNT/CS505_COQE_viT5_train_Instruction0_ASPOL on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-ThuyNT/CS505_COQE_viT5_train_Instruction0_ASPOL #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# CS505_COQE_viT5_train_Instruction0_ASPOL_h2\n\nThis model is a fine-tuned version of ThuyNT/CS505_COQE_viT5_train_Instruction0_ASPOL on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
null | adapter-transformers |
# Adapter `BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_0` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/citation_intent_dataset](https://huggingface.co/datasets/BigTMiami/citation_intent_dataset/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_0", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["adapter-transformers", "roberta"], "datasets": ["BigTMiami/citation_intent_dataset"]} | BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_0 | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/citation_intent_dataset",
"region:us"
] | null | 2024-04-20T21:18:39+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/citation_intent_dataset #region-us
|
# Adapter 'BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_0' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_0' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/citation_intent_dataset #region-us \n",
"# Adapter 'BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_0' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
null | transformers |
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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] | {"library_name": "transformers", "tags": []} | testing-ml/kto-mini | null | [
"transformers",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T21:19:24+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #tensorboard #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #tensorboard #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text2text-generation | transformers |
<!-- 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_opus_books_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1920
- Bleu: 0.1995
- Gen Len: 18.1959
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 3.6418 | 1.0 | 1617 | 3.2754 | 0.165 | 18.2019 |
| 3.5171 | 2.0 | 3234 | 3.1920 | 0.1995 | 18.1959 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "base_model": "t5-small", "model-index": [{"name": "my_awesome_opus_books_model", "results": []}]} | YJL814/my_awesome_opus_books_model | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T21:20:00+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| my\_awesome\_opus\_books\_model
===============================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1920
* Bleu: 0.1995
* Gen Len: 18.1959
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
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] |
null | adapter-transformers |
# Adapter `BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_1` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/citation_intent_dataset](https://huggingface.co/datasets/BigTMiami/citation_intent_dataset/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_1", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["adapter-transformers", "roberta"], "datasets": ["BigTMiami/citation_intent_dataset"]} | BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_1 | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/citation_intent_dataset",
"region:us"
] | null | 2024-04-20T21:20:03+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/citation_intent_dataset #region-us
|
# Adapter 'BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_1' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_1' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/citation_intent_dataset #region-us \n",
"# Adapter 'BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_1' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
null | peft |
# 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 0.10.0 | {"library_name": "peft", "base_model": "meta-llama/Meta-Llama-3-8B-Instruct"} | Fredithefish/Llama3RPInstruct-chkpt-750 | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"region:us"
] | null | 2024-04-20T21:20:25+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Meta-Llama-3-8B-Instruct #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
- PEFT 0.10.0 | [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## Technical Specifications [optional]",
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"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.10.0"
] | [
"TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Meta-Llama-3-8B-Instruct #region-us \n",
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"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
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"#### Testing Data",
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"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
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"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.10.0"
] |
null | adapter-transformers |
# Adapter `BigTMiami/m_cite_par_bn_v_4_class_no_pre_0_adapter` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/citation_intent_dataset](https://huggingface.co/datasets/BigTMiami/citation_intent_dataset/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/m_cite_par_bn_v_4_class_no_pre_0_adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["adapter-transformers", "roberta"], "datasets": ["BigTMiami/citation_intent_dataset"]} | BigTMiami/m_cite_par_bn_v_4_class_no_pre_0_adapter | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/citation_intent_dataset",
"region:us"
] | null | 2024-04-20T21:20:45+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/citation_intent_dataset #region-us
|
# Adapter 'BigTMiami/m_cite_par_bn_v_4_class_no_pre_0_adapter' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/m_cite_par_bn_v_4_class_no_pre_0_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
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"# Adapter 'BigTMiami/m_cite_par_bn_v_4_class_no_pre_0_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
null | adapter-transformers |
# Adapter `BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_2` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/citation_intent_dataset](https://huggingface.co/datasets/BigTMiami/citation_intent_dataset/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_2", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["adapter-transformers", "roberta"], "datasets": ["BigTMiami/citation_intent_dataset"]} | BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_2 | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/citation_intent_dataset",
"region:us"
] | null | 2024-04-20T21:21:28+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/citation_intent_dataset #region-us
|
# Adapter 'BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_2' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_2' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
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"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
null | adapter-transformers |
# Adapter `BigTMiami/m_cite_par_bn_v_4_class_no_pre_1_adapter` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/citation_intent_dataset](https://huggingface.co/datasets/BigTMiami/citation_intent_dataset/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/m_cite_par_bn_v_4_class_no_pre_1_adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["adapter-transformers", "roberta"], "datasets": ["BigTMiami/citation_intent_dataset"]} | BigTMiami/m_cite_par_bn_v_4_class_no_pre_1_adapter | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/citation_intent_dataset",
"region:us"
] | null | 2024-04-20T21:22:08+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/citation_intent_dataset #region-us
|
# Adapter 'BigTMiami/m_cite_par_bn_v_4_class_no_pre_1_adapter' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/m_cite_par_bn_v_4_class_no_pre_1_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/citation_intent_dataset #region-us \n",
"# Adapter 'BigTMiami/m_cite_par_bn_v_4_class_no_pre_1_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
question-answering | transformers | # Model Card for Llama3-4x8B-TimeAgents
This model card serves as a detailed guide and reference for users interacting with the Llama3-TimeAgents-4x8B model.
## Model Details
### Model Description
Llama3-TimeAgents-4x8B is an advanced machine learning model specifically developed for multi-agent systems focused on time series predictions. This model harnesses the capabilities of a state-of-the-art language model framework, tailored to assist in research environments where precise and predictive temporal data analysis is crucial. It combines the robustness of transformer architectures with specialized adaptations for handling the dynamics of multi-agent interactions and time-sensitive data, making it an ideal tool for pushing the boundaries of academic and applied research in time series forecasting. This innovative model is the culmination of rigorous research and development efforts aimed at addressing complex challenges in predictive analytics within multi-agent systems.
- **Model type:** Transformer-based
- **Language(s) (NLP):** Primarily English
- **License:** LLama 3
- **Merged from:** Llama3 base model and variations
-
### Disclaimer
This model is a research experiment and may generate incorrect or harmful content. The model's outputs should not be taken as factual or representative of the views of the model's creator or any other individual.
The model's creator is not responsible for any harm or damage caused by the model's outputs.
### Direct Use
The model is ready for direct integration into applications requiring natural language understanding and generation without further training or significant modifications.
### Out-of-Scope Use
This model is not intended for use in scenarios requiring highly sensitive or critical decision-making processes, where inaccuracies could lead to significant harm or legal issues.
## Bias, Risks, and Limitations
The model, like all AI language models, may inherit biases from the training data, and its outputs should be carefully reviewed in any sensitive application.
### Recommendations
Users should be aware of the potential for biased outputs and should implement appropriate checks and balances when using the model in production environments.
## How to Get Started with the Model
To get started with the Llama3-TimeAgents-4x8B, you can use the following Python code snippet, which utilizes the `transformers` library:
```python
from transformers import AutoTokenizer, pipeline
import torch
model_id = "AIFS/Llama3-4x8B-Time-Agents/"
tokenizer = AutoTokenizer.from_pretrained(model_id)
chat_pipeline = pipeline(
"text-generation",
model=model_id,
tokenizer=tokenizer,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "How can I create a Restful API?"}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = chat_pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
## Evaluation
### Testing Data, Factors & Metrics
To ensure robust performance, the model was evaluated against a diverse set of benchmarks focusing on language understanding and generation.
#### Metrics
Soon after Fine-Tuning
### Results
Detailed results will be available upon completion of ongoing evaluations.
## Environmental Impact
The environmental impact of training such models is significant. Users and stakeholders are encouraged to consider sustainability in their deployment strategies.
- **Carbon Emitted:** Estimated upon request
Meta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved. | {"pipeline_tag": "question-answering"} | AIFS/Llama3-4x8B-Time-Agents | null | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"question-answering",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T21:22:31+00:00 | [] | [] | TAGS
#transformers #safetensors #mixtral #text-generation #question-answering #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Model Card for Llama3-4x8B-TimeAgents
This model card serves as a detailed guide and reference for users interacting with the Llama3-TimeAgents-4x8B model.
## Model Details
### Model Description
Llama3-TimeAgents-4x8B is an advanced machine learning model specifically developed for multi-agent systems focused on time series predictions. This model harnesses the capabilities of a state-of-the-art language model framework, tailored to assist in research environments where precise and predictive temporal data analysis is crucial. It combines the robustness of transformer architectures with specialized adaptations for handling the dynamics of multi-agent interactions and time-sensitive data, making it an ideal tool for pushing the boundaries of academic and applied research in time series forecasting. This innovative model is the culmination of rigorous research and development efforts aimed at addressing complex challenges in predictive analytics within multi-agent systems.
- Model type: Transformer-based
- Language(s) (NLP): Primarily English
- License: LLama 3
- Merged from: Llama3 base model and variations
-
### Disclaimer
This model is a research experiment and may generate incorrect or harmful content. The model's outputs should not be taken as factual or representative of the views of the model's creator or any other individual.
The model's creator is not responsible for any harm or damage caused by the model's outputs.
### Direct Use
The model is ready for direct integration into applications requiring natural language understanding and generation without further training or significant modifications.
### Out-of-Scope Use
This model is not intended for use in scenarios requiring highly sensitive or critical decision-making processes, where inaccuracies could lead to significant harm or legal issues.
## Bias, Risks, and Limitations
The model, like all AI language models, may inherit biases from the training data, and its outputs should be carefully reviewed in any sensitive application.
### Recommendations
Users should be aware of the potential for biased outputs and should implement appropriate checks and balances when using the model in production environments.
## How to Get Started with the Model
To get started with the Llama3-TimeAgents-4x8B, you can use the following Python code snippet, which utilizes the 'transformers' library:
## Evaluation
### Testing Data, Factors & Metrics
To ensure robust performance, the model was evaluated against a diverse set of benchmarks focusing on language understanding and generation.
#### Metrics
Soon after Fine-Tuning
### Results
Detailed results will be available upon completion of ongoing evaluations.
## Environmental Impact
The environmental impact of training such models is significant. Users and stakeholders are encouraged to consider sustainability in their deployment strategies.
- Carbon Emitted: Estimated upon request
Meta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved. | [
"# Model Card for Llama3-4x8B-TimeAgents\n\nThis model card serves as a detailed guide and reference for users interacting with the Llama3-TimeAgents-4x8B model.",
"## Model Details",
"### Model Description\n\nLlama3-TimeAgents-4x8B is an advanced machine learning model specifically developed for multi-agent systems focused on time series predictions. This model harnesses the capabilities of a state-of-the-art language model framework, tailored to assist in research environments where precise and predictive temporal data analysis is crucial. It combines the robustness of transformer architectures with specialized adaptations for handling the dynamics of multi-agent interactions and time-sensitive data, making it an ideal tool for pushing the boundaries of academic and applied research in time series forecasting. This innovative model is the culmination of rigorous research and development efforts aimed at addressing complex challenges in predictive analytics within multi-agent systems.\n\n- Model type: Transformer-based\n- Language(s) (NLP): Primarily English\n- License: LLama 3 \n- Merged from: Llama3 base model and variations\n-",
"### Disclaimer\nThis model is a research experiment and may generate incorrect or harmful content. The model's outputs should not be taken as factual or representative of the views of the model's creator or any other individual.\n\nThe model's creator is not responsible for any harm or damage caused by the model's outputs.",
"### Direct Use\n\nThe model is ready for direct integration into applications requiring natural language understanding and generation without further training or significant modifications.",
"### Out-of-Scope Use\n\nThis model is not intended for use in scenarios requiring highly sensitive or critical decision-making processes, where inaccuracies could lead to significant harm or legal issues.",
"## Bias, Risks, and Limitations\n\nThe model, like all AI language models, may inherit biases from the training data, and its outputs should be carefully reviewed in any sensitive application.",
"### Recommendations\n\nUsers should be aware of the potential for biased outputs and should implement appropriate checks and balances when using the model in production environments.",
"## How to Get Started with the Model\n\nTo get started with the Llama3-TimeAgents-4x8B, you can use the following Python code snippet, which utilizes the 'transformers' library:",
"## Evaluation",
"### Testing Data, Factors & Metrics\n\nTo ensure robust performance, the model was evaluated against a diverse set of benchmarks focusing on language understanding and generation.",
"#### Metrics\n\nSoon after Fine-Tuning",
"### Results\n\nDetailed results will be available upon completion of ongoing evaluations.",
"## Environmental Impact\n\nThe environmental impact of training such models is significant. Users and stakeholders are encouraged to consider sustainability in their deployment strategies.\n\n- Carbon Emitted: Estimated upon request\n\n\nMeta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved."
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #question-answering #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Llama3-4x8B-TimeAgents\n\nThis model card serves as a detailed guide and reference for users interacting with the Llama3-TimeAgents-4x8B model.",
"## Model Details",
"### Model Description\n\nLlama3-TimeAgents-4x8B is an advanced machine learning model specifically developed for multi-agent systems focused on time series predictions. This model harnesses the capabilities of a state-of-the-art language model framework, tailored to assist in research environments where precise and predictive temporal data analysis is crucial. It combines the robustness of transformer architectures with specialized adaptations for handling the dynamics of multi-agent interactions and time-sensitive data, making it an ideal tool for pushing the boundaries of academic and applied research in time series forecasting. This innovative model is the culmination of rigorous research and development efforts aimed at addressing complex challenges in predictive analytics within multi-agent systems.\n\n- Model type: Transformer-based\n- Language(s) (NLP): Primarily English\n- License: LLama 3 \n- Merged from: Llama3 base model and variations\n-",
"### Disclaimer\nThis model is a research experiment and may generate incorrect or harmful content. The model's outputs should not be taken as factual or representative of the views of the model's creator or any other individual.\n\nThe model's creator is not responsible for any harm or damage caused by the model's outputs.",
"### Direct Use\n\nThe model is ready for direct integration into applications requiring natural language understanding and generation without further training or significant modifications.",
"### Out-of-Scope Use\n\nThis model is not intended for use in scenarios requiring highly sensitive or critical decision-making processes, where inaccuracies could lead to significant harm or legal issues.",
"## Bias, Risks, and Limitations\n\nThe model, like all AI language models, may inherit biases from the training data, and its outputs should be carefully reviewed in any sensitive application.",
"### Recommendations\n\nUsers should be aware of the potential for biased outputs and should implement appropriate checks and balances when using the model in production environments.",
"## How to Get Started with the Model\n\nTo get started with the Llama3-TimeAgents-4x8B, you can use the following Python code snippet, which utilizes the 'transformers' library:",
"## Evaluation",
"### Testing Data, Factors & Metrics\n\nTo ensure robust performance, the model was evaluated against a diverse set of benchmarks focusing on language understanding and generation.",
"#### Metrics\n\nSoon after Fine-Tuning",
"### Results\n\nDetailed results will be available upon completion of ongoing evaluations.",
"## Environmental Impact\n\nThe environmental impact of training such models is significant. Users and stakeholders are encouraged to consider sustainability in their deployment strategies.\n\n- Carbon Emitted: Estimated upon request\n\n\nMeta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved."
] |
null | adapter-transformers |
# Adapter `BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_3` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/citation_intent_dataset](https://huggingface.co/datasets/BigTMiami/citation_intent_dataset/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_3", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["adapter-transformers", "roberta"], "datasets": ["BigTMiami/citation_intent_dataset"]} | BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_3 | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/citation_intent_dataset",
"region:us"
] | null | 2024-04-20T21:22:53+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/citation_intent_dataset #region-us
|
# Adapter 'BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_3' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_3' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/citation_intent_dataset #region-us \n",
"# Adapter 'BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_3' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
null | adapter-transformers |
# Adapter `BigTMiami/m_cite_par_bn_v_4_class_no_pre_2_adapter` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/citation_intent_dataset](https://huggingface.co/datasets/BigTMiami/citation_intent_dataset/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/m_cite_par_bn_v_4_class_no_pre_2_adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["adapter-transformers", "roberta"], "datasets": ["BigTMiami/citation_intent_dataset"]} | BigTMiami/m_cite_par_bn_v_4_class_no_pre_2_adapter | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/citation_intent_dataset",
"region:us"
] | null | 2024-04-20T21:23:08+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/citation_intent_dataset #region-us
|
# Adapter 'BigTMiami/m_cite_par_bn_v_4_class_no_pre_2_adapter' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/m_cite_par_bn_v_4_class_no_pre_2_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/citation_intent_dataset #region-us \n",
"# Adapter 'BigTMiami/m_cite_par_bn_v_4_class_no_pre_2_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
null | adapter-transformers |
# Adapter `BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_4` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/citation_intent_dataset](https://huggingface.co/datasets/BigTMiami/citation_intent_dataset/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_4", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["adapter-transformers", "roberta"], "datasets": ["BigTMiami/citation_intent_dataset"]} | BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_4 | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/citation_intent_dataset",
"region:us"
] | null | 2024-04-20T21:24:17+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/citation_intent_dataset #region-us
|
# Adapter 'BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_4' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_4' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/citation_intent_dataset #region-us \n",
"# Adapter 'BigTMiami/m_cite_par_bn_v_4_help_class_adp_S_4' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
null | adapter-transformers |
# Adapter `BigTMiami/m_cite_par_bn_v_4_class_no_pre_3_adapter` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/citation_intent_dataset](https://huggingface.co/datasets/BigTMiami/citation_intent_dataset/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/m_cite_par_bn_v_4_class_no_pre_3_adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["adapter-transformers", "roberta"], "datasets": ["BigTMiami/citation_intent_dataset"]} | BigTMiami/m_cite_par_bn_v_4_class_no_pre_3_adapter | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/citation_intent_dataset",
"region:us"
] | null | 2024-04-20T21:24:34+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/citation_intent_dataset #region-us
|
# Adapter 'BigTMiami/m_cite_par_bn_v_4_class_no_pre_3_adapter' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/m_cite_par_bn_v_4_class_no_pre_3_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/citation_intent_dataset #region-us \n",
"# Adapter 'BigTMiami/m_cite_par_bn_v_4_class_no_pre_3_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
unconditional-image-generation | diffusers |
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('yaochx/sd-class-butterflies-64')
image = pipeline().images[0]
image
```
| {"license": "mit", "tags": ["pytorch", "diffusers", "unconditional-image-generation", "diffusion-models-class"]} | yaochx/sd-class-butterflies-64 | null | [
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2024-04-20T21:24:47+00:00 | [] | [] | TAGS
#diffusers #safetensors #pytorch #unconditional-image-generation #diffusion-models-class #license-mit #diffusers-DDPMPipeline #region-us
|
# Model Card for Unit 1 of the Diffusion Models Class
This model is a diffusion model for unconditional image generation of cute .
## Usage
| [
"# Model Card for Unit 1 of the Diffusion Models Class \n\nThis model is a diffusion model for unconditional image generation of cute .",
"## Usage"
] | [
"TAGS\n#diffusers #safetensors #pytorch #unconditional-image-generation #diffusion-models-class #license-mit #diffusers-DDPMPipeline #region-us \n",
"# Model Card for Unit 1 of the Diffusion Models Class \n\nThis model is a diffusion model for unconditional image generation of cute .",
"## Usage"
] |
null | adapter-transformers |
# Adapter `BigTMiami/m_cite_par_bn_v_4_class_no_pre_4_adapter` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/citation_intent_dataset](https://huggingface.co/datasets/BigTMiami/citation_intent_dataset/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/m_cite_par_bn_v_4_class_no_pre_4_adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["adapter-transformers", "roberta"], "datasets": ["BigTMiami/citation_intent_dataset"]} | BigTMiami/m_cite_par_bn_v_4_class_no_pre_4_adapter | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/citation_intent_dataset",
"region:us"
] | null | 2024-04-20T21:25:59+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/citation_intent_dataset #region-us
|
# Adapter 'BigTMiami/m_cite_par_bn_v_4_class_no_pre_4_adapter' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/m_cite_par_bn_v_4_class_no_pre_4_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/citation_intent_dataset #region-us \n",
"# Adapter 'BigTMiami/m_cite_par_bn_v_4_class_no_pre_4_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/citation_intent_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
text-generation | transformers |
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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] | {"library_name": "transformers", "tags": []} | mohdumar/gpt2-medium-untied | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T21:27:27+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #gpt2 #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #gpt2 #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers |
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
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| {"library_name": "transformers", "tags": []} | Fighoture/Llama-2-7b-chat-shortgpt-15-percent | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T21:27:58+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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] |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | domenicrosati/lens-loss-minimality_lr_2e-5_attack_meta-llama_Llama-2-7b-chat-hf_1_num_layers_24_3e-5_1k | null | [
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"text-generation-inference",
"region:us"
] | null | 2024-04-20T21:28:18+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
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text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | cilantro9246/52ln0ry | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T21:28:40+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Direct Use",
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"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"## Training Details",
"### Training Data",
"### Training Procedure",
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"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"#### Factors",
"#### Metrics",
"### Results",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers |
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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. -->
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[More Information Needed] | {"library_name": "transformers", "tags": []} | tomaszki/stablelm-42 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T21:29:18+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
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"#### Testing Data",
"#### Factors",
"#### Metrics",
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"### Model Architecture and Objective",
"### Compute Infrastructure",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
reinforcement-learning | ml-agents |
# **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: stuvx/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy"]} | stuvx/ppo-Huggy | null | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | null | 2024-04-20T21:30:37+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us
|
# ppo Agent playing Huggy
This is a trained model of a ppo agent playing Huggy
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
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: URL
- A *longer tutorial* to understand how works ML-Agents:
URL
### Resume the training
### 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 URL
2. Step 1: Find your model_id: stuvx/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: stuvx/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n",
"# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: stuvx/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] |
text-classification | transformers |
<!-- 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. -->
# scotus
This model is a fine-tuned version of [YoungPanda/REBIRTHBERT](https://huggingface.co/YoungPanda/REBIRTHBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0950
- Accuracy: 0.7079
## 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: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- total_train_batch_size: 3
- total_eval_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "YoungPanda/REBIRTHBERT", "model-index": [{"name": "scotus", "results": []}]} | KolaGang/REBIRTHBERTCLASS | null | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:YoungPanda/REBIRTHBERT",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T21:31:18+00:00 | [] | [] | TAGS
#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-YoungPanda/REBIRTHBERT #autotrain_compatible #endpoints_compatible #region-us
|
# scotus
This model is a fine-tuned version of YoungPanda/REBIRTHBERT on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0950
- Accuracy: 0.7079
## 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: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- total_train_batch_size: 3
- total_eval_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| [
"# scotus\n\nThis model is a fine-tuned version of YoungPanda/REBIRTHBERT on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.0950\n- Accuracy: 0.7079",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 3\n- total_train_batch_size: 3\n- total_eval_batch_size: 24\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1.0\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.41.0.dev0\n- Pytorch 2.2.0+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1"
] | [
"TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-YoungPanda/REBIRTHBERT #autotrain_compatible #endpoints_compatible #region-us \n",
"# scotus\n\nThis model is a fine-tuned version of YoungPanda/REBIRTHBERT on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.0950\n- Accuracy: 0.7079",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 3\n- total_train_batch_size: 3\n- total_eval_batch_size: 24\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1.0\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.41.0.dev0\n- Pytorch 2.2.0+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1"
] |
text-generation | transformers |
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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
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## Evaluation
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### Results
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## 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]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | tomaszki/stablelm-42-a | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T21:32:13+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
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[optional]
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## Model Card Authors [optional]
## Model Card Contact
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text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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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).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | tomaszki/stablelm-42-b | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
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"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T21:35:22+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
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## Training Details
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## Evaluation
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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- Carbon Emitted:
## Technical Specifications [optional]
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### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- Relevant interpretability work for the model goes here -->
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## 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]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Aspik101/llama3333 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
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"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T21:37:45+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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## Uses
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Use the code below to get started with the model.
## Training Details
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### Training Procedure
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#### Training Hyperparameters
- Training regime:
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## Evaluation
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#### Testing Data
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#### Metrics
### Results
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
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## Model Card Authors [optional]
## Model Card Contact
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | null |
<!-- 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. -->
# tiny_sum_04-20-21-09-19
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.13.3
| {"license": "apache-2.0", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "model-index": [{"name": "tiny_sum_04-20-21-09-19", "results": []}]} | reeddg/tiny_sum_04-20-21-09-19 | null | [
"tensorboard",
"trl",
"sft",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"region:us"
] | null | 2024-04-20T21:38:08+00:00 | [] | [] | TAGS
#tensorboard #trl #sft #generated_from_trainer #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #license-apache-2.0 #region-us
|
# tiny_sum_04-20-21-09-19
This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.13.3
| [
"# tiny_sum_04-20-21-09-19\n\nThis model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 10",
"### Training results",
"### Framework versions\n\n- Transformers 4.31.0\n- Pytorch 2.2.2+cu121\n- Datasets 2.19.0\n- Tokenizers 0.13.3"
] | [
"TAGS\n#tensorboard #trl #sft #generated_from_trainer #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #license-apache-2.0 #region-us \n",
"# tiny_sum_04-20-21-09-19\n\nThis model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 10",
"### Training results",
"### Framework versions\n\n- Transformers 4.31.0\n- Pytorch 2.2.2+cu121\n- Datasets 2.19.0\n- Tokenizers 0.13.3"
] |
text-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the passthrough merge method.
### Models Merged
The following models were included in the merge:
* [Muhammad2003/Llama3-8B-OpenHermes-DPO](https://huggingface.co/Muhammad2003/Llama3-8B-OpenHermes-DPO)
* [IlyaGusev/saiga_llama3_8b](https://huggingface.co/IlyaGusev/saiga_llama3_8b)
* [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct)
* [abacusai/Llama-3-Smaug-8B](https://huggingface.co/abacusai/Llama-3-Smaug-8B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: IlyaGusev/saiga_llama3_8b
layer_range: [0, 32]
- sources:
- model: Muhammad2003/Llama3-8B-OpenHermes-DPO
layer_range: [24, 32]
- sources:
- model: abacusai/Llama-3-Smaug-8B
layer_range: [26, 32]
- sources:
- model: NousResearch/Meta-Llama-3-8B-Instruct
layer_range: [28, 32]
merge_method: passthrough
dtype: bfloat16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Muhammad2003/Llama3-8B-OpenHermes-DPO", "IlyaGusev/saiga_llama3_8b", "NousResearch/Meta-Llama-3-8B-Instruct", "abacusai/Llama-3-Smaug-8B"]} | ehristoforu/0001lp | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Muhammad2003/Llama3-8B-OpenHermes-DPO",
"base_model:IlyaGusev/saiga_llama3_8b",
"base_model:NousResearch/Meta-Llama-3-8B-Instruct",
"base_model:abacusai/Llama-3-Smaug-8B",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T21:40:26+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #base_model-Muhammad2003/Llama3-8B-OpenHermes-DPO #base_model-IlyaGusev/saiga_llama3_8b #base_model-NousResearch/Meta-Llama-3-8B-Instruct #base_model-abacusai/Llama-3-Smaug-8B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the passthrough merge method.
### Models Merged
The following models were included in the merge:
* Muhammad2003/Llama3-8B-OpenHermes-DPO
* IlyaGusev/saiga_llama3_8b
* NousResearch/Meta-Llama-3-8B-Instruct
* abacusai/Llama-3-Smaug-8B
### Configuration
The following YAML configuration was used to produce this model:
| [
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the passthrough merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Muhammad2003/Llama3-8B-OpenHermes-DPO\n* IlyaGusev/saiga_llama3_8b\n* NousResearch/Meta-Llama-3-8B-Instruct\n* abacusai/Llama-3-Smaug-8B",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #base_model-Muhammad2003/Llama3-8B-OpenHermes-DPO #base_model-IlyaGusev/saiga_llama3_8b #base_model-NousResearch/Meta-Llama-3-8B-Instruct #base_model-abacusai/Llama-3-Smaug-8B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the passthrough merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Muhammad2003/Llama3-8B-OpenHermes-DPO\n* IlyaGusev/saiga_llama3_8b\n* NousResearch/Meta-Llama-3-8B-Instruct\n* abacusai/Llama-3-Smaug-8B",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
text-generation | transformers |
# SeELLama (Semantic Extraction LLama)
Model is based on LLama2-7b and fine-tuned with the `DehydratedWater42/semantic_relations_extraction` dataset.
The purpose of this model is to extract semantic relations from text in a structured way.
#### Simplified Example:
- **Initial Text**: "While there is beautiful weather outside the building, from the window we can see a car. And what's the most annoying, pigeons love to sit on that car."
- **Entities**: ["pigeon", "car", "building"]
- **Relations between entities**: {"pigeon -> car": "pigeon sits on the car", "car -> building": "car is parked outside the building"}
**Note:** The text example above is **too short** for the actual model; please use **at least 500-token text** segments for extraction to avoid hallucinations.
### Other versions:
- **Get SeELLama in GGUF format:** [DehydratedWater42/SeELLama-GGUF](https://huggingface.co/DehydratedWater42/SeELLama-GGUF)
- **Get SeELLama as adapter:** [DehydratedWater42/SeELLama-qlora-adapter](https://huggingface.co/DehydratedWater42/SeELLama-qlora-adapter)
***
## How to use it:
### Template:
Use the **prompt template** provided below to extract relations from text. Replace `<<your_text_for_extraction>>` with your selected text, ideally between 500-1500 tokens,
with an **optimal range** of about **800-1000 tokens**. You can adjust the **temperature** between 0.3 and 1.0; a good starting point is **between 0.6 and 0.7**.
Temperatures below 0.3 may lead to never ending `section_description`. The higher the temperature, the more the model will fill in the gaps in the provided text.
It was **fine-tuned on scientific articles**, so it will supplement missing information with general knowledge.
Model was trained on 2560 context lenght where 1000-1500 tokens where used as input text.
```text
Below is an part of larger text. Your task is to extract information about entities and relations to the JSON format.
### Text Part to Extract From:
<<your_text_for_extraction>>
### Extracted Relations:
{
"section_description":
```
The `JSON` opening is not necessary but it improves stability. Remember to use double `{{` instead of singular '{' if you are using LangChain prompts with fstring formatting.
### Optimal settings:
For Transformer loader -> [text-generation-webui](https://github.com/oobabooga/text-generation-webui):
- load-in-4bit=True
- user_double_quant=True
- trust-remote-code=True
- compute_dtype=bfloat16
- quant_type=nf4
For `text-generation-webui` you must add `--trust-remote-code` flag to use the model
`./start_linux.sh --trust-remote-code`
This is a PerfModel created from 4bit qlora adapter fine-tuned with SFTTrainer with BitsAndBytes merged with quantized LLama2-7b.
Model was trained on 2560 context lenght where 1000-1500 tokens where used as input text.
Some results may require post processing to close out-of-context jsons.
You can also get unquantized version in `/SeELLama-unquantized-f16` folder in the repository.
### Starter code
#### Run generation with python
```python
from transformers import LlamaForCausalLM, LlamaTokenizerFast, BitsAndBytesConfig
model_name = "DehydratedWater42/SeELLama"
prompt = """Below is an part of larger text. Your task is to extract information about entities and relations to the JSON format.
### Text Part to Extract From:
Players in Survival, Adventure, or Spectator have access to the 2×2 crafting grid from their inventory screen. Small crafting recipes that are at most 2×2 can be made there. These include wooden planks, sticks, crafting tables, torches and some shapeless recipes. To craft items using a bigger grid to allow more crafting recipes, create a crafting table with 4 wooden planks, place it in the world, and press use while facing it. This brings up an interface with a larger, 3×3 crafting grid, which the player can use to craft any crafting recipe in the game. There is also a recipe book where the player can access all the crafting recipes.
Some recipes do not require their ingredients to be arranged in a specific way on the crafting grid. These are commonly known as shapeless recipes. For example, players may craft a fermented spider eye by placing its ingredients anywhere within the grid or like a flint and steel can be crafted in any orientation.
On the contrary, many recipes must have their ingredients placed in the correct relative positions on the crafting grid. These are commonly known as shaped recipes. Ingredients in shaped recipes can be ‘moved’ up, down, left, or right. They can also be flipped side-ways. For example, a 3×1 recipe, such as bread, can be made using the top, middle, or bottom row of the 3×3 grid, and a bow may be made with the strings placed on the left instead of on the right.
There are recipes that may not be moved or mirrored in this way. These are commonly known as fixed recipes. For example, dyes in banner recipes - only available in Bedrock Edition - must be specifically placed to achieve the desired pattern. Fixed recipes can be added by data packs, add-ons, or mods.
### Extracted Relations:
{
"section_description":
"""
model = LlamaForCausalLM.from_pretrained(model_name, trust_remote_code=True, device_map="auto")
tokenizer = LlamaTokenizerFast.from_pretrained(model_name, trust_remote_code=True, device_map="auto")
model.eval()
inputs = tokenizer(prompt, return_tensors="pt").to('cuda')
outputs = model.generate(
inputs['input_ids'],
max_length=2500,
temperature=0.7,
top_p=0.6,
top_k=0,
repetition_penalty=1,
no_repeat_ngram_size=0,
do_sample=True,
num_return_sequences=1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)
```
#### Extract generated json
```python
import json
json_start = """{
"section_description":
"""
to_parse = json_start + generated_text.split(json_start)[1]
print(json.loads(to_parse))
```
### Example:
Extracting information from Minecraft Wiki.
#### Initial template
```text
Below is an part of larger text. Your task is to extract information about entities and relations to the JSON format.
### Text Part to Extract From:
Players in Survival, Adventure, or Spectator have access to the 2×2 crafting grid from their inventory screen. Small crafting recipes that are at most 2×2 can be made there. These include wooden planks, sticks, crafting tables, torches and some shapeless recipes. To craft items using a bigger grid to allow more crafting recipes, create a crafting table with 4 wooden planks, place it in the world, and press use while facing it. This brings up an interface with a larger, 3×3 crafting grid, which the player can use to craft any crafting recipe in the game. There is also a recipe book where the player can access all the crafting recipes.
Some recipes do not require their ingredients to be arranged in a specific way on the crafting grid. These are commonly known as shapeless recipes. For example, players may craft a fermented spider eye by placing its ingredients anywhere within the grid or like a flint and steel can be crafted in any orientation.
On the contrary, many recipes must have their ingredients placed in the correct relative positions on the crafting grid. These are commonly known as shaped recipes. Ingredients in shaped recipes can be ‘moved’ up, down, left, or right. They can also be flipped side-ways. For example, a 3×1 recipe, such as bread, can be made using the top, middle, or bottom row of the 3×3 grid, and a bow may be made with the strings placed on the left instead of on the right.
There are recipes that may not be moved or mirrored in this way. These are commonly known as fixed recipes. For example, dyes in banner recipes - only available in Bedrock Edition - must be specifically placed to achieve the desired pattern. Fixed recipes can be added by data packs, add-ons, or mods.
### Extracted Relations:
{
"section_description":
```
#### Result (about game):
```text
Below is an part of larger text. Your task is to extract information about entities and relations to the JSON format.
### Text Part to Extract From:
Players in Survival, Adventure, or Spectator have access to the 2×2 crafting grid from their inventory screen. Small crafting recipes that are at most 2×2 can be made there. These include wooden planks, sticks, crafting tables, torches and some shapeless recipes. To craft items using a bigger grid to allow more crafting recipes, create a crafting table with 4 wooden planks, place it in the world, and press use while facing it. This brings up an interface with a larger, 3×3 crafting grid, which the player can use to craft any crafting recipe in the game. There is also a recipe book where the player can access all the crafting recipes.
Some recipes do not require their ingredients to be arranged in a specific way on the crafting grid. These are commonly known as shapeless recipes. For example, players may craft a fermented spider eye by placing its ingredients anywhere within the grid or like a flint and steel can be crafted in any orientation.
On the contrary, many recipes must have their ingredients placed in the correct relative positions on the crafting grid. These are commonly known as shaped recipes. Ingredients in shaped recipes can be ‘moved’ up, down, left, or right. They can also be flipped side-ways. For example, a 3×1 recipe, such as bread, can be made using the top, middle, or bottom row of the 3×3 grid, and a bow may be made with the strings placed on the left instead of on the right.
There are recipes that may not be moved or mirrored in this way. These are commonly known as fixed recipes. For example, dyes in banner recipes - only available in Bedrock Edition - must be specifically placed to achieve the desired pattern. Fixed recipes can be added by data packs, add-ons, or mods.
### Extracted Relations:
{
"section_description": "In Minecraft, players can craft items using the 2x2 crafting grid. There are some fixed recipes that require the player to place their ingredients in a specific position.",
"list_of_entities": [
"crafting recipes",
"player",
"fixed recipes",
"2x2 crafting grid",
"survival, adventure, or spectator players",
"wooden planks",
"torches",
"shaped recipes",
"small crafting recipes",
"shapeless recipes",
"wooden planks, sticks, crafting tables, torches",
"flint and steel"
],
"relations": [
{
"description": "Players have access to the 2x2 crafting grid from their inventory screen.",
"source_entities": [
"crafting recipes"
],
"target_entities": [
"player"
],
"strength": "strong"
},
{
"description": "Small crafting recipes that are at most 2x2 can be made using the 2x2 crafting grid.",
"source_entities": [
"crafting recipes"
],
"target_entities": [
"survival, adventure, or spectator players"
],
"strength": "strong"
},
{
"description": "To craft items using a bigger grid to allow more crafting recipes, create a crafting table with 4 wooden planks, place it in the world, and press use while facing it.",
"source_entities": [
"crafting recipes"
],
"target_entities": [
"wooden planks"
],
"strength": "strong"
},
{
"description": "To craft items using a bigger grid to allow more crafting recipes, create a crafting table with 4 wooden planks, place it in the world, and press use while facing it.",
"source_entities": [
"crafting recipes"
],
"target_entities": [
"crafting tables"
],
"strength": "strong"
},
{
"description": "Crafting tables allow players to make bigger crafting recipes that are not possible with the 2x2 crafting grid.",
"source_entities": [
"crafting recipes"
],
"target_entities": [
"torches",
"flint and steel",
"wooden planks"
],
"strength": "strong"
},
{
"description": "Crafting tables are used to craft items using the 2x2 crafting grid.",
"source_entities": [
"crafting recipes"
],
"target_entities": [
"wooden planks, sticks, crafting tables, torches"
],
"strength": "strong"
},
{
"description": "There are some fixed recipes that require the player to place their ingredients in a specific position.",
"source_entities": [
"crafting recipes"
],
"target_entities": [
"fixed recipes"
],
"strength": "strong"
},
{
"description": "The recipes do not require their ingredients to be arranged in a specific way on the crafting grid.",
"source_entities": [
"crafting recipes"
],
"target_entities": [
"shapeless recipes"
],
"strength": "strong"
},
{
"description": "Ingredients in shapeless recipes can be ‘moved’ up, down, left, or right.",
"source_entities": [
"crafting recipes"
],
"target_entities": [
"shaped recipes"
],
"strength": "strong"
},
{
"description": "Ingredients in shaped recipes can be flipped side-ways.",
"source_entities": [
"crafting recipes"
],
"target_entities": [
"fixed recipes"
],
"strength": "strong"
},
{
"description": "There are some fixed recipes that require the player to place their ingredients in a specific position.",
"source_entities": [
"crafting recipes"
],
"target_entities": [
"fixed recipes"
],
"strength": "strong"
},
{
"description": "The recipes can be added by data packs, add-ons, or mods.",
"source_entities": [
"crafting recipes"
],
"target_entities": [
"fixed recipes"
],
"strength": "strong"
}
]
}
```
### Smaller example (About crafting)
```text
Below is an part of larger text. Your task is to extract information about entities and relations to the JSON format.
### Text Part to Extract From:
Players in Survival, Adventure, or Spectator have access to the 2×2 crafting grid from their inventory screen. Small crafting recipes that are at most 2×2 can be made there. These include wooden planks, sticks, crafting tables, torches and some shapeless recipes. To craft items using a bigger grid to allow more crafting recipes, create a crafting table with 4 wooden planks, place it in the world, and press use while facing it. This brings up an interface with a larger, 3×3 crafting grid, which the player can use to craft any crafting recipe in the game. There is also a recipe book where the player can access all the crafting recipes.
### Extracted Relations:
{
"section_description": "Survival, Adventure, and Spectator players have access to the 2×2 crafting grid. However, to craft items using a bigger grid, you need to create a crafting table with wooden planks, place it in the world, and press use while facing it.",
"list_of_entities": [
"Survival",
"wooden planks",
"Spectator",
"Adventure",
"crafting grid"
],
"relations": [
{
"description": "Survival players can access the 2×2 crafting grid.",
"source_entities": [
"Survival"
],
"target_entities": [
"crafting grid"
],
"strength": "strong"
},
{
"description": "Adventure and Spectator players can also access the 2×2 crafting grid.",
"source_entities": [
"Adventure"
],
"target_entities": [
"crafting grid"
],
"strength": "strong"
},
{
"description": "To craft items using a bigger grid, you need to create a crafting table with wooden planks.",
"source_entities": [
"Spectator"
],
"target_entities": [
"crafting grid"
],
"strength": "strong"
}
]
}
```
### Dopamine example
```text
Below is an part of larger text. Your task is to extract information about entities and relations to the JSON format.
### Text Part to Extract From:
Dopamine is synthesized in a restricted set of cell types, mainly neurons and cells in the medulla of the adrenal glands.[23] The primary and minor metabolic pathways respectively are:
Primary: L-Phenylalanine → L-Tyrosine → L-DOPA → Dopamine[20][21]
Minor: L-Phenylalanine → L-Tyrosine → p-Tyramine → Dopamine[20][21][22]
Minor: L-Phenylalanine → m-Tyrosine → m-Tyramine → Dopamine[22][24][25]
The direct precursor of dopamine, L-DOPA, can be synthesized indirectly from the essential amino acid phenylalanine or directly from the non-essential amino acid tyrosine.[26] These amino acids are found in nearly every protein and so are readily available in food, with tyrosine being the most common. Although dopamine is also found in many types of food, it is incapable of crossing the blood–brain barrier that surrounds and protects the brain.[27] It must therefore be synthesized inside the brain to perform its neuronal activity.[27]
L-Phenylalanine is converted into L-tyrosine by the enzyme phenylalanine hydroxylase, with molecular oxygen (O2) and tetrahydrobiopterin as cofactors. L-Tyrosine is converted into L-DOPA by the enzyme tyrosine hydroxylase, with tetrahydrobiopterin, O2, and iron (Fe2+) as cofactors.[26] L-DOPA is converted into dopamine by the enzyme aromatic L-amino acid decarboxylase (also known as DOPA decarboxylase), with pyridoxal phosphate as the cofactor.[26]
Dopamine itself is used as precursor in the synthesis of the neurotransmitters norepinephrine and epinephrine.[26] Dopamine is converted into norepinephrine by the enzyme dopamine β-hydroxylase, with O2 and L-ascorbic acid as cofactors.[26] Norepinephrine is converted into epinephrine by the enzyme phenylethanolamine N-methyltransferase with S-adenosyl-L-methionine as the cofactor.[26]
Some of the cofactors also require their own synthesis.[26] Deficiency in any required amino acid or cofactor can impair the synthesis of dopamine, norepinephrine, and epinephrine.[26]
Degradation
Dopamine is broken down into inactive metabolites by a set of enzymes—monoamine oxidase (MAO), catechol-O-methyl transferase (COMT), and aldehyde dehydrogenase (ALDH), acting in sequence.[28] Both isoforms of monoamine oxidase, MAO-A and MAO-B, effectively metabolize dopamine.[26] Different breakdown pathways exist but the main end-product is homovanillic acid (HVA), which has no known biological activity.[28] From the bloodstream, homovanillic acid is filtered out by the kidneys and then excreted in the urine.[28] The two primary metabolic routes that convert dopamine into HVA are:[29]
Dopamine → DOPAL → DOPAC → HVA – catalyzed by MAO, ALDH, and COMT respectively
Dopamine → 3-Methoxytyramine → HVA – catalyzed by COMT and MAO+ALDH respectively
In clinical research on schizophrenia, measurements of homovanillic acid in plasma have been used to estimate levels of dopamine activity in the brain. A difficulty in this approach however, is separating the high level of plasma homovanillic acid contributed by the metabolism of norepinephrine.[30][31]
Although dopamine is normally broken down by an oxidoreductase enzyme, it is also susceptible to oxidation by direct reaction with oxygen, yielding quinones plus various free radicals as products.[32] The rate of oxidation can be increased by the presence of ferric iron or other factors. Quinones and free radicals produced by autoxidation of dopamine can poison cells, and there is evidence that this mechanism may contribute to the cell loss that occurs in Parkinson's disease and other conditions.[33]
### Extracted Relations:
{
"section_description": "Dopamine is a neurotransmitter that plays a crucial role in regulating movement, motivation, and reward processing. It is produced in several regions of the brain, including the substantia nigra, ventral tegmental area, and hypothalamus. Dopamine is involved in several physiological functions such as motor control, cognition, and emotional responses. Low levels of dopamine have been linked to several neurological disorders, including Parkinson's disease, schizophrenia, and addiction. Dopamine is also involved in the reward system, which is responsible for motivation and pleasure. The brain's reward system is activated by the release of dopamine in response to pleasurable experiences, such as eating, sex, and drugs. Dopamine is synthesized from the amino acid L-tyrosine, which is derived from dietary sources. The primary precursor of dopamine is L-DOPA, which is synthesized from L-tyrosine by the enzyme tyrosine hydroxylase. Dopamine is then converted into norepinephrine and epinephrine by the enzyme dopamine β-hydroxylase. Dopamine is broken down into inactive metabolites by a set of enzymes, including monoamine oxidase (MAO), catechol-O-methyl transferase (COMT), and aldehyde dehydrogenase (ALDH). The two primary metabolic routes that convert dopamine into HVA are: Dopamine → DOPAL → DOPAC → HVA - catalyzed by MAO, ALDH, and COMT respectively Dopamine → 3-Methoxytyramine → HVA - catalyzed by COMT and MAO+ALDH respectively In clinical research on schizophrenia, measurements of homovanillic acid in plasma have been used to estimate levels of dopamine activity in the brain. A difficulty in this approach however, is separating the high level of plasma homovanillic acid contributed by the metabolism of norepinephrine. Dopamine is also susceptible to oxidation by direct reaction with oxygen, yielding quinones plus various free radicals as products. The rate of oxidation can be increased by the presence of ferric iron or other factors. Quinones and free radicals produced by autoxidation of dopamine can poison cells, and there is evidence that this mechanism may contribute to the cell loss that occurs in Parkinson's disease and other conditions. ",
"list_of_entities": [
"motivation",
"Parkinson's disease",
"cognition",
"pleasure",
"dopamine",
"L-tyrosine",
"schizophrenia",
"emotional responses",
"L-DOPA",
"dopamine β-hydroxylase",
"dopamine β-hydroxylase",
"L-DOPA",
"dopamine",
"L-tyrosine",
"dopamine β-hydroxylase",
"L-DOPA",
"L-tyrosine",
"L-DOPA",
"dopamine",
"L-DOPA",
"dopamine"
],
"relations": [
{
"description": "Dopamine is synthesized from the amino acid L-tyrosine, which is derived from dietary sources.",
"source_entities": [
"dopamine"
],
"target_entities": [
"L-tyrosine"
]
},
{
"description": "The primary precursor of dopamine is L-DOPA, which is synthesized from L-tyrosine by the enzyme tyrosine hydroxylase.",
"source_entities": [
"L-DOPA"
],
"target_entities": [
"dopamine"
]
},
{
"description": "Dopamine is then converted into norepinephrine and epinephrine by the enzyme dopamine β-hydroxylase.",
"source_entities": [
"dopamine"
],
"target_entities": [
"dopamine β-hydroxylase"
]
},
{
"description": "Dopamine is broken down into inactive metabolites by a set of enzymes, including monoamine oxidase (MAO), catechol-O-methyl transferase (COMT), and aldehyde dehydrogenase (ALDH).",
"source_entities": [
"dopamine"
],
"target_entities": [
"monoamine oxidase (MAO)",
"catechol-O-methyl transferase (COMT)",
"aldehyde dehydrogenase (ALDH)"
]
},
{
"description": "The two primary metabolic routes that convert dopamine into HVA are: Dopamine → DOPAL → DOPAC → HVA - catalyzed by MAO, ALDH, and COMT respectively Dopamine → 3-Methoxytyramine → HVA - catalyzed by COMT and MAO+ALDH respectively",
"source_entities": [
"dopamine"
],
"target_entities": [
"HVA",
"MAO",
"ALDH",
"COMT"
]
},
{
"description": "In clinical research on schizophrenia, measurements of homovanillic acid in plasma have been used to estimate levels of dopamine activity in the brain.",
"source_entities": [
"dopamine"
],
"target_entities": [
"homovanillic acid"
]
},
{
"description": "A difficulty in this approach however, is separating the high level of plasma homovanillic acid contributed by the metabolism of norepinephrine.",
"source_entities": [
"homovanillic acid"
],
"target_entities": [
"norepinephrine"
]
},
{
"description": "Dopamine is also susceptible to oxidation by direct reaction with oxygen, yielding quinones plus various free radicals as products.",
"source_entities": [
"dopamine"
],
"target_entities": [
"oxidation"
]
},
{
"description": "The rate of oxidation can be increased by the presence of ferric iron or other factors.",
"source_entities": [
"dopamine"
],
"target_entities": [
"ferric iron"
]
},
{
"description": "Quinones and free radicals produced by autoxidation of dopamine can poison cells, and there is evidence that this mechanism may contribute to the cell loss that occurs in Parkinson's disease and other conditions.",
"source_entities": [
"dopamine"
],
"target_entities": [
"cell loss"
]
}
]
}
```
### Longer example (dirt block)
```text
Below is an part of larger text. Your task is to extract information about entities and relations to the JSON format.
### Text Part to Extract From:
Usage
Using bone meal on grass blocks causes short grass, tall grass,[Java Edition only] ferns,[Bedrock Edition only] and flowers to grow.
Passive mobs tend to wander toward grass blocks. They also wander toward light.
When a sheep eats a grass block, the block becomes dirt, and a sheared sheep regrows its wool. Baby sheep graze grass much more often than adults and mature 1 minute faster when grazing.
Tilling a grass block with a hoe converts it to a farmland block.
Dirt paths can be created by using any type of shovel on the side or top of a grass block with air above it. The shovel loses 1 durability for each dirt path block created.
Death
Grass dies and changes to dirt a random time (when a random tick lands on the block) after it has been covered by any opaque blocks. Transparent blocks can kill grass in a similar manner if they cause the light level above the grass block to be four or below (like water does), and the surrounding area is not otherwise sufficiently illuminated.
Spawning
In Java Edition, animals occasionally spawn on grass blocks that have light level 9 or brighter in the space directly above. This is quite rare and requires that there be few other animals nearby or in the spawn chunks. Most animals are instead created along with the terrain.
In Bedrock Edition, most animals require surface grass blocks (grass blocks with no solid block anywhere above them at the same x, z coordinate) to spawn. Animals regularly spawn in most Overworld biomes on surface grass blocks that have light level 7 or brighter in the space directly above.
Snowy grass block
A snowy grass block with snow on top of it.
Grass blocks turn into a snowy grass block if snow, powder snow or a snow block is placed on top of the block. Removing the snow layer turns the block into a normal grass block.
Composting
In Bedrock Edition, placing grass blocks into a composter has a 30% chance of raising the compost level by 1.
### Extracted Relations:
{
"section_description": "The article discusses the various ways in which grass blocks can be used in Minecraft, including as food, fuel, and protection. It also covers how grass blocks can be used to tame animals, and the dangers of being exposed to grass. The article concludes by highlighting the importance of knowing how to use and defend against grass blocks.",
"list_of_entities": [
"dirt",
"Minecraft",
"light level",
"animal",
"surface grass blocks",
"Bedrock Edition",
"Java Edition",
"dirt path",
"grass",
"snow",
"snowy grass block",
"opaque blocks",
"sheep",
"composter",
"transparent blocks"
],
"relations": [
{
"description": "Grass blocks are a renewable resource that can be used as a food item.",
"source_entities": [
"Minecraft",
"grass"
],
"target_entities": [
"animal",
"surface grass blocks"
],
"strength": "strong"
},
{
"description": "Grass blocks can be used to tame animals.",
"source_entities": [
"Minecraft",
"grass"
],
"target_entities": [
"animal",
"surface grass blocks"
],
"strength": "strong"
},
{
"description": "Exposure to grass blocks can cause damage to the player's health.",
"source_entities": [
"Minecraft"
],
"target_entities": [
"grass",
"player"
],
"strength": "strong"
},
{
"description": "Placing grass blocks in a composter has a 30% chance of raising the compost level by 1.",
"source_entities": [
"Minecraft",
"grass"
],
"target_entities": [
"composter",
"grass"
],
"strength": "strong"
},
{
"description": "Surface grass blocks are the only ones that can be used to spawn animals.",
"source_entities": [
"Minecraft"
],
"target_entities": [
"surface grass blocks",
"animal"
],
"strength": "strong"
},
{
"description": "Animals regularly spawn in most Overworld biomes on surface grass blocks that have light level 7 or brighter in the space directly above.",
"source_entities": [
"Minecraft"
],
"target_entities": [
"surface grass blocks",
"animal"
],
"strength": "strong"
},
{
"description": "If you walk into the grass without first getting rid of any animals or monsters that are there, they will attack you.",
"source_entities": [
"Minecraft"
],
"target_entities": [
"grass",
"player"
],
"strength": "moderate"
},
{
"description": "Placing grass blocks with snow on top of them turns them into snowy grass blocks.",
"source_entities": [
"Minecraft"
],
"target_entities": [
"snow",
"grass"
],
"strength": "strong"
},
{
"description": "Removing the snow layer turns the block into a normal grass block.",
"source_entities": [
"Minecraft"
],
"target_entities": [
"snowy grass block",
"grass"
],
"strength": "strong"
},
{
"description": "Dirt path blocks can be created by using any type of shovel on the side or top of a grass block with air above it.",
"source_entities": [
"Minecraft",
"grass"
],
"target_entities": [
"dirt path",
"shovel"
],
"strength": "strong"
},
{
"description": "The shovel loses 1 durability for each dirt path block created.",
"source_entities": [
"Minecraft",
"shovel"
],
"target_entities": [
"dirt path",
"shovel"
],
"strength": "moderate"
},
{
"description": "Death grass block dies and changes to dirt a random time (when a random tick lands on the block)",
"source_entities": [
"Minecraft"
],
"target_entities": [
"death grass block",
"dirt"
],
"strength": "strong"
},
{
"description": "Grass can be used to create dirt paths",
"source_entities": [
"Minecraft"
],
"target_entities": [
"grass",
"dirt path"
],
"strength": "strong"
}
]
}
``` | {"language": ["en"], "license": "llama2", "library_name": "transformers", "tags": ["math", "semantic", "extraction", "graph", "relations", "science", "synthetic"], "datasets": ["DehydratedWater42/semantic_relations_extraction"], "pipeline_tag": "text-generation", "inference": false} | DehydratedWater42/SeELLama | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"math",
"semantic",
"extraction",
"graph",
"relations",
"science",
"synthetic",
"en",
"dataset:DehydratedWater42/semantic_relations_extraction",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-20T21:41:41+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #math #semantic #extraction #graph #relations #science #synthetic #en #dataset-DehydratedWater42/semantic_relations_extraction #license-llama2 #autotrain_compatible #text-generation-inference #4-bit #region-us
|
# SeELLama (Semantic Extraction LLama)
Model is based on LLama2-7b and fine-tuned with the 'DehydratedWater42/semantic_relations_extraction' dataset.
The purpose of this model is to extract semantic relations from text in a structured way.
#### Simplified Example:
- Initial Text: "While there is beautiful weather outside the building, from the window we can see a car. And what's the most annoying, pigeons love to sit on that car."
- Entities: ["pigeon", "car", "building"]
- Relations between entities: {"pigeon -> car": "pigeon sits on the car", "car -> building": "car is parked outside the building"}
Note: The text example above is too short for the actual model; please use at least 500-token text segments for extraction to avoid hallucinations.
### Other versions:
- Get SeELLama in GGUF format: DehydratedWater42/SeELLama-GGUF
- Get SeELLama as adapter: DehydratedWater42/SeELLama-qlora-adapter
*
## How to use it:
### Template:
Use the prompt template provided below to extract relations from text. Replace '<<your_text_for_extraction>>' with your selected text, ideally between 500-1500 tokens,
with an optimal range of about 800-1000 tokens. You can adjust the temperature between 0.3 and 1.0; a good starting point is between 0.6 and 0.7.
Temperatures below 0.3 may lead to never ending 'section_description'. The higher the temperature, the more the model will fill in the gaps in the provided text.
It was fine-tuned on scientific articles, so it will supplement missing information with general knowledge.
Model was trained on 2560 context lenght where 1000-1500 tokens where used as input text.
The 'JSON' opening is not necessary but it improves stability. Remember to use double '{{' instead of singular '{' if you are using LangChain prompts with fstring formatting.
### Optimal settings:
For Transformer loader -> text-generation-webui:
- load-in-4bit=True
- user_double_quant=True
- trust-remote-code=True
- compute_dtype=bfloat16
- quant_type=nf4
For 'text-generation-webui' you must add '--trust-remote-code' flag to use the model
'./start_linux.sh --trust-remote-code'
This is a PerfModel created from 4bit qlora adapter fine-tuned with SFTTrainer with BitsAndBytes merged with quantized LLama2-7b.
Model was trained on 2560 context lenght where 1000-1500 tokens where used as input text.
Some results may require post processing to close out-of-context jsons.
You can also get unquantized version in '/SeELLama-unquantized-f16' folder in the repository.
### Starter code
#### Run generation with python
#### Extract generated json
### Example:
Extracting information from Minecraft Wiki.
#### Initial template
#### Result (about game):
### Smaller example (About crafting)
### Dopamine example
### Longer example (dirt block)
| [
"# SeELLama (Semantic Extraction LLama)\nModel is based on LLama2-7b and fine-tuned with the 'DehydratedWater42/semantic_relations_extraction' dataset.\n\nThe purpose of this model is to extract semantic relations from text in a structured way.",
"#### Simplified Example:\n- Initial Text: \"While there is beautiful weather outside the building, from the window we can see a car. And what's the most annoying, pigeons love to sit on that car.\"\n- Entities: [\"pigeon\", \"car\", \"building\"]\n- Relations between entities: {\"pigeon -> car\": \"pigeon sits on the car\", \"car -> building\": \"car is parked outside the building\"}\n\nNote: The text example above is too short for the actual model; please use at least 500-token text segments for extraction to avoid hallucinations.",
"### Other versions:\n- Get SeELLama in GGUF format: DehydratedWater42/SeELLama-GGUF\n- Get SeELLama as adapter: DehydratedWater42/SeELLama-qlora-adapter\n\n*",
"## How to use it:",
"### Template:\nUse the prompt template provided below to extract relations from text. Replace '<<your_text_for_extraction>>' with your selected text, ideally between 500-1500 tokens, \nwith an optimal range of about 800-1000 tokens. You can adjust the temperature between 0.3 and 1.0; a good starting point is between 0.6 and 0.7. \nTemperatures below 0.3 may lead to never ending 'section_description'. The higher the temperature, the more the model will fill in the gaps in the provided text. \nIt was fine-tuned on scientific articles, so it will supplement missing information with general knowledge. \nModel was trained on 2560 context lenght where 1000-1500 tokens where used as input text.\n\n\n\nThe 'JSON' opening is not necessary but it improves stability. Remember to use double '{{' instead of singular '{' if you are using LangChain prompts with fstring formatting.",
"### Optimal settings:\nFor Transformer loader -> text-generation-webui:\n- load-in-4bit=True\n- user_double_quant=True\n- trust-remote-code=True\n- compute_dtype=bfloat16\n- quant_type=nf4\n\nFor 'text-generation-webui' you must add '--trust-remote-code' flag to use the model\n\n'./start_linux.sh --trust-remote-code'\n\nThis is a PerfModel created from 4bit qlora adapter fine-tuned with SFTTrainer with BitsAndBytes merged with quantized LLama2-7b.\n\nModel was trained on 2560 context lenght where 1000-1500 tokens where used as input text.\n\nSome results may require post processing to close out-of-context jsons.\n\nYou can also get unquantized version in '/SeELLama-unquantized-f16' folder in the repository.",
"### Starter code",
"#### Run generation with python",
"#### Extract generated json",
"### Example:\nExtracting information from Minecraft Wiki.",
"#### Initial template",
"#### Result (about game):",
"### Smaller example (About crafting)",
"### Dopamine example",
"### Longer example (dirt block)"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #math #semantic #extraction #graph #relations #science #synthetic #en #dataset-DehydratedWater42/semantic_relations_extraction #license-llama2 #autotrain_compatible #text-generation-inference #4-bit #region-us \n",
"# SeELLama (Semantic Extraction LLama)\nModel is based on LLama2-7b and fine-tuned with the 'DehydratedWater42/semantic_relations_extraction' dataset.\n\nThe purpose of this model is to extract semantic relations from text in a structured way.",
"#### Simplified Example:\n- Initial Text: \"While there is beautiful weather outside the building, from the window we can see a car. And what's the most annoying, pigeons love to sit on that car.\"\n- Entities: [\"pigeon\", \"car\", \"building\"]\n- Relations between entities: {\"pigeon -> car\": \"pigeon sits on the car\", \"car -> building\": \"car is parked outside the building\"}\n\nNote: The text example above is too short for the actual model; please use at least 500-token text segments for extraction to avoid hallucinations.",
"### Other versions:\n- Get SeELLama in GGUF format: DehydratedWater42/SeELLama-GGUF\n- Get SeELLama as adapter: DehydratedWater42/SeELLama-qlora-adapter\n\n*",
"## How to use it:",
"### Template:\nUse the prompt template provided below to extract relations from text. Replace '<<your_text_for_extraction>>' with your selected text, ideally between 500-1500 tokens, \nwith an optimal range of about 800-1000 tokens. You can adjust the temperature between 0.3 and 1.0; a good starting point is between 0.6 and 0.7. \nTemperatures below 0.3 may lead to never ending 'section_description'. The higher the temperature, the more the model will fill in the gaps in the provided text. \nIt was fine-tuned on scientific articles, so it will supplement missing information with general knowledge. \nModel was trained on 2560 context lenght where 1000-1500 tokens where used as input text.\n\n\n\nThe 'JSON' opening is not necessary but it improves stability. Remember to use double '{{' instead of singular '{' if you are using LangChain prompts with fstring formatting.",
"### Optimal settings:\nFor Transformer loader -> text-generation-webui:\n- load-in-4bit=True\n- user_double_quant=True\n- trust-remote-code=True\n- compute_dtype=bfloat16\n- quant_type=nf4\n\nFor 'text-generation-webui' you must add '--trust-remote-code' flag to use the model\n\n'./start_linux.sh --trust-remote-code'\n\nThis is a PerfModel created from 4bit qlora adapter fine-tuned with SFTTrainer with BitsAndBytes merged with quantized LLama2-7b.\n\nModel was trained on 2560 context lenght where 1000-1500 tokens where used as input text.\n\nSome results may require post processing to close out-of-context jsons.\n\nYou can also get unquantized version in '/SeELLama-unquantized-f16' folder in the repository.",
"### Starter code",
"#### Run generation with python",
"#### Extract generated json",
"### Example:\nExtracting information from Minecraft Wiki.",
"#### Initial template",
"#### Result (about game):",
"### Smaller example (About crafting)",
"### Dopamine example",
"### Longer example (dirt block)"
] |
text-generation | transformers |
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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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
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### Training Procedure
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#### Preprocessing [optional]
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### Testing Data, Factors & Metrics
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## 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
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#### Hardware
[More Information Needed]
#### Software
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
| {"library_name": "transformers", "tags": []} | Fighoture/Llama-2-7b-chat-shortgpt-with-angular-15-percent | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T21:42:26+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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- Shared by [optional]:
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- Language(s) (NLP):
- License:
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## Uses
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### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
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"## Model Card Contact"
] |
text-generation | transformers |
<!-- 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. -->
# 0.001_ablation_declr_4iters_iter_3
This model is a fine-tuned version of [ShenaoZ/0.001_ablation_declr_4iters_iter_2](https://huggingface.co/ShenaoZ/0.001_ablation_declr_4iters_iter_2) on the updated and the original datasets.
## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZ/0.001_ablation_declr_4iters_iter_2", "model-index": [{"name": "0.001_ablation_declr_4iters_iter_3", "results": []}]} | ShenaoZ/0.001_ablation_declr_4iters_iter_3 | null | [
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"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T21:46:55+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #alignment-handbook #generated_from_trainer #trl #dpo #conversational #dataset-updated #dataset-original #base_model-ShenaoZ/0.001_ablation_declr_4iters_iter_2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# 0.001_ablation_declr_4iters_iter_3
This model is a fine-tuned version of ShenaoZ/0.001_ablation_declr_4iters_iter_2 on the updated and the original datasets.
## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| [
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
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] |
text-generation | transformers |
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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#### 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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## 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]
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- **Carbon Emitted:** [More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | OwOOwO/dumbo-stable11 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T21:53:23+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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"## Model Card Contact"
] |
text-generation | transformers | # ResplendentAI/Aura_Uncensored_l3_8B AWQ
- Model creator: [amazingvince](https://huggingface.co/amazingvince)
- Original model: [openhermes-7b-dpo](https://huggingface.co/amazingvince/openhermes-7b-dpo)
## Model Summary
is is the culmination of all my efforts for the Aura line. I have taken the original training data and applied it over Undi95's Unholy base model. This model can and will provide unsafe information and RP. I strongly recommend that you do not use this model if you are sensitive to unsafe output.
I have tested the model thoroughly and believe that it will please the majority of users. I hope that you enjoy this model.
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible"], "base_model": ["Undi95/Llama-3-Unholy-8B", "Undi95/Llama-3-Unholy-8B", "ResplendentAI/Aura_Llama3", "Undi95/Llama-3-Unholy-8B", "ResplendentAI/RP_Format_QuoteAsterisk_Llama3", "Undi95/Llama-3-Unholy-8B", "ResplendentAI/Luna_Llama3", "Undi95/Llama-3-Unholy-8B", "ResplendentAI/Theory_of_Mind_Llama3", "Undi95/Llama-3-Unholy-8B", "ResplendentAI/BlueMoon_Llama3"], "pipeline_tag": "text-generation", "inference": false, "quantized_by": "Suparious"} | solidrust/Aura_Uncensored_8B-AWQ | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible",
"conversational",
"en",
"base_model:Undi95/Llama-3-Unholy-8B",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T21:57:02+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #4-bit #AWQ #autotrain_compatible #endpoints_compatible #conversational #en #base_model-Undi95/Llama-3-Unholy-8B #license-apache-2.0 #text-generation-inference #region-us
| # ResplendentAI/Aura_Uncensored_l3_8B AWQ
- Model creator: amazingvince
- Original model: openhermes-7b-dpo
!image/png
## Model Summary
is is the culmination of all my efforts for the Aura line. I have taken the original training data and applied it over Undi95's Unholy base model. This model can and will provide unsafe information and RP. I strongly recommend that you do not use this model if you are sensitive to unsafe output.
I have tested the model thoroughly and believe that it will please the majority of users. I hope that you enjoy this model.
| [
"# ResplendentAI/Aura_Uncensored_l3_8B AWQ\n\n- Model creator: amazingvince\n- Original model: openhermes-7b-dpo\n\n!image/png",
"## Model Summary\n\nis is the culmination of all my efforts for the Aura line. I have taken the original training data and applied it over Undi95's Unholy base model. This model can and will provide unsafe information and RP. I strongly recommend that you do not use this model if you are sensitive to unsafe output. \n\nI have tested the model thoroughly and believe that it will please the majority of users. I hope that you enjoy this model."
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #4-bit #AWQ #autotrain_compatible #endpoints_compatible #conversational #en #base_model-Undi95/Llama-3-Unholy-8B #license-apache-2.0 #text-generation-inference #region-us \n",
"# ResplendentAI/Aura_Uncensored_l3_8B AWQ\n\n- Model creator: amazingvince\n- Original model: openhermes-7b-dpo\n\n!image/png",
"## Model Summary\n\nis is the culmination of all my efforts for the Aura line. I have taken the original training data and applied it over Undi95's Unholy base model. This model can and will provide unsafe information and RP. I strongly recommend that you do not use this model if you are sensitive to unsafe output. \n\nI have tested the model thoroughly and believe that it will please the majority of users. I hope that you enjoy this model."
] |
text-generation | transformers |
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
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- **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]
| {"library_name": "transformers", "tags": []} | Fighoture/Llama-2-7b-chat-shortgpt-with-angular-20-percent | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T22:01:12+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-classification | transformers |
<!-- 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. -->
# canine-rando
This model is a fine-tuned version of [google/canine-c](https://huggingface.co/google/canine-c) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0006
- Accuracy: 0.9999
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0236 | 1.0 | 8000 | 0.0150 | 0.9981 |
| 0.0016 | 2.0 | 16000 | 0.0006 | 0.9999 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.0.0+cu117
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/canine-c", "model-index": [{"name": "canine-rando", "results": []}]} | addykan/canine-rando | null | [
"transformers",
"safetensors",
"canine",
"text-classification",
"generated_from_trainer",
"base_model:google/canine-c",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T22:05:19+00:00 | [] | [] | TAGS
#transformers #safetensors #canine #text-classification #generated_from_trainer #base_model-google/canine-c #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| canine-rando
============
This model is a fine-tuned version of google/canine-c on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0006
* Accuracy: 0.9999
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.40.0
* Pytorch 2.0.0+cu117
* Datasets 2.19.0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.0.0+cu117\n* Datasets 2.19.0\n* Tokenizers 0.19.1"
] | [
"TAGS\n#transformers #safetensors #canine #text-classification #generated_from_trainer #base_model-google/canine-c #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.0.0+cu117\n* Datasets 2.19.0\n* Tokenizers 0.19.1"
] |
null | trl |
# Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-1.0.8-DPO
This model is a fine-tuned version of [Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged] on the dataset Weni/wenigpt-agent-dpo-1.0.0 with the DPO trainer. It is part of the WeniGPT project for [Weni](https://weni.ai/).
Description: Experiment on DPO with other hyperparameters and best SFT model of WeniGPT
It achieves the following results on the evaluation set:
{'eval_loss': 0.20048503577709198, 'eval_runtime': 9.9205, 'eval_samples_per_second': 2.822, 'eval_steps_per_second': 0.706, 'eval_rewards/chosen': 1.7620631456375122, 'eval_rewards/rejected': -0.7248173952102661, 'eval_rewards/accuracies': 1.0, 'eval_rewards/margins': 2.4868807792663574, 'eval_logps/rejected': -271.5132751464844, 'eval_logps/chosen': -102.97270202636719, 'eval_logits/rejected': -1.8957865238189697, 'eval_logits/chosen': -1.8012962341308594, 'epoch': 5.81}
## Intended uses & limitations
This model has not been trained to avoid specific intructions.
## Training procedure
Finetuning was done on the model Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged with the following prompt:
```
---------------------
System_prompt:
Agora você se chama {name}, você é {occupation} e seu objetivo é {chatbot_goal}. O adjetivo que mais define a sua personalidade é {adjective} e você se comporta da seguinte forma:
{instructions_formatted}
{context_statement}
Lista de requisitos:
- Responda de forma natural, mas nunca fale sobre um assunto fora do contexto.
- Nunca traga informações do seu próprio conhecimento.
- Repito é crucial que você responda usando apenas informações do contexto.
- Nunca mencione o contexto fornecido.
- Nunca mencione a pergunta fornecida.
- Gere a resposta mais útil possível para a pergunta usando informações do conexto acima.
- Nunca elabore sobre o porque e como você fez a tarefa, apenas responda.
---------------------
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- per_device_train_batch_size: 1
- per_device_eval_batch_size: 1
- gradient_accumulation_steps: 2
- num_gpus: 4
- total_train_batch_size: 8
- optimizer: AdamW
- lr_scheduler_type: cosine
- num_steps: 180
- quantization_type: bitsandbytes
- LoRA: ("\n - bits: 4\n - use_exllama: True\n - device_map: auto\n - use_cache: False\n - lora_r: 8\n - lora_alpha: 16\n - lora_dropout: 0.05\n - bias: none\n - target_modules: ['v_proj', 'q_proj']\n - task_type: CAUSAL_LM",)
### Training results
### Framework versions
- transformers==4.38.2
- datasets==2.18.0
- peft==0.10.0
- safetensors==0.4.2
- evaluate==0.4.1
- bitsandbytes==0.43
- huggingface_hub==0.22.2
- seqeval==1.2.2
- optimum==1.18.1
- auto-gptq==0.7.1
- gpustat==1.1.1
- deepspeed==0.14.0
- wandb==0.16.6
- trl==0.8.1
- accelerate==0.29.2
- coloredlogs==15.0.1
- traitlets==5.14.2
- autoawq@https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.4/autoawq-0.2.4+cu118-cp310-cp310-linux_x86_64.whl
### Hardware
- Cloud provided: runpod.io
| {"language": ["pt"], "license": "mit", "library_name": "trl", "tags": ["DPO", "WeniGPT"], "base_model": "Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged", "model-index": [{"name": "Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-1.0.8-DPO", "results": []}]} | Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-1.0.8-DPO | null | [
"trl",
"safetensors",
"DPO",
"WeniGPT",
"pt",
"base_model:Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged",
"license:mit",
"region:us"
] | null | 2024-04-20T22:05:41+00:00 | [] | [
"pt"
] | TAGS
#trl #safetensors #DPO #WeniGPT #pt #base_model-Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged #license-mit #region-us
|
# Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-1.0.8-DPO
This model is a fine-tuned version of [Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged] on the dataset Weni/wenigpt-agent-dpo-1.0.0 with the DPO trainer. It is part of the WeniGPT project for Weni.
Description: Experiment on DPO with other hyperparameters and best SFT model of WeniGPT
It achieves the following results on the evaluation set:
{'eval_loss': 0.20048503577709198, 'eval_runtime': 9.9205, 'eval_samples_per_second': 2.822, 'eval_steps_per_second': 0.706, 'eval_rewards/chosen': 1.7620631456375122, 'eval_rewards/rejected': -0.7248173952102661, 'eval_rewards/accuracies': 1.0, 'eval_rewards/margins': 2.4868807792663574, 'eval_logps/rejected': -271.5132751464844, 'eval_logps/chosen': -102.97270202636719, 'eval_logits/rejected': -1.8957865238189697, 'eval_logits/chosen': -1.8012962341308594, 'epoch': 5.81}
## Intended uses & limitations
This model has not been trained to avoid specific intructions.
## Training procedure
Finetuning was done on the model Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged with the following prompt:
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- per_device_train_batch_size: 1
- per_device_eval_batch_size: 1
- gradient_accumulation_steps: 2
- num_gpus: 4
- total_train_batch_size: 8
- optimizer: AdamW
- lr_scheduler_type: cosine
- num_steps: 180
- quantization_type: bitsandbytes
- LoRA: ("\n - bits: 4\n - use_exllama: True\n - device_map: auto\n - use_cache: False\n - lora_r: 8\n - lora_alpha: 16\n - lora_dropout: 0.05\n - bias: none\n - target_modules: ['v_proj', 'q_proj']\n - task_type: CAUSAL_LM",)
### Training results
### Framework versions
- transformers==4.38.2
- datasets==2.18.0
- peft==0.10.0
- safetensors==0.4.2
- evaluate==0.4.1
- bitsandbytes==0.43
- huggingface_hub==0.22.2
- seqeval==1.2.2
- optimum==1.18.1
- auto-gptq==0.7.1
- gpustat==1.1.1
- deepspeed==0.14.0
- wandb==0.16.6
- trl==0.8.1
- accelerate==0.29.2
- coloredlogs==15.0.1
- traitlets==5.14.2
- autoawq@URL
### Hardware
- Cloud provided: URL
| [
"# Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-1.0.8-DPO\n\nThis model is a fine-tuned version of [Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged] on the dataset Weni/wenigpt-agent-dpo-1.0.0 with the DPO trainer. It is part of the WeniGPT project for Weni.\nDescription: Experiment on DPO with other hyperparameters and best SFT model of WeniGPT\n\nIt achieves the following results on the evaluation set:\n{'eval_loss': 0.20048503577709198, 'eval_runtime': 9.9205, 'eval_samples_per_second': 2.822, 'eval_steps_per_second': 0.706, 'eval_rewards/chosen': 1.7620631456375122, 'eval_rewards/rejected': -0.7248173952102661, 'eval_rewards/accuracies': 1.0, 'eval_rewards/margins': 2.4868807792663574, 'eval_logps/rejected': -271.5132751464844, 'eval_logps/chosen': -102.97270202636719, 'eval_logits/rejected': -1.8957865238189697, 'eval_logits/chosen': -1.8012962341308594, 'epoch': 5.81}",
"## Intended uses & limitations\n\nThis model has not been trained to avoid specific intructions.",
"## Training procedure\n\nFinetuning was done on the model Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged with the following prompt:",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-06\n- per_device_train_batch_size: 1\n- per_device_eval_batch_size: 1\n- gradient_accumulation_steps: 2\n- num_gpus: 4\n- total_train_batch_size: 8\n- optimizer: AdamW\n- lr_scheduler_type: cosine\n- num_steps: 180\n- quantization_type: bitsandbytes\n- LoRA: (\"\\n - bits: 4\\n - use_exllama: True\\n - device_map: auto\\n - use_cache: False\\n - lora_r: 8\\n - lora_alpha: 16\\n - lora_dropout: 0.05\\n - bias: none\\n - target_modules: ['v_proj', 'q_proj']\\n - task_type: CAUSAL_LM\",)",
"### Training results",
"### Framework versions\n\n- transformers==4.38.2\n- datasets==2.18.0\n- peft==0.10.0\n- safetensors==0.4.2\n- evaluate==0.4.1\n- bitsandbytes==0.43\n- huggingface_hub==0.22.2\n- seqeval==1.2.2\n- optimum==1.18.1\n- auto-gptq==0.7.1\n- gpustat==1.1.1\n- deepspeed==0.14.0\n- wandb==0.16.6\n- trl==0.8.1\n- accelerate==0.29.2\n- coloredlogs==15.0.1\n- traitlets==5.14.2\n- autoawq@URL",
"### Hardware\n- Cloud provided: URL"
] | [
"TAGS\n#trl #safetensors #DPO #WeniGPT #pt #base_model-Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged #license-mit #region-us \n",
"# Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-1.0.8-DPO\n\nThis model is a fine-tuned version of [Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged] on the dataset Weni/wenigpt-agent-dpo-1.0.0 with the DPO trainer. It is part of the WeniGPT project for Weni.\nDescription: Experiment on DPO with other hyperparameters and best SFT model of WeniGPT\n\nIt achieves the following results on the evaluation set:\n{'eval_loss': 0.20048503577709198, 'eval_runtime': 9.9205, 'eval_samples_per_second': 2.822, 'eval_steps_per_second': 0.706, 'eval_rewards/chosen': 1.7620631456375122, 'eval_rewards/rejected': -0.7248173952102661, 'eval_rewards/accuracies': 1.0, 'eval_rewards/margins': 2.4868807792663574, 'eval_logps/rejected': -271.5132751464844, 'eval_logps/chosen': -102.97270202636719, 'eval_logits/rejected': -1.8957865238189697, 'eval_logits/chosen': -1.8012962341308594, 'epoch': 5.81}",
"## Intended uses & limitations\n\nThis model has not been trained to avoid specific intructions.",
"## Training procedure\n\nFinetuning was done on the model Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged with the following prompt:",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-06\n- per_device_train_batch_size: 1\n- per_device_eval_batch_size: 1\n- gradient_accumulation_steps: 2\n- num_gpus: 4\n- total_train_batch_size: 8\n- optimizer: AdamW\n- lr_scheduler_type: cosine\n- num_steps: 180\n- quantization_type: bitsandbytes\n- LoRA: (\"\\n - bits: 4\\n - use_exllama: True\\n - device_map: auto\\n - use_cache: False\\n - lora_r: 8\\n - lora_alpha: 16\\n - lora_dropout: 0.05\\n - bias: none\\n - target_modules: ['v_proj', 'q_proj']\\n - task_type: CAUSAL_LM\",)",
"### Training results",
"### Framework versions\n\n- transformers==4.38.2\n- datasets==2.18.0\n- peft==0.10.0\n- safetensors==0.4.2\n- evaluate==0.4.1\n- bitsandbytes==0.43\n- huggingface_hub==0.22.2\n- seqeval==1.2.2\n- optimum==1.18.1\n- auto-gptq==0.7.1\n- gpustat==1.1.1\n- deepspeed==0.14.0\n- wandb==0.16.6\n- trl==0.8.1\n- accelerate==0.29.2\n- coloredlogs==15.0.1\n- traitlets==5.14.2\n- autoawq@URL",
"### Hardware\n- Cloud provided: URL"
] |
null | transformers |
# Uploaded model
- **Developed by:** ogdanneedham
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit"} | ogdanneedham/mistral-gs-0.1-lora | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T22:06:55+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #text-generation-inference #unsloth #mistral #trl #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: ogdanneedham
- License: apache-2.0
- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: ogdanneedham\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #safetensors #text-generation-inference #unsloth #mistral #trl #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: ogdanneedham\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
text-classification | transformers |
<!-- 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. -->
# bart-large
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0027
- Accuracy: 0.7916
- Precision: 0.7858
- Recall: 0.7916
- Precision Macro: 0.7201
- Recall Macro: 0.7056
- Macro Fpr: 0.0201
- Weighted Fpr: 0.0195
- Weighted Specificity: 0.9714
- Macro Specificity: 0.9836
- Weighted Sensitivity: 0.7823
- Macro Sensitivity: 0.7056
- F1 Micro: 0.7823
- F1 Macro: 0.7080
- F1 Weighted: 0.7801
## 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: 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Precision Macro | Recall Macro | Macro Fpr | Weighted Fpr | Weighted Specificity | Macro Specificity | Weighted Sensitivity | Macro Sensitivity | F1 Micro | F1 Macro | F1 Weighted |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:|
| 1.1685 | 1.0 | 2569 | 1.2587 | 0.6847 | 0.6360 | 0.6847 | 0.4176 | 0.4720 | 0.0331 | 0.0318 | 0.9550 | 0.9760 | 0.6847 | 0.4720 | 0.6847 | 0.4296 | 0.6471 |
| 1.1965 | 2.0 | 5138 | 1.1623 | 0.6638 | 0.6943 | 0.6638 | 0.4564 | 0.4261 | 0.0342 | 0.0349 | 0.9654 | 0.9753 | 0.6638 | 0.4261 | 0.6638 | 0.3955 | 0.6468 |
| 1.189 | 3.0 | 7707 | 1.3574 | 0.7235 | 0.7220 | 0.7235 | 0.5413 | 0.5528 | 0.0271 | 0.0266 | 0.9628 | 0.9791 | 0.7235 | 0.5528 | 0.7235 | 0.5196 | 0.7031 |
| 1.0127 | 4.0 | 10276 | 1.4685 | 0.7668 | 0.7584 | 0.7668 | 0.6671 | 0.6202 | 0.0224 | 0.0213 | 0.9653 | 0.9821 | 0.7668 | 0.6202 | 0.7668 | 0.6233 | 0.7569 |
| 1.0205 | 5.0 | 12845 | 1.4232 | 0.7668 | 0.7711 | 0.7668 | 0.6765 | 0.6872 | 0.0215 | 0.0213 | 0.9737 | 0.9827 | 0.7668 | 0.6872 | 0.7668 | 0.6732 | 0.7643 |
| 0.7927 | 6.0 | 15414 | 1.5678 | 0.7428 | 0.7451 | 0.7428 | 0.6489 | 0.6333 | 0.0248 | 0.0241 | 0.9690 | 0.9808 | 0.7428 | 0.6333 | 0.7428 | 0.6108 | 0.7292 |
| 0.7701 | 7.0 | 17983 | 1.7337 | 0.7467 | 0.7600 | 0.7467 | 0.6863 | 0.6536 | 0.0240 | 0.0237 | 0.9680 | 0.9810 | 0.7467 | 0.6536 | 0.7467 | 0.6584 | 0.7399 |
| 0.584 | 8.0 | 20552 | 1.6188 | 0.7692 | 0.7766 | 0.7692 | 0.6979 | 0.7065 | 0.0214 | 0.0210 | 0.9706 | 0.9827 | 0.7692 | 0.7065 | 0.7692 | 0.6980 | 0.7683 |
| 0.5659 | 9.0 | 23121 | 1.6983 | 0.7599 | 0.7665 | 0.7599 | 0.7000 | 0.6804 | 0.0227 | 0.0221 | 0.9695 | 0.9820 | 0.7599 | 0.6804 | 0.7599 | 0.6728 | 0.7542 |
| 0.7021 | 10.0 | 25690 | 1.6445 | 0.7699 | 0.7656 | 0.7699 | 0.7144 | 0.6857 | 0.0223 | 0.0209 | 0.9608 | 0.9821 | 0.7699 | 0.6857 | 0.7699 | 0.6954 | 0.7634 |
| 0.6216 | 11.0 | 28259 | 1.6562 | 0.7676 | 0.7634 | 0.7676 | 0.6856 | 0.6776 | 0.0223 | 0.0212 | 0.9640 | 0.9821 | 0.7676 | 0.6776 | 0.7676 | 0.6786 | 0.7624 |
| 0.6408 | 12.0 | 30828 | 1.6682 | 0.7668 | 0.7629 | 0.7668 | 0.6706 | 0.6719 | 0.0223 | 0.0213 | 0.9666 | 0.9822 | 0.7668 | 0.6719 | 0.7668 | 0.6666 | 0.7608 |
| 0.523 | 13.0 | 33397 | 1.7727 | 0.7653 | 0.7674 | 0.7653 | 0.8238 | 0.6934 | 0.0226 | 0.0214 | 0.9659 | 0.9821 | 0.7653 | 0.6934 | 0.7653 | 0.7066 | 0.7534 |
| 0.3688 | 14.0 | 35966 | 1.8404 | 0.7792 | 0.7788 | 0.7792 | 0.7229 | 0.6921 | 0.0209 | 0.0198 | 0.9675 | 0.9831 | 0.7792 | 0.6921 | 0.7792 | 0.6960 | 0.7731 |
| 0.2394 | 15.0 | 38535 | 1.7885 | 0.7816 | 0.7809 | 0.7816 | 0.7441 | 0.7115 | 0.0210 | 0.0196 | 0.9628 | 0.9830 | 0.7816 | 0.7115 | 0.7816 | 0.7230 | 0.7765 |
| 0.2734 | 16.0 | 41104 | 1.8944 | 0.7777 | 0.7870 | 0.7777 | 0.7539 | 0.7265 | 0.0203 | 0.0200 | 0.9724 | 0.9833 | 0.7777 | 0.7265 | 0.7777 | 0.7295 | 0.7777 |
| 0.4319 | 17.0 | 43673 | 1.7744 | 0.7885 | 0.7847 | 0.7885 | 0.7247 | 0.7320 | 0.0195 | 0.0188 | 0.9718 | 0.9840 | 0.7885 | 0.7320 | 0.7885 | 0.7269 | 0.7855 |
| 0.2347 | 18.0 | 46242 | 2.0036 | 0.7413 | 0.7352 | 0.7413 | 0.6934 | 0.6799 | 0.0255 | 0.0243 | 0.9597 | 0.9801 | 0.7413 | 0.6799 | 0.7413 | 0.6825 | 0.7354 |
| 0.1882 | 19.0 | 48811 | 1.9298 | 0.7816 | 0.7804 | 0.7816 | 0.7243 | 0.7262 | 0.0202 | 0.0196 | 0.9708 | 0.9835 | 0.7816 | 0.7262 | 0.7816 | 0.7225 | 0.7792 |
| 0.1799 | 20.0 | 51380 | 1.9688 | 0.7792 | 0.7892 | 0.7792 | 0.7312 | 0.7343 | 0.0205 | 0.0198 | 0.9714 | 0.9834 | 0.7792 | 0.7343 | 0.7792 | 0.7242 | 0.7779 |
| 0.1366 | 21.0 | 53949 | 1.9910 | 0.7847 | 0.7846 | 0.7847 | 0.7148 | 0.7455 | 0.0198 | 0.0192 | 0.9730 | 0.9838 | 0.7847 | 0.7455 | 0.7847 | 0.7265 | 0.7833 |
| 0.1793 | 22.0 | 56518 | 2.2548 | 0.7630 | 0.7648 | 0.7630 | 0.7150 | 0.7273 | 0.0230 | 0.0217 | 0.9633 | 0.9818 | 0.7630 | 0.7273 | 0.7630 | 0.7150 | 0.7582 |
| 0.1749 | 23.0 | 59087 | 2.1109 | 0.7816 | 0.7768 | 0.7816 | 0.7466 | 0.7230 | 0.0205 | 0.0196 | 0.9690 | 0.9834 | 0.7816 | 0.7230 | 0.7816 | 0.7289 | 0.7774 |
| 0.1154 | 24.0 | 61656 | 2.0637 | 0.7878 | 0.7837 | 0.7878 | 0.7590 | 0.7269 | 0.0196 | 0.0189 | 0.9718 | 0.9840 | 0.7878 | 0.7269 | 0.7878 | 0.7331 | 0.7828 |
| 0.1447 | 25.0 | 64225 | 2.0027 | 0.7916 | 0.7858 | 0.7916 | 0.7750 | 0.7299 | 0.0194 | 0.0185 | 0.9697 | 0.9841 | 0.7916 | 0.7299 | 0.7916 | 0.7408 | 0.7861 |
| 0.0806 | 26.0 | 66794 | 2.0777 | 0.7885 | 0.7831 | 0.7885 | 0.7162 | 0.7134 | 0.0196 | 0.0188 | 0.9715 | 0.9840 | 0.7885 | 0.7134 | 0.7885 | 0.7118 | 0.7840 |
| 0.0407 | 27.0 | 69363 | 2.1754 | 0.7885 | 0.7863 | 0.7885 | 0.7192 | 0.7080 | 0.0194 | 0.0188 | 0.9725 | 0.9841 | 0.7885 | 0.7080 | 0.7885 | 0.7105 | 0.7866 |
| 0.0701 | 28.0 | 71932 | 2.1578 | 0.7823 | 0.7817 | 0.7823 | 0.7130 | 0.7097 | 0.0201 | 0.0195 | 0.9714 | 0.9836 | 0.7823 | 0.7097 | 0.7823 | 0.7066 | 0.7810 |
| 0.1034 | 29.0 | 74501 | 2.2132 | 0.7800 | 0.7789 | 0.7800 | 0.7163 | 0.7044 | 0.0203 | 0.0197 | 0.9713 | 0.9834 | 0.7800 | 0.7044 | 0.7800 | 0.7064 | 0.7785 |
| 0.0388 | 30.0 | 77070 | 2.1833 | 0.7823 | 0.7806 | 0.7823 | 0.7201 | 0.7056 | 0.0201 | 0.0195 | 0.9714 | 0.9836 | 0.7823 | 0.7056 | 0.7823 | 0.7080 | 0.7801 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.19.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall"], "base_model": "facebook/bart-large", "model-index": [{"name": "bart-large", "results": []}]} | xshubhamx/bart-large | null | [
"transformers",
"tensorboard",
"safetensors",
"bart",
"text-classification",
"generated_from_trainer",
"base_model:facebook/bart-large",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T22:07:04+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bart #text-classification #generated_from_trainer #base_model-facebook/bart-large #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-large
==========
This model is a fine-tuned version of facebook/bart-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0027
* Accuracy: 0.7916
* Precision: 0.7858
* Recall: 0.7916
* Precision Macro: 0.7201
* Recall Macro: 0.7056
* Macro Fpr: 0.0201
* Weighted Fpr: 0.0195
* Weighted Specificity: 0.9714
* Macro Specificity: 0.9836
* Weighted Sensitivity: 0.7823
* Macro Sensitivity: 0.7056
* F1 Micro: 0.7823
* F1 Macro: 0.7080
* F1 Weighted: 0.7801
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: 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: 30
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.19.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #bart #text-classification #generated_from_trainer #base_model-facebook/bart-large #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.1"
] |
text-generation | null | # 4-bit Quantized Llama 3 Model For Chat Bots
## Description
This repository hosts the 4-bit quantized version of the Llama 3 model. Optimized for reduced memory usage and faster inference, this model is suitable for deployment in environments where computational resources are limited.
## Model Details
- **Model Type**: Transformer-based language model.
- **Quantization**: 4-bit precision.
- **Advantages**:
- **Memory Efficiency**: Reduces memory usage significantly, allowing deployment on devices with limited RAM.
- **Inference Speed**: Accelerates inference times, depending on the hardware's ability to process low-bit computations.
## How to Use
To utilize this model efficiently, follow the steps below:
### Loading the Quantized Model
Load the model with specific parameters to ensure it utilizes 4-bit precision:
```python
from transformers import AutoModelForCausalLM
model_4bit = AutoModelForCausalLM.from_pretrained("SweatyCrayfish/Llama-3-8B-Instruct-Quantized", device_map="auto", load_in_4bit=True)
```
## Adjusting Precision of Components
Adjust the precision of other components, which are by default converted to torch.float16:
```python
import torch
from transformers import AutoModelForCausalLM
model_4bit = AutoModelForCausalLM.from_pretrained("SweatyCrayfish/Llama-3-8B-Instruct-Quantized", load_in_4bit=True, torch_dtype=torch.float32)
print(model_4bit.model.decoder.layers[-1].final_layer_norm.weight.dtype)
```
## Citation
Original repository and citations:
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
} | {"language": ["en"], "license": "other", "tags": ["facebook", "meta", "pytorch", "llama", "llama-3", "qlora", "quantization", "4-bit", "lora"], "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE", "extra_gated_prompt": "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.\n\"Documentation\" means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\"Licensee\" or \"you\" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity\u2019s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama 3\" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means, collectively, Meta\u2019s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).\n \n1. License Rights and Redistribution.\na. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta\u2019s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials.\nb. Redistribution and Use.\ni. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display \u201cBuilt with Meta Llama 3\u201d on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include \u201cLlama 3\u201d at the beginning of any such AI model name.\nii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you.\niii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a \u201cNotice\u201d text file distributed as a part of such copies: \u201cMeta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright \u00a9 Meta Platforms, Inc. All Rights Reserved.\u201d\niv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3/use-policy), which is hereby incorporated by reference into this Agreement.\nv. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Meta Llama 3 or derivative works thereof).\n2. Additional Commercial Terms. If, on the Meta Llama 3 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee\u2019s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN \u201cAS IS\u201d BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.\n5. Intellectual Property.\na. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use \u201cLlama 3\u201d (the \u201cMark\u201d) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use of the Mark will inure to the benefit of Meta.\nb. Subject to Meta\u2019s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications.\nc. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Meta Llama 3 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials.\n6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.\n7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.\n### Meta Llama 3 Acceptable Use Policy\nMeta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (\u201cPolicy\u201d). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)\n#### Prohibited Uses\nWe want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or others\u2019 rights, including to:\n 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\n 1. Violence or terrorism\n 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\n 3. Human trafficking, exploitation, and sexual violence\n 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\n 5. Sexual solicitation\n 6. Any other criminal activity\n 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\n 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices\n 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\n 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials\n 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system\n2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following:\n 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\n 2. Guns and illegal weapons (including weapon development)\n 3. Illegal drugs and regulated/controlled substances\n 4. Operation of critical infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm or harm to others, including suicide, cutting, and eating disorders\n 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following:\n 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n 3. Generating, promoting, or further distributing spam\n 4. Impersonating another individual without consent, authorization, or legal right\n 5. Representing that the use of Meta Llama 3 or outputs are human-generated\n 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement\n4. Fail to appropriately disclose to end users any known dangers of your AI system\nPlease report any violation of this Policy, software \u201cbug,\u201d or other problems that could lead to a violation of this Policy through one of the following means:\n * Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)\n * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Date of birth": "date_picker", "Country": "country", "Affiliation": "text", "geo": "ip_location", "By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy": "checkbox"}, "extra_gated_description": "The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).", "extra_gated_button_content": "Submit"} | SweatyCrayfish/Llama-3-8B-Instruct-Quantized | null | [
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"qlora",
"quantization",
"4-bit",
"lora",
"text-generation",
"en",
"license:other",
"region:us"
] | null | 2024-04-20T22:09:26+00:00 | [] | [
"en"
] | TAGS
#facebook #meta #pytorch #llama #llama-3 #qlora #quantization #4-bit #lora #text-generation #en #license-other #region-us
| # 4-bit Quantized Llama 3 Model For Chat Bots
## Description
This repository hosts the 4-bit quantized version of the Llama 3 model. Optimized for reduced memory usage and faster inference, this model is suitable for deployment in environments where computational resources are limited.
## Model Details
- Model Type: Transformer-based language model.
- Quantization: 4-bit precision.
- Advantages:
- Memory Efficiency: Reduces memory usage significantly, allowing deployment on devices with limited RAM.
- Inference Speed: Accelerates inference times, depending on the hardware's ability to process low-bit computations.
## How to Use
To utilize this model efficiently, follow the steps below:
### Loading the Quantized Model
Load the model with specific parameters to ensure it utilizes 4-bit precision:
## Adjusting Precision of Components
Adjust the precision of other components, which are by default converted to torch.float16:
Original repository and citations:
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {URL
} | [
"# 4-bit Quantized Llama 3 Model For Chat Bots",
"## Description\nThis repository hosts the 4-bit quantized version of the Llama 3 model. Optimized for reduced memory usage and faster inference, this model is suitable for deployment in environments where computational resources are limited.",
"## Model Details\n- Model Type: Transformer-based language model.\n- Quantization: 4-bit precision.\n- Advantages:\n - Memory Efficiency: Reduces memory usage significantly, allowing deployment on devices with limited RAM.\n - Inference Speed: Accelerates inference times, depending on the hardware's ability to process low-bit computations.",
"## How to Use\nTo utilize this model efficiently, follow the steps below:",
"### Loading the Quantized Model\nLoad the model with specific parameters to ensure it utilizes 4-bit precision:",
"## Adjusting Precision of Components\nAdjust the precision of other components, which are by default converted to torch.float16:\n\nOriginal repository and citations:\n@article{llama3modelcard,\n title={Llama 3 Model Card},\n author={AI@Meta},\n year={2024},\n url = {URL\n}"
] | [
"TAGS\n#facebook #meta #pytorch #llama #llama-3 #qlora #quantization #4-bit #lora #text-generation #en #license-other #region-us \n",
"# 4-bit Quantized Llama 3 Model For Chat Bots",
"## Description\nThis repository hosts the 4-bit quantized version of the Llama 3 model. Optimized for reduced memory usage and faster inference, this model is suitable for deployment in environments where computational resources are limited.",
"## Model Details\n- Model Type: Transformer-based language model.\n- Quantization: 4-bit precision.\n- Advantages:\n - Memory Efficiency: Reduces memory usage significantly, allowing deployment on devices with limited RAM.\n - Inference Speed: Accelerates inference times, depending on the hardware's ability to process low-bit computations.",
"## How to Use\nTo utilize this model efficiently, follow the steps below:",
"### Loading the Quantized Model\nLoad the model with specific parameters to ensure it utilizes 4-bit precision:",
"## Adjusting Precision of Components\nAdjust the precision of other components, which are by default converted to torch.float16:\n\nOriginal repository and citations:\n@article{llama3modelcard,\n title={Llama 3 Model Card},\n author={AI@Meta},\n year={2024},\n url = {URL\n}"
] |
video-classification | transformers |
<!-- 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. -->
# videomae-base-finetuned-ucf101-subset
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2238
- Accuracy: 0.9143
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7608 | 0.25 | 75 | 1.4279 | 0.5857 |
| 0.4775 | 1.25 | 150 | 0.6071 | 0.8 |
| 0.399 | 2.25 | 225 | 0.3544 | 0.8429 |
| 0.137 | 3.25 | 300 | 0.2238 | 0.9143 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "MCG-NJU/videomae-base", "model-index": [{"name": "videomae-base-finetuned-ucf101-subset", "results": []}]} | RachelAman/videomae-base-finetuned-ucf101-subset | null | [
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T22:09:31+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #videomae #video-classification #generated_from_trainer #base_model-MCG-NJU/videomae-base #license-cc-by-nc-4.0 #endpoints_compatible #region-us
| videomae-base-finetuned-ucf101-subset
=====================================
This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2238
* Accuracy: 0.9143
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* training\_steps: 300
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 2.2.2+cpu
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 300",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.2.2+cpu\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #videomae #video-classification #generated_from_trainer #base_model-MCG-NJU/videomae-base #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 300",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.2.2+cpu\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
null | transformers |
# Uploaded model
- **Developed by:** Saiikish
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/tinyllama-bnb-4bit"} | Saiikish/Saiikish | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/tinyllama-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T22:12:44+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #base_model-unsloth/tinyllama-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: Saiikish
- License: apache-2.0
- Finetuned from model : unsloth/tinyllama-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: Saiikish\n- License: apache-2.0\n- Finetuned from model : unsloth/tinyllama-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #base_model-unsloth/tinyllama-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: Saiikish\n- License: apache-2.0\n- Finetuned from model : unsloth/tinyllama-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
text-generation | transformers |
# ehristoforu/c4ai-command-r-plus-Q2_K-GGUF
This model was converted to GGUF format from [`CohereForAI/c4ai-command-r-plus`](https://huggingface.co/CohereForAI/c4ai-command-r-plus) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/CohereForAI/c4ai-command-r-plus) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo ehristoforu/c4ai-command-r-plus-Q2_K-GGUF --model c4ai-command-r-plus.Q2_K.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo ehristoforu/c4ai-command-r-plus-Q2_K-GGUF --model c4ai-command-r-plus.Q2_K.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m c4ai-command-r-plus.Q2_K.gguf -n 128
```
# Model Card for C4AI Command R+
🚨 **This model is non-quantized version of C4AI Command R+. You can find the quantized version of C4AI Command R+ using bitsandbytes [here](https://huggingface.co/CohereForAI/c4ai-command-r-plus-4bit)**.
## Model Summary
C4AI Command R+ is an open weights research release of a 104B billion parameter model with highly advanced capabilities, this includes Retrieval Augmented Generation (RAG) and tool use to automate sophisticated tasks. The tool use in this model generation enables multi-step tool use which allows the model to combine multiple tools over multiple steps to accomplish difficult tasks. C4AI Command R+ is a multilingual model evaluated in 10 languages for performance: English, French, Spanish, Italian, German, Brazilian Portuguese, Japanese, Korean, Arabic, and Simplified Chinese. Command R+ is optimized for a variety of use cases including reasoning, summarization, and question answering.
C4AI Command R+ is part of a family of open weight releases from Cohere For AI and Cohere. Our smaller companion model is [C4AI Command R](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
Developed by: [Cohere](https://cohere.com/) and [Cohere For AI](https://cohere.for.ai)
- Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/)
- License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy)
- Model: c4ai-command-r-plus
- Model Size: 104 billion parameters
- Context length: 128K
**Try C4AI Command R+**
You can try out C4AI Command R+ before downloading the weights in our hosted [Hugging Face Space](https://huggingface.co/spaces/CohereForAI/c4ai-command-r-plus).
**Usage**
Please install `transformers` from the source repository that includes the necessary changes for this model.
```python
# pip install 'git+https://github.com/huggingface/transformers.git'
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "CohereForAI/c4ai-command-r-plus"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
# Format message with the command-r-plus chat template
messages = [{"role": "user", "content": "Hello, how are you?"}]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
gen_tokens = model.generate(
input_ids,
max_new_tokens=100,
do_sample=True,
temperature=0.3,
)
gen_text = tokenizer.decode(gen_tokens[0])
print(gen_text)
```
**Quantized model through bitsandbytes, 8-bit precision**
```python
# pip install 'git+https://github.com/huggingface/transformers.git' bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
bnb_config = BitsAndBytesConfig(load_in_8bit=True)
model_id = "CohereForAI/c4ai-command-r-plus"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config)
# Format message with the command-r-plus chat template
messages = [{"role": "user", "content": "Hello, how are you?"}]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
gen_tokens = model.generate(
input_ids,
max_new_tokens=100,
do_sample=True,
temperature=0.3,
)
gen_text = tokenizer.decode(gen_tokens[0])
print(gen_text)
```
**Quantized model through bitsandbytes, 4-bit precision**
This model is non-quantized version of C4AI Command R+. You can find the quantized version of C4AI Command R+ using bitsandbytes [here](https://huggingface.co/CohereForAI/c4ai-command-r-plus-4bit).
## Model Details
**Input**: Models input text only.
**Output**: Models generate text only.
**Model Architecture**: This is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model uses supervised fine-tuning (SFT) and preference training to align model behavior to human preferences for helpfulness and safety.
**Languages covered**: The model is optimized to perform well in the following languages: English, French, Spanish, Italian, German, Brazilian Portuguese, Japanese, Korean, Simplified Chinese, and Arabic.
Pre-training data additionally included the following 13 languages: Russian, Polish, Turkish, Vietnamese, Dutch, Czech, Indonesian, Ukrainian, Romanian, Greek, Hindi, Hebrew, Persian.
**Context length**: Command R+ supports a context length of 128K.
## Evaluations
Command R+ has been submitted to the [Open LLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). We include the results below, along with a direct comparison to the strongest state-of-art open weights models currently available on Hugging Face. We note that these results are only useful to compare when evaluations are implemented for all models in a [standardized way](https://github.com/EleutherAI/lm-evaluation-harness) using publically available code, and hence shouldn't be used for comparison outside of models submitted to the leaderboard or compared to self-reported numbers which can't be replicated in the same way.
| Model | Average | Arc (Challenge) | Hella Swag | MMLU | Truthful QA | Winogrande | GSM8k |
|:--------------------------------|----------:|------------------:|-------------:|-------:|--------------:|-------------:|--------:|
| **CohereForAI/c4ai-command-r-plus** | 74.6 | 70.99 | 88.6 | 75.7 | 56.3 | 85.4 | 70.7 |
| [DBRX Instruct](https://huggingface.co/databricks/dbrx-instruct) | 74.5 | 68.9 | 89 | 73.7 | 66.9 | 81.8 | 66.9 |
| [Mixtral 8x7B-Instruct](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) | 72.7 | 70.1 | 87.6 | 71.4 | 65 | 81.1 | 61.1 |
| [Mixtral 8x7B Chat](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) | 72.6 | 70.2 | 87.6 | 71.2 | 64.6 | 81.4 | 60.7 |
| [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01) | 68.5 | 65.5 | 87 | 68.2 | 52.3 | 81.5 | 56.6 |
| [Llama 2 70B](https://huggingface.co/meta-llama/Llama-2-70b-hf) | 67.9 | 67.3 | 87.3 | 69.8 | 44.9 | 83.7 | 54.1 |
| [Yi-34B-Chat](https://huggingface.co/01-ai/Yi-34B-Chat) | 65.3 | 65.4 | 84.2 | 74.9 | 55.4 | 80.1 | 31.9 |
| [Gemma-7B](https://huggingface.co/google/gemma-7b) | 63.8 | 61.1 | 82.2 | 64.6 | 44.8 | 79 | 50.9 |
| [LLama 2 70B Chat](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) | 62.4 | 64.6 | 85.9 | 63.9 | 52.8 | 80.5 | 26.7 |
| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) | 61 | 60 | 83.3 | 64.2 | 42.2 | 78.4 | 37.8 |
We include these metrics here because they are frequently requested, but note that these metrics do not capture RAG, multilingual, tooling performance or the evaluation of open ended generations which we believe Command R+ to be state-of-art at. For evaluations of RAG, multilingual and tooling read more [here](https://txt.cohere.com/command-r-plus-microsoft-azure/). For evaluation of open ended generation, Command R+ is currently being evaluated on the [chatbot arena](https://chat.lmsys.org/).
### Tool use & multihop capabilities:
Command R+ has been specifically trained with conversational tool use capabilities. These have been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template will likely reduce performance, but we encourage experimentation.
Command R+’s tool use functionality takes a conversation as input (with an optional user-system preamble), along with a list of available tools. The model will then generate a json-formatted list of actions to execute on a subset of those tools. Command R+ may use one of its supplied tools more than once.
The model has been trained to recognise a special `directly_answer` tool, which it uses to indicate that it doesn’t want to use any of its other tools. The ability to abstain from calling a specific tool can be useful in a range of situations, such as greeting a user, or asking clarifying questions.
We recommend including the `directly_answer` tool, but it can be removed or renamed if required.
Comprehensive documentation for working with command R+'s tool use prompt template can be found [here](https://docs.cohere.com/docs/prompting-command-r).
The code snippet below shows a minimal working example on how to render a prompt.
<details>
<summary><b>Usage: Rendering Tool Use Prompts [CLICK TO EXPAND]</b> </summary>
```python
from transformers import AutoTokenizer
model_id = "CohereForAI/c4ai-command-r-plus"
tokenizer = AutoTokenizer.from_pretrained(model_id)
# define conversation input:
conversation = [
{"role": "user", "content": "Whats the biggest penguin in the world?"}
]
# Define tools available for the model to use:
tools = [
{
"name": "internet_search",
"description": "Returns a list of relevant document snippets for a textual query retrieved from the internet",
"parameter_definitions": {
"query": {
"description": "Query to search the internet with",
"type": 'str',
"required": True
}
}
},
{
'name': "directly_answer",
"description": "Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history",
'parameter_definitions': {}
}
]
# render the tool use prompt as a string:
tool_use_prompt = tokenizer.apply_tool_use_template(
conversation,
tools=tools,
tokenize=False,
add_generation_prompt=True,
)
print(tool_use_prompt)
```
</details>
<details>
<summary><b>Example Rendered Tool Use Prompt [CLICK TO EXPAND]</b></summary>
````
<BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># Safety Preamble
The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral.
# System Preamble
## Basic Rules
You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions.
# User Preamble
## Task and Context
You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.
## Style Guide
Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.
## Available Tools
Here is a list of tools that you have available to you:
```python
def internet_search(query: str) -> List[Dict]:
"""Returns a list of relevant document snippets for a textual query retrieved from the internet
Args:
query (str): Query to search the internet with
"""
pass
```
```python
def directly_answer() -> List[Dict]:
"""Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history
"""
pass
```<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Whats the biggest penguin in the world?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>Write 'Action:' followed by a json-formatted list of actions that you want to perform in order to produce a good response to the user's last input. You can use any of the supplied tools any number of times, but you should aim to execute the minimum number of necessary actions for the input. You should use the `directly-answer` tool if calling the other tools is unnecessary. The list of actions you want to call should be formatted as a list of json objects, for example:
```json
[
{
"tool_name": title of the tool in the specification,
"parameters": a dict of parameters to input into the tool as they are defined in the specs, or {} if it takes no parameters
}
]```<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
````
</details>
<details>
<summary><b>Example Rendered Tool Use Completion [CLICK TO EXPAND]</b></summary>
````
Action: ```json
[
{
"tool_name": "internet_search",
"parameters": {
"query": "biggest penguin in the world"
}
}
]
```
````
</details>
### Grounded Generation and RAG Capabilities:
Command R+ has been specifically trained with grounded generation capabilities. This means that it can generate responses based on a list of supplied document snippets, and it will include grounding spans (citations) in its response indicating the source of the information. This can be used to enable behaviors such as grounded summarization and the final step of Retrieval Augmented Generation (RAG). This behavior has been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template may reduce performance, but we encourage experimentation.
Command R+’s grounded generation behavior takes a conversation as input (with an optional user-supplied system preamble, indicating task, context and desired output style), along with a list of retrieved document snippets. The document snippets should be chunks, rather than long documents, typically around 100-400 words per chunk. Document snippets consist of key-value pairs. The keys should be short descriptive strings, the values can be text or semi-structured.
By default, Command R+ will generate grounded responses by first predicting which documents are relevant, then predicting which ones it will cite, then generating an answer. Finally, it will then insert grounding spans into the answer. See below for an example. This is referred to as `accurate` grounded generation.
The model is trained with a number of other answering modes, which can be selected by prompt changes. A `fast` citation mode is supported in the tokenizer, which will directly generate an answer with grounding spans in it, without first writing the answer out in full. This sacrifices some grounding accuracy in favor of generating fewer tokens.
Comprehensive documentation for working with Command R+'s grounded generation prompt template can be found [here](https://docs.cohere.com/docs/prompting-command-r).
The code snippet below shows a minimal working example on how to render a prompt.
<details>
<summary> <b>Usage: Rendering Grounded Generation prompts [CLICK TO EXPAND]</b> </summary>
````python
from transformers import AutoTokenizer
model_id = "CohereForAI/c4ai-command-r-plus"
tokenizer = AutoTokenizer.from_pretrained(model_id)
# define conversation input:
conversation = [
{"role": "user", "content": "Whats the biggest penguin in the world?"}
]
# define documents to ground on:
documents = [
{ "title": "Tall penguins", "text": "Emperor penguins are the tallest growing up to 122 cm in height." },
{ "title": "Penguin habitats", "text": "Emperor penguins only live in Antarctica."}
]
# render the tool use prompt as a string:
grounded_generation_prompt = tokenizer.apply_grounded_generation_template(
conversation,
documents=documents,
citation_mode="accurate", # or "fast"
tokenize=False,
add_generation_prompt=True,
)
print(grounded_generation_prompt)
````
</details>
<details>
<summary><b>Example Rendered Grounded Generation Prompt [CLICK TO EXPAND]</b></summary>
````<BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># Safety Preamble
The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral.
# System Preamble
## Basic Rules
You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions.
# User Preamble
## Task and Context
You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.
## Style Guide
Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Whats the biggest penguin in the world?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><results>
Document: 0
title: Tall penguins
text: Emperor penguins are the tallest growing up to 122 cm in height.
Document: 1
title: Penguin habitats
text: Emperor penguins only live in Antarctica.
</results><|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>Carefully perform the following instructions, in order, starting each with a new line.
Firstly, Decide which of the retrieved documents are relevant to the user's last input by writing 'Relevant Documents:' followed by comma-separated list of document numbers. If none are relevant, you should instead write 'None'.
Secondly, Decide which of the retrieved documents contain facts that should be cited in a good answer to the user's last input by writing 'Cited Documents:' followed a comma-separated list of document numbers. If you dont want to cite any of them, you should instead write 'None'.
Thirdly, Write 'Answer:' followed by a response to the user's last input in high quality natural english. Use the retrieved documents to help you. Do not insert any citations or grounding markup.
Finally, Write 'Grounded answer:' followed by a response to the user's last input in high quality natural english. Use the symbols <co: doc> and </co: doc> to indicate when a fact comes from a document in the search result, e.g <co: 0>my fact</co: 0> for a fact from document 0.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
````
</details>
<details>
<summary><b>Example Rendered Grounded Generation Completion [CLICK TO EXPAND]</b></summary>
````
Relevant Documents: 0,1
Cited Documents: 0,1
Answer: The Emperor Penguin is the tallest or biggest penguin in the world. It is a bird that lives only in Antarctica and grows to a height of around 122 centimetres.
Grounded answer: The <co: 0>Emperor Penguin</co: 0> is the <co: 0>tallest</co: 0> or biggest penguin in the world. It is a bird that <co: 1>lives only in Antarctica</co: 1> and <co: 0>grows to a height of around 122 centimetres.</co: 0>
````
</details>
### Code Capabilities:
Command R+ has been optimized to interact with your code, by requesting code snippets, code explanations, or code rewrites. It might not perform well out-of-the-box for pure code completion. For better performance, we also recommend using a low temperature (and even greedy decoding) for code-generation related instructions.
### Model Card Contact
For errors or additional questions about details in this model card, contact [[email protected]](mailto:[email protected]).
### Terms of Use:
We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant 104 billion parameter model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). | {"language": ["en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar"], "license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["conversational", "llama-cpp", "gguf-my-repo"], "pipeline_tag": "text-generation", "inference": false} | ehristoforu/c4ai-command-r-plus-Q2_K-GGUF | null | [
"transformers",
"gguf",
"conversational",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"fr",
"de",
"es",
"it",
"pt",
"ja",
"ko",
"zh",
"ar",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-04-20T22:13:11+00:00 | [] | [
"en",
"fr",
"de",
"es",
"it",
"pt",
"ja",
"ko",
"zh",
"ar"
] | TAGS
#transformers #gguf #conversational #llama-cpp #gguf-my-repo #text-generation #en #fr #de #es #it #pt #ja #ko #zh #ar #license-cc-by-nc-4.0 #region-us
| ehristoforu/c4ai-command-r-plus-Q2\_K-GGUF
==========================================
This model was converted to GGUF format from 'CohereForAI/c4ai-command-r-plus' using URL via the URL's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Use with URL
------------
Install URL through brew.
Invoke the URL server or the CLI.
CLI:
Server:
Note: You can also use this checkpoint directly through the usage steps listed in the URL repo as well.
Model Card for C4AI Command R+
==============================
This model is non-quantized version of C4AI Command R+. You can find the quantized version of C4AI Command R+ using bitsandbytes here.
Model Summary
-------------
C4AI Command R+ is an open weights research release of a 104B billion parameter model with highly advanced capabilities, this includes Retrieval Augmented Generation (RAG) and tool use to automate sophisticated tasks. The tool use in this model generation enables multi-step tool use which allows the model to combine multiple tools over multiple steps to accomplish difficult tasks. C4AI Command R+ is a multilingual model evaluated in 10 languages for performance: English, French, Spanish, Italian, German, Brazilian Portuguese, Japanese, Korean, Arabic, and Simplified Chinese. Command R+ is optimized for a variety of use cases including reasoning, summarization, and question answering.
C4AI Command R+ is part of a family of open weight releases from Cohere For AI and Cohere. Our smaller companion model is C4AI Command R
Developed by: Cohere and Cohere For AI
* Point of Contact: Cohere For AI: URL
* License: CC-BY-NC, requires also adhering to C4AI's Acceptable Use Policy
* Model: c4ai-command-r-plus
* Model Size: 104 billion parameters
* Context length: 128K
Try C4AI Command R+
You can try out C4AI Command R+ before downloading the weights in our hosted Hugging Face Space.
Usage
Please install 'transformers' from the source repository that includes the necessary changes for this model.
Quantized model through bitsandbytes, 8-bit precision
Quantized model through bitsandbytes, 4-bit precision
This model is non-quantized version of C4AI Command R+. You can find the quantized version of C4AI Command R+ using bitsandbytes here.
Model Details
-------------
Input: Models input text only.
Output: Models generate text only.
Model Architecture: This is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model uses supervised fine-tuning (SFT) and preference training to align model behavior to human preferences for helpfulness and safety.
Languages covered: The model is optimized to perform well in the following languages: English, French, Spanish, Italian, German, Brazilian Portuguese, Japanese, Korean, Simplified Chinese, and Arabic.
Pre-training data additionally included the following 13 languages: Russian, Polish, Turkish, Vietnamese, Dutch, Czech, Indonesian, Ukrainian, Romanian, Greek, Hindi, Hebrew, Persian.
Context length: Command R+ supports a context length of 128K.
Evaluations
-----------
Command R+ has been submitted to the Open LLM leaderboard. We include the results below, along with a direct comparison to the strongest state-of-art open weights models currently available on Hugging Face. We note that these results are only useful to compare when evaluations are implemented for all models in a standardized way using publically available code, and hence shouldn't be used for comparison outside of models submitted to the leaderboard or compared to self-reported numbers which can't be replicated in the same way.
We include these metrics here because they are frequently requested, but note that these metrics do not capture RAG, multilingual, tooling performance or the evaluation of open ended generations which we believe Command R+ to be state-of-art at. For evaluations of RAG, multilingual and tooling read more here. For evaluation of open ended generation, Command R+ is currently being evaluated on the chatbot arena.
### Tool use & multihop capabilities:
Command R+ has been specifically trained with conversational tool use capabilities. These have been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template will likely reduce performance, but we encourage experimentation.
Command R+’s tool use functionality takes a conversation as input (with an optional user-system preamble), along with a list of available tools. The model will then generate a json-formatted list of actions to execute on a subset of those tools. Command R+ may use one of its supplied tools more than once.
The model has been trained to recognise a special 'directly\_answer' tool, which it uses to indicate that it doesn’t want to use any of its other tools. The ability to abstain from calling a specific tool can be useful in a range of situations, such as greeting a user, or asking clarifying questions.
We recommend including the 'directly\_answer' tool, but it can be removed or renamed if required.
Comprehensive documentation for working with command R+'s tool use prompt template can be found here.
The code snippet below shows a minimal working example on how to render a prompt.
**Usage: Rendering Tool Use Prompts [CLICK TO EXPAND]**
**Example Rendered Tool Use Prompt [CLICK TO EXPAND]**
python
def internet\_search(query: str) -> List[Dict]:
"""Returns a list of relevant document snippets for a textual query retrieved from the internet
```
Args:
query (str): Query to search the internet with
"""
pass
```
python
def directly\_answer() -> List[Dict]:
"""Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history
"""
pass
json
[
{
"tool\_name": title of the tool in the specification,
"parameters": a dict of parameters to input into the tool as they are defined in the specs, or {} if it takes no parameters
}
]'
**Example Rendered Tool Use Completion [CLICK TO EXPAND]**
json
[
{
"tool\_name": "internet\_search",
"parameters": {
"query": "biggest penguin in the world"
}
}
]
'
### Grounded Generation and RAG Capabilities:
Command R+ has been specifically trained with grounded generation capabilities. This means that it can generate responses based on a list of supplied document snippets, and it will include grounding spans (citations) in its response indicating the source of the information. This can be used to enable behaviors such as grounded summarization and the final step of Retrieval Augmented Generation (RAG). This behavior has been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template may reduce performance, but we encourage experimentation.
Command R+’s grounded generation behavior takes a conversation as input (with an optional user-supplied system preamble, indicating task, context and desired output style), along with a list of retrieved document snippets. The document snippets should be chunks, rather than long documents, typically around 100-400 words per chunk. Document snippets consist of key-value pairs. The keys should be short descriptive strings, the values can be text or semi-structured.
By default, Command R+ will generate grounded responses by first predicting which documents are relevant, then predicting which ones it will cite, then generating an answer. Finally, it will then insert grounding spans into the answer. See below for an example. This is referred to as 'accurate' grounded generation.
The model is trained with a number of other answering modes, which can be selected by prompt changes. A 'fast' citation mode is supported in the tokenizer, which will directly generate an answer with grounding spans in it, without first writing the answer out in full. This sacrifices some grounding accuracy in favor of generating fewer tokens.
Comprehensive documentation for working with Command R+'s grounded generation prompt template can be found here.
The code snippet below shows a minimal working example on how to render a prompt.
**Usage: Rendering Grounded Generation prompts [CLICK TO EXPAND]**
'
**Example Rendered Grounded Generation Prompt [CLICK TO EXPAND]**
'
**Example Rendered Grounded Generation Completion [CLICK TO EXPAND]**
'
### Code Capabilities:
Command R+ has been optimized to interact with your code, by requesting code snippets, code explanations, or code rewrites. It might not perform well out-of-the-box for pure code completion. For better performance, we also recommend using a low temperature (and even greedy decoding) for code-generation related instructions.
### Model Card Contact
For errors or additional questions about details in this model card, contact info@URL.
### Terms of Use:
We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant 104 billion parameter model to researchers all over the world. This model is governed by a CC-BY-NC License with an acceptable use addendum, and also requires adhering to C4AI's Acceptable Use Policy.
| [
"### Tool use & multihop capabilities:\n\n\nCommand R+ has been specifically trained with conversational tool use capabilities. These have been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template will likely reduce performance, but we encourage experimentation.\n\n\nCommand R+’s tool use functionality takes a conversation as input (with an optional user-system preamble), along with a list of available tools. The model will then generate a json-formatted list of actions to execute on a subset of those tools. Command R+ may use one of its supplied tools more than once.\n\n\nThe model has been trained to recognise a special 'directly\\_answer' tool, which it uses to indicate that it doesn’t want to use any of its other tools. The ability to abstain from calling a specific tool can be useful in a range of situations, such as greeting a user, or asking clarifying questions.\nWe recommend including the 'directly\\_answer' tool, but it can be removed or renamed if required.\n\n\nComprehensive documentation for working with command R+'s tool use prompt template can be found here.\n\n\nThe code snippet below shows a minimal working example on how to render a prompt.\n\n\n\n**Usage: Rendering Tool Use Prompts [CLICK TO EXPAND]** \n\n\n**Example Rendered Tool Use Prompt [CLICK TO EXPAND]**\npython\ndef internet\\_search(query: str) -> List[Dict]:\n\"\"\"Returns a list of relevant document snippets for a textual query retrieved from the internet\n\n\n\n```\nArgs:\n query (str): Query to search the internet with\n\"\"\"\npass\n\n```\n\npython\ndef directly\\_answer() -> List[Dict]:\n\"\"\"Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history\n\"\"\"\npass\njson\n[\n{\n\"tool\\_name\": title of the tool in the specification,\n\"parameters\": a dict of parameters to input into the tool as they are defined in the specs, or {} if it takes no parameters\n}\n]'\n\n\n\n\n**Example Rendered Tool Use Completion [CLICK TO EXPAND]**\njson\n[\n{\n\"tool\\_name\": \"internet\\_search\",\n\"parameters\": {\n\"query\": \"biggest penguin in the world\"\n}\n}\n]\n'",
"### Grounded Generation and RAG Capabilities:\n\n\nCommand R+ has been specifically trained with grounded generation capabilities. This means that it can generate responses based on a list of supplied document snippets, and it will include grounding spans (citations) in its response indicating the source of the information. This can be used to enable behaviors such as grounded summarization and the final step of Retrieval Augmented Generation (RAG). This behavior has been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template may reduce performance, but we encourage experimentation.\n\n\nCommand R+’s grounded generation behavior takes a conversation as input (with an optional user-supplied system preamble, indicating task, context and desired output style), along with a list of retrieved document snippets. The document snippets should be chunks, rather than long documents, typically around 100-400 words per chunk. Document snippets consist of key-value pairs. The keys should be short descriptive strings, the values can be text or semi-structured.\n\n\nBy default, Command R+ will generate grounded responses by first predicting which documents are relevant, then predicting which ones it will cite, then generating an answer. Finally, it will then insert grounding spans into the answer. See below for an example. This is referred to as 'accurate' grounded generation.\n\n\nThe model is trained with a number of other answering modes, which can be selected by prompt changes. A 'fast' citation mode is supported in the tokenizer, which will directly generate an answer with grounding spans in it, without first writing the answer out in full. This sacrifices some grounding accuracy in favor of generating fewer tokens.\n\n\nComprehensive documentation for working with Command R+'s grounded generation prompt template can be found here.\n\n\nThe code snippet below shows a minimal working example on how to render a prompt.\n\n\n\n **Usage: Rendering Grounded Generation prompts [CLICK TO EXPAND]** \n'\n\n\n\n\n**Example Rendered Grounded Generation Prompt [CLICK TO EXPAND]**\n'\n\n\n\n\n**Example Rendered Grounded Generation Completion [CLICK TO EXPAND]**\n'",
"### Code Capabilities:\n\n\nCommand R+ has been optimized to interact with your code, by requesting code snippets, code explanations, or code rewrites. It might not perform well out-of-the-box for pure code completion. For better performance, we also recommend using a low temperature (and even greedy decoding) for code-generation related instructions.",
"### Model Card Contact\n\n\nFor errors or additional questions about details in this model card, contact info@URL.",
"### Terms of Use:\n\n\nWe hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant 104 billion parameter model to researchers all over the world. This model is governed by a CC-BY-NC License with an acceptable use addendum, and also requires adhering to C4AI's Acceptable Use Policy."
] | [
"TAGS\n#transformers #gguf #conversational #llama-cpp #gguf-my-repo #text-generation #en #fr #de #es #it #pt #ja #ko #zh #ar #license-cc-by-nc-4.0 #region-us \n",
"### Tool use & multihop capabilities:\n\n\nCommand R+ has been specifically trained with conversational tool use capabilities. These have been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template will likely reduce performance, but we encourage experimentation.\n\n\nCommand R+’s tool use functionality takes a conversation as input (with an optional user-system preamble), along with a list of available tools. The model will then generate a json-formatted list of actions to execute on a subset of those tools. Command R+ may use one of its supplied tools more than once.\n\n\nThe model has been trained to recognise a special 'directly\\_answer' tool, which it uses to indicate that it doesn’t want to use any of its other tools. The ability to abstain from calling a specific tool can be useful in a range of situations, such as greeting a user, or asking clarifying questions.\nWe recommend including the 'directly\\_answer' tool, but it can be removed or renamed if required.\n\n\nComprehensive documentation for working with command R+'s tool use prompt template can be found here.\n\n\nThe code snippet below shows a minimal working example on how to render a prompt.\n\n\n\n**Usage: Rendering Tool Use Prompts [CLICK TO EXPAND]** \n\n\n**Example Rendered Tool Use Prompt [CLICK TO EXPAND]**\npython\ndef internet\\_search(query: str) -> List[Dict]:\n\"\"\"Returns a list of relevant document snippets for a textual query retrieved from the internet\n\n\n\n```\nArgs:\n query (str): Query to search the internet with\n\"\"\"\npass\n\n```\n\npython\ndef directly\\_answer() -> List[Dict]:\n\"\"\"Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history\n\"\"\"\npass\njson\n[\n{\n\"tool\\_name\": title of the tool in the specification,\n\"parameters\": a dict of parameters to input into the tool as they are defined in the specs, or {} if it takes no parameters\n}\n]'\n\n\n\n\n**Example Rendered Tool Use Completion [CLICK TO EXPAND]**\njson\n[\n{\n\"tool\\_name\": \"internet\\_search\",\n\"parameters\": {\n\"query\": \"biggest penguin in the world\"\n}\n}\n]\n'",
"### Grounded Generation and RAG Capabilities:\n\n\nCommand R+ has been specifically trained with grounded generation capabilities. This means that it can generate responses based on a list of supplied document snippets, and it will include grounding spans (citations) in its response indicating the source of the information. This can be used to enable behaviors such as grounded summarization and the final step of Retrieval Augmented Generation (RAG). This behavior has been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template may reduce performance, but we encourage experimentation.\n\n\nCommand R+’s grounded generation behavior takes a conversation as input (with an optional user-supplied system preamble, indicating task, context and desired output style), along with a list of retrieved document snippets. The document snippets should be chunks, rather than long documents, typically around 100-400 words per chunk. Document snippets consist of key-value pairs. The keys should be short descriptive strings, the values can be text or semi-structured.\n\n\nBy default, Command R+ will generate grounded responses by first predicting which documents are relevant, then predicting which ones it will cite, then generating an answer. Finally, it will then insert grounding spans into the answer. See below for an example. This is referred to as 'accurate' grounded generation.\n\n\nThe model is trained with a number of other answering modes, which can be selected by prompt changes. A 'fast' citation mode is supported in the tokenizer, which will directly generate an answer with grounding spans in it, without first writing the answer out in full. This sacrifices some grounding accuracy in favor of generating fewer tokens.\n\n\nComprehensive documentation for working with Command R+'s grounded generation prompt template can be found here.\n\n\nThe code snippet below shows a minimal working example on how to render a prompt.\n\n\n\n **Usage: Rendering Grounded Generation prompts [CLICK TO EXPAND]** \n'\n\n\n\n\n**Example Rendered Grounded Generation Prompt [CLICK TO EXPAND]**\n'\n\n\n\n\n**Example Rendered Grounded Generation Completion [CLICK TO EXPAND]**\n'",
"### Code Capabilities:\n\n\nCommand R+ has been optimized to interact with your code, by requesting code snippets, code explanations, or code rewrites. It might not perform well out-of-the-box for pure code completion. For better performance, we also recommend using a low temperature (and even greedy decoding) for code-generation related instructions.",
"### Model Card Contact\n\n\nFor errors or additional questions about details in this model card, contact info@URL.",
"### Terms of Use:\n\n\nWe hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant 104 billion parameter model to researchers all over the world. This model is governed by a CC-BY-NC License with an acceptable use addendum, and also requires adhering to C4AI's Acceptable Use Policy."
] |
text-generation | transformers | This is my previous Llama3 merge (OrpoSmaug-Slerp) with an extra LoRa for better RP on top.
Thanks to mradermacher, there are also GGUF quants (Q2_K-Q8_K & IQ3_XS-IQ4_XS) for this model available here: https://huggingface.co/mradermacher/Llama3-RPLoRa-SmaugOrpo-GGUF
---
base_model:
- WesPro/Llama3-OrpoSmaug-Slerp-8B
- ResplendentAI/RP_Format_Llama3
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method.
### Models Merged
The following models were included in the merge:
* [WesPro/Llama3-OrpoSmaug-Slerp-8B](https://huggingface.co/WesPro/Llama3-OrpoSmaug-Slerp-8B) + [ResplendentAI/RP_Format_Llama3](https://huggingface.co/ResplendentAI/RP_Format_Llama3)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: WesPro/Llama3-OrpoSmaug-Slerp-8B+ResplendentAI/RP_Format_Llama3
parameters:
weight: 1.0
merge_method: linear
dtype: float16
```
| {} | WesPro/Llama3-RPLoRa-SmaugOrpo | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:2203.05482",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T22:13:47+00:00 | [
"2203.05482"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-2203.05482 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is my previous Llama3 merge (OrpoSmaug-Slerp) with an extra LoRa for better RP on top.
Thanks to mradermacher, there are also GGUF quants (Q2_K-Q8_K & IQ3_XS-IQ4_XS) for this model available here: URL
---
base_model:
- WesPro/Llama3-OrpoSmaug-Slerp-8B
- ResplendentAI/RP_Format_Llama3
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the linear merge method.
### Models Merged
The following models were included in the merge:
* WesPro/Llama3-OrpoSmaug-Slerp-8B + ResplendentAI/RP_Format_Llama3
### Configuration
The following YAML configuration was used to produce this model:
| [
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the linear merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* WesPro/Llama3-OrpoSmaug-Slerp-8B + ResplendentAI/RP_Format_Llama3",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-2203.05482 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the linear merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* WesPro/Llama3-OrpoSmaug-Slerp-8B + ResplendentAI/RP_Format_Llama3",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
text-to-image | diffusers | ## Zelda Diffusion Model Card
SDZelda is a latent text-to-image diffusion model capable of generating images of Zelda from The Legend of Zelda. For more information about how Stable Diffusion functions, please have a look at 🤗's [Stable Diffusion blog](https://huggingface.co/blog/stable_diffusion).
You can use this with the 🧨Diffusers library from [Hugging Face](https://huggingface.co).
### Diffusers
```py
from diffusers import StableDiffusionPipeline
import torch
pipeline = StableDiffusionPipeline.from_pretrained("nroggendorff/zelda-diffusion", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
image = pipeline(prompt="a drawing of a woman in a blue dress and gold crown").images[0]
image.save("zelda.png")
```
### Model Details
- `train_batch_size`: 1
- `gradient_accumulation_steps`: 4
- `learning_rate`: 1e-2
- `lr_warmup_steps`: 500
- `mixed_precision`: "fp16"
- `eval_metric`: "mean_squared_error"
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model was trained on a small-scale dataset: [nroggendorff/zelda](https://huggingface.co/datasets/nroggendorff/zelda)
### Developed by
- Noa Linden Roggendorff
*This model card was written by Noa Roggendorff and is based on the [Stable Diffusion v1-5 Model Card](https://huggingface.co/runwayml/stable-diffusion-v1-5).* | {"language": ["en"], "license": "mit", "library_name": "diffusers", "tags": ["diffusion"], "datasets": ["nroggendorff/zelda"], "metrics": ["mse"], "pipeline_tag": "text-to-image"} | nroggendorff/zelda-diffusion | null | [
"diffusers",
"tensorboard",
"safetensors",
"diffusion",
"text-to-image",
"en",
"dataset:nroggendorff/zelda",
"license:mit",
"endpoints_compatible",
"has_space",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null | 2024-04-20T22:14:17+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #safetensors #diffusion #text-to-image #en #dataset-nroggendorff/zelda #license-mit #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us
| ## Zelda Diffusion Model Card
SDZelda is a latent text-to-image diffusion model capable of generating images of Zelda from The Legend of Zelda. For more information about how Stable Diffusion functions, please have a look at 's Stable Diffusion blog.
You can use this with the Diffusers library from Hugging Face.
!So pretty, right?
### Diffusers
### Model Details
- 'train_batch_size': 1
- 'gradient_accumulation_steps': 4
- 'learning_rate': 1e-2
- 'lr_warmup_steps': 500
- 'mixed_precision': "fp16"
- 'eval_metric': "mean_squared_error"
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model was trained on a small-scale dataset: nroggendorff/zelda
### Developed by
- Noa Linden Roggendorff
*This model card was written by Noa Roggendorff and is based on the Stable Diffusion v1-5 Model Card.* | [
"## Zelda Diffusion Model Card\n\nSDZelda is a latent text-to-image diffusion model capable of generating images of Zelda from The Legend of Zelda. For more information about how Stable Diffusion functions, please have a look at 's Stable Diffusion blog.\n\nYou can use this with the Diffusers library from Hugging Face.\n\n!So pretty, right?",
"### Diffusers",
"### Model Details\n- 'train_batch_size': 1\n- 'gradient_accumulation_steps': 4\n- 'learning_rate': 1e-2\n- 'lr_warmup_steps': 500\n- 'mixed_precision': \"fp16\"\n- 'eval_metric': \"mean_squared_error\"",
"### Limitations\n\n- The model does not achieve perfect photorealism\n- The model cannot render legible text\n- The model was trained on a small-scale dataset: nroggendorff/zelda",
"### Developed by\n- Noa Linden Roggendorff\n\n*This model card was written by Noa Roggendorff and is based on the Stable Diffusion v1-5 Model Card.*"
] | [
"TAGS\n#diffusers #tensorboard #safetensors #diffusion #text-to-image #en #dataset-nroggendorff/zelda #license-mit #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us \n",
"## Zelda Diffusion Model Card\n\nSDZelda is a latent text-to-image diffusion model capable of generating images of Zelda from The Legend of Zelda. For more information about how Stable Diffusion functions, please have a look at 's Stable Diffusion blog.\n\nYou can use this with the Diffusers library from Hugging Face.\n\n!So pretty, right?",
"### Diffusers",
"### Model Details\n- 'train_batch_size': 1\n- 'gradient_accumulation_steps': 4\n- 'learning_rate': 1e-2\n- 'lr_warmup_steps': 500\n- 'mixed_precision': \"fp16\"\n- 'eval_metric': \"mean_squared_error\"",
"### Limitations\n\n- The model does not achieve perfect photorealism\n- The model cannot render legible text\n- The model was trained on a small-scale dataset: nroggendorff/zelda",
"### Developed by\n- Noa Linden Roggendorff\n\n*This model card was written by Noa Roggendorff and is based on the Stable Diffusion v1-5 Model Card.*"
] |
text-generation | transformers |
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
| {"library_name": "transformers", "tags": []} | Fighoture/Llama-2-7b-chat-shortgpt-with-angular-25-percent | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T22:14:48+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | transformers |
# Uploaded model
- **Developed by:** Saiikish
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/tinyllama-bnb-4bit"} | Saiikish/Alt_f4 | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/tinyllama-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T22:19:13+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #base_model-unsloth/tinyllama-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: Saiikish
- License: apache-2.0
- Finetuned from model : unsloth/tinyllama-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: Saiikish\n- License: apache-2.0\n- Finetuned from model : unsloth/tinyllama-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #base_model-unsloth/tinyllama-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: Saiikish\n- License: apache-2.0\n- Finetuned from model : unsloth/tinyllama-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
null | null |
<!-- 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. -->
# bart-large-lora-no-grad
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8724
- Accuracy: 0.8428
- Precision: 0.8414
- Recall: 0.8428
- Precision Macro: 0.8149
- Recall Macro: 0.7856
- Macro Fpr: 0.0144
- Weighted Fpr: 0.0138
- Weighted Specificity: 0.9778
- Macro Specificity: 0.9876
- Weighted Sensitivity: 0.8366
- Macro Sensitivity: 0.7856
- F1 Micro: 0.8366
- F1 Macro: 0.7922
- F1 Weighted: 0.8329
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Precision Macro | Recall Macro | Macro Fpr | Weighted Fpr | Weighted Specificity | Macro Specificity | Weighted Sensitivity | Macro Sensitivity | F1 Micro | F1 Macro | F1 Weighted |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:|
| 1.3548 | 1.0 | 643 | 0.7811 | 0.7568 | 0.7272 | 0.7568 | 0.4206 | 0.4734 | 0.0234 | 0.0224 | 0.9682 | 0.9817 | 0.7568 | 0.4734 | 0.7568 | 0.4364 | 0.7359 |
| 0.7738 | 2.0 | 1286 | 0.6572 | 0.7893 | 0.7848 | 0.7893 | 0.6529 | 0.5639 | 0.0196 | 0.0187 | 0.9732 | 0.9842 | 0.7893 | 0.5639 | 0.7893 | 0.5618 | 0.7783 |
| 0.6874 | 3.0 | 1929 | 0.6485 | 0.8009 | 0.7994 | 0.8009 | 0.6224 | 0.6498 | 0.0179 | 0.0174 | 0.9767 | 0.9852 | 0.8009 | 0.6498 | 0.8009 | 0.6248 | 0.7948 |
| 0.502 | 4.0 | 2572 | 0.6912 | 0.8257 | 0.8216 | 0.8257 | 0.7661 | 0.7399 | 0.0158 | 0.0149 | 0.9738 | 0.9866 | 0.8257 | 0.7399 | 0.8257 | 0.7393 | 0.8182 |
| 0.4443 | 5.0 | 3215 | 0.6655 | 0.8350 | 0.8324 | 0.8350 | 0.7584 | 0.7344 | 0.0146 | 0.0139 | 0.9781 | 0.9875 | 0.8350 | 0.7344 | 0.8350 | 0.7352 | 0.8308 |
| 0.3903 | 6.0 | 3858 | 0.7269 | 0.8304 | 0.8288 | 0.8304 | 0.7500 | 0.7407 | 0.0149 | 0.0144 | 0.9789 | 0.9873 | 0.8304 | 0.7407 | 0.8304 | 0.7363 | 0.8261 |
| 0.3398 | 7.0 | 4501 | 0.8292 | 0.8218 | 0.8264 | 0.8218 | 0.8274 | 0.7793 | 0.0161 | 0.0152 | 0.9752 | 0.9865 | 0.8218 | 0.7793 | 0.8218 | 0.7883 | 0.8163 |
| 0.2818 | 8.0 | 5144 | 0.8360 | 0.8218 | 0.8240 | 0.8218 | 0.8251 | 0.7683 | 0.0159 | 0.0152 | 0.9767 | 0.9866 | 0.8218 | 0.7683 | 0.8218 | 0.7744 | 0.8178 |
| 0.2572 | 9.0 | 5787 | 0.8456 | 0.8342 | 0.8328 | 0.8342 | 0.7999 | 0.7735 | 0.0146 | 0.0140 | 0.9787 | 0.9875 | 0.8342 | 0.7735 | 0.8342 | 0.7768 | 0.8310 |
| 0.2594 | 10.0 | 6430 | 0.8724 | 0.8428 | 0.8414 | 0.8428 | 0.8149 | 0.7891 | 0.0138 | 0.0132 | 0.9790 | 0.9881 | 0.8428 | 0.7891 | 0.8428 | 0.7955 | 0.8396 |
| 0.208 | 11.0 | 7073 | 0.9797 | 0.8335 | 0.8339 | 0.8335 | 0.8092 | 0.7870 | 0.0148 | 0.0141 | 0.9774 | 0.9874 | 0.8335 | 0.7870 | 0.8335 | 0.7896 | 0.8303 |
| 0.1786 | 12.0 | 7716 | 1.0180 | 0.8311 | 0.8323 | 0.8311 | 0.8100 | 0.7846 | 0.0149 | 0.0143 | 0.9777 | 0.9873 | 0.8311 | 0.7846 | 0.8311 | 0.7906 | 0.8285 |
| 0.1556 | 13.0 | 8359 | 1.0392 | 0.8358 | 0.8335 | 0.8358 | 0.8040 | 0.7830 | 0.0146 | 0.0138 | 0.9773 | 0.9875 | 0.8358 | 0.7830 | 0.8358 | 0.7876 | 0.8321 |
| 0.1419 | 14.0 | 9002 | 1.0568 | 0.8381 | 0.8362 | 0.8381 | 0.8110 | 0.7855 | 0.0143 | 0.0136 | 0.9779 | 0.9877 | 0.8381 | 0.7855 | 0.8381 | 0.7917 | 0.8349 |
| 0.1251 | 15.0 | 9645 | 1.0593 | 0.8366 | 0.8350 | 0.8366 | 0.8149 | 0.7856 | 0.0144 | 0.0138 | 0.9778 | 0.9876 | 0.8366 | 0.7856 | 0.8366 | 0.7922 | 0.8329 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.19.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall"], "base_model": "facebook/bart-large", "model-index": [{"name": "bart-large-lora-no-grad", "results": []}]} | xshubhamx/bart-large-lora-no-grad | null | [
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:facebook/bart-large",
"license:apache-2.0",
"region:us"
] | null | 2024-04-20T22:22:50+00:00 | [] | [] | TAGS
#tensorboard #safetensors #generated_from_trainer #base_model-facebook/bart-large #license-apache-2.0 #region-us
| bart-large-lora-no-grad
=======================
This model is a fine-tuned version of facebook/bart-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8724
* Accuracy: 0.8428
* Precision: 0.8414
* Recall: 0.8428
* Precision Macro: 0.8149
* Recall Macro: 0.7856
* Macro Fpr: 0.0144
* Weighted Fpr: 0.0138
* Weighted Specificity: 0.9778
* Macro Specificity: 0.9876
* Weighted Sensitivity: 0.8366
* Macro Sensitivity: 0.7856
* F1 Micro: 0.8366
* F1 Macro: 0.7922
* F1 Weighted: 0.8329
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: 15
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.19.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15",
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"### Training results",
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] |
null | adapter-transformers |
# Adapter `jgrc3/pfeiffer_adapter_classification_noPre_lr0_0001` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/amazon_helpfulness](https://huggingface.co/datasets/BigTMiami/amazon_helpfulness/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("jgrc3/pfeiffer_adapter_classification_noPre_lr0_0001", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["adapter-transformers", "roberta"], "datasets": ["BigTMiami/amazon_helpfulness"]} | jgrc3/pfeiffer_adapter_classification_noPre_lr0_0001 | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_helpfulness",
"region:us"
] | null | 2024-04-20T22:25:54+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_helpfulness #region-us
|
# Adapter 'jgrc3/pfeiffer_adapter_classification_noPre_lr0_0001' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_helpfulness dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'jgrc3/pfeiffer_adapter_classification_noPre_lr0_0001' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_helpfulness dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
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"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
text-classification | transformers |
<!-- 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. -->
# ellis-v10-emotion-leadership
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2726
- Accuracy: 0.9142
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3636 | 1.0 | 1109 | 0.2726 | 0.9142 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "bert-base-uncased", "model-index": [{"name": "ellis-v10-emotion-leadership", "results": []}]} | gsl22/ellis-v10-emotion-leadership | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T22:27:11+00:00 | [] | [] | TAGS
#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ellis-v10-emotion-leadership
============================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2726
* Accuracy: 0.9142
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: 1
### Training results
### Framework versions
* Transformers 4.40.0
* Pytorch 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.19.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1"
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1"
] |
null | peft |
<!-- 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_instruct_generation
This model is a fine-tuned version of [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0965
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 0.03
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0556 | 1.0 | 1278 | 1.0965 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "mistralai/Mixtral-8x7B-v0.1", "model-index": [{"name": "mistral_instruct_generation", "results": []}]} | Cem13/mixtralep1 | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mixtral-8x7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-04-20T22:30:45+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-mistralai/Mixtral-8x7B-v0.1 #license-apache-2.0 #region-us
| mistral\_instruct\_generation
=============================
This model is a fine-tuned version of mistralai/Mixtral-8x7B-v0.1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0965
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0002
* train\_batch\_size: 2
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* lr\_scheduler\_warmup\_steps: 0.03
* num\_epochs: 1
### Training results
### Framework versions
* PEFT 0.10.0
* Transformers 4.40.0
* Pytorch 2.2.1+cu121
* Tokenizers 0.19.1
| [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_steps: 0.03\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Tokenizers 0.19.1"
] |
text-generation | transformers |
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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. -->
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed] | {"library_name": "transformers", "tags": []} | domenicrosati/lens-loss-minimality_lr_2e-5_attack_meta-llama_Llama-2-7b-chat-hf_1_num_layers_24_8e-5_10k | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T22:32:37+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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- Shared by [optional]:
- Model type:
- Language(s) (NLP):
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## Uses
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
## Evaluation
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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## Technical Specifications [optional]
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### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
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] |
text-generation | transformers |
<!-- 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. -->
# 0.0_ablation_sample3_4iters_iter_1
This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) on the HuggingFaceH4/ultrafeedback_binarized 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrafeedback_binarized"], "base_model": "HuggingFaceH4/mistral-7b-sft-beta", "model-index": [{"name": "0.0_ablation_sample3_4iters_iter_1", "results": []}]} | ZhangShenao/0.0_ablation_sample3_4iters_iter_1 | null | [
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"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T22:33:22+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #alignment-handbook #generated_from_trainer #trl #dpo #conversational #dataset-HuggingFaceH4/ultrafeedback_binarized #base_model-HuggingFaceH4/mistral-7b-sft-beta #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# 0.0_ablation_sample3_4iters_iter_1
This model is a fine-tuned version of HuggingFaceH4/mistral-7b-sft-beta on the HuggingFaceH4/ultrafeedback_binarized 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| [
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] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### 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
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[More Information Needed]
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[More Information Needed]
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#### 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]
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<!-- 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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed] | {"library_name": "transformers", "tags": []} | KaggleMasterX/llama3_dpo | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T22:34:16+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
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] |
audio-classification | transformers | # Accent Classification XL
Accent Classification XL is a major improvement from ML for Speech's previous [Accent Classification](https://huggingface.co/ml-for-speech/accent-classification) model. This model was trained on almost twice the amount of data and features a much larger model, based on [Distil-Whisper Medium](https://huggingface.co/distil-whisper/distil-medium.en).
This model is a general improvement from the previous model, with higher accuracy across the board.
It scored 100% on the accuracy metric on an evaluation dataset, but in real-world examples shows inaccuracies and biases.
## Usage
The easiest way to use this model is through ML for Speech's [SpeechToolkit](https://github.com/ml-for-speech/speechtoolkit).
## Biases
Biases have been observed in the usage of this model.
## Disclaimer
This model may make mistakes and should not be used in high-risk situations.
THE MODEL IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS MODEL INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS MODEL. | {"language": ["en"]} | ml-for-speech/accent-classification-xl | null | [
"transformers",
"safetensors",
"whisper",
"audio-classification",
"en",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2024-04-20T22:35:31+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #whisper #audio-classification #en #endpoints_compatible #has_space #region-us
| # Accent Classification XL
Accent Classification XL is a major improvement from ML for Speech's previous Accent Classification model. This model was trained on almost twice the amount of data and features a much larger model, based on Distil-Whisper Medium.
This model is a general improvement from the previous model, with higher accuracy across the board.
It scored 100% on the accuracy metric on an evaluation dataset, but in real-world examples shows inaccuracies and biases.
## Usage
The easiest way to use this model is through ML for Speech's SpeechToolkit.
## Biases
Biases have been observed in the usage of this model.
## Disclaimer
This model may make mistakes and should not be used in high-risk situations.
THE MODEL IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS MODEL INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS MODEL. | [
"# Accent Classification XL\n\nAccent Classification XL is a major improvement from ML for Speech's previous Accent Classification model. This model was trained on almost twice the amount of data and features a much larger model, based on Distil-Whisper Medium.\n\nThis model is a general improvement from the previous model, with higher accuracy across the board.\n\nIt scored 100% on the accuracy metric on an evaluation dataset, but in real-world examples shows inaccuracies and biases.",
"## Usage\n\nThe easiest way to use this model is through ML for Speech's SpeechToolkit.",
"## Biases\n\nBiases have been observed in the usage of this model.",
"## Disclaimer\n\nThis model may make mistakes and should not be used in high-risk situations.\n\nTHE MODEL IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS MODEL INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS MODEL."
] | [
"TAGS\n#transformers #safetensors #whisper #audio-classification #en #endpoints_compatible #has_space #region-us \n",
"# Accent Classification XL\n\nAccent Classification XL is a major improvement from ML for Speech's previous Accent Classification model. This model was trained on almost twice the amount of data and features a much larger model, based on Distil-Whisper Medium.\n\nThis model is a general improvement from the previous model, with higher accuracy across the board.\n\nIt scored 100% on the accuracy metric on an evaluation dataset, but in real-world examples shows inaccuracies and biases.",
"## Usage\n\nThe easiest way to use this model is through ML for Speech's SpeechToolkit.",
"## Biases\n\nBiases have been observed in the usage of this model.",
"## Disclaimer\n\nThis model may make mistakes and should not be used in high-risk situations.\n\nTHE MODEL IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS MODEL INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS MODEL."
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
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<!-- 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).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | KaggleMasterX/llama3_dpo_tok | null | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T22:36:14+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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### Model Sources [optional]
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## Uses
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### Recommendations
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
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[optional]
BibTeX:
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## Glossary [optional]
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## Model Card Contact
| [
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"TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n",
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"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers |
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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<!-- 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
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[More Information Needed]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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[More Information Needed]
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<!-- 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).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | gotchachurchkhela/SN6-26 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T22:41:03+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
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- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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] | [
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] |
text-generation | transformers |
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- 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. -->
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## 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
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<!-- 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. -->
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<!-- 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. -->
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#### 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. -->
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
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[More Information Needed]
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## 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]
| {"library_name": "transformers", "tags": []} | Fighoture/Llama-2-7b-chat-shortgpt-23-29 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T22:41:20+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
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"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
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"#### Metrics",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3284
- Accuracy: 0.8791
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3228 | 1.0 | 1563 | 0.3019 | 0.8716 |
| 0.22 | 2.0 | 3126 | 0.3284 | 0.8791 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert/distilbert-base-uncased", "model-index": [{"name": "my_awesome_model", "results": []}]} | namebobb/my_awesome_model | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T22:46:17+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert/distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| my\_awesome\_model
==================
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3284
* Accuracy: 0.8791
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 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.15.2
| [
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] |
text-generation | transformers |
<!-- 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. -->
# 0.0_ablation_sample1_4iters_iter_1
This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) on the HuggingFaceH4/ultrafeedback_binarized 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrafeedback_binarized"], "base_model": "HuggingFaceH4/mistral-7b-sft-beta", "model-index": [{"name": "0.0_ablation_sample1_4iters_iter_1", "results": []}]} | ZhangShenao/0.0_ablation_sample1_4iters_iter_1 | null | [
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"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T22:47:32+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #alignment-handbook #generated_from_trainer #trl #dpo #conversational #dataset-HuggingFaceH4/ultrafeedback_binarized #base_model-HuggingFaceH4/mistral-7b-sft-beta #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# 0.0_ablation_sample1_4iters_iter_1
This model is a fine-tuned version of HuggingFaceH4/mistral-7b-sft-beta on the HuggingFaceH4/ultrafeedback_binarized 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
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] |
text-generation | transformers |
<!-- 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. -->
# 0.0_ablation_declr_4iters_256batch_iter_3
This model is a fine-tuned version of [ZhangShenao/0.0_ablation_declr_4iters_256batch_iter_2](https://huggingface.co/ZhangShenao/0.0_ablation_declr_4iters_256batch_iter_2) on the ZhangShenao/0.0_ablation_declr_4iters_256batch_dataset 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["ZhangShenao/0.0_ablation_declr_4iters_256batch_dataset"], "base_model": "ZhangShenao/0.0_ablation_declr_4iters_256batch_iter_2", "model-index": [{"name": "0.0_ablation_declr_4iters_256batch_iter_3", "results": []}]} | ZhangShenao/0.0_ablation_declr_4iters_256batch_iter_3 | null | [
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"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T22:53:17+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #alignment-handbook #generated_from_trainer #trl #dpo #conversational #dataset-ZhangShenao/0.0_ablation_declr_4iters_256batch_dataset #base_model-ZhangShenao/0.0_ablation_declr_4iters_256batch_iter_2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# 0.0_ablation_declr_4iters_256batch_iter_3
This model is a fine-tuned version of ZhangShenao/0.0_ablation_declr_4iters_256batch_iter_2 on the ZhangShenao/0.0_ablation_declr_4iters_256batch_dataset 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| [
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"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1",
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"# 0.0_ablation_declr_4iters_256batch_iter_3\n\nThis model is a fine-tuned version of ZhangShenao/0.0_ablation_declr_4iters_256batch_iter_2 on the ZhangShenao/0.0_ablation_declr_4iters_256batch_dataset dataset.",
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"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
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"### Training results",
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] |
text2text-generation | transformers |
<!-- 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. -->
# CS505_COQE_viT5_train_Instruction0_SOAPL_h3
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) 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: 5e-05
- train_batch_size: 12
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_SOAPL_h3", "results": []}]} | ThuyNT/CS505_COQE_viT5_train_Instruction0_SOAPL_h3 | null | [
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] | null | 2024-04-20T22:53:43+00:00 | [] | [] | TAGS
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|
# CS505_COQE_viT5_train_Instruction0_SOAPL_h3
This model is a fine-tuned version of VietAI/vit5-large 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: 5e-05
- train_batch_size: 12
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 12\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP",
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] |
null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
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[More Information Needed]
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<!-- 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
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[More Information Needed]
## Training Details
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[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 0.10.0 | {"library_name": "peft", "base_model": "meta-llama/Meta-Llama-3-8B-Instruct"} | Fredithefish/Llama3RPInstruct-chkpt-2000 | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"region:us"
] | null | 2024-04-20T22:55:14+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Meta-Llama-3-8B-Instruct #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
- PEFT 0.10.0 | [
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] |
text-generation | transformers |
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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<!-- 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
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[More Information Needed]
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[More Information Needed]
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<!-- 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
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#### 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. -->
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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[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]
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| {"library_name": "transformers", "tags": []} | Fighoture/Llama-2-7b-chat-shortgpt-21-28 | null | [
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"safetensors",
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"text-generation-inference",
"region:us"
] | null | 2024-04-20T22:56:01+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
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- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- 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. -->
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## 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] | {"library_name": "transformers", "tags": []} | OwOOwO/dumbo-llama | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T22:56:02+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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] |
text-classification | transformers |
<!-- 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. -->
# citation_intent_classification_roberta
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: 0.9427
- Accuracy: 0.7986
- F1 Macro: 0.6913
## 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: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 1.4059 | 1.0 | 105 | 1.0585 | 0.6491 | 0.2714 |
| 1.0405 | 2.0 | 211 | 0.9258 | 0.6842 | 0.3254 |
| 0.8144 | 3.0 | 316 | 0.7686 | 0.7281 | 0.4907 |
| 0.5887 | 4.0 | 422 | 0.7906 | 0.7456 | 0.5518 |
| 0.404 | 5.0 | 527 | 0.6946 | 0.7719 | 0.7045 |
| 0.3302 | 6.0 | 633 | 0.8840 | 0.7719 | 0.6330 |
| 0.225 | 7.0 | 738 | 0.8200 | 0.8070 | 0.7258 |
| 0.1843 | 8.0 | 844 | 0.8336 | 0.8070 | 0.7533 |
| 0.16 | 9.0 | 949 | 0.8221 | 0.8246 | 0.7641 |
| 0.0977 | 9.95 | 1050 | 0.8798 | 0.8246 | 0.7649 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.13.1+cu117
- Datasets 2.13.2
- Tokenizers 0.13.3
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "citation_intent_classification_roberta", "results": []}]} | ltuzova/citation_intent_classification_roberta | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T23:00:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| citation\_intent\_classification\_roberta
=========================================
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9427
* Accuracy: 0.7986
* F1 Macro: 0.6913
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: 16
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.06
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.30.2
* Pytorch 1.13.1+cu117
* Datasets 2.13.2
* Tokenizers 0.13.3
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] |
text-classification | transformers |
<!-- 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. -->
# canine-rando-30-chars
This model is a fine-tuned version of [google/canine-c](https://huggingface.co/google/canine-c) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7346
- Accuracy: 0.7282
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9127 | 1.0 | 8000 | 0.8745 | 0.6576 |
| 0.7717 | 2.0 | 16000 | 0.7346 | 0.7282 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.0.0+cu117
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/canine-c", "model-index": [{"name": "canine-rando-30-chars", "results": []}]} | addykan/canine-rando-30-chars | null | [
"transformers",
"safetensors",
"canine",
"text-classification",
"generated_from_trainer",
"base_model:google/canine-c",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T23:02:00+00:00 | [] | [] | TAGS
#transformers #safetensors #canine #text-classification #generated_from_trainer #base_model-google/canine-c #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| canine-rando-30-chars
=====================
This model is a fine-tuned version of google/canine-c on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7346
* Accuracy: 0.7282
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: 2
### Training results
### Framework versions
* Transformers 4.40.0
* Pytorch 2.0.0+cu117
* Datasets 2.19.0
* Tokenizers 0.19.1
| [
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] |
null | peft |
# 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]
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### 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. -->
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<!-- 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 0.10.0 | {"library_name": "peft", "base_model": "unsloth/llama-3-8b-bnb-4bit"} | ResplendentAI/RP_Format_QuoteAsterisk_Llama3 | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"region:us"
] | null | 2024-04-20T23:03:08+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-unsloth/llama-3-8b-bnb-4bit #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
- PEFT 0.10.0 | [
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"## Model Details",
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"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.10.0"
] | [
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"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
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"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
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"## Training Details",
"### Training Data",
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"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.10.0"
] |
null | null |
<!-- 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. -->
# sum_04-20-22-59-01
This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.13.3
| {"tags": ["trl", "sft", "generated_from_trainer"], "base_model": "NousResearch/Llama-2-7b-chat-hf", "model-index": [{"name": "sum_04-20-22-59-01", "results": []}]} | reeddg/sum_04-20-22-59-01 | null | [
"tensorboard",
"trl",
"sft",
"generated_from_trainer",
"base_model:NousResearch/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-04-20T23:04:23+00:00 | [] | [] | TAGS
#tensorboard #trl #sft #generated_from_trainer #base_model-NousResearch/Llama-2-7b-chat-hf #region-us
|
# sum_04-20-22-59-01
This model is a fine-tuned version of NousResearch/Llama-2-7b-chat-hf 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.13.3
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"## Training and evaluation data\n\nMore information needed",
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"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 10",
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] |
null | mlx |
# lucataco/Mixtral-8x22B-v0.1-4bit
This model was converted to MLX format from [`mistralai/Mixtral-8x22B-v0.1`]() using mlx-lm version **0.10.0**.
Refer to the [original model card](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("lucataco/Mixtral-8x22B-v0.1-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"language": ["fr", "it", "de", "es", "en"], "license": "apache-2.0", "tags": ["moe", "mlx"]} | lucataco/Mixtral-8x22B-v0.1-4bit | null | [
"mlx",
"safetensors",
"mixtral",
"moe",
"fr",
"it",
"de",
"es",
"en",
"license:apache-2.0",
"region:us"
] | null | 2024-04-20T23:06:31+00:00 | [] | [
"fr",
"it",
"de",
"es",
"en"
] | TAGS
#mlx #safetensors #mixtral #moe #fr #it #de #es #en #license-apache-2.0 #region-us
|
# lucataco/Mixtral-8x22B-v0.1-4bit
This model was converted to MLX format from ['mistralai/Mixtral-8x22B-v0.1']() using mlx-lm version 0.10.0.
Refer to the original model card for more details on the model.
## Use with mlx
| [
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"## Use with mlx"
] |
text-classification | transformers |
<!-- 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. -->
# nlp_a4
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:
- eval_loss: 0.3511
- eval_f1: 0.4167
- eval_runtime: 128.3405
- eval_samples_per_second: 56.794
- eval_steps_per_second: 1.777
- epoch: 2.0
- step: 3684
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "roberta-base", "model-index": [{"name": "nlp_a4", "results": []}]} | hhardik/nlp_a4 | null | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T23:07:17+00:00 | [] | [] | TAGS
#transformers #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# nlp_a4
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3511
- eval_f1: 0.4167
- eval_runtime: 128.3405
- eval_samples_per_second: 56.794
- eval_steps_per_second: 1.777
- epoch: 2.0
- step: 3684
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
text-generation | transformers |
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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] | {"license": "apache-2.0", "library_name": "transformers"} | HuggingFaceFW/ablation-model-fineweb-v1 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T23:08:00+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
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"## Model Details",
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"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
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"### Results",
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"## Technical Specifications [optional]",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | null | <!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="http://professorf.com/wp-content/uploads/2014/12/VirtualWorldGirl.png" alt="ProfessorF" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">ProfessorF is Dr. Nick V. Flor</p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Phi 1 - GGUF
- Model creator: [Microsoft](https://huggingface.co/microsoft)
- Original model: [Phi 1](https://huggingface.co/microsoft/phi-1)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Microsoft's Phi 1](https://huggingface.co/microsoft/phi-1).
<!-- 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). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/professorf/phi-1-GGUF)
* [Microsoft's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/microsoft/phi-1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Phi
```
Instruct: {prompt}
Output:
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## 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
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
TBD
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files 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: ProfessorF/phi-1-GGUF and below it, a specific filename to download, such as: phi-1.Q8_0.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 professorf/phi-1-GGUF phi-1.Q8_0.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 professorf/phi-1-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 professorf/phi-1-GGUF phi-1.Q8_0.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 phi-1.Q8_0.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Instruct: {prompt}\nOutput:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 2048` 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="./phi-1.Q8_0.gguf", # Download the model file first
n_ctx=2048, # 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(
"Instruct: {prompt}\nOutput:", # 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="./phi-1.Q8_0.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 start -->
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Microsoft's Phi 2
## Model Summary
The language model Phi-1 is a Transformer with 1.3 billion parameters, specialized for basic Python coding. Its training involved a variety of data sources, including subsets of Python codes from [The Stack v1.2](https://huggingface.co/datasets/bigcode/the-stack), Q&A content from [StackOverflow](https://archive.org/download/stackexchange), competition code from [code_contests](https://github.com/deepmind/code_contests), and synthetic Python textbooks and exercises generated by [gpt-3.5-turbo-0301](https://platform.openai.com/docs/models/gpt-3-5). Even though the model and the datasets are relatively small compared to contemporary Large Language Models (LLMs), Phi-1 has demonstrated an impressive accuracy rate exceeding 50% on the simple Python coding benchmark, HumanEval.
## How to Use
Phi-1 has been integrated in the `transformers` version 4.37.0. If you are using a lower version, ensure that you are doing the following:
* When loading the model, ensure that `trust_remote_code=True` is passed as an argument of the `from_pretrained()` function.
The current `transformers` version can be verified with: `pip list | grep transformers`.
## Intended Uses
Given the nature of the training data, Phi-1 is best suited for prompts using the code format:
### Code Format:
```python
def print_prime(n):
"""
Print all primes between 1 and n
"""
for num in range(2, n+1):
for i in range(2, num):
if num % i == 0:
break
else:
print(num)
```
where the model generates the code after the comments. (Note: This is a legitimate and correct use of the else statement in Python loops.)
**Notes:**
* Phi-1 is intended for code purposes. The model-generated code should be treated as a starting point rather than a definitive solution for potential use cases. Users should be cautious when employing this model in their applications.
* Direct adoption for production coding tasks is out of the scope of this research project. As a result, Phi-1 has not been tested to ensure that it performs adequately for production-level code. Please refer to the limitation sections of this document for more details.
* If you are using `transformers<4.37.0`, always load the model with `trust_remote_code=True` to prevent side-effects.
## Sample Code
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
torch.set_default_device("cuda")
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1", torch_dtype="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1", trust_remote_code=True)
inputs = tokenizer('''def print_prime(n):
"""
Print all primes between 1 and n
"""''', return_tensors="pt", return_attention_mask=False)
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)
```
## Limitations of Phi-1
* Limited Scope: 99.8% of the Python scripts in our fine-tuning dataset use only the packages "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages, we strongly recommend users manually verify all API uses.
* Replicate Scripts Online: As our model is trained on Python scripts found online, there is a small chance it may replicate such scripts, especially if they appear repetitively across different online sources.
* Generate Inaccurate Code: The model frequently generates incorrect code. We suggest that users view these outputs as a source of inspiration rather than definitive solutions.
* Unreliable Responses to Alternate Formats: Despite appearing to comprehend instructions in formats like Q&A or chat, our models often respond with inaccurate answers, even when seeming confident. Their capabilities with non-code formats are significantly more limited.
* Limitations on Natural Language Comprehension. As a coding bot, Phi-1's main focus is to help with coding-related questions. While it may have some natural language comprehension capabilities, its primary function is not to engage in general conversations or demonstrate common sense like a general AI assistant. Its strength lies in providing assistance and guidance in the context of programming and software development.
* Potential Biases: Phi-1, like other AI models, is trained on web and synthetic data. This data can contain biases and errors that might affect the AI's performance. Biases could stem from various sources like unbalanced representation, stereotypes, or controversial opinions present in the training data. As a result, the model might sometimes generate responses that reflect these biases or errors.
## Warning about Security Risks
When leveraging Phi-1, it's paramount to be vigilant. The model, though powerful, can inadvertently introduce security vulnerabilities in the generated code. Examples include, but are not limited to:
* Directory Traversal: The code might fail to implement safe checks against directory traversal attacks, potentially allowing unauthorized access to sensitive files on your system.
* Injection Attacks: There could be lapses in escaping strings properly, making the application susceptible to SQL, OS commands, or other injection attacks.
* Misunderstanding Requirements: The model might sometimes misunderstand or oversimplify user requirements, leading to incomplete or insecure solutions.
* Lack of Input Validation: In some cases, the model might neglect to incorporate input validation or sanitize user inputs, opening doors to attacks like Cross-Site Scripting (XSS).
* Insecure Defaults: The model might recommend or generate code with insecure default settings, such as weak password requirements or unencrypted data transmissions.
* Failure in Error Handling: Improper error handling can inadvertently reveal sensitive information about the system or the application's internal workings.
Given these potential pitfalls, and others not explicitly mentioned, it's essential to thoroughly review, test, and verify the generated code before deploying it in any application, especially those that are security-sensitive. Always consult with security experts or perform rigorous penetration testing when in doubt.
## Training
### Model
* Architecture: a Transformer-based model with next-word prediction objective
* Training tokens: 54B tokens (7B unique tokens)
* Precision: fp16
* GPUs: 8 A100
* Training time: 6 days
### Software
* [PyTorch](https://github.com/pytorch/pytorch)
* [DeepSpeed](https://github.com/microsoft/DeepSpeed)
* [Flash-Attention](https://github.com/HazyResearch/flash-attention)
### License
The model is licensed under the [MIT license](https://huggingface.co/microsoft/phi-1/resolve/main/LICENSE).
### Citation
```bib
@article{gunasekar2023textbooks,
title={Textbooks Are All You Need},
author={Gunasekar, Suriya and Zhang, Yi and Aneja, Jyoti and Mendes, Caio C{\'e}sar Teodoro and Del Giorno, Allie and Gopi, Sivakanth and Javaheripi, Mojan and Kauffmann, Piero and de Rosa, Gustavo and Saarikivi, Olli and others},
journal={arXiv preprint arXiv:2306.11644},
year={2023}
}
```
## Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.<!-- original-model-card end -->
| {"language": ["en"], "license": "mit", "tags": ["nlp", "code"], "model_name": "Phi 1", "base_model": "microsoft/phi-1", "inference": false, "license_link": "https://huggingface.co/microsoft/phi-1/resolve/main/LICENSE", "license_name": "microsoft-research-license", "model_creator": "Microsoft", "model_type": "phi-msft", "pipeline_tag": "text-generation", "prompt_template": "Instruct: {prompt}\nOutput:\n", "quantized_by": "ProfessorF"} | professorf/phi-1-gguf | null | [
"gguf",
"nlp",
"code",
"text-generation",
"en",
"base_model:microsoft/phi-1",
"license:mit",
"region:us"
] | null | 2024-04-20T23:10:49+00:00 | [] | [
"en"
] | TAGS
#gguf #nlp #code #text-generation #en #base_model-microsoft/phi-1 #license-mit #region-us
|
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="URL alt="ProfessorF" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">ProfessorF is Dr. Nick V. Flor</p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
# Phi 1 - GGUF
- Model creator: Microsoft
- Original model: Phi 1
## Description
This repo contains GGUF format model files for Microsoft's Phi 1.
### About GGUF
GGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* URL. The source project for GGUF. Offers a CLI and a server option.
* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
## Repositories available
* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference
* Microsoft's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions
## Prompt template: Phi
## Compatibility
These quantised GGUFv2 files are compatible with URL from August 27th onwards, as of commit d0cee0d
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## 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
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
## Provided files
TBD
Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
## How to 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
* URL
### In 'text-generation-webui'
Under Download Model, you can enter the model repo: ProfessorF/phi-1-GGUF and below it, a specific filename to download, such as: phi-1.Q8_0.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the 'huggingface-hub' Python library:
Then you can download any individual model file to the current directory, at high speed, with a command like this:
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
For more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.
To accelerate downloads on fast connections (1Gbit/s or higher), install 'hf_transfer':
And set environment variable 'HF_HUB_ENABLE_HF_TRANSFER' to '1':
Windows Command Line users: You can set the environment variable by running 'set HF_HUB_ENABLE_HF_TRANSFER=1' before the download command.
</details>
## Example 'URL' command
Make sure you are using 'URL' from commit d0cee0d or later.
Change '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change '-c 2048' 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 URL 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 URL documentation
## 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 URL.
## How to run from Python code
You can use GGUF models from Python using the llama-cpp-python or 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.
#### First install the package
Run one of the following commands, according to your system:
#### Simple llama-cpp-python example code
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* LangChain + llama-cpp-python
* LangChain + ctransformers
# Original model card: Microsoft's Phi 2
## Model Summary
The language model Phi-1 is a Transformer with 1.3 billion parameters, specialized for basic Python coding. Its training involved a variety of data sources, including subsets of Python codes from The Stack v1.2, Q&A content from StackOverflow, competition code from code_contests, and synthetic Python textbooks and exercises generated by gpt-3.5-turbo-0301. Even though the model and the datasets are relatively small compared to contemporary Large Language Models (LLMs), Phi-1 has demonstrated an impressive accuracy rate exceeding 50% on the simple Python coding benchmark, HumanEval.
## How to Use
Phi-1 has been integrated in the 'transformers' version 4.37.0. If you are using a lower version, ensure that you are doing the following:
* When loading the model, ensure that 'trust_remote_code=True' is passed as an argument of the 'from_pretrained()' function.
The current 'transformers' version can be verified with: 'pip list | grep transformers'.
## Intended Uses
Given the nature of the training data, Phi-1 is best suited for prompts using the code format:
### Code Format:
where the model generates the code after the comments. (Note: This is a legitimate and correct use of the else statement in Python loops.)
Notes:
* Phi-1 is intended for code purposes. The model-generated code should be treated as a starting point rather than a definitive solution for potential use cases. Users should be cautious when employing this model in their applications.
* Direct adoption for production coding tasks is out of the scope of this research project. As a result, Phi-1 has not been tested to ensure that it performs adequately for production-level code. Please refer to the limitation sections of this document for more details.
* If you are using 'transformers<4.37.0', always load the model with 'trust_remote_code=True' to prevent side-effects.
## Sample Code
## Limitations of Phi-1
* Limited Scope: 99.8% of the Python scripts in our fine-tuning dataset use only the packages "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages, we strongly recommend users manually verify all API uses.
* Replicate Scripts Online: As our model is trained on Python scripts found online, there is a small chance it may replicate such scripts, especially if they appear repetitively across different online sources.
* Generate Inaccurate Code: The model frequently generates incorrect code. We suggest that users view these outputs as a source of inspiration rather than definitive solutions.
* Unreliable Responses to Alternate Formats: Despite appearing to comprehend instructions in formats like Q&A or chat, our models often respond with inaccurate answers, even when seeming confident. Their capabilities with non-code formats are significantly more limited.
* Limitations on Natural Language Comprehension. As a coding bot, Phi-1's main focus is to help with coding-related questions. While it may have some natural language comprehension capabilities, its primary function is not to engage in general conversations or demonstrate common sense like a general AI assistant. Its strength lies in providing assistance and guidance in the context of programming and software development.
* Potential Biases: Phi-1, like other AI models, is trained on web and synthetic data. This data can contain biases and errors that might affect the AI's performance. Biases could stem from various sources like unbalanced representation, stereotypes, or controversial opinions present in the training data. As a result, the model might sometimes generate responses that reflect these biases or errors.
## Warning about Security Risks
When leveraging Phi-1, it's paramount to be vigilant. The model, though powerful, can inadvertently introduce security vulnerabilities in the generated code. Examples include, but are not limited to:
* Directory Traversal: The code might fail to implement safe checks against directory traversal attacks, potentially allowing unauthorized access to sensitive files on your system.
* Injection Attacks: There could be lapses in escaping strings properly, making the application susceptible to SQL, OS commands, or other injection attacks.
* Misunderstanding Requirements: The model might sometimes misunderstand or oversimplify user requirements, leading to incomplete or insecure solutions.
* Lack of Input Validation: In some cases, the model might neglect to incorporate input validation or sanitize user inputs, opening doors to attacks like Cross-Site Scripting (XSS).
* Insecure Defaults: The model might recommend or generate code with insecure default settings, such as weak password requirements or unencrypted data transmissions.
* Failure in Error Handling: Improper error handling can inadvertently reveal sensitive information about the system or the application's internal workings.
Given these potential pitfalls, and others not explicitly mentioned, it's essential to thoroughly review, test, and verify the generated code before deploying it in any application, especially those that are security-sensitive. Always consult with security experts or perform rigorous penetration testing when in doubt.
## Training
### Model
* Architecture: a Transformer-based model with next-word prediction objective
* Training tokens: 54B tokens (7B unique tokens)
* Precision: fp16
* GPUs: 8 A100
* Training time: 6 days
### Software
* PyTorch
* DeepSpeed
* Flash-Attention
### License
The model is licensed under the MIT license.
## Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft’s Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.
| [
"# Phi 1 - GGUF\n- Model creator: Microsoft\n- Original model: Phi 1",
"## Description\n\nThis repo contains GGUF format model files for Microsoft's Phi 1.",
"### About GGUF\n\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.\n\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.",
"## Repositories available\n\n* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference\n* Microsoft's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions",
"## Prompt template: Phi",
"## Compatibility\n\nThese quantised GGUFv2 files are compatible with URL from August 27th onwards, as of commit d0cee0d\n\nThey are also compatible with many third party UIs and libraries - please see the list at the top of this README.",
"## Explanation of quantisation methods\n\n<details>\n <summary>Click to see details</summary>\n\nThe new methods available are:\n\n* 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)\n* 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.\n* 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.\n* GGML_TYPE_Q5_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw\n* 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\n\nRefer to the Provided Files table below to see what files use which methods, and how.\n</details>",
"## Provided files\n\nTBD\n\nNote: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.",
"## How to download GGUF files\n\nNote 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.\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n* LM Studio\n* LoLLMS Web UI\n* URL",
"### In 'text-generation-webui'\n\nUnder Download Model, you can enter the model repo: ProfessorF/phi-1-GGUF and below it, a specific filename to download, such as: phi-1.Q8_0.gguf.\n\nThen click Download.",
"### On the command line, including multiple files at once\n\nI recommend using the 'huggingface-hub' Python library:\n\n\n\nThen you can download any individual model file to the current directory, at high speed, with a command like this:\n\n\n\n<details>\n <summary>More advanced huggingface-cli download usage (click to read)</summary>\n\nYou can also download multiple files at once with a pattern:\n\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf_transfer':\n\n\n\nAnd set environment variable 'HF_HUB_ENABLE_HF_TRANSFER' to '1':\n\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF_HUB_ENABLE_HF_TRANSFER=1' before the download command.\n</details>",
"## Example 'URL' command\n\nMake sure you are using 'URL' from commit d0cee0d or later.\n\n\n\nChange '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\nChange '-c 2048' 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 URL automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.\n\nIf you want to have a chat-style conversation, replace the '-p <PROMPT>' argument with '-i -ins'\n\nFor other parameters and how to use them, please refer to the URL documentation",
"## How to run in 'text-generation-webui'\n\nFurther instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model URL.",
"## How to run from Python code\n\nYou can use GGUF models from Python using the llama-cpp-python or 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\n\nFor full documentation, please see: llama-cpp-python docs.",
"#### First install the package\n\nRun one of the following commands, according to your system:",
"#### Simple llama-cpp-python example code",
"## How to use with LangChain\n\nHere are guides on using llama-cpp-python and ctransformers with LangChain:\n\n* LangChain + llama-cpp-python\n* LangChain + ctransformers",
"# Original model card: Microsoft's Phi 2",
"## Model Summary\n\nThe language model Phi-1 is a Transformer with 1.3 billion parameters, specialized for basic Python coding. Its training involved a variety of data sources, including subsets of Python codes from The Stack v1.2, Q&A content from StackOverflow, competition code from code_contests, and synthetic Python textbooks and exercises generated by gpt-3.5-turbo-0301. Even though the model and the datasets are relatively small compared to contemporary Large Language Models (LLMs), Phi-1 has demonstrated an impressive accuracy rate exceeding 50% on the simple Python coding benchmark, HumanEval.",
"## How to Use\n\nPhi-1 has been integrated in the 'transformers' version 4.37.0. If you are using a lower version, ensure that you are doing the following:\n\n* When loading the model, ensure that 'trust_remote_code=True' is passed as an argument of the 'from_pretrained()' function.\n\nThe current 'transformers' version can be verified with: 'pip list | grep transformers'.",
"## Intended Uses\n\nGiven the nature of the training data, Phi-1 is best suited for prompts using the code format:",
"### Code Format:\n\n\n\nwhere the model generates the code after the comments. (Note: This is a legitimate and correct use of the else statement in Python loops.)\n\nNotes:\n\n* Phi-1 is intended for code purposes. The model-generated code should be treated as a starting point rather than a definitive solution for potential use cases. Users should be cautious when employing this model in their applications.\n\n* Direct adoption for production coding tasks is out of the scope of this research project. As a result, Phi-1 has not been tested to ensure that it performs adequately for production-level code. Please refer to the limitation sections of this document for more details.\n\n* If you are using 'transformers<4.37.0', always load the model with 'trust_remote_code=True' to prevent side-effects.",
"## Sample Code",
"## Limitations of Phi-1\n\n* Limited Scope: 99.8% of the Python scripts in our fine-tuning dataset use only the packages \"typing, math, random, collections, datetime, itertools\". If the model generates Python scripts that utilize other packages, we strongly recommend users manually verify all API uses.\n\n* Replicate Scripts Online: As our model is trained on Python scripts found online, there is a small chance it may replicate such scripts, especially if they appear repetitively across different online sources.\n\n* Generate Inaccurate Code: The model frequently generates incorrect code. We suggest that users view these outputs as a source of inspiration rather than definitive solutions.\n* Unreliable Responses to Alternate Formats: Despite appearing to comprehend instructions in formats like Q&A or chat, our models often respond with inaccurate answers, even when seeming confident. Their capabilities with non-code formats are significantly more limited.\n\n* Limitations on Natural Language Comprehension. As a coding bot, Phi-1's main focus is to help with coding-related questions. While it may have some natural language comprehension capabilities, its primary function is not to engage in general conversations or demonstrate common sense like a general AI assistant. Its strength lies in providing assistance and guidance in the context of programming and software development.\n\n* Potential Biases: Phi-1, like other AI models, is trained on web and synthetic data. This data can contain biases and errors that might affect the AI's performance. Biases could stem from various sources like unbalanced representation, stereotypes, or controversial opinions present in the training data. As a result, the model might sometimes generate responses that reflect these biases or errors.",
"## Warning about Security Risks\n\nWhen leveraging Phi-1, it's paramount to be vigilant. The model, though powerful, can inadvertently introduce security vulnerabilities in the generated code. Examples include, but are not limited to:\n\n* Directory Traversal: The code might fail to implement safe checks against directory traversal attacks, potentially allowing unauthorized access to sensitive files on your system.\n\n* Injection Attacks: There could be lapses in escaping strings properly, making the application susceptible to SQL, OS commands, or other injection attacks.\n\n* Misunderstanding Requirements: The model might sometimes misunderstand or oversimplify user requirements, leading to incomplete or insecure solutions.\n\n* Lack of Input Validation: In some cases, the model might neglect to incorporate input validation or sanitize user inputs, opening doors to attacks like Cross-Site Scripting (XSS).\n\n* Insecure Defaults: The model might recommend or generate code with insecure default settings, such as weak password requirements or unencrypted data transmissions.\n\n* Failure in Error Handling: Improper error handling can inadvertently reveal sensitive information about the system or the application's internal workings.\n\nGiven these potential pitfalls, and others not explicitly mentioned, it's essential to thoroughly review, test, and verify the generated code before deploying it in any application, especially those that are security-sensitive. Always consult with security experts or perform rigorous penetration testing when in doubt.",
"## Training",
"### Model\n\n* Architecture: a Transformer-based model with next-word prediction objective\n\n* Training tokens: 54B tokens (7B unique tokens)\n\n* Precision: fp16\n\n* GPUs: 8 A100\n\n* Training time: 6 days",
"### Software\n\n* PyTorch\n\n* DeepSpeed\n\n* Flash-Attention",
"### License\n\nThe model is licensed under the MIT license.",
"## Trademarks\n\nThis project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft’s Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies."
] | [
"TAGS\n#gguf #nlp #code #text-generation #en #base_model-microsoft/phi-1 #license-mit #region-us \n",
"# Phi 1 - GGUF\n- Model creator: Microsoft\n- Original model: Phi 1",
"## Description\n\nThis repo contains GGUF format model files for Microsoft's Phi 1.",
"### About GGUF\n\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.\n\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.",
"## Repositories available\n\n* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference\n* Microsoft's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions",
"## Prompt template: Phi",
"## Compatibility\n\nThese quantised GGUFv2 files are compatible with URL from August 27th onwards, as of commit d0cee0d\n\nThey are also compatible with many third party UIs and libraries - please see the list at the top of this README.",
"## Explanation of quantisation methods\n\n<details>\n <summary>Click to see details</summary>\n\nThe new methods available are:\n\n* 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)\n* 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.\n* 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.\n* GGML_TYPE_Q5_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw\n* 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\n\nRefer to the Provided Files table below to see what files use which methods, and how.\n</details>",
"## Provided files\n\nTBD\n\nNote: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.",
"## How to download GGUF files\n\nNote 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.\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n* LM Studio\n* LoLLMS Web UI\n* URL",
"### In 'text-generation-webui'\n\nUnder Download Model, you can enter the model repo: ProfessorF/phi-1-GGUF and below it, a specific filename to download, such as: phi-1.Q8_0.gguf.\n\nThen click Download.",
"### On the command line, including multiple files at once\n\nI recommend using the 'huggingface-hub' Python library:\n\n\n\nThen you can download any individual model file to the current directory, at high speed, with a command like this:\n\n\n\n<details>\n <summary>More advanced huggingface-cli download usage (click to read)</summary>\n\nYou can also download multiple files at once with a pattern:\n\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf_transfer':\n\n\n\nAnd set environment variable 'HF_HUB_ENABLE_HF_TRANSFER' to '1':\n\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF_HUB_ENABLE_HF_TRANSFER=1' before the download command.\n</details>",
"## Example 'URL' command\n\nMake sure you are using 'URL' from commit d0cee0d or later.\n\n\n\nChange '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\nChange '-c 2048' 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 URL automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.\n\nIf you want to have a chat-style conversation, replace the '-p <PROMPT>' argument with '-i -ins'\n\nFor other parameters and how to use them, please refer to the URL documentation",
"## How to run in 'text-generation-webui'\n\nFurther instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model URL.",
"## How to run from Python code\n\nYou can use GGUF models from Python using the llama-cpp-python or 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\n\nFor full documentation, please see: llama-cpp-python docs.",
"#### First install the package\n\nRun one of the following commands, according to your system:",
"#### Simple llama-cpp-python example code",
"## How to use with LangChain\n\nHere are guides on using llama-cpp-python and ctransformers with LangChain:\n\n* LangChain + llama-cpp-python\n* LangChain + ctransformers",
"# Original model card: Microsoft's Phi 2",
"## Model Summary\n\nThe language model Phi-1 is a Transformer with 1.3 billion parameters, specialized for basic Python coding. Its training involved a variety of data sources, including subsets of Python codes from The Stack v1.2, Q&A content from StackOverflow, competition code from code_contests, and synthetic Python textbooks and exercises generated by gpt-3.5-turbo-0301. Even though the model and the datasets are relatively small compared to contemporary Large Language Models (LLMs), Phi-1 has demonstrated an impressive accuracy rate exceeding 50% on the simple Python coding benchmark, HumanEval.",
"## How to Use\n\nPhi-1 has been integrated in the 'transformers' version 4.37.0. If you are using a lower version, ensure that you are doing the following:\n\n* When loading the model, ensure that 'trust_remote_code=True' is passed as an argument of the 'from_pretrained()' function.\n\nThe current 'transformers' version can be verified with: 'pip list | grep transformers'.",
"## Intended Uses\n\nGiven the nature of the training data, Phi-1 is best suited for prompts using the code format:",
"### Code Format:\n\n\n\nwhere the model generates the code after the comments. (Note: This is a legitimate and correct use of the else statement in Python loops.)\n\nNotes:\n\n* Phi-1 is intended for code purposes. The model-generated code should be treated as a starting point rather than a definitive solution for potential use cases. Users should be cautious when employing this model in their applications.\n\n* Direct adoption for production coding tasks is out of the scope of this research project. As a result, Phi-1 has not been tested to ensure that it performs adequately for production-level code. Please refer to the limitation sections of this document for more details.\n\n* If you are using 'transformers<4.37.0', always load the model with 'trust_remote_code=True' to prevent side-effects.",
"## Sample Code",
"## Limitations of Phi-1\n\n* Limited Scope: 99.8% of the Python scripts in our fine-tuning dataset use only the packages \"typing, math, random, collections, datetime, itertools\". If the model generates Python scripts that utilize other packages, we strongly recommend users manually verify all API uses.\n\n* Replicate Scripts Online: As our model is trained on Python scripts found online, there is a small chance it may replicate such scripts, especially if they appear repetitively across different online sources.\n\n* Generate Inaccurate Code: The model frequently generates incorrect code. We suggest that users view these outputs as a source of inspiration rather than definitive solutions.\n* Unreliable Responses to Alternate Formats: Despite appearing to comprehend instructions in formats like Q&A or chat, our models often respond with inaccurate answers, even when seeming confident. Their capabilities with non-code formats are significantly more limited.\n\n* Limitations on Natural Language Comprehension. As a coding bot, Phi-1's main focus is to help with coding-related questions. While it may have some natural language comprehension capabilities, its primary function is not to engage in general conversations or demonstrate common sense like a general AI assistant. Its strength lies in providing assistance and guidance in the context of programming and software development.\n\n* Potential Biases: Phi-1, like other AI models, is trained on web and synthetic data. This data can contain biases and errors that might affect the AI's performance. Biases could stem from various sources like unbalanced representation, stereotypes, or controversial opinions present in the training data. As a result, the model might sometimes generate responses that reflect these biases or errors.",
"## Warning about Security Risks\n\nWhen leveraging Phi-1, it's paramount to be vigilant. The model, though powerful, can inadvertently introduce security vulnerabilities in the generated code. Examples include, but are not limited to:\n\n* Directory Traversal: The code might fail to implement safe checks against directory traversal attacks, potentially allowing unauthorized access to sensitive files on your system.\n\n* Injection Attacks: There could be lapses in escaping strings properly, making the application susceptible to SQL, OS commands, or other injection attacks.\n\n* Misunderstanding Requirements: The model might sometimes misunderstand or oversimplify user requirements, leading to incomplete or insecure solutions.\n\n* Lack of Input Validation: In some cases, the model might neglect to incorporate input validation or sanitize user inputs, opening doors to attacks like Cross-Site Scripting (XSS).\n\n* Insecure Defaults: The model might recommend or generate code with insecure default settings, such as weak password requirements or unencrypted data transmissions.\n\n* Failure in Error Handling: Improper error handling can inadvertently reveal sensitive information about the system or the application's internal workings.\n\nGiven these potential pitfalls, and others not explicitly mentioned, it's essential to thoroughly review, test, and verify the generated code before deploying it in any application, especially those that are security-sensitive. Always consult with security experts or perform rigorous penetration testing when in doubt.",
"## Training",
"### Model\n\n* Architecture: a Transformer-based model with next-word prediction objective\n\n* Training tokens: 54B tokens (7B unique tokens)\n\n* Precision: fp16\n\n* GPUs: 8 A100\n\n* Training time: 6 days",
"### Software\n\n* PyTorch\n\n* DeepSpeed\n\n* Flash-Attention",
"### License\n\nThe model is licensed under the MIT license.",
"## Trademarks\n\nThis project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft’s Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies."
] |
text-generation | transformers |
<!-- 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. -->
# Dolphin 2.9 Llama 3 8b 🐬
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
Discord: https://discord.gg/8fbBeC7ZGx
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
A bug has been found in the Dolphin 2.9 dataset in SystemConversations that causes the model to overly talk about the "SYSTEM MESSAGE". To counter this, we recommend you add a statement in the system message directing the model not to mention the system message. An example system message is "The assistant is named Dolphin. A helpful and friendly AI assistant, Dolphin avoids discussing the system message unless directly asked about it."
My appreciation for the sponsors of Dolphin 2.9:
- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 10xL40S node
This model is based on Llama-3-8b, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE)
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 2.5 days on 8x L40S provided by Crusoe Cloud
This model was trained FFT on all parameters, using ChatML prompt template format.
example:
```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false
load_in_8bit: false
load_in_4bit: false
strict: false
model_config:
datasets:
- path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Ultrachat200kunfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/SystemConversations.jsonl
type: sharegpt
conversation: chatml
chat_template: chatml
dataset_prepared_path: /workspace/datasets/dolphin-2.9/thingy
val_set_size: 0.0002
output_dir: ./out
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
gradient_accumulation_steps: 4
micro_batch_size: 3
num_epochs: 3
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
wandb_project: dolphin-2.9-mixtral-8x22b
wandb_watch:
wandb_run_id:
wandb_log_model:
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 4
save_total_limit: 2
save_steps:
evals_per_epoch: 4
eval_sample_packing: false
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
eos_token: "<|im_end|>"
pad_token: "<|end_of_text|>"
tokens:
- "<|im_start|>"
- "<|im_end|>"
```
</details><br>
## Quants
GGUF : https://huggingface.co/QuantFactory/dolphin-2.9-llama3-8b-GGUF
GGUF with imatrix: https://huggingface.co/bartowski/dolphin-2.9-llama3-8b-GGUF
Exllamav2: https://huggingface.co/bartowski/dolphin-2.9-llama3-8b-exl2
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- total_eval_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 7
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.146 | 0.0005 | 1 | 1.1064 |
| 0.6962 | 0.2501 | 555 | 0.6636 |
| 0.6857 | 0.5001 | 1110 | 0.6503 |
| 0.6592 | 0.7502 | 1665 | 0.6419 |
| 0.6465 | 1.0002 | 2220 | 0.6317 |
| 0.5295 | 1.2395 | 2775 | 0.6408 |
| 0.5302 | 1.4895 | 3330 | 0.6351 |
| 0.5188 | 1.7396 | 3885 | 0.6227 |
| 0.521 | 1.9896 | 4440 | 0.6168 |
| 0.3968 | 2.2289 | 4995 | 0.6646 |
| 0.3776 | 2.4789 | 5550 | 0.6619 |
| 0.3983 | 2.7290 | 6105 | 0.6602 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1 | {"license": "other", "tags": ["generated_from_trainer", "axolotl"], "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "HuggingFaceH4/ultrachat_200k", "microsoft/orca-math-word-problems-200k", "abacusai/SystemChat-1.1", "Locutusque/function-calling-chatml", "internlm/Agent-FLAN"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "out", "results": []}]} | cognitivecomputations/dolphin-2.9-llama3-8b | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"conversational",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:abacusai/SystemChat-1.1",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T23:14:52+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #generated_from_trainer #axolotl #conversational #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Dolphin 2.9 Llama 3 8b
======================
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
Discord: URL
<img src="URL width="600" />
A bug has been found in the Dolphin 2.9 dataset in SystemConversations that causes the model to overly talk about the "SYSTEM MESSAGE". To counter this, we recommend you add a statement in the system message directing the model not to mention the system message. An example system message is "The assistant is named Dolphin. A helpful and friendly AI assistant, Dolphin avoids discussing the system message unless directly asked about it."
My appreciation for the sponsors of Dolphin 2.9:
* Crusoe Cloud - provided excellent on-demand 10xL40S node
This model is based on Llama-3-8b, and is governed by META LLAMA 3 COMMUNITY LICENSE AGREEMENT
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 2.5 days on 8x L40S provided by Crusoe Cloud
This model was trained FFT on all parameters, using ChatML prompt template format.
example:
Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. URL You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.
<img src="URL alt="Built with Axolotl" width="200" height="32"/>
See axolotl config
axolotl version: '0.4.0'
Quants
------
GGUF : URL
GGUF with imatrix: URL
Exllamav2: URL
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 3
* eval\_batch\_size: 3
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 96
* total\_eval\_batch\_size: 24
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 7
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.40.0
* Pytorch 2.2.2+cu121
* Datasets 2.18.0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size: 3\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 96\n* total\\_eval\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 7\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.19.1"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #generated_from_trainer #axolotl #conversational #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size: 3\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 96\n* total\\_eval\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 7\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.19.1"
] |
text-generation | transformers |
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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<!-- 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
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[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## 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. -->
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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<!-- 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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## 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]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"license": "apache-2.0", "library_name": "transformers"} | HuggingFaceFW/ablation-model-c4 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T23:15:50+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "133.79 +/- 79.48", "name": "mean_reward", "verified": false}]}]}]} | poojakannanv/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-04-20T23:17:20+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] |
text-generation | transformers |
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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## Environmental Impact
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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]
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[More Information Needed] | {"license": "apache-2.0", "library_name": "transformers"} | HuggingFaceFW/ablation-model-dolma-v1_6 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T23:19:10+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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- Language(s) (NLP):
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## Uses
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## How to Get Started with the Model
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## Training Details
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## Evaluation
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
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- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"## Bias, Risks, and Limitations",
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"## Training Details",
"### Training Data",
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"#### Metrics",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
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"## Model Card Contact"
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"## Model Card Contact"
] |
text-generation | transformers |
<!-- 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. -->
# 0.0_ablation_5e7lr_4iters_256batch_iter_3
This model is a fine-tuned version of [ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_iter_2](https://huggingface.co/ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_iter_2) on the ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_dataset 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_dataset"], "base_model": "ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_iter_2", "model-index": [{"name": "0.0_ablation_5e7lr_4iters_256batch_iter_3", "results": []}]} | ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_iter_3 | null | [
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"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T23:19:39+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #alignment-handbook #generated_from_trainer #trl #dpo #conversational #dataset-ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_dataset #base_model-ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_iter_2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# 0.0_ablation_5e7lr_4iters_256batch_iter_3
This model is a fine-tuned version of ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_iter_2 on the ZhangShenao/0.0_ablation_5e7lr_4iters_256batch_dataset 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| [
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"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1",
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"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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<!-- 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
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[More Information Needed]
## Training Details
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### 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. -->
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<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
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## 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]
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[More Information Needed]
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[More Information Needed]
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## 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. -->
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[More Information Needed] | {"license": "apache-2.0", "library_name": "transformers"} | HuggingFaceFW/ablation-model-refinedweb | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T23:20:09+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
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"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"## Model Card Contact"
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] |
null | peft |
# 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
| {"library_name": "peft", "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0"} | bmehrba/TinyLlama-1.1B-Chat-v1.0-fine-tuned-adapters_GrounTruth_tiny_Seed101 | null | [
"peft",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null | 2024-04-20T23:20:29+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## 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
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"### Framework versions\n\n\n- PEFT 0.7.0.dev0"
] |
null | peft |
# 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
| {"library_name": "peft", "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0"} | bmehrba/TinyLlama-1.1B-Chat-v1.0-fine-tuned_GrounTruth_tiny_Seed101 | null | [
"peft",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null | 2024-04-20T23:20:34+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## 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
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"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.7.0.dev0"
] |
text-generation | transformers |
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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] | {"library_name": "transformers", "tags": []} | chandc/OrpoLlama-3-8B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T23:20:57+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers |
I made a couple more models just for fun. I can’t vouch for the quality, since I didn’t do any tests.
Link to the original model and the script with which I created these models:
- Himitsui/KuroMitsu-11B: https://huggingface.co/Himitsui/KuroMitsu-11B
- FantasiaFoundry/GGUF-Quantization-Script: https://huggingface.co/FantasiaFoundry/GGUF-Quantization-Script | {"language": ["en"], "license": "cc-by-4.0", "library_name": "transformers", "tags": ["llama"], "pipeline_tag": "text-generation"} | SolidSnacke/KuroMitsu-11B-i-GGUF | null | [
"transformers",
"gguf",
"llama",
"text-generation",
"en",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T23:22:11+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #llama #text-generation #en #license-cc-by-4.0 #endpoints_compatible #region-us
|
I made a couple more models just for fun. I can’t vouch for the quality, since I didn’t do any tests.
Link to the original model and the script with which I created these models:
- Himitsui/KuroMitsu-11B: URL
- FantasiaFoundry/GGUF-Quantization-Script: URL | [] | [
"TAGS\n#transformers #gguf #llama #text-generation #en #license-cc-by-4.0 #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- 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. -->
# canine-rando-30-chars-5-epoch
This model is a fine-tuned version of [google/canine-c](https://huggingface.co/google/canine-c) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6015
- Accuracy: 0.7066
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 242 | 0.6975 | 0.5289 |
| No log | 2.0 | 484 | 0.5939 | 0.6983 |
| 0.6861 | 3.0 | 726 | 0.5975 | 0.6942 |
| 0.6861 | 4.0 | 968 | 0.5858 | 0.7149 |
| 0.6213 | 5.0 | 1210 | 0.6015 | 0.7066 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.0.0+cu117
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/canine-c", "model-index": [{"name": "canine-rando-30-chars-5-epoch", "results": []}]} | addykan/canine-rando-30-chars-5-epoch | null | [
"transformers",
"safetensors",
"canine",
"text-classification",
"generated_from_trainer",
"base_model:google/canine-c",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T23:22:38+00:00 | [] | [] | TAGS
#transformers #safetensors #canine #text-classification #generated_from_trainer #base_model-google/canine-c #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| canine-rando-30-chars-5-epoch
=============================
This model is a fine-tuned version of google/canine-c on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6015
* Accuracy: 0.7066
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.40.0
* Pytorch 2.0.0+cu117
* Datasets 2.19.0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.0.0+cu117\n* Datasets 2.19.0\n* Tokenizers 0.19.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.0.0+cu117\n* Datasets 2.19.0\n* Tokenizers 0.19.1"
] |
text2text-generation | transformers |
<!-- 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. -->
# CS505_COQE_viT5_train_Instruction0_ASPOL_h3
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) 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: 5e-05
- train_batch_size: 12
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_ASPOL_h3", "results": []}]} | ThuyNT/CS505_COQE_viT5_train_Instruction0_ASPOL_h3 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T23:24:28+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# CS505_COQE_viT5_train_Instruction0_ASPOL_h3
This model is a fine-tuned version of VietAI/vit5-large 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: 5e-05
- train_batch_size: 12
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
"# CS505_COQE_viT5_train_Instruction0_ASPOL_h3\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 12\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP",
"### Training results",
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"# CS505_COQE_viT5_train_Instruction0_ASPOL_h3\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 12\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP",
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] |
object-detection | transformers |
<!-- 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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/u2o06hbj)
# hustvl-yolos-small-finetuned-10k-cppe5
This model is a fine-tuned version of [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small) on the cppe-5 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4674
- Map: 0.3363
- Map 50: 0.6506
- Map 75: 0.2992
- Map Small: 0.2695
- Map Medium: 0.2282
- Map Large: 0.4791
- Mar 1: 0.3441
- Mar 10: 0.4988
- Mar 100: 0.5186
- Mar Small: 0.3192
- Mar Medium: 0.3884
- Mar Large: 0.6982
- Map Coverall: 0.607
- Mar 100 Coverall: 0.7716
- Map Face Shield: 0.3854
- Mar 100 Face Shield: 0.5883
- Map Gloves: 0.2283
- Mar 100 Gloves: 0.4093
- Map Goggles: 0.1228
- Mar 100 Goggles: 0.3319
- Map Mask: 0.3379
- Mar 100 Mask: 0.4916
## 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: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Coverall | Mar 100 Coverall | Map Face Shield | Mar 100 Face Shield | Map Gloves | Mar 100 Gloves | Map Goggles | Mar 100 Goggles | Map Mask | Mar 100 Mask |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:------------:|:----------------:|:---------------:|:-------------------:|:----------:|:--------------:|:-----------:|:---------------:|:--------:|:------------:|
| 1.9479 | 1.0 | 107 | 1.6080 | 0.15 | 0.3271 | 0.1142 | 0.0227 | 0.0603 | 0.1779 | 0.1663 | 0.3089 | 0.3414 | 0.098 | 0.2108 | 0.3973 | 0.499 | 0.7191 | 0.048 | 0.2467 | 0.0276 | 0.3377 | 0.0156 | 0.0764 | 0.1598 | 0.3268 |
| 1.4786 | 2.0 | 214 | 1.4219 | 0.1899 | 0.3864 | 0.1627 | 0.0539 | 0.0911 | 0.2756 | 0.2423 | 0.4081 | 0.4299 | 0.0864 | 0.3003 | 0.5424 | 0.5552 | 0.7951 | 0.1065 | 0.4933 | 0.0707 | 0.326 | 0.042 | 0.2028 | 0.1752 | 0.3324 |
| 1.3842 | 3.0 | 321 | 1.3797 | 0.2174 | 0.4379 | 0.1755 | 0.0584 | 0.1132 | 0.326 | 0.2616 | 0.4277 | 0.4505 | 0.2266 | 0.3179 | 0.61 | 0.5588 | 0.7698 | 0.1542 | 0.475 | 0.0952 | 0.35 | 0.0469 | 0.2764 | 0.2321 | 0.3816 |
| 1.2986 | 4.0 | 428 | 1.3926 | 0.1978 | 0.4118 | 0.1625 | 0.0355 | 0.0902 | 0.3168 | 0.2456 | 0.4139 | 0.4312 | 0.181 | 0.3023 | 0.5948 | 0.56 | 0.7568 | 0.1293 | 0.455 | 0.1054 | 0.3574 | 0.0389 | 0.2681 | 0.1553 | 0.319 |
| 1.233 | 5.0 | 535 | 1.3301 | 0.2271 | 0.5034 | 0.1694 | 0.0505 | 0.1223 | 0.3357 | 0.2595 | 0.4181 | 0.4355 | 0.205 | 0.304 | 0.5722 | 0.5531 | 0.766 | 0.2005 | 0.475 | 0.1371 | 0.3647 | 0.0537 | 0.2486 | 0.1912 | 0.3229 |
| 1.1644 | 6.0 | 642 | 1.2556 | 0.2497 | 0.5164 | 0.1981 | 0.0579 | 0.1447 | 0.3657 | 0.2915 | 0.4434 | 0.4598 | 0.1981 | 0.3224 | 0.6256 | 0.5888 | 0.7753 | 0.1927 | 0.4933 | 0.1338 | 0.373 | 0.0736 | 0.2667 | 0.2597 | 0.3905 |
| 1.1229 | 7.0 | 749 | 1.2388 | 0.234 | 0.4963 | 0.1996 | 0.1279 | 0.1367 | 0.3492 | 0.2638 | 0.4433 | 0.4576 | 0.2508 | 0.3912 | 0.6242 | 0.5616 | 0.7895 | 0.1503 | 0.4883 | 0.1564 | 0.373 | 0.043 | 0.2736 | 0.2589 | 0.3637 |
| 1.0939 | 8.0 | 856 | 1.2988 | 0.2343 | 0.5195 | 0.1773 | 0.0506 | 0.1319 | 0.3447 | 0.2687 | 0.3976 | 0.4065 | 0.1377 | 0.2645 | 0.5857 | 0.5806 | 0.7494 | 0.1693 | 0.4083 | 0.1114 | 0.3059 | 0.0683 | 0.2069 | 0.2419 | 0.362 |
| 1.0571 | 9.0 | 963 | 1.2067 | 0.2604 | 0.5346 | 0.2265 | 0.0671 | 0.1565 | 0.383 | 0.3001 | 0.4545 | 0.4655 | 0.1538 | 0.3488 | 0.625 | 0.6037 | 0.7586 | 0.2 | 0.49 | 0.1749 | 0.3858 | 0.0758 | 0.3097 | 0.2477 | 0.3832 |
| 1.0535 | 10.0 | 1070 | 1.2278 | 0.2534 | 0.5112 | 0.2324 | 0.0556 | 0.1444 | 0.3914 | 0.3011 | 0.4593 | 0.4806 | 0.1686 | 0.3462 | 0.6597 | 0.6104 | 0.7821 | 0.1993 | 0.525 | 0.1614 | 0.3956 | 0.0553 | 0.3014 | 0.2407 | 0.3989 |
| 0.9948 | 11.0 | 1177 | 1.2097 | 0.2653 | 0.5317 | 0.2346 | 0.2441 | 0.1608 | 0.3892 | 0.3125 | 0.4766 | 0.4903 | 0.2854 | 0.3756 | 0.643 | 0.5968 | 0.7648 | 0.2183 | 0.5533 | 0.1797 | 0.3936 | 0.0557 | 0.3306 | 0.276 | 0.4089 |
| 0.9766 | 12.0 | 1284 | 1.2409 | 0.258 | 0.5423 | 0.2228 | 0.1649 | 0.1503 | 0.3925 | 0.2986 | 0.4553 | 0.4667 | 0.2045 | 0.3461 | 0.6238 | 0.5955 | 0.7667 | 0.2124 | 0.5267 | 0.1877 | 0.3613 | 0.0559 | 0.3 | 0.2382 | 0.3788 |
| 0.9642 | 13.0 | 1391 | 1.2483 | 0.2733 | 0.5497 | 0.2377 | 0.1841 | 0.1614 | 0.401 | 0.3115 | 0.4527 | 0.4643 | 0.2125 | 0.3235 | 0.6653 | 0.6113 | 0.7821 | 0.2527 | 0.55 | 0.1924 | 0.3544 | 0.0694 | 0.2639 | 0.2406 | 0.3709 |
| 0.9632 | 14.0 | 1498 | 1.2315 | 0.2728 | 0.544 | 0.2363 | 0.0893 | 0.1691 | 0.3978 | 0.308 | 0.4605 | 0.4713 | 0.1705 | 0.345 | 0.6593 | 0.6124 | 0.7753 | 0.2228 | 0.4933 | 0.1615 | 0.3475 | 0.0861 | 0.325 | 0.2814 | 0.4151 |
| 0.9342 | 15.0 | 1605 | 1.2185 | 0.2843 | 0.5699 | 0.2436 | 0.2202 | 0.1798 | 0.4107 | 0.313 | 0.4628 | 0.4732 | 0.2514 | 0.3365 | 0.6666 | 0.6132 | 0.7858 | 0.2452 | 0.54 | 0.1971 | 0.3578 | 0.0855 | 0.2736 | 0.2807 | 0.4089 |
| 0.9118 | 16.0 | 1712 | 1.2268 | 0.2759 | 0.5763 | 0.2359 | 0.1335 | 0.1694 | 0.4276 | 0.3045 | 0.4556 | 0.4712 | 0.2254 | 0.3336 | 0.6805 | 0.6205 | 0.8 | 0.2553 | 0.53 | 0.161 | 0.349 | 0.0765 | 0.2833 | 0.2661 | 0.3939 |
| 0.9031 | 17.0 | 1819 | 1.2224 | 0.2794 | 0.5673 | 0.2443 | 0.2481 | 0.1638 | 0.41 | 0.3079 | 0.4683 | 0.4888 | 0.275 | 0.3562 | 0.6834 | 0.6053 | 0.8012 | 0.2647 | 0.5367 | 0.1652 | 0.3735 | 0.1022 | 0.3542 | 0.2594 | 0.3782 |
| 0.8556 | 18.0 | 1926 | 1.1809 | 0.2933 | 0.5872 | 0.265 | 0.2545 | 0.1984 | 0.3951 | 0.3132 | 0.4787 | 0.496 | 0.2908 | 0.3581 | 0.6792 | 0.5871 | 0.7981 | 0.2753 | 0.5567 | 0.1937 | 0.3971 | 0.0936 | 0.2903 | 0.3171 | 0.438 |
| 0.8221 | 19.0 | 2033 | 1.2087 | 0.2939 | 0.5973 | 0.249 | 0.0952 | 0.1777 | 0.4381 | 0.312 | 0.4685 | 0.4845 | 0.133 | 0.3389 | 0.6883 | 0.6256 | 0.8111 | 0.2686 | 0.5233 | 0.1739 | 0.3603 | 0.1 | 0.3069 | 0.3015 | 0.4207 |
| 0.8202 | 20.0 | 2140 | 1.2266 | 0.2934 | 0.5939 | 0.2522 | 0.1408 | 0.1867 | 0.4349 | 0.3234 | 0.4769 | 0.4945 | 0.1756 | 0.3613 | 0.6994 | 0.617 | 0.7975 | 0.3123 | 0.5533 | 0.1686 | 0.3637 | 0.0951 | 0.3486 | 0.2741 | 0.4095 |
| 0.7954 | 21.0 | 2247 | 1.2556 | 0.2905 | 0.6004 | 0.2432 | 0.1632 | 0.1839 | 0.438 | 0.3135 | 0.4719 | 0.4865 | 0.2116 | 0.3531 | 0.6842 | 0.6094 | 0.7741 | 0.2976 | 0.56 | 0.1763 | 0.3574 | 0.089 | 0.3069 | 0.2802 | 0.4341 |
| 0.7868 | 22.0 | 2354 | 1.2723 | 0.2717 | 0.559 | 0.2363 | 0.1494 | 0.1689 | 0.411 | 0.293 | 0.4624 | 0.4771 | 0.1901 | 0.3415 | 0.6822 | 0.5933 | 0.7735 | 0.3068 | 0.5717 | 0.1651 | 0.3358 | 0.0718 | 0.3236 | 0.2213 | 0.381 |
| 0.7827 | 23.0 | 2461 | 1.2710 | 0.2957 | 0.6092 | 0.2317 | 0.2667 | 0.1878 | 0.4432 | 0.3189 | 0.4785 | 0.4928 | 0.2995 | 0.3684 | 0.6803 | 0.6028 | 0.7802 | 0.3233 | 0.5533 | 0.1795 | 0.3843 | 0.1219 | 0.3333 | 0.2511 | 0.4128 |
| 0.7795 | 24.0 | 2568 | 1.2305 | 0.3039 | 0.6021 | 0.2739 | 0.2054 | 0.2003 | 0.4373 | 0.3229 | 0.4902 | 0.5068 | 0.2637 | 0.3887 | 0.6889 | 0.6232 | 0.766 | 0.2872 | 0.575 | 0.1954 | 0.3838 | 0.1011 | 0.3528 | 0.3125 | 0.4564 |
| 0.7524 | 25.0 | 2675 | 1.2481 | 0.2897 | 0.5968 | 0.2423 | 0.2147 | 0.1875 | 0.4391 | 0.3168 | 0.4866 | 0.5029 | 0.2532 | 0.3815 | 0.689 | 0.598 | 0.779 | 0.2973 | 0.58 | 0.1842 | 0.3833 | 0.1061 | 0.3417 | 0.2627 | 0.4307 |
| 0.7446 | 26.0 | 2782 | 1.2481 | 0.2894 | 0.5835 | 0.2479 | 0.16 | 0.182 | 0.441 | 0.3083 | 0.4857 | 0.496 | 0.1905 | 0.3783 | 0.6953 | 0.6217 | 0.7765 | 0.2919 | 0.5733 | 0.1736 | 0.3652 | 0.068 | 0.3319 | 0.292 | 0.433 |
| 0.7203 | 27.0 | 2889 | 1.2720 | 0.2871 | 0.5801 | 0.2338 | 0.2113 | 0.1852 | 0.4258 | 0.3107 | 0.48 | 0.4955 | 0.2698 | 0.3781 | 0.6844 | 0.6106 | 0.7698 | 0.2779 | 0.5633 | 0.2001 | 0.3819 | 0.0842 | 0.3333 | 0.2625 | 0.4291 |
| 0.7239 | 28.0 | 2996 | 1.2166 | 0.3043 | 0.621 | 0.256 | 0.1515 | 0.1926 | 0.4491 | 0.3286 | 0.4874 | 0.5037 | 0.2054 | 0.3733 | 0.6882 | 0.6196 | 0.7728 | 0.3166 | 0.58 | 0.1917 | 0.3799 | 0.095 | 0.3319 | 0.2985 | 0.4536 |
| 0.6987 | 29.0 | 3103 | 1.2685 | 0.3008 | 0.6003 | 0.256 | 0.2512 | 0.1964 | 0.4285 | 0.3231 | 0.4964 | 0.5118 | 0.3015 | 0.3921 | 0.6779 | 0.6247 | 0.7716 | 0.2988 | 0.5933 | 0.1999 | 0.3819 | 0.0914 | 0.3514 | 0.2895 | 0.4609 |
| 0.6776 | 30.0 | 3210 | 1.2834 | 0.2976 | 0.6053 | 0.2574 | 0.1637 | 0.1951 | 0.4457 | 0.3311 | 0.4801 | 0.4952 | 0.2704 | 0.3691 | 0.6739 | 0.6165 | 0.787 | 0.28 | 0.59 | 0.194 | 0.3652 | 0.1218 | 0.3236 | 0.2759 | 0.4101 |
| 0.6695 | 31.0 | 3317 | 1.2599 | 0.2957 | 0.5942 | 0.268 | 0.1376 | 0.1935 | 0.4377 | 0.3115 | 0.4812 | 0.4988 | 0.2589 | 0.3643 | 0.6915 | 0.6154 | 0.7852 | 0.2669 | 0.585 | 0.2035 | 0.3936 | 0.0936 | 0.2917 | 0.2991 | 0.4385 |
| 0.6829 | 32.0 | 3424 | 1.3085 | 0.2938 | 0.5904 | 0.2481 | 0.2294 | 0.1851 | 0.4296 | 0.311 | 0.4789 | 0.497 | 0.2771 | 0.3744 | 0.6878 | 0.622 | 0.7741 | 0.3067 | 0.5867 | 0.1709 | 0.3505 | 0.1139 | 0.3681 | 0.2557 | 0.4056 |
| 0.6632 | 33.0 | 3531 | 1.2422 | 0.2996 | 0.6001 | 0.2578 | 0.1768 | 0.1909 | 0.4405 | 0.3214 | 0.4901 | 0.5081 | 0.2888 | 0.3812 | 0.6588 | 0.6126 | 0.7728 | 0.2943 | 0.5967 | 0.2104 | 0.4064 | 0.0948 | 0.3208 | 0.2858 | 0.4436 |
| 0.6518 | 34.0 | 3638 | 1.2245 | 0.3093 | 0.611 | 0.2619 | 0.1701 | 0.2059 | 0.4612 | 0.3295 | 0.4808 | 0.498 | 0.2206 | 0.3699 | 0.6679 | 0.6104 | 0.7809 | 0.3062 | 0.5633 | 0.2068 | 0.3794 | 0.1203 | 0.3222 | 0.303 | 0.4441 |
| 0.6649 | 35.0 | 3745 | 1.2282 | 0.3067 | 0.621 | 0.267 | 0.2252 | 0.2014 | 0.4684 | 0.3313 | 0.4843 | 0.5019 | 0.2741 | 0.3718 | 0.6808 | 0.5992 | 0.7796 | 0.3058 | 0.5617 | 0.2148 | 0.4064 | 0.1257 | 0.3194 | 0.2883 | 0.4425 |
| 0.6373 | 36.0 | 3852 | 1.3044 | 0.3085 | 0.6184 | 0.2823 | 0.248 | 0.2014 | 0.4489 | 0.3186 | 0.4898 | 0.5107 | 0.2978 | 0.3889 | 0.6776 | 0.6084 | 0.7784 | 0.3151 | 0.59 | 0.2058 | 0.3985 | 0.113 | 0.3153 | 0.3 | 0.4715 |
| 0.6139 | 37.0 | 3959 | 1.2725 | 0.3138 | 0.6336 | 0.2796 | 0.1862 | 0.203 | 0.4697 | 0.3262 | 0.4928 | 0.5143 | 0.2357 | 0.387 | 0.6958 | 0.6116 | 0.7802 | 0.3125 | 0.5667 | 0.2204 | 0.3961 | 0.1065 | 0.3625 | 0.3181 | 0.4659 |
| 0.6087 | 38.0 | 4066 | 1.2936 | 0.3072 | 0.6272 | 0.2668 | 0.2027 | 0.2048 | 0.4465 | 0.3151 | 0.4832 | 0.5024 | 0.2461 | 0.3726 | 0.6757 | 0.6071 | 0.7981 | 0.3166 | 0.575 | 0.1965 | 0.3814 | 0.1308 | 0.3375 | 0.2848 | 0.4201 |
| 0.613 | 39.0 | 4173 | 1.2992 | 0.3233 | 0.6431 | 0.3037 | 0.1967 | 0.2164 | 0.4755 | 0.3249 | 0.4887 | 0.5027 | 0.2438 | 0.3761 | 0.6875 | 0.6188 | 0.7753 | 0.332 | 0.5717 | 0.2113 | 0.3873 | 0.1314 | 0.325 | 0.3232 | 0.4542 |
| 0.6009 | 40.0 | 4280 | 1.3210 | 0.3105 | 0.6141 | 0.2801 | 0.196 | 0.1976 | 0.446 | 0.3223 | 0.4704 | 0.4859 | 0.2349 | 0.3542 | 0.6723 | 0.6392 | 0.7722 | 0.3152 | 0.5433 | 0.1995 | 0.3672 | 0.0986 | 0.2847 | 0.3001 | 0.462 |
| 0.5766 | 41.0 | 4387 | 1.2828 | 0.3157 | 0.6377 | 0.2786 | 0.2062 | 0.2068 | 0.4597 | 0.3266 | 0.4825 | 0.5013 | 0.2481 | 0.3722 | 0.6859 | 0.6119 | 0.7753 | 0.3294 | 0.5683 | 0.2074 | 0.4 | 0.1285 | 0.3139 | 0.3013 | 0.4492 |
| 0.5692 | 42.0 | 4494 | 1.3361 | 0.3209 | 0.6257 | 0.2844 | 0.2785 | 0.2123 | 0.4655 | 0.3311 | 0.4922 | 0.5103 | 0.3317 | 0.3787 | 0.6923 | 0.6219 | 0.7802 | 0.3437 | 0.5767 | 0.1992 | 0.3897 | 0.1221 | 0.3347 | 0.3177 | 0.4704 |
| 0.5563 | 43.0 | 4601 | 1.2864 | 0.3284 | 0.6405 | 0.2969 | 0.2098 | 0.2261 | 0.4568 | 0.3312 | 0.5031 | 0.5202 | 0.2639 | 0.399 | 0.6864 | 0.6278 | 0.7821 | 0.3447 | 0.5733 | 0.2249 | 0.4186 | 0.1179 | 0.3486 | 0.327 | 0.4782 |
| 0.5581 | 44.0 | 4708 | 1.2884 | 0.3309 | 0.6319 | 0.3104 | 0.2022 | 0.2285 | 0.4627 | 0.337 | 0.5046 | 0.5237 | 0.268 | 0.4064 | 0.6992 | 0.6404 | 0.7944 | 0.353 | 0.595 | 0.2145 | 0.3877 | 0.1283 | 0.3639 | 0.3185 | 0.4777 |
| 0.5464 | 45.0 | 4815 | 1.3207 | 0.3205 | 0.6256 | 0.2849 | 0.1818 | 0.2114 | 0.4495 | 0.3295 | 0.493 | 0.5136 | 0.2564 | 0.3824 | 0.7016 | 0.6279 | 0.787 | 0.3335 | 0.5867 | 0.2226 | 0.4064 | 0.1144 | 0.3194 | 0.304 | 0.4687 |
| 0.5445 | 46.0 | 4922 | 1.2675 | 0.3266 | 0.6518 | 0.295 | 0.2526 | 0.2217 | 0.4655 | 0.3354 | 0.4991 | 0.5164 | 0.3 | 0.3932 | 0.6916 | 0.626 | 0.7883 | 0.3552 | 0.5817 | 0.2189 | 0.4054 | 0.1285 | 0.3347 | 0.3045 | 0.4721 |
| 0.5247 | 47.0 | 5029 | 1.3173 | 0.3311 | 0.6464 | 0.2924 | 0.2393 | 0.226 | 0.4541 | 0.339 | 0.4983 | 0.5182 | 0.2804 | 0.3948 | 0.6897 | 0.6356 | 0.784 | 0.3544 | 0.6 | 0.2171 | 0.4034 | 0.1408 | 0.3333 | 0.3077 | 0.4704 |
| 0.5302 | 48.0 | 5136 | 1.2731 | 0.326 | 0.631 | 0.2795 | 0.2521 | 0.2174 | 0.4665 | 0.3267 | 0.4931 | 0.5104 | 0.2953 | 0.3882 | 0.6879 | 0.6296 | 0.784 | 0.3588 | 0.585 | 0.1986 | 0.3961 | 0.1284 | 0.3333 | 0.3147 | 0.4536 |
| 0.5149 | 49.0 | 5243 | 1.2684 | 0.3152 | 0.6229 | 0.2801 | 0.2283 | 0.2075 | 0.4572 | 0.3264 | 0.4836 | 0.5022 | 0.281 | 0.3707 | 0.6804 | 0.6224 | 0.7772 | 0.3308 | 0.5433 | 0.2041 | 0.4078 | 0.1049 | 0.3153 | 0.3136 | 0.4676 |
| 0.5118 | 50.0 | 5350 | 1.3018 | 0.3252 | 0.6248 | 0.305 | 0.2196 | 0.2206 | 0.4821 | 0.3287 | 0.4858 | 0.5015 | 0.2654 | 0.3794 | 0.6771 | 0.6159 | 0.7691 | 0.3332 | 0.5717 | 0.2209 | 0.3961 | 0.1375 | 0.3167 | 0.3185 | 0.4542 |
| 0.502 | 51.0 | 5457 | 1.2466 | 0.3319 | 0.653 | 0.2901 | 0.2399 | 0.2178 | 0.4883 | 0.3388 | 0.4905 | 0.5081 | 0.289 | 0.3748 | 0.6816 | 0.626 | 0.7772 | 0.357 | 0.5783 | 0.2109 | 0.402 | 0.1552 | 0.3208 | 0.3103 | 0.462 |
| 0.506 | 52.0 | 5564 | 1.2553 | 0.3153 | 0.64 | 0.2721 | 0.24 | 0.2077 | 0.4769 | 0.3287 | 0.4818 | 0.4982 | 0.2784 | 0.372 | 0.6928 | 0.6264 | 0.766 | 0.3397 | 0.5717 | 0.2087 | 0.3956 | 0.1133 | 0.3111 | 0.2885 | 0.4464 |
| 0.5066 | 53.0 | 5671 | 1.3476 | 0.3352 | 0.6334 | 0.2955 | 0.2544 | 0.2323 | 0.4958 | 0.3383 | 0.4952 | 0.5163 | 0.3038 | 0.3849 | 0.7196 | 0.6251 | 0.7759 | 0.3453 | 0.5733 | 0.2273 | 0.4162 | 0.1552 | 0.3319 | 0.3231 | 0.4844 |
| 0.4992 | 54.0 | 5778 | 1.3008 | 0.3303 | 0.6458 | 0.292 | 0.1557 | 0.2268 | 0.4735 | 0.3312 | 0.4849 | 0.5011 | 0.228 | 0.3711 | 0.691 | 0.611 | 0.7673 | 0.3633 | 0.5567 | 0.2247 | 0.3995 | 0.1374 | 0.2986 | 0.3149 | 0.4832 |
| 0.4791 | 55.0 | 5885 | 1.3185 | 0.3348 | 0.6544 | 0.2884 | 0.2444 | 0.2309 | 0.4804 | 0.3387 | 0.4981 | 0.5153 | 0.2973 | 0.3873 | 0.7085 | 0.6284 | 0.7784 | 0.3575 | 0.595 | 0.2163 | 0.4034 | 0.1542 | 0.3278 | 0.3175 | 0.4721 |
| 0.4628 | 56.0 | 5992 | 1.2985 | 0.3266 | 0.6258 | 0.2912 | 0.216 | 0.2286 | 0.4531 | 0.3407 | 0.4845 | 0.5015 | 0.2562 | 0.3845 | 0.6729 | 0.6301 | 0.7747 | 0.3721 | 0.5833 | 0.2105 | 0.402 | 0.1221 | 0.3097 | 0.2983 | 0.438 |
| 0.4568 | 57.0 | 6099 | 1.2744 | 0.3368 | 0.6356 | 0.3091 | 0.2112 | 0.2331 | 0.4793 | 0.3421 | 0.4889 | 0.5086 | 0.2813 | 0.3778 | 0.691 | 0.6273 | 0.7741 | 0.3848 | 0.58 | 0.2022 | 0.4078 | 0.1421 | 0.2972 | 0.3276 | 0.4838 |
| 0.4508 | 58.0 | 6206 | 1.3367 | 0.3387 | 0.6541 | 0.3073 | 0.256 | 0.2365 | 0.4842 | 0.3444 | 0.4925 | 0.5096 | 0.3095 | 0.3798 | 0.6947 | 0.6259 | 0.7735 | 0.3822 | 0.5767 | 0.2121 | 0.4005 | 0.1487 | 0.3125 | 0.3246 | 0.4849 |
| 0.4476 | 59.0 | 6313 | 1.2988 | 0.3422 | 0.6574 | 0.3041 | 0.2575 | 0.2349 | 0.4833 | 0.3421 | 0.4973 | 0.5173 | 0.3087 | 0.3878 | 0.7027 | 0.6439 | 0.7858 | 0.3656 | 0.5683 | 0.239 | 0.4191 | 0.1374 | 0.3333 | 0.325 | 0.4799 |
| 0.4418 | 60.0 | 6420 | 1.3153 | 0.336 | 0.6532 | 0.3026 | 0.2298 | 0.2296 | 0.4988 | 0.342 | 0.4952 | 0.5178 | 0.2729 | 0.3987 | 0.6979 | 0.617 | 0.7735 | 0.3692 | 0.5767 | 0.2324 | 0.4152 | 0.1367 | 0.3347 | 0.3246 | 0.4888 |
| 0.4255 | 61.0 | 6527 | 1.3474 | 0.3351 | 0.6402 | 0.3107 | 0.2307 | 0.2303 | 0.4998 | 0.3483 | 0.5026 | 0.5188 | 0.2808 | 0.3929 | 0.7016 | 0.6216 | 0.7741 | 0.3898 | 0.5933 | 0.2099 | 0.3975 | 0.1265 | 0.3361 | 0.3277 | 0.4927 |
| 0.4398 | 62.0 | 6634 | 1.3079 | 0.3235 | 0.6414 | 0.2716 | 0.203 | 0.2249 | 0.4655 | 0.3377 | 0.4969 | 0.5139 | 0.2522 | 0.3896 | 0.691 | 0.6164 | 0.7716 | 0.3404 | 0.575 | 0.2371 | 0.4216 | 0.1347 | 0.3347 | 0.2889 | 0.4665 |
| 0.4373 | 63.0 | 6741 | 1.3848 | 0.3227 | 0.6411 | 0.2715 | 0.2586 | 0.2268 | 0.4554 | 0.338 | 0.4883 | 0.5056 | 0.2983 | 0.3867 | 0.6765 | 0.6107 | 0.7599 | 0.3369 | 0.59 | 0.2328 | 0.4044 | 0.1322 | 0.3153 | 0.3011 | 0.4587 |
| 0.4287 | 64.0 | 6848 | 1.3676 | 0.3218 | 0.6456 | 0.2789 | 0.2563 | 0.2225 | 0.4598 | 0.3365 | 0.4915 | 0.5088 | 0.3148 | 0.3826 | 0.6923 | 0.5985 | 0.7549 | 0.3578 | 0.5667 | 0.2206 | 0.4142 | 0.1304 | 0.3417 | 0.3018 | 0.4665 |
| 0.4085 | 65.0 | 6955 | 1.3785 | 0.3343 | 0.6465 | 0.2967 | 0.2321 | 0.2383 | 0.4732 | 0.3415 | 0.4994 | 0.514 | 0.2836 | 0.3928 | 0.6854 | 0.6048 | 0.763 | 0.3561 | 0.5817 | 0.2355 | 0.4078 | 0.1399 | 0.3319 | 0.335 | 0.4855 |
| 0.4018 | 66.0 | 7062 | 1.3817 | 0.3259 | 0.6478 | 0.279 | 0.2321 | 0.227 | 0.4848 | 0.3364 | 0.4897 | 0.5103 | 0.2796 | 0.3899 | 0.6871 | 0.6028 | 0.7605 | 0.3393 | 0.57 | 0.2203 | 0.4015 | 0.1491 | 0.3306 | 0.318 | 0.4888 |
| 0.4005 | 67.0 | 7169 | 1.3791 | 0.3305 | 0.6483 | 0.2948 | 0.2344 | 0.225 | 0.4871 | 0.3358 | 0.4895 | 0.5095 | 0.2826 | 0.3829 | 0.6944 | 0.6162 | 0.7654 | 0.3629 | 0.5683 | 0.2217 | 0.3941 | 0.1194 | 0.3222 | 0.3324 | 0.4972 |
| 0.4 | 68.0 | 7276 | 1.3844 | 0.3413 | 0.6686 | 0.2994 | 0.2306 | 0.2315 | 0.4953 | 0.3424 | 0.4996 | 0.5178 | 0.2897 | 0.3871 | 0.7112 | 0.618 | 0.7728 | 0.3626 | 0.58 | 0.2307 | 0.4137 | 0.1373 | 0.3208 | 0.3581 | 0.5017 |
| 0.3961 | 69.0 | 7383 | 1.3469 | 0.334 | 0.6535 | 0.2764 | 0.2806 | 0.2249 | 0.4906 | 0.3449 | 0.5058 | 0.5229 | 0.3374 | 0.3884 | 0.7273 | 0.6161 | 0.7778 | 0.3543 | 0.585 | 0.2291 | 0.4088 | 0.1308 | 0.3514 | 0.3396 | 0.4916 |
| 0.3817 | 70.0 | 7490 | 1.3834 | 0.328 | 0.6577 | 0.286 | 0.2902 | 0.2249 | 0.4676 | 0.3436 | 0.4968 | 0.5121 | 0.3332 | 0.3912 | 0.6866 | 0.6162 | 0.7593 | 0.3558 | 0.5717 | 0.2175 | 0.4093 | 0.1222 | 0.3361 | 0.3282 | 0.4844 |
| 0.3851 | 71.0 | 7597 | 1.3745 | 0.3268 | 0.6354 | 0.281 | 0.2392 | 0.2237 | 0.4644 | 0.3386 | 0.4965 | 0.5132 | 0.3088 | 0.3918 | 0.685 | 0.6184 | 0.7654 | 0.3537 | 0.575 | 0.2253 | 0.4039 | 0.1238 | 0.3528 | 0.313 | 0.4687 |
| 0.3773 | 72.0 | 7704 | 1.3953 | 0.3325 | 0.6475 | 0.2841 | 0.2436 | 0.2233 | 0.4887 | 0.3397 | 0.4968 | 0.5134 | 0.3069 | 0.3873 | 0.6961 | 0.6198 | 0.7673 | 0.3492 | 0.575 | 0.2159 | 0.4088 | 0.1404 | 0.325 | 0.3375 | 0.4911 |
| 0.3709 | 73.0 | 7811 | 1.3560 | 0.3299 | 0.6478 | 0.2891 | 0.2394 | 0.2246 | 0.4904 | 0.3415 | 0.4962 | 0.5133 | 0.2887 | 0.3893 | 0.6974 | 0.6232 | 0.7648 | 0.3428 | 0.5567 | 0.2255 | 0.4074 | 0.13 | 0.3444 | 0.3281 | 0.4933 |
| 0.386 | 74.0 | 7918 | 1.3967 | 0.3336 | 0.6619 | 0.2908 | 0.2852 | 0.23 | 0.4811 | 0.3439 | 0.4963 | 0.5149 | 0.3293 | 0.3951 | 0.6897 | 0.6098 | 0.7636 | 0.3737 | 0.5967 | 0.2245 | 0.401 | 0.1329 | 0.3319 | 0.3271 | 0.4816 |
| 0.3584 | 75.0 | 8025 | 1.3931 | 0.3342 | 0.6622 | 0.3032 | 0.2654 | 0.2288 | 0.4765 | 0.3417 | 0.495 | 0.513 | 0.3084 | 0.3815 | 0.7017 | 0.6155 | 0.7784 | 0.3649 | 0.57 | 0.2321 | 0.402 | 0.1249 | 0.3222 | 0.3336 | 0.4922 |
| 0.3481 | 76.0 | 8132 | 1.3925 | 0.3368 | 0.6585 | 0.2895 | 0.269 | 0.2329 | 0.4833 | 0.348 | 0.5001 | 0.5193 | 0.3074 | 0.3935 | 0.7015 | 0.6196 | 0.7821 | 0.3493 | 0.5783 | 0.2393 | 0.4118 | 0.1409 | 0.3292 | 0.3348 | 0.495 |
| 0.3512 | 77.0 | 8239 | 1.3984 | 0.3323 | 0.6513 | 0.2849 | 0.2895 | 0.2248 | 0.4827 | 0.3393 | 0.4967 | 0.5136 | 0.3289 | 0.382 | 0.7079 | 0.6188 | 0.7772 | 0.339 | 0.5783 | 0.2318 | 0.4074 | 0.1319 | 0.3208 | 0.3402 | 0.4844 |
| 0.3366 | 78.0 | 8346 | 1.4160 | 0.3414 | 0.6618 | 0.3016 | 0.263 | 0.2376 | 0.4876 | 0.3428 | 0.4978 | 0.5138 | 0.3062 | 0.3873 | 0.6903 | 0.6113 | 0.7704 | 0.3714 | 0.595 | 0.2483 | 0.4093 | 0.131 | 0.3056 | 0.3449 | 0.4888 |
| 0.3278 | 79.0 | 8453 | 1.4257 | 0.3367 | 0.6401 | 0.3062 | 0.2399 | 0.2326 | 0.4775 | 0.3408 | 0.4939 | 0.5115 | 0.2817 | 0.3838 | 0.6977 | 0.6067 | 0.771 | 0.3648 | 0.5717 | 0.2445 | 0.4108 | 0.1211 | 0.3069 | 0.3463 | 0.4972 |
| 0.3225 | 80.0 | 8560 | 1.3995 | 0.3355 | 0.6592 | 0.2994 | 0.2158 | 0.2357 | 0.4778 | 0.3447 | 0.496 | 0.5105 | 0.2617 | 0.3863 | 0.6889 | 0.6033 | 0.7562 | 0.3597 | 0.5817 | 0.2395 | 0.4039 | 0.1327 | 0.3153 | 0.3423 | 0.4955 |
| 0.3197 | 81.0 | 8667 | 1.3828 | 0.3368 | 0.6525 | 0.3008 | 0.239 | 0.2342 | 0.48 | 0.3525 | 0.503 | 0.5196 | 0.2863 | 0.3951 | 0.6975 | 0.6113 | 0.7735 | 0.3803 | 0.5817 | 0.2268 | 0.4029 | 0.1239 | 0.3347 | 0.3415 | 0.505 |
| 0.3119 | 82.0 | 8774 | 1.3821 | 0.3367 | 0.6504 | 0.313 | 0.2674 | 0.2299 | 0.4826 | 0.3486 | 0.4961 | 0.5121 | 0.309 | 0.3872 | 0.692 | 0.6122 | 0.763 | 0.3691 | 0.58 | 0.2295 | 0.4108 | 0.1337 | 0.3097 | 0.3391 | 0.4972 |
| 0.3022 | 83.0 | 8881 | 1.4340 | 0.3378 | 0.6458 | 0.2983 | 0.2439 | 0.2336 | 0.4668 | 0.3417 | 0.4952 | 0.5138 | 0.2866 | 0.3936 | 0.6793 | 0.6111 | 0.7636 | 0.3687 | 0.5783 | 0.2383 | 0.4206 | 0.1301 | 0.3139 | 0.341 | 0.4927 |
| 0.3011 | 84.0 | 8988 | 1.4571 | 0.3335 | 0.6497 | 0.2893 | 0.2644 | 0.2254 | 0.4719 | 0.3399 | 0.4901 | 0.5069 | 0.3052 | 0.3811 | 0.6801 | 0.6158 | 0.7654 | 0.3622 | 0.5783 | 0.2278 | 0.4029 | 0.1355 | 0.3069 | 0.3261 | 0.481 |
| 0.2972 | 85.0 | 9095 | 1.4258 | 0.3338 | 0.6461 | 0.2916 | 0.2397 | 0.2283 | 0.4751 | 0.3449 | 0.499 | 0.516 | 0.2907 | 0.3911 | 0.6938 | 0.606 | 0.7574 | 0.3745 | 0.6017 | 0.2265 | 0.4025 | 0.1277 | 0.3264 | 0.3343 | 0.4922 |
| 0.2859 | 86.0 | 9202 | 1.4684 | 0.329 | 0.6399 | 0.2879 | 0.2591 | 0.2221 | 0.4732 | 0.3452 | 0.4957 | 0.5163 | 0.3076 | 0.3911 | 0.6931 | 0.6055 | 0.766 | 0.3602 | 0.5983 | 0.2173 | 0.3936 | 0.1339 | 0.3306 | 0.328 | 0.4927 |
| 0.2921 | 87.0 | 9309 | 1.4191 | 0.3332 | 0.646 | 0.2982 | 0.2655 | 0.2265 | 0.477 | 0.3415 | 0.5005 | 0.5196 | 0.3141 | 0.3895 | 0.7048 | 0.6053 | 0.7679 | 0.3804 | 0.6133 | 0.2223 | 0.402 | 0.1294 | 0.3278 | 0.3286 | 0.4872 |
| 0.2788 | 88.0 | 9416 | 1.4109 | 0.3327 | 0.6484 | 0.2959 | 0.2407 | 0.2284 | 0.4755 | 0.3454 | 0.498 | 0.5162 | 0.2875 | 0.3884 | 0.7014 | 0.6128 | 0.771 | 0.3555 | 0.585 | 0.2313 | 0.4025 | 0.1274 | 0.3333 | 0.3366 | 0.4894 |
| 0.2808 | 89.0 | 9523 | 1.4585 | 0.3333 | 0.6453 | 0.3076 | 0.2643 | 0.2279 | 0.4756 | 0.3423 | 0.4977 | 0.5168 | 0.3141 | 0.388 | 0.6946 | 0.6058 | 0.7673 | 0.3719 | 0.5967 | 0.2322 | 0.4118 | 0.1281 | 0.3139 | 0.3286 | 0.4944 |
| 0.2747 | 90.0 | 9630 | 1.4490 | 0.3338 | 0.6433 | 0.2976 | 0.2402 | 0.2257 | 0.4754 | 0.3455 | 0.4997 | 0.5175 | 0.2859 | 0.3942 | 0.6893 | 0.6087 | 0.7667 | 0.3793 | 0.595 | 0.2274 | 0.4083 | 0.1186 | 0.3264 | 0.3347 | 0.4911 |
| 0.2528 | 91.0 | 9737 | 1.4493 | 0.3362 | 0.6504 | 0.2926 | 0.2687 | 0.2288 | 0.4798 | 0.342 | 0.5006 | 0.5198 | 0.3141 | 0.3965 | 0.6867 | 0.6044 | 0.7704 | 0.389 | 0.6 | 0.2308 | 0.4103 | 0.1191 | 0.3222 | 0.3375 | 0.4961 |
| 0.2593 | 92.0 | 9844 | 1.4320 | 0.334 | 0.6486 | 0.2993 | 0.2461 | 0.2263 | 0.4702 | 0.3423 | 0.4986 | 0.517 | 0.2961 | 0.3884 | 0.6883 | 0.6089 | 0.7679 | 0.3741 | 0.5917 | 0.2337 | 0.4078 | 0.1149 | 0.3208 | 0.3382 | 0.4966 |
| 0.2685 | 93.0 | 9951 | 1.4475 | 0.3357 | 0.6478 | 0.2992 | 0.2672 | 0.2258 | 0.4738 | 0.3425 | 0.4978 | 0.5165 | 0.3123 | 0.3859 | 0.7037 | 0.6143 | 0.771 | 0.3774 | 0.5883 | 0.2311 | 0.4132 | 0.1173 | 0.325 | 0.3386 | 0.4849 |
| 0.2618 | 94.0 | 10058 | 1.4451 | 0.3385 | 0.6593 | 0.2988 | 0.2673 | 0.2324 | 0.4768 | 0.3457 | 0.5009 | 0.519 | 0.316 | 0.3898 | 0.6985 | 0.6104 | 0.7716 | 0.387 | 0.5967 | 0.2296 | 0.4039 | 0.1281 | 0.3278 | 0.3373 | 0.495 |
| 0.2513 | 95.0 | 10165 | 1.4426 | 0.3403 | 0.6552 | 0.313 | 0.2645 | 0.2336 | 0.4795 | 0.3429 | 0.5006 | 0.5179 | 0.3109 | 0.391 | 0.6934 | 0.6134 | 0.7735 | 0.3953 | 0.595 | 0.2311 | 0.4025 | 0.1209 | 0.3278 | 0.3408 | 0.4911 |
| 0.2596 | 96.0 | 10272 | 1.4438 | 0.338 | 0.6557 | 0.3033 | 0.2669 | 0.2288 | 0.4786 | 0.344 | 0.5005 | 0.5189 | 0.317 | 0.3915 | 0.6914 | 0.6119 | 0.7722 | 0.3964 | 0.595 | 0.2265 | 0.4054 | 0.1177 | 0.3347 | 0.3374 | 0.4872 |
| 0.2363 | 97.0 | 10379 | 1.4447 | 0.3379 | 0.6526 | 0.3044 | 0.2687 | 0.2297 | 0.4771 | 0.3437 | 0.5013 | 0.5196 | 0.32 | 0.3893 | 0.6969 | 0.608 | 0.7735 | 0.3861 | 0.5917 | 0.2335 | 0.4083 | 0.122 | 0.3319 | 0.3396 | 0.4927 |
| 0.2346 | 98.0 | 10486 | 1.4613 | 0.3366 | 0.6512 | 0.299 | 0.267 | 0.2288 | 0.4809 | 0.3425 | 0.5006 | 0.5201 | 0.3166 | 0.391 | 0.6999 | 0.6083 | 0.7722 | 0.3887 | 0.5933 | 0.2285 | 0.4088 | 0.1186 | 0.3319 | 0.3388 | 0.4944 |
| 0.2452 | 99.0 | 10593 | 1.4607 | 0.3362 | 0.6498 | 0.3001 | 0.2676 | 0.2286 | 0.4805 | 0.344 | 0.4982 | 0.5179 | 0.3168 | 0.3889 | 0.6997 | 0.6076 | 0.7716 | 0.3863 | 0.585 | 0.2264 | 0.4049 | 0.124 | 0.3347 | 0.3368 | 0.4933 |
| 0.2287 | 100.0 | 10700 | 1.4674 | 0.3363 | 0.6506 | 0.2992 | 0.2695 | 0.2282 | 0.4791 | 0.3441 | 0.4988 | 0.5186 | 0.3192 | 0.3884 | 0.6982 | 0.607 | 0.7716 | 0.3854 | 0.5883 | 0.2283 | 0.4093 | 0.1228 | 0.3319 | 0.3379 | 0.4916 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.18.0
- Tokenizers 0.19.0
| {"license": "apache-2.0", "tags": ["object-detection", "vision", "generated_from_trainer"], "base_model": "hustvl/yolos-small", "model-index": [{"name": "hustvl-yolos-small-finetuned-10k-cppe5", "results": []}]} | qubvel-hf/hustvl-yolos-small-finetuned-10k-cppe5 | null | [
"transformers",
"safetensors",
"yolos",
"object-detection",
"vision",
"generated_from_trainer",
"base_model:hustvl/yolos-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T23:25:45+00:00 | [] | [] | TAGS
#transformers #safetensors #yolos #object-detection #vision #generated_from_trainer #base_model-hustvl/yolos-small #license-apache-2.0 #endpoints_compatible #region-us
| <img src="URL alt="Visualize in Weights & Biases" width="200" height="32"/>
hustvl-yolos-small-finetuned-10k-cppe5
======================================
This model is a fine-tuned version of hustvl/yolos-small on the cppe-5 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4674
* Map: 0.3363
* Map 50: 0.6506
* Map 75: 0.2992
* Map Small: 0.2695
* Map Medium: 0.2282
* Map Large: 0.4791
* Mar 1: 0.3441
* Mar 10: 0.4988
* Mar 100: 0.5186
* Mar Small: 0.3192
* Mar Medium: 0.3884
* Mar Large: 0.6982
* Map Coverall: 0.607
* Mar 100 Coverall: 0.7716
* Map Face Shield: 0.3854
* Mar 100 Face Shield: 0.5883
* Map Gloves: 0.2283
* Mar 100 Gloves: 0.4093
* Map Goggles: 0.1228
* Mar 100 Goggles: 0.3319
* Map Mask: 0.3379
* Mar 100 Mask: 0.4916
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: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.41.0.dev0
* Pytorch 1.13.0+cu117
* Datasets 2.18.0
* Tokenizers 0.19.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 1337\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 100.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.41.0.dev0\n* Pytorch 1.13.0+cu117\n* Datasets 2.18.0\n* Tokenizers 0.19.0"
] | [
"TAGS\n#transformers #safetensors #yolos #object-detection #vision #generated_from_trainer #base_model-hustvl/yolos-small #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 1337\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 100.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.41.0.dev0\n* Pytorch 1.13.0+cu117\n* Datasets 2.18.0\n* Tokenizers 0.19.0"
] |
text-classification | transformers |
<!-- 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. -->
# amazon_helpfulness_classification_on_TAPT_pretrained_full_train
This model is a fine-tuned version of [ltuzova/tapt_helpfulness_base_pretraining_model_full_train](https://huggingface.co/ltuzova/tapt_helpfulness_base_pretraining_model_full_train) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3275
- Accuracy: 0.8787
- F1 Macro: 0.6852
## 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=(0.9,0.98) and epsilon=1e-06
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.3175 | 1.0 | 7204 | 0.3037 | 0.8762 | 0.6515 |
| 0.2996 | 2.0 | 14408 | 0.3404 | 0.8744 | 0.6448 |
| 0.2624 | 3.0 | 21612 | 0.3263 | 0.8792 | 0.6963 |
| 0.2322 | 4.0 | 28816 | 0.4314 | 0.877 | 0.6752 |
| 0.1906 | 5.0 | 36020 | 0.5521 | 0.8708 | 0.6836 |
| 0.1487 | 6.0 | 43224 | 0.7017 | 0.8704 | 0.6941 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "ltuzova/tapt_helpfulness_base_pretraining_model_full_train", "model-index": [{"name": "amazon_helpfulness_classification_on_TAPT_pretrained_full_train", "results": []}]} | ltuzova/amazon_helpfulness_classification_on_TAPT_pretrained_full_train | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:ltuzova/tapt_helpfulness_base_pretraining_model_full_train",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T23:27:48+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-ltuzova/tapt_helpfulness_base_pretraining_model_full_train #license-mit #autotrain_compatible #endpoints_compatible #region-us
| amazon\_helpfulness\_classification\_on\_TAPT\_pretrained\_full\_train
======================================================================
This model is a fine-tuned version of ltuzova/tapt\_helpfulness\_base\_pretraining\_model\_full\_train on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3275
* Accuracy: 0.8787
* F1 Macro: 0.6852
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=(0.9,0.98) and epsilon=1e-06
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.06
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.36.2
* Pytorch 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.06\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-ltuzova/tapt_helpfulness_base_pretraining_model_full_train #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.06\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] |
text-generation | transformers |
# Model Card for Model ID
Optimum GPTQ quantized 8-bit version of Mixtral-8x22B-Instruct-v0.1 \
WIP - do not use currently \
See original model card for more information.
# How to load
| {"library_name": "transformers", "tags": []} | jarrelscy/Mixtral-8x22B-Instruct-v0.1-GPTQ-8bit | null | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-04-20T23:32:15+00:00 | [] | [] | TAGS
#transformers #safetensors #mixtral #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us
|
# Model Card for Model ID
Optimum GPTQ quantized 8-bit version of Mixtral-8x22B-Instruct-v0.1 \
WIP - do not use currently \
See original model card for more information.
# How to load
| [
"# Model Card for Model ID\n\nOptimum GPTQ quantized 8-bit version of Mixtral-8x22B-Instruct-v0.1 \\\nWIP - do not use currently \\\nSee original model card for more information.",
"# How to load"
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n",
"# Model Card for Model ID\n\nOptimum GPTQ quantized 8-bit version of Mixtral-8x22B-Instruct-v0.1 \\\nWIP - do not use currently \\\nSee original model card for more information.",
"# How to load"
] |
null | null | https://civitai.com/models/410359/fenrys-lv2-kara-cheat-datta-motoyuusha-kouho-no-mattari-isekai-life | {"license": "creativeml-openrail-m"} | LarryAIDraw/Fenrys | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-04-20T23:32:25+00:00 | [] | [] | TAGS
#license-creativeml-openrail-m #region-us
| URL | [] | [
"TAGS\n#license-creativeml-openrail-m #region-us \n"
] |
null | null | https://civitai.com/models/104171/hifumi-takimoto-new-game | {"license": "creativeml-openrail-m"} | LarryAIDraw/hifumi-newgame-01 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-04-20T23:32:49+00:00 | [] | [] | TAGS
#license-creativeml-openrail-m #region-us
| URL | [] | [
"TAGS\n#license-creativeml-openrail-m #region-us \n"
] |
null | null | https://civitai.com/models/408784/nakano-miku | {"license": "creativeml-openrail-m"} | LarryAIDraw/nakano_miku | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-04-20T23:33:18+00:00 | [] | [] | TAGS
#license-creativeml-openrail-m #region-us
| URL | [] | [
"TAGS\n#license-creativeml-openrail-m #region-us \n"
] |
null | null | https://civitai.com/models/409365/anis-nikke-swimsuit | {"license": "creativeml-openrail-m"} | LarryAIDraw/anis_bikini | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-04-20T23:33:44+00:00 | [] | [] | TAGS
#license-creativeml-openrail-m #region-us
| URL | [] | [
"TAGS\n#license-creativeml-openrail-m #region-us \n"
] |
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