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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_total_Instruction0_OPASL_v1_h1
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: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- 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_total_Instruction0_OPASL_v1_h1", "results": []}]} | ThuyNT/CS505_COQE_viT5_total_Instruction0_OPASL_v1_h1 | 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 | 2024-04-28T00:52:20+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_total_Instruction0_OPASL_v1_h1
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: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- 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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"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
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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",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
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/vn7jioeq)
# microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5-auto-pad
This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the cppe-5 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: 1
- 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
### 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": "microsoft/conditional-detr-resnet-50", "model-index": [{"name": "microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5-auto-pad", "results": []}]} | qubvel-hf/microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5-auto-pad | null | [
"transformers",
"safetensors",
"conditional_detr",
"object-detection",
"vision",
"generated_from_trainer",
"base_model:microsoft/conditional-detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T00:52:26+00:00 | [] | [] | TAGS
#transformers #safetensors #conditional_detr #object-detection #vision #generated_from_trainer #base_model-microsoft/conditional-detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us
|
<img src="URL alt="Visualize in Weights & Biases" width="200" height="32"/>
# microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5-auto-pad
This model is a fine-tuned version of microsoft/conditional-detr-resnet-50 on the cppe-5 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: 1
- 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
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.18.0
- Tokenizers 0.19.0
| [
"# microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5-auto-pad\n\nThis model is a fine-tuned version of microsoft/conditional-detr-resnet-50 on the cppe-5 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: 1\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",
"### Framework versions\n\n- Transformers 4.41.0.dev0\n- Pytorch 1.13.0+cu117\n- Datasets 2.18.0\n- Tokenizers 0.19.0"
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"# microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5-auto-pad\n\nThis model is a fine-tuned version of microsoft/conditional-detr-resnet-50 on the cppe-5 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: 1\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",
"### Framework versions\n\n- Transformers 4.41.0.dev0\n- Pytorch 1.13.0+cu117\n- Datasets 2.18.0\n- Tokenizers 0.19.0"
] |
reinforcement-learning | ml-agents |
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: hossniper/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos"]} | hossniper/poca-SoccerTwos | null | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | null | 2024-04-28T00:56:41+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us
|
# poca Agent playing SoccerTwos
This is a trained model of a poca agent playing SoccerTwos
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: hossniper/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
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] | [
"TAGS\n#ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us \n",
"# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\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: hossniper/poca-SoccerTwos\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] |
text-to-image | null | # Juggernaut X v10 - Onnx Olive DirectML Optimized
## Original Model
https://huggingface.co/RunDiffusion/Juggernaut-X-v10
## C# Inference Demo
https://github.com/saddam213/OnnxStack
```csharp
// Create Pipeline
var pipeline = StableDiffusionXLPipeline.CreatePipeline("D:\\Models\\Juggernaut-X-v10-onnx");
// Prompt
var promptOptions = new PromptOptions
{
Prompt = "a brain connected with cable and computers, dreamlike, hyperrealistic, 8k, hyperdetailed, steampunk, cyberpunk, cyborg"
};
// Run pipeline
var result = await pipeline.GenerateImageAsync(promptOptions);
// Save Image Result
await result.SaveAsync("Result.png");
```
## Inference Result
 | {"pipeline_tag": "text-to-image"} | saddam213/Juggernaut-X-v10-onnx | null | [
"onnx",
"text-to-image",
"region:us"
] | null | 2024-04-28T00:57:30+00:00 | [] | [] | TAGS
#onnx #text-to-image #region-us
| # Juggernaut X v10 - Onnx Olive DirectML Optimized
## Original Model
URL
## C# Inference Demo
URL
## Inference Result
!Intro Image | [
"# Juggernaut X v10 - Onnx Olive DirectML Optimized",
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"## C# Inference Demo\nURL",
"## Inference Result\n!Intro Image"
] |
null | transformers |
# Uploaded model
- **Developed by:** jspr
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-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/llama-3-8b-bnb-4bit"} | jspr/smut_llama_8b_smutromance_32k_peft | null | [
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"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T00:58:15+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: jspr
- License: apache-2.0
- Finetuned from model : unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
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] | [
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"# Uploaded model\n\n- Developed by: jspr\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
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_total_Instruction0_SPAOL_v1_h1
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: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- 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_total_Instruction0_SPAOL_v1_h1", "results": []}]} | ThuyNT/CS505_COQE_viT5_total_Instruction0_SPAOL_v1_h1 | 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 | 2024-04-28T00:58:52+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_total_Instruction0_SPAOL_v1_h1
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: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- 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: 8\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: 25\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"
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"# CS505_COQE_viT5_total_Instruction0_SPAOL_v1_h1\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: 8\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: 25\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"
] |
text-generation | transformers |
# Uploaded model
- **Developed by:** jspr
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-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", "sft"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | jspr/smut_llama_8b_smutromance_32k_merged | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T00:59:55+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #text-generation-inference #unsloth #trl #sft #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: jspr
- License: apache-2.0
- Finetuned from model : unsloth/llama-3-8b-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: jspr\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-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 #llama #text-generation #text-generation-inference #unsloth #trl #sft #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: jspr\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-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 | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Duosion/llama-3-tsuki-unsloth-8b
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/llama-3-tsuki-unsloth-8b-GGUF/resolve/main/llama-3-tsuki-unsloth-8b.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-tsuki-unsloth-8b-GGUF/resolve/main/llama-3-tsuki-unsloth-8b.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-tsuki-unsloth-8b-GGUF/resolve/main/llama-3-tsuki-unsloth-8b.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-tsuki-unsloth-8b-GGUF/resolve/main/llama-3-tsuki-unsloth-8b.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/llama-3-tsuki-unsloth-8b-GGUF/resolve/main/llama-3-tsuki-unsloth-8b.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-tsuki-unsloth-8b-GGUF/resolve/main/llama-3-tsuki-unsloth-8b.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-tsuki-unsloth-8b-GGUF/resolve/main/llama-3-tsuki-unsloth-8b.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-tsuki-unsloth-8b-GGUF/resolve/main/llama-3-tsuki-unsloth-8b.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-tsuki-unsloth-8b-GGUF/resolve/main/llama-3-tsuki-unsloth-8b.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama-3-tsuki-unsloth-8b-GGUF/resolve/main/llama-3-tsuki-unsloth-8b.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama-3-tsuki-unsloth-8b-GGUF/resolve/main/llama-3-tsuki-unsloth-8b.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-tsuki-unsloth-8b-GGUF/resolve/main/llama-3-tsuki-unsloth-8b.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-tsuki-unsloth-8b-GGUF/resolve/main/llama-3-tsuki-unsloth-8b.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-tsuki-unsloth-8b-GGUF/resolve/main/llama-3-tsuki-unsloth-8b.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-tsuki-unsloth-8b-GGUF/resolve/main/llama-3-tsuki-unsloth-8b.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "Duosion/llama-3-tsuki-unsloth-8b", "quantized_by": "mradermacher"} | mradermacher/llama-3-tsuki-unsloth-8b-GGUF | null | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"sft",
"en",
"base_model:Duosion/llama-3-tsuki-unsloth-8b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:02:56+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #text-generation-inference #unsloth #llama #trl #sft #en #base_model-Duosion/llama-3-tsuki-unsloth-8b #license-apache-2.0 #endpoints_compatible #region-us
| About
-----
static quants of URL
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
-----
If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs for
more details, including on how to concatenate multi-part files.
Provided Quants
---------------
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #text-generation-inference #unsloth #llama #trl #sft #en #base_model-Duosion/llama-3-tsuki-unsloth-8b #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-CartPole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "500.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | Epoching/Reinforce-CartPole-v1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2024-04-28T01:03:51+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] |
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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<!-- Provide the basic links for the model. -->
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## 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
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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. -->
[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
- **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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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/8ogm5vt | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T01:04:51+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
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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]
## 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 |
# 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]
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#### 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": []} | quickstep3621/62v6d5q | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:04:51+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
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### 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]
## 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
### 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": []} | quickstep3621/fbec7qx | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:04:56+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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] |
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": []} | quickstep3621/b2bwzee | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:05:00+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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] |
feature-extraction | 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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### Model Sources [optional]
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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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## 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]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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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]
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## 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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## 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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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | stvhuang/rcr-run-5pqr6lwp-90396-master-0_20240402T105012-ep37 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:06:38+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #xlm-roberta #feature-extraction #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]:",
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"### Direct Use",
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"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## Training Details",
"### Training Data",
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"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
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"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #xlm-roberta #feature-extraction #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"
] |
text-generation | transformers |
### Model Description
<!-- Provide a longer summary of what this model is. -->
This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on [m-a-p/Code-Feedback](https://huggingface.co/datasets/m-a-p/Code-Feedback) in order to answer questions related to programming better. Trained by making small modifications on [sample_finetune.py](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/sample_finetune.py) provided by Microsoft.
- **Developed by:** [Can Deniz Koçak](https://www.linkedin.com/in/candenizkocak/)
- **Finetuned from model:** [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)
### Fine-tuning Data
[m-a-p/Code-Feedback](https://huggingface.co/datasets/m-a-p/Code-Feedback)
### Training Procedure
Trained on a single A100 on Google Colab. | {"library_name": "transformers", "tags": ["trl", "sft"]} | candenizkocak/coder-Phi-3-mini-4k-instruct | null | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"trl",
"sft",
"conversational",
"custom_code",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2024-04-28T01:09:22+00:00 | [] | [] | TAGS
#transformers #safetensors #phi3 #text-generation #trl #sft #conversational #custom_code #autotrain_compatible #endpoints_compatible #has_space #region-us
|
### Model Description
This model is a fine-tuned version of microsoft/Phi-3-mini-4k-instruct on m-a-p/Code-Feedback in order to answer questions related to programming better. Trained by making small modifications on sample_finetune.py provided by Microsoft.
- Developed by: Can Deniz Koçak
- Finetuned from model: microsoft/Phi-3-mini-4k-instruct
### Fine-tuning Data
m-a-p/Code-Feedback
### Training Procedure
Trained on a single A100 on Google Colab. | [
"### Model Description\n\n\n\nThis model is a fine-tuned version of microsoft/Phi-3-mini-4k-instruct on m-a-p/Code-Feedback in order to answer questions related to programming better. Trained by making small modifications on sample_finetune.py provided by Microsoft.\n\n- Developed by: Can Deniz Koçak\n- Finetuned from model: microsoft/Phi-3-mini-4k-instruct",
"### Fine-tuning Data\n\nm-a-p/Code-Feedback",
"### Training Procedure\n\nTrained on a single A100 on Google Colab."
] | [
"TAGS\n#transformers #safetensors #phi3 #text-generation #trl #sft #conversational #custom_code #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Model Description\n\n\n\nThis model is a fine-tuned version of microsoft/Phi-3-mini-4k-instruct on m-a-p/Code-Feedback in order to answer questions related to programming better. Trained by making small modifications on sample_finetune.py provided by Microsoft.\n\n- Developed by: Can Deniz Koçak\n- Finetuned from model: microsoft/Phi-3-mini-4k-instruct",
"### Fine-tuning Data\n\nm-a-p/Code-Feedback",
"### Training Procedure\n\nTrained on a single A100 on Google Colab."
] |
null | null |
# MergerixYamshadowexperiment28-7B
MergerixYamshadowexperiment28-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
## 🧩 Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
- model: MiniMoog/Mergerix-7b-v0.3
- model: automerger/YamshadowExperiment28-7B
merge_method: model_stock
base_model: mistralai/Mistral-7B-v0.1
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/MergerixYamshadowexperiment28-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"]} | automerger/MergerixYamshadowexperiment28-7B | null | [
"merge",
"mergekit",
"lazymergekit",
"automerger",
"license:apache-2.0",
"region:us"
] | null | 2024-04-28T01:09:24+00:00 | [] | [] | TAGS
#merge #mergekit #lazymergekit #automerger #license-apache-2.0 #region-us
|
# MergerixYamshadowexperiment28-7B
MergerixYamshadowexperiment28-7B is an automated merge created by Maxime Labonne using the following configuration.
## Configuration
## Usage
| [
"# MergerixYamshadowexperiment28-7B\n\nMergerixYamshadowexperiment28-7B is an automated merge created by Maxime Labonne using the following configuration.",
"## Configuration",
"## Usage"
] | [
"TAGS\n#merge #mergekit #lazymergekit #automerger #license-apache-2.0 #region-us \n",
"# MergerixYamshadowexperiment28-7B\n\nMergerixYamshadowexperiment28-7B is an automated merge created by Maxime Labonne using the following configuration.",
"## Configuration",
"## Usage"
] |
visual-question-answering | 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. -->
### 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]
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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
- **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]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | lazyghost/blip2-fnt | null | [
"transformers",
"safetensors",
"blip-2",
"visual-question-answering",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:10:03+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #blip-2 #visual-question-answering #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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"### Results",
"#### Summary",
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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:",
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"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #blip-2 #visual-question-answering #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]:",
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"### 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:",
"#### 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 | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/gradientai/Llama-3-8B-Instruct-262k
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-GGUF/resolve/main/Llama-3-8B-Instruct-262k.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-GGUF/resolve/main/Llama-3-8B-Instruct-262k.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-GGUF/resolve/main/Llama-3-8B-Instruct-262k.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-GGUF/resolve/main/Llama-3-8B-Instruct-262k.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-GGUF/resolve/main/Llama-3-8B-Instruct-262k.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-GGUF/resolve/main/Llama-3-8B-Instruct-262k.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-GGUF/resolve/main/Llama-3-8B-Instruct-262k.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-GGUF/resolve/main/Llama-3-8B-Instruct-262k.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-GGUF/resolve/main/Llama-3-8B-Instruct-262k.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-GGUF/resolve/main/Llama-3-8B-Instruct-262k.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-GGUF/resolve/main/Llama-3-8B-Instruct-262k.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-GGUF/resolve/main/Llama-3-8B-Instruct-262k.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-GGUF/resolve/main/Llama-3-8B-Instruct-262k.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-GGUF/resolve/main/Llama-3-8B-Instruct-262k.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-GGUF/resolve/main/Llama-3-8B-Instruct-262k.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "llama3", "library_name": "transformers", "tags": ["meta", "llama-3"], "base_model": "gradientai/Llama-3-8B-Instruct-262k", "quantized_by": "mradermacher"} | mradermacher/Llama-3-8B-Instruct-262k-GGUF | null | [
"transformers",
"gguf",
"meta",
"llama-3",
"en",
"base_model:gradientai/Llama-3-8B-Instruct-262k",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:12:26+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #meta #llama-3 #en #base_model-gradientai/Llama-3-8B-Instruct-262k #license-llama3 #endpoints_compatible #region-us
| About
-----
static quants of URL
weighted/imatrix quants are available at URL
Usage
-----
If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs for
more details, including on how to concatenate multi-part files.
Provided Quants
---------------
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #meta #llama-3 #en #base_model-gradientai/Llama-3-8B-Instruct-262k #license-llama3 #endpoints_compatible #region-us \n"
] |
null | peft |
# Model Card for Model ID
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### Framework versions
- PEFT 0.7.2.dev0 | {"library_name": "peft", "base_model": "meta-llama/Meta-Llama-3-8B"} | yiyic/llama-text-labels-lora-clf-epoch-1 | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Meta-Llama-3-8B",
"region:us"
] | null | 2024-04-28T01:12:27+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Meta-Llama-3-8B #region-us
|
# Model Card for Model ID
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- PEFT 0.7.2.dev0 | [
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"### Framework versions\n\n- PEFT 0.7.2.dev0"
] |
null | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | tenzintridhe/phi2-model-B | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:15:06+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
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- Hardware Type:
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| [
"# 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]:",
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"#### 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 | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/erfanzar/Xerxes-8B-Instruct-v0.4
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Xerxes-8B-Instruct-v0.4-GGUF/resolve/main/Xerxes-8B-Instruct-v0.4.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Xerxes-8B-Instruct-v0.4-GGUF/resolve/main/Xerxes-8B-Instruct-v0.4.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Xerxes-8B-Instruct-v0.4-GGUF/resolve/main/Xerxes-8B-Instruct-v0.4.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Xerxes-8B-Instruct-v0.4-GGUF/resolve/main/Xerxes-8B-Instruct-v0.4.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Xerxes-8B-Instruct-v0.4-GGUF/resolve/main/Xerxes-8B-Instruct-v0.4.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Xerxes-8B-Instruct-v0.4-GGUF/resolve/main/Xerxes-8B-Instruct-v0.4.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Xerxes-8B-Instruct-v0.4-GGUF/resolve/main/Xerxes-8B-Instruct-v0.4.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Xerxes-8B-Instruct-v0.4-GGUF/resolve/main/Xerxes-8B-Instruct-v0.4.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Xerxes-8B-Instruct-v0.4-GGUF/resolve/main/Xerxes-8B-Instruct-v0.4.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Xerxes-8B-Instruct-v0.4-GGUF/resolve/main/Xerxes-8B-Instruct-v0.4.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Xerxes-8B-Instruct-v0.4-GGUF/resolve/main/Xerxes-8B-Instruct-v0.4.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Xerxes-8B-Instruct-v0.4-GGUF/resolve/main/Xerxes-8B-Instruct-v0.4.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Xerxes-8B-Instruct-v0.4-GGUF/resolve/main/Xerxes-8B-Instruct-v0.4.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Xerxes-8B-Instruct-v0.4-GGUF/resolve/main/Xerxes-8B-Instruct-v0.4.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Xerxes-8B-Instruct-v0.4-GGUF/resolve/main/Xerxes-8B-Instruct-v0.4.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "library_name": "transformers", "tags": [], "base_model": "erfanzar/Xerxes-8B-Instruct-v0.4", "quantized_by": "mradermacher"} | mradermacher/Xerxes-8B-Instruct-v0.4-GGUF | null | [
"transformers",
"gguf",
"en",
"base_model:erfanzar/Xerxes-8B-Instruct-v0.4",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:15:25+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #en #base_model-erfanzar/Xerxes-8B-Instruct-v0.4 #endpoints_compatible #region-us
| About
-----
static quants of URL
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
-----
If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs for
more details, including on how to concatenate multi-part files.
Provided Quants
---------------
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #en #base_model-erfanzar/Xerxes-8B-Instruct-v0.4 #endpoints_compatible #region-us \n"
] |
text-to-image | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - Pandluru/SDXL-Base
<Gallery />
## Model description
These are Pandluru/SDXL-Base LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of TOK dog to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](Pandluru/SDXL-Base/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | {"license": "openrail++", "library_name": "diffusers", "tags": ["text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of TOK dog", "widget": []} | Pandluru/SDXL-Base | null | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"dora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null | 2024-04-28T01:17:28+00:00 | [] | [] | TAGS
#diffusers #tensorboard #text-to-image #diffusers-training #dora #template-sd-lora #stable-diffusion-xl #stable-diffusion-xl-diffusers #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us
|
# SDXL LoRA DreamBooth - Pandluru/SDXL-Base
<Gallery />
## Model description
These are Pandluru/SDXL-Base LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using DreamBooth.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of TOK dog to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | [
"# SDXL LoRA DreamBooth - Pandluru/SDXL-Base\n\n<Gallery />",
"## Model description\n\nThese are Pandluru/SDXL-Base LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.",
"## Trigger words\n\nYou should use a photo of TOK dog to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Training details\n\n[TODO: describe the data used to train the model]"
] | [
"TAGS\n#diffusers #tensorboard #text-to-image #diffusers-training #dora #template-sd-lora #stable-diffusion-xl #stable-diffusion-xl-diffusers #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us \n",
"# SDXL LoRA DreamBooth - Pandluru/SDXL-Base\n\n<Gallery />",
"## Model description\n\nThese are Pandluru/SDXL-Base LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.",
"## Trigger words\n\nYou should use a photo of TOK dog to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Training details\n\n[TODO: describe the data used to train the model]"
] |
image-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. -->
# Boya1_RMSProp_1-e5_10Epoch_swinv2-base-patch4_fold4
This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window12-192-22k](https://huggingface.co/microsoft/swinv2-base-patch4-window12-192-22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3001
- Accuracy: 0.6711
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1488 | 1.0 | 924 | 1.1016 | 0.6245 |
| 0.9595 | 2.0 | 1848 | 0.9926 | 0.6535 |
| 0.766 | 3.0 | 2772 | 0.9713 | 0.6662 |
| 0.7722 | 4.0 | 3696 | 1.0042 | 0.6743 |
| 0.6923 | 5.0 | 4620 | 1.0252 | 0.6689 |
| 0.384 | 6.0 | 5544 | 1.1090 | 0.6646 |
| 0.4933 | 7.0 | 6468 | 1.1429 | 0.6654 |
| 0.5012 | 8.0 | 7392 | 1.2321 | 0.6678 |
| 0.3141 | 9.0 | 8316 | 1.2959 | 0.6695 |
| 0.3701 | 10.0 | 9240 | 1.3001 | 0.6711 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swinv2-base-patch4-window12-192-22k", "model-index": [{"name": "Boya1_RMSProp_1-e5_10Epoch_swinv2-base-patch4_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6710918450284475, "name": "Accuracy"}]}]}]} | onizukal/Boya1_RMSProp_1-e5_10Epoch_swinv2-base-patch4_fold4 | null | [
"transformers",
"safetensors",
"swinv2",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swinv2-base-patch4-window12-192-22k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:19:03+00:00 | [] | [] | TAGS
#transformers #safetensors #swinv2 #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swinv2-base-patch4-window12-192-22k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Boya1\_RMSProp\_1-e5\_10Epoch\_swinv2-base-patch4\_fold4
========================================================
This model is a fine-tuned version of microsoft/swinv2-base-patch4-window12-192-22k on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3001
* Accuracy: 0.6711
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.1.0
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #safetensors #swinv2 #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swinv2-base-patch4-window12-192-22k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] |
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]
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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]
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<!-- 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
### 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": []} | HC-85/distilbert-lora-r64-arxiv-multilabel | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:20:54+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
| [
"# 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 #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"
] |
text-to-image | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - Pandluru/SDXL-Lightning
<Gallery />
## Model description
These are Pandluru/SDXL-Lightning LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of TOK dog to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](Pandluru/SDXL-Lightning/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | {"license": "openrail++", "library_name": "diffusers", "tags": ["text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of TOK dog", "widget": []} | Pandluru/SDXL-Lightning | null | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"dora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null | 2024-04-28T01:23:27+00:00 | [] | [] | TAGS
#diffusers #tensorboard #text-to-image #diffusers-training #dora #template-sd-lora #stable-diffusion-xl #stable-diffusion-xl-diffusers #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us
|
# SDXL LoRA DreamBooth - Pandluru/SDXL-Lightning
<Gallery />
## Model description
These are Pandluru/SDXL-Lightning LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using DreamBooth.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of TOK dog to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | [
"# SDXL LoRA DreamBooth - Pandluru/SDXL-Lightning\n\n<Gallery />",
"## Model description\n\nThese are Pandluru/SDXL-Lightning LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.",
"## Trigger words\n\nYou should use a photo of TOK dog to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Training details\n\n[TODO: describe the data used to train the model]"
] | [
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"# SDXL LoRA DreamBooth - Pandluru/SDXL-Lightning\n\n<Gallery />",
"## Model description\n\nThese are Pandluru/SDXL-Lightning LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.",
"## Trigger words\n\nYou should use a photo of TOK dog to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Training details\n\n[TODO: describe the data used to train the model]"
] |
text2text-generation | transformers |
*Author - Hayden Beadles*
This model is meant to evaluate the results of creating an Encoder / Decoder generative model using BERT. The model is then finetuned on 30000 samples of the PubMedQA dataset. Instead of being finetuned on the columns question and final_answer, where final_answer is a set of yes / no answers, we instead fine tune on the more challenging long_answer column, which gives a short answer to the question.
The model was fine-tuned over 3 epochs, using the Adam learning rate scheduler, with a max length of 128 tokens.
The results are to help gauge BERT's abilities to answer (generate an answer) directly to a question, with no context provided. It is meant to evaluate the overall models training and attention towards a more focused topic, to see if BERTs base training gives it any advantages.
| {"language": ["en"], "license": "mit", "tags": ["medical"], "datasets": ["qiaojin/PubMedQA"]} | GeorgiaTech/bert-generative-pubmedqa | null | [
"transformers",
"safetensors",
"encoder-decoder",
"text2text-generation",
"medical",
"en",
"dataset:qiaojin/PubMedQA",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:23:45+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #encoder-decoder #text2text-generation #medical #en #dataset-qiaojin/PubMedQA #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
*Author - Hayden Beadles*
This model is meant to evaluate the results of creating an Encoder / Decoder generative model using BERT. The model is then finetuned on 30000 samples of the PubMedQA dataset. Instead of being finetuned on the columns question and final_answer, where final_answer is a set of yes / no answers, we instead fine tune on the more challenging long_answer column, which gives a short answer to the question.
The model was fine-tuned over 3 epochs, using the Adam learning rate scheduler, with a max length of 128 tokens.
The results are to help gauge BERT's abilities to answer (generate an answer) directly to a question, with no context provided. It is meant to evaluate the overall models training and attention towards a more focused topic, to see if BERTs base training gives it any advantages.
| [] | [
"TAGS\n#transformers #safetensors #encoder-decoder #text2text-generation #medical #en #dataset-qiaojin/PubMedQA #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
reinforcement-learning | null |
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'hossniper/SPPO-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
| {"tags": ["LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "-182.55 +/- 105.80", "name": "mean_reward", "verified": false}]}]}]} | hossniper/SPPO-LunarLander-v2 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | null | 2024-04-28T01:25:44+00:00 | [] | [] | TAGS
#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us
|
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
| [
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n \n # Hyperparameters"
] | [
"TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us \n",
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n \n # Hyperparameters"
] |
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. -->
# gemma-2b-dolly-qa
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0223
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- training_steps: 1480
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 2.9293 | 1.6393 | 100 | 2.5752 |
| 2.4386 | 3.2787 | 200 | 2.2894 |
| 2.2528 | 4.9180 | 300 | 2.1724 |
| 2.1653 | 6.5574 | 400 | 2.1080 |
| 2.1144 | 8.1967 | 500 | 2.0766 |
| 2.087 | 9.8361 | 600 | 2.0583 |
| 2.0697 | 11.4754 | 700 | 2.0473 |
| 2.0493 | 13.1148 | 800 | 2.0395 |
| 2.0472 | 14.7541 | 900 | 2.0341 |
| 2.0311 | 16.3934 | 1000 | 2.0300 |
| 2.029 | 18.0328 | 1100 | 2.0267 |
| 2.0233 | 19.6721 | 1200 | 2.0245 |
| 2.0177 | 21.3115 | 1300 | 2.0230 |
| 2.0136 | 22.9508 | 1400 | 2.0223 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.1.0.post0+cxx11.abi
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "gemma", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "google/gemma-2b", "model-index": [{"name": "gemma-2b-dolly-qa", "results": []}]} | Codingjackking/gemma-2b-dolly-qa | null | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:google/gemma-2b",
"license:gemma",
"region:us"
] | null | 2024-04-28T01:25:51+00:00 | [] | [] | TAGS
#peft #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-google/gemma-2b #license-gemma #region-us
| gemma-2b-dolly-qa
=================
This model is a fine-tuned version of google/gemma-2b on the generator dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0223
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* training\_steps: 1480
### Training results
### Framework versions
* PEFT 0.10.0
* Transformers 4.40.1
* Pytorch 2.1.0.post0+URL
* Datasets 2.19.0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\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\\_ratio: 0.05\n* training\\_steps: 1480",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.40.1\n* Pytorch 2.1.0.post0+URL\n* Datasets 2.19.0\n* Tokenizers 0.19.1"
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"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.40.1\n* Pytorch 2.1.0.post0+URL\n* Datasets 2.19.0\n* Tokenizers 0.19.1"
] |
text-to-image | diffusers | ## Import to Automatic1111
Create a folder named WorldDiffusion and insert the 3 files found to use it. | {"language": ["en"], "library_name": "diffusers", "tags": ["art"], "pipeline_tag": "text-to-image"} | GamerC0der/WorldDiffusion | null | [
"diffusers",
"art",
"text-to-image",
"en",
"region:us"
] | null | 2024-04-28T01:26:29+00:00 | [] | [
"en"
] | TAGS
#diffusers #art #text-to-image #en #region-us
| ## Import to Automatic1111
Create a folder named WorldDiffusion and insert the 3 files found to use it. | [
"## Import to Automatic1111\nCreate a folder named WorldDiffusion and insert the 3 files found to use it."
] | [
"TAGS\n#diffusers #art #text-to-image #en #region-us \n",
"## Import to Automatic1111\nCreate a folder named WorldDiffusion and insert the 3 files found to use it."
] |
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_total_Instruction0_PASOL_v1_h1
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: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- 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_total_Instruction0_PASOL_v1_h1", "results": []}]} | ThuyNT/CS505_COQE_viT5_total_Instruction0_PASOL_v1_h1 | 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 | 2024-04-28T01:27:15+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_total_Instruction0_PASOL_v1_h1
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: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- 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_total_Instruction0_PASOL_v1_h1\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: 8\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: 25\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"
] | [
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"# CS505_COQE_viT5_total_Instruction0_PASOL_v1_h1\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: 8\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: 25\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 | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/vincentoh/llama3-alpaca-dpo-instruct
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/llama3-alpaca-dpo-instruct-GGUF/resolve/main/llama3-alpaca-dpo-instruct.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-alpaca-dpo-instruct-GGUF/resolve/main/llama3-alpaca-dpo-instruct.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-alpaca-dpo-instruct-GGUF/resolve/main/llama3-alpaca-dpo-instruct.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-alpaca-dpo-instruct-GGUF/resolve/main/llama3-alpaca-dpo-instruct.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/llama3-alpaca-dpo-instruct-GGUF/resolve/main/llama3-alpaca-dpo-instruct.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-alpaca-dpo-instruct-GGUF/resolve/main/llama3-alpaca-dpo-instruct.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/llama3-alpaca-dpo-instruct-GGUF/resolve/main/llama3-alpaca-dpo-instruct.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-alpaca-dpo-instruct-GGUF/resolve/main/llama3-alpaca-dpo-instruct.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-alpaca-dpo-instruct-GGUF/resolve/main/llama3-alpaca-dpo-instruct.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama3-alpaca-dpo-instruct-GGUF/resolve/main/llama3-alpaca-dpo-instruct.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama3-alpaca-dpo-instruct-GGUF/resolve/main/llama3-alpaca-dpo-instruct.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-alpaca-dpo-instruct-GGUF/resolve/main/llama3-alpaca-dpo-instruct.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-alpaca-dpo-instruct-GGUF/resolve/main/llama3-alpaca-dpo-instruct.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/llama3-alpaca-dpo-instruct-GGUF/resolve/main/llama3-alpaca-dpo-instruct.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/llama3-alpaca-dpo-instruct-GGUF/resolve/main/llama3-alpaca-dpo-instruct.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "vincentoh/llama3-alpaca-dpo-instruct", "quantized_by": "mradermacher"} | mradermacher/llama3-alpaca-dpo-instruct-GGUF | null | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:vincentoh/llama3-alpaca-dpo-instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:28:02+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #text-generation-inference #unsloth #llama #trl #en #base_model-vincentoh/llama3-alpaca-dpo-instruct #license-apache-2.0 #endpoints_compatible #region-us
| About
-----
static quants of URL
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
-----
If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs for
more details, including on how to concatenate multi-part files.
Provided Quants
---------------
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #text-generation-inference #unsloth #llama #trl #en #base_model-vincentoh/llama3-alpaca-dpo-instruct #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
text-to-image | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - Pandluru/Hyper-SDXL
<Gallery />
## Model description
These are Pandluru/Hyper-SDXL LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of TOK dog to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](Pandluru/Hyper-SDXL/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | {"license": "openrail++", "library_name": "diffusers", "tags": ["text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of TOK dog", "widget": []} | Pandluru/Hyper-SDXL | null | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"dora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null | 2024-04-28T01:28:17+00:00 | [] | [] | TAGS
#diffusers #tensorboard #text-to-image #diffusers-training #dora #template-sd-lora #stable-diffusion-xl #stable-diffusion-xl-diffusers #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us
|
# SDXL LoRA DreamBooth - Pandluru/Hyper-SDXL
<Gallery />
## Model description
These are Pandluru/Hyper-SDXL LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using DreamBooth.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of TOK dog to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | [
"# SDXL LoRA DreamBooth - Pandluru/Hyper-SDXL\n\n<Gallery />",
"## Model description\n\nThese are Pandluru/Hyper-SDXL LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.",
"## Trigger words\n\nYou should use a photo of TOK dog to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Training details\n\n[TODO: describe the data used to train the model]"
] | [
"TAGS\n#diffusers #tensorboard #text-to-image #diffusers-training #dora #template-sd-lora #stable-diffusion-xl #stable-diffusion-xl-diffusers #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us \n",
"# SDXL LoRA DreamBooth - Pandluru/Hyper-SDXL\n\n<Gallery />",
"## Model description\n\nThese are Pandluru/Hyper-SDXL LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.",
"## Trigger words\n\nYou should use a photo of TOK dog to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Training details\n\n[TODO: describe the data used to train the model]"
] |
feature-extraction | 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": []} | Mariofm02/bart_NLP_10000 | null | [
"transformers",
"safetensors",
"bart",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:29:02+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #bart #feature-extraction #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 #safetensors #bart #feature-extraction #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"
] |
text-to-audio | 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. -->
# zlm_b128_le4_s4000
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3181
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.385 | 0.8377 | 500 | 0.3593 |
| 0.3735 | 1.6754 | 1000 | 0.3449 |
| 0.37 | 2.5131 | 1500 | 0.3446 |
| 0.366 | 3.3508 | 2000 | 0.3387 |
| 0.3576 | 4.1885 | 2500 | 0.3337 |
| 0.3561 | 5.0262 | 3000 | 0.3288 |
| 0.3482 | 5.8639 | 3500 | 0.3197 |
| 0.3469 | 6.7016 | 4000 | 0.3181 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "zlm_b128_le4_s4000", "results": []}]} | mikhail-panzo/zlm_b128_le4_s4000 | null | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"base_model:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:30:44+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us
| zlm\_b128\_le4\_s4000
=====================
This model is a fine-tuned version of microsoft/speecht5\_tts on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3181
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* training\_steps: 4000
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.41.0.dev0
* Pytorch 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.41.0.dev0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.41.0.dev0\n* Pytorch 2.2.1+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
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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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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": []} | shallow6414/4urq346 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T01:30:59+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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[optional]
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## Glossary [optional]
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| [
"# Model Card for Model ID",
"## Model Details",
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"### 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",
"## 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",
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"## Glossary [optional]",
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"## Model Card Contact"
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"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
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"### 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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"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
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"#### Training Hyperparameters\n\n- Training regime:",
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"## Technical Specifications [optional]",
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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-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.
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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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).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/7y05xeo | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:33:44+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):
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- Paper [optional]:
- Demo [optional]:
## Uses
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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#### Preprocessing [optional]
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## Evaluation
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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]
## 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]:",
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"## 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",
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"### 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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"## 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
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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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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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### 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
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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## How to Get Started with the Model
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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#### 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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<!-- 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]
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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": []} | golf2248/5xgnt4j | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:33:49+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
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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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## How to Get Started with the Model
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## Training Details
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| [
"# 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.",
"## 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.
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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": []} | golf2248/6lgbvrk | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:33:54+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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## Uses
### Direct Use
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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- Hardware Type:
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[optional]
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## 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:",
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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 #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.",
"## 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 | null |
# Qwen1.5-110B-Chat-GGUF
## Introduction
Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:
* 9 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, 32B, 72B, and 110B dense models, and an MoE model of 14B with 2.7B activated;
* Significant performance improvement in human preference for chat models;
* Multilingual support of both base and chat models;
* Stable support of 32K context length for models of all sizes
* No need of `trust_remote_code`.
For more details, please refer to our [blog post](https://qwenlm.github.io/blog/qwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5).
In this repo, we provide quantized models in the GGUF formats, including `q2_k`, `q3_k_m`, `q4_0`, `q4_k_m`, `q5_0`, `q5_k_m`, `q6_k` and `q8_0`.
## Model Details
Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA (except for 32B and 110B) and the mixture of SWA and full attention.
## Training details
We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization.
## Requirements
We advise you to clone [`llama.cpp`](https://github.com/ggerganov/llama.cpp) and install it following the official guide.
## How to use
For starters, the 110B model is large and for most GGUF files, due to the limitation of uploading, we split the byte strings into 2 or 3 segments, so you can see files with theirs names ended with `.a` or `.b`.
Cloning the repo may be inefficient, and thus you can manually download the GGUF file that you need or use `huggingface-cli` (`pip install huggingface_hub`). For each GGUF model, you need to download all the files with the same prefix. For example, for the q_5_k_m model, you need to download both files with `.a` and `.b` at the end.
```bash
huggingface-cli download Qwen/Qwen1.5-110B-Chat-GGUF qwen1_5-110b-chat-q5_k_m.gguf.a --local-dir . --local-dir-use-symlinks False
huggingface-cli download Qwen/Qwen1.5-110B-Chat-GGUF qwen1_5-110b-chat-q5_k_m.gguf.b --local-dir . --local-dir-use-symlinks False
```
After, you need to concatenate them to obtain a whole GGUF file:
```bash
cat qwen1_5-110b-chat-q5_k_m.gguf.* > qwen1_5-110b-chat-q5_k_m.gguf
```
We demonstrate how to use `llama.cpp` to run Qwen1.5:
```shell
./main -m qwen1_5-110b-chat-q5_k_m.gguf -n 512 --color -i -cml -f prompts/chat-with-qwen.txt
```
## Citation
If you find our work helpful, feel free to give us a cite.
```
@article{qwen,
title={Qwen Technical Report},
author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu},
journal={arXiv preprint arXiv:2309.16609},
year={2023}
}
```
| {"language": ["en"], "license": "other", "tags": ["chat"], "license_name": "tongyi-qianwen", "license_link": "https://huggingface.co/Qwen/Qwen1.5-110B-Chat-GGUF/blob/main/LICENSE", "pipeline_tag": "text-generation"} | Qwen/Qwen1.5-110B-Chat-GGUF | null | [
"gguf",
"chat",
"text-generation",
"en",
"license:other",
"region:us"
] | null | 2024-04-28T01:38:30+00:00 | [] | [
"en"
] | TAGS
#gguf #chat #text-generation #en #license-other #region-us
|
# Qwen1.5-110B-Chat-GGUF
## Introduction
Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:
* 9 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, 32B, 72B, and 110B dense models, and an MoE model of 14B with 2.7B activated;
* Significant performance improvement in human preference for chat models;
* Multilingual support of both base and chat models;
* Stable support of 32K context length for models of all sizes
* No need of 'trust_remote_code'.
For more details, please refer to our blog post and GitHub repo.
In this repo, we provide quantized models in the GGUF formats, including 'q2_k', 'q3_k_m', 'q4_0', 'q4_k_m', 'q5_0', 'q5_k_m', 'q6_k' and 'q8_0'.
## Model Details
Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA (except for 32B and 110B) and the mixture of SWA and full attention.
## Training details
We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization.
## Requirements
We advise you to clone 'URL' and install it following the official guide.
## How to use
For starters, the 110B model is large and for most GGUF files, due to the limitation of uploading, we split the byte strings into 2 or 3 segments, so you can see files with theirs names ended with '.a' or '.b'.
Cloning the repo may be inefficient, and thus you can manually download the GGUF file that you need or use 'huggingface-cli' ('pip install huggingface_hub'). For each GGUF model, you need to download all the files with the same prefix. For example, for the q_5_k_m model, you need to download both files with '.a' and '.b' at the end.
After, you need to concatenate them to obtain a whole GGUF file:
We demonstrate how to use 'URL' to run Qwen1.5:
If you find our work helpful, feel free to give us a cite.
| [
"# Qwen1.5-110B-Chat-GGUF",
"## Introduction\n\nQwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include: \n\n* 9 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, 32B, 72B, and 110B dense models, and an MoE model of 14B with 2.7B activated;\n* Significant performance improvement in human preference for chat models;\n* Multilingual support of both base and chat models;\n* Stable support of 32K context length for models of all sizes\n* No need of 'trust_remote_code'.\n\nFor more details, please refer to our blog post and GitHub repo. \nIn this repo, we provide quantized models in the GGUF formats, including 'q2_k', 'q3_k_m', 'q4_0', 'q4_k_m', 'q5_0', 'q5_k_m', 'q6_k' and 'q8_0'.",
"## Model Details\nQwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA (except for 32B and 110B) and the mixture of SWA and full attention.",
"## Training details\nWe pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization.",
"## Requirements\nWe advise you to clone 'URL' and install it following the official guide.",
"## How to use\n\nFor starters, the 110B model is large and for most GGUF files, due to the limitation of uploading, we split the byte strings into 2 or 3 segments, so you can see files with theirs names ended with '.a' or '.b'. \n\nCloning the repo may be inefficient, and thus you can manually download the GGUF file that you need or use 'huggingface-cli' ('pip install huggingface_hub'). For each GGUF model, you need to download all the files with the same prefix. For example, for the q_5_k_m model, you need to download both files with '.a' and '.b' at the end.\n\n\nAfter, you need to concatenate them to obtain a whole GGUF file:\n\n\n\nWe demonstrate how to use 'URL' to run Qwen1.5:\n\n\n\nIf you find our work helpful, feel free to give us a cite."
] | [
"TAGS\n#gguf #chat #text-generation #en #license-other #region-us \n",
"# Qwen1.5-110B-Chat-GGUF",
"## Introduction\n\nQwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include: \n\n* 9 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, 32B, 72B, and 110B dense models, and an MoE model of 14B with 2.7B activated;\n* Significant performance improvement in human preference for chat models;\n* Multilingual support of both base and chat models;\n* Stable support of 32K context length for models of all sizes\n* No need of 'trust_remote_code'.\n\nFor more details, please refer to our blog post and GitHub repo. \nIn this repo, we provide quantized models in the GGUF formats, including 'q2_k', 'q3_k_m', 'q4_0', 'q4_k_m', 'q5_0', 'q5_k_m', 'q6_k' and 'q8_0'.",
"## Model Details\nQwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA (except for 32B and 110B) and the mixture of SWA and full attention.",
"## Training details\nWe pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization.",
"## Requirements\nWe advise you to clone 'URL' and install it following the official guide.",
"## How to use\n\nFor starters, the 110B model is large and for most GGUF files, due to the limitation of uploading, we split the byte strings into 2 or 3 segments, so you can see files with theirs names ended with '.a' or '.b'. \n\nCloning the repo may be inefficient, and thus you can manually download the GGUF file that you need or use 'huggingface-cli' ('pip install huggingface_hub'). For each GGUF model, you need to download all the files with the same prefix. For example, for the q_5_k_m model, you need to download both files with '.a' and '.b' at the end.\n\n\nAfter, you need to concatenate them to obtain a whole GGUF file:\n\n\n\nWe demonstrate how to use 'URL' to run Qwen1.5:\n\n\n\nIf you find our work helpful, feel free to give us a cite."
] |
text2text-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": []} | kssumanth6/t5_small_sentence_polishing_generator_v1 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T01:38:38+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #t5 #text2text-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 #t5 #text2text-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"
] |
null | transformers |
# SilverFan/IceCoffeeRP-7b-Q6_K-GGUF
This model was converted to GGUF format from [`icefog72/IceCoffeeRP-7b`](https://huggingface.co/icefog72/IceCoffeeRP-7b) 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/icefog72/IceCoffeeRP-7b) 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 SilverFan/IceCoffeeRP-7b-Q6_K-GGUF --model icecoffeerp-7b.Q6_K.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo SilverFan/IceCoffeeRP-7b-Q6_K-GGUF --model icecoffeerp-7b.Q6_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 icecoffeerp-7b.Q6_K.gguf -n 128
```
| {"license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["mergekit", "merge", "alpaca", "mistral", "not-for-all-audiences", "nsfw", "llama-cpp", "gguf-my-repo"], "model-index": [{"name": "IceCoffeeTest11", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 71.16, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceCoffeeTest11", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HellaSwag (10-Shot)", "type": "hellaswag", "split": "validation", "args": {"num_few_shot": 10}}, "metrics": [{"type": "acc_norm", "value": 87.74, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceCoffeeTest11", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "MMLU (5-Shot)", "type": "cais/mmlu", "config": "all", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 63.54, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceCoffeeTest11", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "TruthfulQA (0-shot)", "type": "truthful_qa", "config": "multiple_choice", "split": "validation", "args": {"num_few_shot": 0}}, "metrics": [{"type": "mc2", "value": 70.03}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceCoffeeTest11", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Winogrande (5-shot)", "type": "winogrande", "config": "winogrande_xl", "split": "validation", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 82.48, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceCoffeeTest11", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "GSM8k (5-shot)", "type": "gsm8k", "config": "main", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 64.22, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceCoffeeTest11", "name": "Open LLM Leaderboard"}}]}]} | SilverFan/IceCoffeeRP-7b-Q6_K-GGUF | null | [
"transformers",
"gguf",
"mergekit",
"merge",
"alpaca",
"mistral",
"not-for-all-audiences",
"nsfw",
"llama-cpp",
"gguf-my-repo",
"license:cc-by-nc-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:42:20+00:00 | [] | [] | TAGS
#transformers #gguf #mergekit #merge #alpaca #mistral #not-for-all-audiences #nsfw #llama-cpp #gguf-my-repo #license-cc-by-nc-4.0 #model-index #endpoints_compatible #region-us
|
# SilverFan/IceCoffeeRP-7b-Q6_K-GGUF
This model was converted to GGUF format from 'icefog72/IceCoffeeRP-7b' 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.
| [
"# SilverFan/IceCoffeeRP-7b-Q6_K-GGUF\nThis model was converted to GGUF format from 'icefog72/IceCoffeeRP-7b' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] | [
"TAGS\n#transformers #gguf #mergekit #merge #alpaca #mistral #not-for-all-audiences #nsfw #llama-cpp #gguf-my-repo #license-cc-by-nc-4.0 #model-index #endpoints_compatible #region-us \n",
"# SilverFan/IceCoffeeRP-7b-Q6_K-GGUF\nThis model was converted to GGUF format from 'icefog72/IceCoffeeRP-7b' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] |
text-classification | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
Given a sentence, our model predicts whether or not the sentence contains "persuasive" language, or language designed to elicit emotions or change
readers' opinions. The model was tuned on the SemEval 2020 Task 11 dataset. However, we preprocessed the dataset to adapt it from
multilabel technique classification and span-classification to our binary classification task.
There are two revisions:
* BERT - we finetuned `bert-large-cased` on our main branch
* XLM-RoBERTa - we finetuned `xlm-roberta-base` on our `roberta` branch.
## 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:** Ultraviolet Text
- **Model type:** BERT / RoBERTa
- **Language(s) (NLP):** En
- **License:** MIT
- **Finetuned from model [optional]:** bert-large-cased / xlm-roberta-base
## How to Get Started with the Model
Use the code below to get started with the model.
### Loading from the main branch (BERT)
```py
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-large-cased")
model = AutoModelForSequenceClassification.from_pretrained("chreh/persuasive_language_detector")
```
### Loading from the `roberta` branch (XLM RoBERTa)
```py
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
model = AutoModelForSequenceClassification.from_pretrained("chreh/persuasive_language_detector", revision="roberta")
```
## 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. -->
Training data can be downloaded from [the Semeval website](https://propaganda.qcri.org/semeval2020-task11/).
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
The training was done using Huggingface Trainer on both our local machines and Intel Developer Cloud kernels, enabling us to prototype multiple models simultaneously.
#### Preprocessing [optional]
All sentences containing spans of persuasive language techniques were labeled as persuasive language examples, while all others
were labeled as examples of non-persuasive language.
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
The test data is from the test data of `sem_eval_2020_task_11`, which can be downloaded from [the original website](https://propaganda.qcri.org/semeval2020-task11/).
The test data contains 38.25% persuasive examples and non-persuasive examples 61.75%. Metrics can be found in the following section
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
Metrics are reported in the format (main_branch), (roberta branch)
* Accuracy - 0.7165140725669719, 0.7326693227091633
* Recall - 0.6875584658559402, 0.6822916666666666
* Precision - 0.5941794664510913, 0.6415279138099902
* F1 - 0.6374674761491761, 0.6612821807168097
Overall, the `roberta` branch performs better, and with faster inference times. Thus, we recommend users download from the `roberta` revision.
| {"language": ["en"], "license": "mit", "library_name": "transformers", "datasets": ["sem_eval_2020_task_11"]} | chreh/persuasive_language_detector | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"en",
"dataset:sem_eval_2020_task_11",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:43:37+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #bert #text-classification #en #dataset-sem_eval_2020_task_11 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
Given a sentence, our model predicts whether or not the sentence contains "persuasive" language, or language designed to elicit emotions or change
readers' opinions. The model was tuned on the SemEval 2020 Task 11 dataset. However, we preprocessed the dataset to adapt it from
multilabel technique classification and span-classification to our binary classification task.
There are two revisions:
* BERT - we finetuned 'bert-large-cased' on our main branch
* XLM-RoBERTa - we finetuned 'xlm-roberta-base' on our 'roberta' branch.
## 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: Ultraviolet Text
- Model type: BERT / RoBERTa
- Language(s) (NLP): En
- License: MIT
- Finetuned from model [optional]: bert-large-cased / xlm-roberta-base
## How to Get Started with the Model
Use the code below to get started with the model.
### Loading from the main branch (BERT)
### Loading from the 'roberta' branch (XLM RoBERTa)
## Training Details
### Training Data
Training data can be downloaded from the Semeval website.
### Training Procedure
The training was done using Huggingface Trainer on both our local machines and Intel Developer Cloud kernels, enabling us to prototype multiple models simultaneously.
#### Preprocessing [optional]
All sentences containing spans of persuasive language techniques were labeled as persuasive language examples, while all others
were labeled as examples of non-persuasive language.
### Testing Data, Factors & Metrics
#### Testing Data
The test data is from the test data of 'sem_eval_2020_task_11', which can be downloaded from the original website.
The test data contains 38.25% persuasive examples and non-persuasive examples 61.75%. Metrics can be found in the following section
#### Metrics
Metrics are reported in the format (main_branch), (roberta branch)
* Accuracy - 0.7165140725669719, 0.7326693227091633
* Recall - 0.6875584658559402, 0.6822916666666666
* Precision - 0.5941794664510913, 0.6415279138099902
* F1 - 0.6374674761491761, 0.6612821807168097
Overall, the 'roberta' branch performs better, and with faster inference times. Thus, we recommend users download from the 'roberta' revision.
| [
"# Model Card for Model ID\n\n\nGiven a sentence, our model predicts whether or not the sentence contains \"persuasive\" language, or language designed to elicit emotions or change\nreaders' opinions. The model was tuned on the SemEval 2020 Task 11 dataset. However, we preprocessed the dataset to adapt it from\nmultilabel technique classification and span-classification to our binary classification task.\n\nThere are two revisions:\n* BERT - we finetuned 'bert-large-cased' on our main branch\n* XLM-RoBERTa - we finetuned 'xlm-roberta-base' on our 'roberta' branch.",
"## 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: Ultraviolet Text\n- Model type: BERT / RoBERTa\n- Language(s) (NLP): En\n- License: MIT\n- Finetuned from model [optional]: bert-large-cased / xlm-roberta-base",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"### Loading from the main branch (BERT)",
"### Loading from the 'roberta' branch (XLM RoBERTa)",
"## Training Details",
"### Training Data\n\n\nTraining data can be downloaded from the Semeval website.",
"### Training Procedure\n\n\nThe training was done using Huggingface Trainer on both our local machines and Intel Developer Cloud kernels, enabling us to prototype multiple models simultaneously.",
"#### Preprocessing [optional]\nAll sentences containing spans of persuasive language techniques were labeled as persuasive language examples, while all others\nwere labeled as examples of non-persuasive language.",
"### Testing Data, Factors & Metrics",
"#### Testing Data\n\n\nThe test data is from the test data of 'sem_eval_2020_task_11', which can be downloaded from the original website.\nThe test data contains 38.25% persuasive examples and non-persuasive examples 61.75%. Metrics can be found in the following section",
"#### Metrics\n\n\nMetrics are reported in the format (main_branch), (roberta branch)\n* Accuracy - 0.7165140725669719, 0.7326693227091633\n* Recall - 0.6875584658559402, 0.6822916666666666\n* Precision - 0.5941794664510913, 0.6415279138099902\n* F1 - 0.6374674761491761, 0.6612821807168097\n\nOverall, the 'roberta' branch performs better, and with faster inference times. Thus, we recommend users download from the 'roberta' revision."
] | [
"TAGS\n#transformers #safetensors #bert #text-classification #en #dataset-sem_eval_2020_task_11 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for Model ID\n\n\nGiven a sentence, our model predicts whether or not the sentence contains \"persuasive\" language, or language designed to elicit emotions or change\nreaders' opinions. The model was tuned on the SemEval 2020 Task 11 dataset. However, we preprocessed the dataset to adapt it from\nmultilabel technique classification and span-classification to our binary classification task.\n\nThere are two revisions:\n* BERT - we finetuned 'bert-large-cased' on our main branch\n* XLM-RoBERTa - we finetuned 'xlm-roberta-base' on our 'roberta' branch.",
"## 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: Ultraviolet Text\n- Model type: BERT / RoBERTa\n- Language(s) (NLP): En\n- License: MIT\n- Finetuned from model [optional]: bert-large-cased / xlm-roberta-base",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"### Loading from the main branch (BERT)",
"### Loading from the 'roberta' branch (XLM RoBERTa)",
"## Training Details",
"### Training Data\n\n\nTraining data can be downloaded from the Semeval website.",
"### Training Procedure\n\n\nThe training was done using Huggingface Trainer on both our local machines and Intel Developer Cloud kernels, enabling us to prototype multiple models simultaneously.",
"#### Preprocessing [optional]\nAll sentences containing spans of persuasive language techniques were labeled as persuasive language examples, while all others\nwere labeled as examples of non-persuasive language.",
"### Testing Data, Factors & Metrics",
"#### Testing Data\n\n\nThe test data is from the test data of 'sem_eval_2020_task_11', which can be downloaded from the original website.\nThe test data contains 38.25% persuasive examples and non-persuasive examples 61.75%. Metrics can be found in the following section",
"#### Metrics\n\n\nMetrics are reported in the format (main_branch), (roberta branch)\n* Accuracy - 0.7165140725669719, 0.7326693227091633\n* Recall - 0.6875584658559402, 0.6822916666666666\n* Precision - 0.5941794664510913, 0.6415279138099902\n* F1 - 0.6374674761491761, 0.6612821807168097\n\nOverall, the 'roberta' branch performs better, and with faster inference times. Thus, we recommend users download from the 'roberta' revision."
] |
text-generation | transformers |
# Llama-3-8b-pygmalion-2-7b-v1
Llama-3-8b-pygmalion-2-7b-v1 is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [winglian/Llama-3-8b-64k-PoSE](https://huggingface.co/winglian/Llama-3-8b-64k-PoSE)
* [PygmalionAI/pygmalion-2-7b](https://huggingface.co/PygmalionAI/pygmalion-2-7b)
## 🧩 Configuration
```yamlbase_model: winglian/Llama-3-8b-64k-PoSE
dtype: float16
gate_mode: cheap_embed
experts:
- source_model: winglian/Llama-3-8b-64k-PoSE
positive_prompts: ["You are an intelligent bot that is smart and sassy"]
- source_model: PygmalionAI/pygmalion-2-7b
positive_prompts: ["You are a sexy girl that loves to roleplay"]```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "farhananis005/Llama-3-8b-pygmalion-2-7b-v1"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "apache-2.0", "tags": ["moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "winglian/Llama-3-8b-64k-PoSE", "PygmalionAI/pygmalion-2-7b"], "base_model": ["winglian/Llama-3-8b-64k-PoSE", "PygmalionAI/pygmalion-2-7b"]} | farhananis005/Llama-3-8b-pygmalion-2-7b-v1 | null | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"frankenmoe",
"merge",
"mergekit",
"lazymergekit",
"winglian/Llama-3-8b-64k-PoSE",
"PygmalionAI/pygmalion-2-7b",
"base_model:winglian/Llama-3-8b-64k-PoSE",
"base_model:PygmalionAI/pygmalion-2-7b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T01:46:34+00:00 | [] | [] | TAGS
#transformers #safetensors #mixtral #text-generation #moe #frankenmoe #merge #mergekit #lazymergekit #winglian/Llama-3-8b-64k-PoSE #PygmalionAI/pygmalion-2-7b #base_model-winglian/Llama-3-8b-64k-PoSE #base_model-PygmalionAI/pygmalion-2-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Llama-3-8b-pygmalion-2-7b-v1
Llama-3-8b-pygmalion-2-7b-v1 is a Mixture of Experts (MoE) made with the following models using LazyMergekit:
* winglian/Llama-3-8b-64k-PoSE
* PygmalionAI/pygmalion-2-7b
## Configuration
## Usage
| [
"# Llama-3-8b-pygmalion-2-7b-v1\n\nLlama-3-8b-pygmalion-2-7b-v1 is a Mixture of Experts (MoE) made with the following models using LazyMergekit:\n* winglian/Llama-3-8b-64k-PoSE\n* PygmalionAI/pygmalion-2-7b",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #moe #frankenmoe #merge #mergekit #lazymergekit #winglian/Llama-3-8b-64k-PoSE #PygmalionAI/pygmalion-2-7b #base_model-winglian/Llama-3-8b-64k-PoSE #base_model-PygmalionAI/pygmalion-2-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Llama-3-8b-pygmalion-2-7b-v1\n\nLlama-3-8b-pygmalion-2-7b-v1 is a Mixture of Experts (MoE) made with the following models using LazyMergekit:\n* winglian/Llama-3-8b-64k-PoSE\n* PygmalionAI/pygmalion-2-7b",
"## Configuration",
"## Usage"
] |
null | null | # Getting Started
Create a new conda environment for robomaster
```bash
conda create -n robomaster python=3.8
pip install robomaster dora-rs==0.3.3
```
Create a new conda environment for idefics2. This requirements file suppose that your using cu122.
```bash
conda create -n idefics2 python=3.10
conda activate idefics2
pip install -r requirements.txt
```
## Robomaster Jailbreak
### Installation of the Robomaster S1 Hack
This guide is an updated version of the original [Robomaster S1 SDK Hack Guide](https://www.bug-br.org.br/s1_sdk_hack.zip) and is intended for use on a Windows 11 system.
#### Prerequisites
Before you get started, you'll need the following:
- Robomaster S1 (do not update it to the latest version, as it may block the hack).
- [Robomaster App](https://www.dji.com/fr/robomaster-s1/downloads).
- [Android SDK Platform-Tools](https://developer.android.com/tools/releases/platform-tools). Simply unzip it and keep the path handy.
- A micro USB cable. If this guide doesn't work, there might be an issue with the cable, and you may need to replace it with one that supports data transfer.
#### Instructions
1. Start the Robomaster App and connect the Robomaster S1 using one of the two options provided (via router or via Wi-Fi).
2. While connected, use a micro USB cable to connect the robot to the computer's USB port. You should hear a beep sound, similar to when you connect any device. (Please note that no other Android device should be connected via USB during this process).
3. In the Lab section of the app, create a new Python application and paste the following code:
```python
def root_me(module):
__import__ = rm_define.__dict__['__builtins__']['__import__']
return __import__(module, globals(), locals(), [], 0)
builtins = root_me('builtins')
subprocess = root_me('subprocess')
proc = subprocess.Popen('/system/bin/adb_en.sh', shell=True, executable='/system/bin/sh', stdout=subprocess.PIPE, stderr=subprocess.PIPE)
```
4. Run the code; there should be no errors, and the console should display **Execution Complete**
5. Without closing the app, navigate to the folder containing the Android SDK Platform-Tools and open a terminal inside it.
6. Run the ADP command `.\adb.exe devices `. If everything is working correctly, you should see output similar to this: 
7. Execute the upload.sh script located in the folder `s1_SDK`.
8. Once everything has been executed, restart the S1 by turning it off and then back on. While it's booting up, you should hear two chimes instead of the usual single chime, indicating that the hack has been successful.
## Robomaster Connection
Make sure to be connected using the wifi hotspot of the robomaster which is the most stable one.
The default password for the hotpsot is: 12341234
You might need to have a second wifi card if you want to be able to run the demo with internet on.
## Post-Installation test
Please try running idefics2 with:
```bash
conda activate idefics2
python tests/test_idefics2.py
```
Please try running robomaster with:
```bash
conda activate robomaster
python tests/test_robomaster.py
```
## Running the demo
```bash
export HF_TOKEN=<TOKEN>
conda activate idefics2
# This requires dora==0.3.3, update with:
# cargo install dora-cli
dora up
dora start graphs/dataflow_robot_vlm.yml --attach --hot-reload
```
Current way to interact is by press up arrow key on laptop to record a message and send to the VLM
## Running the demo without robot
```bash
export HF_TOKEN=<TOKEN>
conda activate idefics2
# This requires dora==0.3.3, update with:
# cargo install dora-cli
dora up
dora start graphs/dataflow_vlm_basic.yml --attach --hot-reload
```
Current way to interact is by press up arrow key on laptop to record a message and send to the VLM
## Kill process in case of failure
Due to a Python GIL issue, we currently meed to kill processes manually. You can use the following command to do so:
```bash
pkill -f 'import dora;'
```
## LICENSE
While the source of this library is licensed under Apache-2.0, the usage of the Text to Speech(TTS) SystemEngine is licensed under Mozilla Public License 2.0 and GNU Lesser General Public License (LGPL) version 3.0.
Feel free to remove the TTS SystemEngine.
| {} | TommyZQ/csg-dora-rs | null | [
"region:us"
] | null | 2024-04-28T01:48:29+00:00 | [] | [] | TAGS
#region-us
| # Getting Started
Create a new conda environment for robomaster
Create a new conda environment for idefics2. This requirements file suppose that your using cu122.
## Robomaster Jailbreak
### Installation of the Robomaster S1 Hack
This guide is an updated version of the original Robomaster S1 SDK Hack Guide and is intended for use on a Windows 11 system.
#### Prerequisites
Before you get started, you'll need the following:
- Robomaster S1 (do not update it to the latest version, as it may block the hack).
- Robomaster App.
- Android SDK Platform-Tools. Simply unzip it and keep the path handy.
- A micro USB cable. If this guide doesn't work, there might be an issue with the cable, and you may need to replace it with one that supports data transfer.
#### Instructions
1. Start the Robomaster App and connect the Robomaster S1 using one of the two options provided (via router or via Wi-Fi).
2. While connected, use a micro USB cable to connect the robot to the computer's USB port. You should hear a beep sound, similar to when you connect any device. (Please note that no other Android device should be connected via USB during this process).
3. In the Lab section of the app, create a new Python application and paste the following code:
4. Run the code; there should be no errors, and the console should display Execution Complete
5. Without closing the app, navigate to the folder containing the Android SDK Platform-Tools and open a terminal inside it.
6. Run the ADP command '.\URL devices '. If everything is working correctly, you should see output similar to this: !image
7. Execute the URL script located in the folder 's1_SDK'.
8. Once everything has been executed, restart the S1 by turning it off and then back on. While it's booting up, you should hear two chimes instead of the usual single chime, indicating that the hack has been successful.
## Robomaster Connection
Make sure to be connected using the wifi hotspot of the robomaster which is the most stable one.
The default password for the hotpsot is: 12341234
You might need to have a second wifi card if you want to be able to run the demo with internet on.
## Post-Installation test
Please try running idefics2 with:
Please try running robomaster with:
## Running the demo
Current way to interact is by press up arrow key on laptop to record a message and send to the VLM
## Running the demo without robot
Current way to interact is by press up arrow key on laptop to record a message and send to the VLM
## Kill process in case of failure
Due to a Python GIL issue, we currently meed to kill processes manually. You can use the following command to do so:
## LICENSE
While the source of this library is licensed under Apache-2.0, the usage of the Text to Speech(TTS) SystemEngine is licensed under Mozilla Public License 2.0 and GNU Lesser General Public License (LGPL) version 3.0.
Feel free to remove the TTS SystemEngine.
| [
"# Getting Started\n\nCreate a new conda environment for robomaster\n\n\n\nCreate a new conda environment for idefics2. This requirements file suppose that your using cu122.",
"## Robomaster Jailbreak",
"### Installation of the Robomaster S1 Hack\n\nThis guide is an updated version of the original Robomaster S1 SDK Hack Guide and is intended for use on a Windows 11 system.",
"#### Prerequisites\n\nBefore you get started, you'll need the following:\n\n- Robomaster S1 (do not update it to the latest version, as it may block the hack).\n- Robomaster App.\n- Android SDK Platform-Tools. Simply unzip it and keep the path handy.\n- A micro USB cable. If this guide doesn't work, there might be an issue with the cable, and you may need to replace it with one that supports data transfer.",
"#### Instructions\n\n1. Start the Robomaster App and connect the Robomaster S1 using one of the two options provided (via router or via Wi-Fi).\n2. While connected, use a micro USB cable to connect the robot to the computer's USB port. You should hear a beep sound, similar to when you connect any device. (Please note that no other Android device should be connected via USB during this process).\n3. In the Lab section of the app, create a new Python application and paste the following code:\n\n \n\n4. Run the code; there should be no errors, and the console should display Execution Complete\n5. Without closing the app, navigate to the folder containing the Android SDK Platform-Tools and open a terminal inside it.\n6. Run the ADP command '.\\URL devices '. If everything is working correctly, you should see output similar to this: !image\n7. Execute the URL script located in the folder 's1_SDK'.\n8. Once everything has been executed, restart the S1 by turning it off and then back on. While it's booting up, you should hear two chimes instead of the usual single chime, indicating that the hack has been successful.",
"## Robomaster Connection\n\nMake sure to be connected using the wifi hotspot of the robomaster which is the most stable one.\n\nThe default password for the hotpsot is: 12341234\n\nYou might need to have a second wifi card if you want to be able to run the demo with internet on.",
"## Post-Installation test\n\nPlease try running idefics2 with:\n\n\n\nPlease try running robomaster with:",
"## Running the demo\n\n\n\nCurrent way to interact is by press up arrow key on laptop to record a message and send to the VLM",
"## Running the demo without robot\n\n\n\nCurrent way to interact is by press up arrow key on laptop to record a message and send to the VLM",
"## Kill process in case of failure\n\nDue to a Python GIL issue, we currently meed to kill processes manually. You can use the following command to do so:",
"## LICENSE\n\nWhile the source of this library is licensed under Apache-2.0, the usage of the Text to Speech(TTS) SystemEngine is licensed under Mozilla Public License 2.0 and GNU Lesser General Public License (LGPL) version 3.0.\n\nFeel free to remove the TTS SystemEngine."
] | [
"TAGS\n#region-us \n",
"# Getting Started\n\nCreate a new conda environment for robomaster\n\n\n\nCreate a new conda environment for idefics2. This requirements file suppose that your using cu122.",
"## Robomaster Jailbreak",
"### Installation of the Robomaster S1 Hack\n\nThis guide is an updated version of the original Robomaster S1 SDK Hack Guide and is intended for use on a Windows 11 system.",
"#### Prerequisites\n\nBefore you get started, you'll need the following:\n\n- Robomaster S1 (do not update it to the latest version, as it may block the hack).\n- Robomaster App.\n- Android SDK Platform-Tools. Simply unzip it and keep the path handy.\n- A micro USB cable. If this guide doesn't work, there might be an issue with the cable, and you may need to replace it with one that supports data transfer.",
"#### Instructions\n\n1. Start the Robomaster App and connect the Robomaster S1 using one of the two options provided (via router or via Wi-Fi).\n2. While connected, use a micro USB cable to connect the robot to the computer's USB port. You should hear a beep sound, similar to when you connect any device. (Please note that no other Android device should be connected via USB during this process).\n3. In the Lab section of the app, create a new Python application and paste the following code:\n\n \n\n4. Run the code; there should be no errors, and the console should display Execution Complete\n5. Without closing the app, navigate to the folder containing the Android SDK Platform-Tools and open a terminal inside it.\n6. Run the ADP command '.\\URL devices '. If everything is working correctly, you should see output similar to this: !image\n7. Execute the URL script located in the folder 's1_SDK'.\n8. Once everything has been executed, restart the S1 by turning it off and then back on. While it's booting up, you should hear two chimes instead of the usual single chime, indicating that the hack has been successful.",
"## Robomaster Connection\n\nMake sure to be connected using the wifi hotspot of the robomaster which is the most stable one.\n\nThe default password for the hotpsot is: 12341234\n\nYou might need to have a second wifi card if you want to be able to run the demo with internet on.",
"## Post-Installation test\n\nPlease try running idefics2 with:\n\n\n\nPlease try running robomaster with:",
"## Running the demo\n\n\n\nCurrent way to interact is by press up arrow key on laptop to record a message and send to the VLM",
"## Running the demo without robot\n\n\n\nCurrent way to interact is by press up arrow key on laptop to record a message and send to the VLM",
"## Kill process in case of failure\n\nDue to a Python GIL issue, we currently meed to kill processes manually. You can use the following command to do so:",
"## LICENSE\n\nWhile the source of this library is licensed under Apache-2.0, the usage of the Text to Speech(TTS) SystemEngine is licensed under Mozilla Public License 2.0 and GNU Lesser General Public License (LGPL) version 3.0.\n\nFeel free to remove the TTS SystemEngine."
] |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/TETO101/AIRI-8B-V2
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/AIRI-8B-V2-GGUF/resolve/main/AIRI-8B-V2.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/AIRI-8B-V2-GGUF/resolve/main/AIRI-8B-V2.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/AIRI-8B-V2-GGUF/resolve/main/AIRI-8B-V2.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/AIRI-8B-V2-GGUF/resolve/main/AIRI-8B-V2.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/AIRI-8B-V2-GGUF/resolve/main/AIRI-8B-V2.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/AIRI-8B-V2-GGUF/resolve/main/AIRI-8B-V2.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/AIRI-8B-V2-GGUF/resolve/main/AIRI-8B-V2.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/AIRI-8B-V2-GGUF/resolve/main/AIRI-8B-V2.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/AIRI-8B-V2-GGUF/resolve/main/AIRI-8B-V2.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/AIRI-8B-V2-GGUF/resolve/main/AIRI-8B-V2.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/AIRI-8B-V2-GGUF/resolve/main/AIRI-8B-V2.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/AIRI-8B-V2-GGUF/resolve/main/AIRI-8B-V2.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/AIRI-8B-V2-GGUF/resolve/main/AIRI-8B-V2.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/AIRI-8B-V2-GGUF/resolve/main/AIRI-8B-V2.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/AIRI-8B-V2-GGUF/resolve/main/AIRI-8B-V2.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "TETO101/AIRI-8B-V2", "quantized_by": "mradermacher"} | mradermacher/AIRI-8B-V2-GGUF | null | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"sft",
"en",
"base_model:TETO101/AIRI-8B-V2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:53:03+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #text-generation-inference #unsloth #llama #trl #sft #en #base_model-TETO101/AIRI-8B-V2 #license-apache-2.0 #endpoints_compatible #region-us
| About
-----
static quants of URL
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
-----
If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs for
more details, including on how to concatenate multi-part files.
Provided Quants
---------------
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #text-generation-inference #unsloth #llama #trl #sft #en #base_model-TETO101/AIRI-8B-V2 #license-apache-2.0 #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. -->
# V2-bert-text-classification-model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2017
- Accuracy: 0.9601
- F1: 0.8264
- Precision: 0.8214
- Recall: 0.8331
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.5342 | 0.11 | 50 | 1.6906 | 0.3486 | 0.1581 | 0.1874 | 0.1879 |
| 0.7232 | 0.22 | 100 | 0.7529 | 0.8296 | 0.5057 | 0.5008 | 0.5124 |
| 0.2933 | 0.33 | 150 | 0.4824 | 0.9018 | 0.6709 | 0.6673 | 0.6756 |
| 0.2774 | 0.44 | 200 | 0.4746 | 0.8772 | 0.6543 | 0.6423 | 0.6686 |
| 0.1739 | 0.55 | 250 | 0.4650 | 0.9103 | 0.6760 | 0.6636 | 0.6892 |
| 0.1757 | 0.66 | 300 | 0.3614 | 0.9166 | 0.7175 | 0.7823 | 0.7127 |
| 0.177 | 0.76 | 350 | 0.2602 | 0.9111 | 0.7284 | 0.7568 | 0.7163 |
| 0.1019 | 0.87 | 400 | 0.3053 | 0.9223 | 0.7301 | 0.7881 | 0.7203 |
| 0.1067 | 0.98 | 450 | 0.4436 | 0.9095 | 0.7255 | 0.7598 | 0.7197 |
| 0.1577 | 1.09 | 500 | 0.2348 | 0.9532 | 0.8227 | 0.8285 | 0.8171 |
| 0.0792 | 1.2 | 550 | 0.2429 | 0.9519 | 0.8190 | 0.8218 | 0.8175 |
| 0.086 | 1.31 | 600 | 0.1858 | 0.9595 | 0.8264 | 0.8282 | 0.8258 |
| 0.091 | 1.42 | 650 | 0.1868 | 0.9625 | 0.8279 | 0.8259 | 0.8308 |
| 0.0909 | 1.53 | 700 | 0.2091 | 0.9549 | 0.8244 | 0.8284 | 0.8217 |
| 0.0434 | 1.64 | 750 | 0.1942 | 0.9628 | 0.8303 | 0.8294 | 0.8315 |
| 0.1175 | 1.75 | 800 | 0.1572 | 0.9650 | 0.8317 | 0.8304 | 0.8333 |
| 0.092 | 1.86 | 850 | 0.2515 | 0.9300 | 0.7489 | 0.7995 | 0.7346 |
| 0.06 | 1.97 | 900 | 0.4890 | 0.9136 | 0.7334 | 0.7694 | 0.7261 |
| 0.0652 | 2.07 | 950 | 0.2258 | 0.9541 | 0.8218 | 0.8143 | 0.8309 |
| 0.0436 | 2.18 | 1000 | 0.2224 | 0.9587 | 0.8245 | 0.8184 | 0.8326 |
| 0.0524 | 2.29 | 1050 | 0.2476 | 0.9546 | 0.8193 | 0.8118 | 0.8283 |
| 0.0598 | 2.4 | 1100 | 0.1913 | 0.9669 | 0.8317 | 0.8312 | 0.8328 |
| 0.0503 | 2.51 | 1150 | 0.2179 | 0.9612 | 0.8230 | 0.8298 | 0.8175 |
| 0.0258 | 2.62 | 1200 | 0.2204 | 0.9631 | 0.8298 | 0.8280 | 0.8323 |
| 0.0091 | 2.73 | 1250 | 0.5198 | 0.9218 | 0.7127 | 0.8107 | 0.7092 |
| 0.1076 | 2.84 | 1300 | 0.1853 | 0.9642 | 0.8323 | 0.8338 | 0.8310 |
| 0.0356 | 2.95 | 1350 | 0.2162 | 0.9612 | 0.8273 | 0.8220 | 0.8338 |
| 0.0492 | 3.06 | 1400 | 0.2382 | 0.9573 | 0.8245 | 0.8201 | 0.8296 |
| 0.0088 | 3.17 | 1450 | 0.2252 | 0.9636 | 0.8303 | 0.8285 | 0.8329 |
| 0.0275 | 3.28 | 1500 | 0.3000 | 0.9543 | 0.8234 | 0.8207 | 0.8279 |
| 0.0215 | 3.38 | 1550 | 0.3234 | 0.9497 | 0.8191 | 0.8152 | 0.8255 |
| 0.0294 | 3.49 | 1600 | 0.3486 | 0.9311 | 0.7500 | 0.8114 | 0.7338 |
| 0.0393 | 3.6 | 1650 | 0.2357 | 0.9595 | 0.8291 | 0.8274 | 0.8311 |
| 0.008 | 3.71 | 1700 | 0.2762 | 0.9587 | 0.8277 | 0.8260 | 0.8297 |
| 0.0042 | 3.82 | 1750 | 0.2393 | 0.9666 | 0.8330 | 0.8348 | 0.8312 |
| 0.0329 | 3.93 | 1800 | 0.3012 | 0.9584 | 0.8290 | 0.8267 | 0.8325 |
| 0.0185 | 4.04 | 1850 | 0.2400 | 0.9653 | 0.8324 | 0.8331 | 0.8319 |
| 0.019 | 4.15 | 1900 | 0.3604 | 0.9314 | 0.7489 | 0.8084 | 0.7324 |
| 0.0205 | 4.26 | 1950 | 0.2451 | 0.9653 | 0.8346 | 0.8365 | 0.8328 |
| 0.0202 | 4.37 | 2000 | 0.3619 | 0.9483 | 0.8190 | 0.8174 | 0.8237 |
| 0.019 | 4.48 | 2050 | 0.2573 | 0.9628 | 0.8315 | 0.8332 | 0.8306 |
| 0.0087 | 4.59 | 2100 | 0.2661 | 0.9634 | 0.8316 | 0.8319 | 0.8322 |
| 0.0212 | 4.69 | 2150 | 0.3671 | 0.9311 | 0.7497 | 0.8091 | 0.7378 |
| 0.0087 | 4.8 | 2200 | 0.3005 | 0.9305 | 0.7582 | 0.8108 | 0.7431 |
| 0.0005 | 4.91 | 2250 | 0.2772 | 0.9584 | 0.8257 | 0.8223 | 0.8297 |
| 0.0231 | 5.02 | 2300 | 0.2556 | 0.9634 | 0.8290 | 0.8269 | 0.8318 |
| 0.0006 | 5.13 | 2350 | 0.2798 | 0.9595 | 0.8253 | 0.8219 | 0.8298 |
| 0.0012 | 5.24 | 2400 | 0.2777 | 0.9625 | 0.8305 | 0.8278 | 0.8334 |
| 0.0096 | 5.35 | 2450 | 0.2818 | 0.9614 | 0.8280 | 0.8259 | 0.8308 |
| 0.0145 | 5.46 | 2500 | 0.2449 | 0.9628 | 0.8311 | 0.8286 | 0.8341 |
| 0.032 | 5.57 | 2550 | 0.2480 | 0.9653 | 0.8322 | 0.8296 | 0.8355 |
| 0.0075 | 5.68 | 2600 | 0.2241 | 0.9661 | 0.8341 | 0.8324 | 0.8360 |
| 0.0058 | 5.79 | 2650 | 0.2349 | 0.9645 | 0.8309 | 0.8290 | 0.8332 |
| 0.0079 | 5.9 | 2700 | 0.4499 | 0.9325 | 0.7515 | 0.8158 | 0.7383 |
| 0.0003 | 6.0 | 2750 | 0.2890 | 0.9590 | 0.8268 | 0.8252 | 0.8296 |
| 0.0109 | 6.11 | 2800 | 0.2298 | 0.9669 | 0.8337 | 0.8331 | 0.8346 |
| 0.0004 | 6.22 | 2850 | 0.2356 | 0.9669 | 0.8341 | 0.8334 | 0.8351 |
| 0.0003 | 6.33 | 2900 | 0.2272 | 0.9691 | 0.8364 | 0.8364 | 0.8366 |
| 0.0003 | 6.44 | 2950 | 0.2389 | 0.9669 | 0.8350 | 0.8342 | 0.8362 |
| 0.0201 | 6.55 | 3000 | 0.2427 | 0.9661 | 0.8346 | 0.8343 | 0.8354 |
| 0.0003 | 6.66 | 3050 | 0.2382 | 0.9677 | 0.8347 | 0.8352 | 0.8344 |
| 0.0095 | 6.77 | 3100 | 0.2004 | 0.9705 | 0.8367 | 0.8379 | 0.8354 |
| 0.0187 | 6.88 | 3150 | 0.2470 | 0.9677 | 0.8335 | 0.8332 | 0.8341 |
| 0.0086 | 6.99 | 3200 | 0.2243 | 0.9688 | 0.8348 | 0.8340 | 0.8358 |
| 0.0003 | 7.1 | 3250 | 0.2424 | 0.9677 | 0.8342 | 0.8329 | 0.8357 |
| 0.0067 | 7.21 | 3300 | 0.2754 | 0.9623 | 0.8287 | 0.8268 | 0.8314 |
| 0.0003 | 7.31 | 3350 | 0.2302 | 0.9686 | 0.8348 | 0.8340 | 0.8358 |
| 0.0002 | 7.42 | 3400 | 0.2318 | 0.9688 | 0.8350 | 0.8342 | 0.8359 |
| 0.0002 | 7.53 | 3450 | 0.2327 | 0.9686 | 0.8349 | 0.8342 | 0.8358 |
| 0.0002 | 7.64 | 3500 | 0.2376 | 0.9680 | 0.8346 | 0.8339 | 0.8355 |
| 0.0002 | 7.75 | 3550 | 0.2391 | 0.9680 | 0.8346 | 0.8339 | 0.8355 |
| 0.0002 | 7.86 | 3600 | 0.2435 | 0.9683 | 0.8358 | 0.8349 | 0.8370 |
| 0.0164 | 7.97 | 3650 | 0.2196 | 0.9705 | 0.8359 | 0.8358 | 0.8361 |
| 0.0003 | 8.08 | 3700 | 0.2116 | 0.9718 | 0.8380 | 0.8390 | 0.8369 |
| 0.004 | 8.19 | 3750 | 0.2192 | 0.9702 | 0.8364 | 0.8367 | 0.8362 |
| 0.0002 | 8.3 | 3800 | 0.2213 | 0.9699 | 0.8357 | 0.8356 | 0.8358 |
| 0.0002 | 8.41 | 3850 | 0.2232 | 0.9699 | 0.8357 | 0.8356 | 0.8358 |
| 0.0001 | 8.52 | 3900 | 0.2242 | 0.9699 | 0.8357 | 0.8356 | 0.8358 |
| 0.0001 | 8.62 | 3950 | 0.2230 | 0.9705 | 0.8360 | 0.8357 | 0.8362 |
| 0.0001 | 8.73 | 4000 | 0.2240 | 0.9705 | 0.8360 | 0.8357 | 0.8362 |
| 0.0001 | 8.84 | 4050 | 0.2254 | 0.9705 | 0.8361 | 0.8359 | 0.8364 |
| 0.0001 | 8.95 | 4100 | 0.2265 | 0.9705 | 0.8361 | 0.8359 | 0.8364 |
| 0.0002 | 9.06 | 4150 | 0.2280 | 0.9705 | 0.8364 | 0.8359 | 0.8369 |
| 0.0071 | 9.17 | 4200 | 0.2393 | 0.9694 | 0.8357 | 0.8355 | 0.8362 |
| 0.0001 | 9.28 | 4250 | 0.2564 | 0.9680 | 0.8355 | 0.8347 | 0.8367 |
| 0.0002 | 9.39 | 4300 | 0.2442 | 0.9688 | 0.8354 | 0.8352 | 0.8358 |
| 0.0002 | 9.5 | 4350 | 0.2363 | 0.9699 | 0.8361 | 0.8359 | 0.8365 |
| 0.0001 | 9.61 | 4400 | 0.2365 | 0.9699 | 0.8361 | 0.8359 | 0.8365 |
| 0.0001 | 9.72 | 4450 | 0.2366 | 0.9699 | 0.8361 | 0.8359 | 0.8365 |
| 0.0001 | 9.83 | 4500 | 0.2372 | 0.9699 | 0.8361 | 0.8359 | 0.8365 |
| 0.0001 | 9.93 | 4550 | 0.2372 | 0.9699 | 0.8361 | 0.8359 | 0.8365 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "base_model": "bert-base-uncased", "model-index": [{"name": "V2-bert-text-classification-model", "results": []}]} | AmirlyPhd/V2-bert-text-classification-model | 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 | 2024-04-28T01:53:07+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
| V2-bert-text-classification-model
=================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2017
* Accuracy: 0.9601
* F1: 0.8264
* Precision: 0.8214
* Recall: 0.8331
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 10
* 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
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\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* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #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: 16\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* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
null | transformers |
# Model Card for Model ID
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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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[More Information Needed] | {"library_name": "transformers", "tags": []} | HC-85/distilbert-lora-32r-arxiv-multilabel | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:55:09+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:
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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]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
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#### Metrics
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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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[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 #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
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] |
null | transformers |
# Model Card for Model ID
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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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[More Information Needed] | {"library_name": "transformers", "tags": []} | HC-85/distilbert-lora-64r-arxiv-multilabel | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:55:28+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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| [
"# 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]:",
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"#### 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",
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] | [
"TAGS\n#transformers #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]:",
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"## Uses",
"### Direct Use",
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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",
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] |
null | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | zandfj/LLaMA2-7B-Chat-sft-sft-3epo_dpozf_glod0862_042808 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T01:56:10+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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| [
"# 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]:",
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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",
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"#### Training Hyperparameters\n\n- Training regime:",
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"## 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",
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] | [
"TAGS\n#transformers #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",
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"#### Factors",
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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:",
"## Technical Specifications [optional]",
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] |
text-generation | transformers | ## GOOGLE COLAB IS A SCAM DO NOT USE THE PAID VERSION
## THEY WILL DISCONNECT YOUR RUNTIME BEFORE EVEN 24 HOURS
https://github.com/googlecolab/colabtools/issues/3451
_________________________________________________________________________________________
## PLEASE INSTEAD USE TENSORDOCK ITS CHEAPER AND DOESNT DISCONNECT YOU
tensordock.com
_________________________________________________________________________________________
__________________________________________________________________________________________________________________________________________________________________________________
_________________________________________________________________________________________
_________________________________________________________________________________________
_________________________________________________________________________________________
_________________________________________________________________________________________
_________________________________________________________________________________________
_________________________________________________________________________________________
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_________________________________________________________________________________________
This is unsloth/llama-3-8b-Instruct trained on the Replete-AI/code-test-dataset using the code bellow with unsloth and google colab with under 15gb of vram. This training was complete in about 40 minutes total.
__________________________________________________________________________
Colab doc if you dont want to copy the code by hand:
- https://colab.research.google.com/drive/1bX4BsjLcdNJnoAf7lGXmWOgaY8yekg8p?usp=sharing
__________________________________________________________________________
Copy from my announcement in my discord:
```
If anyone wants to train their own llama-3-8b model for free on any dataset
that has around 1,500 lines of data or less you can now do it easily by using
the code I provided in the model card for my test model in this repo and
google colab. The training for this model uses (Unsloth + Qlora + Galore) to
achieve the ability for training under such low vram.
```
For anyone that is new to coding and training Ai, all your really have to edit is
1. (max_seq_length = 8192) To match the max tokens of the dataset or model you are using
2. (model_name = "unsloth/llama-3-8b-Instruct",) Change what model you are finetuning, this setup is specifically for llama-3-8b
3. (alpaca_prompt =) Change the prompt format, this one is setup to meet llama-3-8b-instruct format, but match it to your specifications.
4. (dataset = load_dataset("Replete-AI/code-test-dataset", split = "train")) What dataset you are using from huggingface
5. (model.push_to_hub_merged("rombodawg/test_dataset_Codellama-3-8B", tokenizer, save_method = "merged_16bit", token = ""))
6. For the above you need to change "rombodawg" to your Hugginface name, "test_dataset_Codellama-3-8B" to the model name you want saved as, and in token = "" you need to put your huggingface write token so the model can be saved.
```Python
%%capture
import torch
major_version, minor_version = torch.cuda.get_device_capability()
# Must install separately since Colab has torch 2.2.1, which breaks packages
!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
if major_version >= 8:
# Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)
!pip install --no-deps packaging ninja einops flash-attn xformers trl peft accelerate bitsandbytes
else:
# Use this for older GPUs (V100, Tesla T4, RTX 20xx)
!pip install --no-deps xformers trl peft accelerate bitsandbytes
pass
```
```Python
!pip install galore_torch
```
```Python
from unsloth import FastLanguageModel
import torch
max_seq_length = 8192 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
# 4bit pre quantized models we support for 4x faster downloading + no OOMs.
fourbit_models = [
"unsloth/mistral-7b-bnb-4bit",
"unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"unsloth/llama-2-7b-bnb-4bit",
"unsloth/gemma-7b-bnb-4bit",
"unsloth/gemma-7b-it-bnb-4bit", # Instruct version of Gemma 7b
"unsloth/gemma-2b-bnb-4bit",
"unsloth/gemma-2b-it-bnb-4bit", # Instruct version of Gemma 2b
"unsloth/llama-3-8b-bnb-4bit", # [NEW] 15 Trillion token Llama-3
] # More models at https://huggingface.co/unsloth
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "unsloth/llama-3-8b-Instruct",
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = load_in_4bit,
# token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)
```
```Python
model = FastLanguageModel.get_peft_model(
model,
r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",],
lora_alpha = 16,
lora_dropout = 0, # Supports any, but = 0 is optimized
bias = "none", # Supports any, but = "none" is optimized
# [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
random_state = 3407,
use_rslora = False, # We support rank stabilized LoRA
loftq_config = None, # And LoftQ
)
```
```Python
alpaca_prompt = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>
Below is an instruction that describes a task, Write a response that appropriately completes the request.<|eot_id|><|start_header_id|>user<|end_header_id|>
{}<|eot_id|><|start_header_id|>assistant<|end_header_id|>{}"""
EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN
def formatting_prompts_func(examples):
inputs = examples["human"]
outputs = examples["assistant"]
texts = []
for input, output in zip(inputs, outputs):
# Must add EOS_TOKEN, otherwise your generation will go on forever!
text = alpaca_prompt.format(input, output) + EOS_TOKEN
texts.append(text)
return { "text" : texts, }
pass
from datasets import load_dataset
dataset = load_dataset("Replete-AI/code-test-dataset", split = "train")
dataset = dataset.map(formatting_prompts_func, batched = True,)
```
```Python
from trl import SFTTrainer
from transformers import TrainingArguments
from galore_torch import GaLoreAdamW8bit
import torch.nn as nn
galore_params = []
target_modules_list = ["attn", "mlp"]
for module_name, module in model.named_modules():
if not isinstance(module, nn.Linear):
continue
if not any(target_key in module_name for target_key in target_modules_list):
continue
print('mod ', module_name)
galore_params.append(module.weight)
id_galore_params = [id(p) for p in galore_params]
regular_params = [p for p in model.parameters() if id(p) not in id_galore_params]
param_groups = [{'params': regular_params},
{'params': galore_params, 'rank': 64, 'update_proj_gap': 200, 'scale': 0.25, 'proj_type': 'std'}]
optimizer = GaLoreAdamW8bit(param_groups, lr=2e-5)
trainer = SFTTrainer(
model = model,
tokenizer = tokenizer,
train_dataset = dataset,
optimizers=(optimizer, None),
dataset_text_field = "text",
max_seq_length = max_seq_length,
dataset_num_proc = 2,
packing = True, # Can make training 5x faster for short sequences.
args = TrainingArguments(
per_device_train_batch_size = 1,
gradient_accumulation_steps = 4,
warmup_steps = 5,
learning_rate = 2e-4,
fp16 = not torch.cuda.is_bf16_supported(),
bf16 = torch.cuda.is_bf16_supported(),
logging_steps = 1,
weight_decay = 0.01,
lr_scheduler_type = "linear",
seed = 3407,
output_dir = "outputs",
),
)
```
```Python
trainer_stats = trainer.train()
model.save_pretrained_merged("model", tokenizer, save_method = "merged_16bit",)
model.push_to_hub_merged("rombodawg/test_dataset_Codellama-3-8B", tokenizer, save_method = "merged_16bit", token = "")
```
| {"language": ["en"], "license": "apache-2.0", "model-index": [{"name": "test_dataset_Codellama-3-8B", "results": [{"task": {"type": "text-generation"}, "dataset": {"name": "HumanEval", "type": "openai_humaneval"}, "metrics": [{"type": "pass@1", "value": 0.63, "name": "pass@1", "verified": false}]}]}]} | rombodawg/test_dataset_Codellama-3-8B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"has_space"
] | null | 2024-04-28T01:56:24+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #conversational #en #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #has_space
| ## GOOGLE COLAB IS A SCAM DO NOT USE THE PAID VERSION
## THEY WILL DISCONNECT YOUR RUNTIME BEFORE EVEN 24 HOURS
URL
_________________________________________________________________________________________
## PLEASE INSTEAD USE TENSORDOCK ITS CHEAPER AND DOESNT DISCONNECT YOU
URL
_________________________________________________________________________________________
__________________________________________________________________________________________________________________________________________________________________________________
_________________________________________________________________________________________
_________________________________________________________________________________________
_________________________________________________________________________________________
_________________________________________________________________________________________
_________________________________________________________________________________________
_________________________________________________________________________________________
_________________________________________________________________________________________
_________________________________________________________________________________________
_________________________________________________________________________________________
_________________________________________________________________________________________
This is unsloth/llama-3-8b-Instruct trained on the Replete-AI/code-test-dataset using the code bellow with unsloth and google colab with under 15gb of vram. This training was complete in about 40 minutes total.
__________________________________________________________________________
Colab doc if you dont want to copy the code by hand:
- URL
__________________________________________________________________________
Copy from my announcement in my discord:
For anyone that is new to coding and training Ai, all your really have to edit is
1. (max_seq_length = 8192) To match the max tokens of the dataset or model you are using
2. (model_name = "unsloth/llama-3-8b-Instruct",) Change what model you are finetuning, this setup is specifically for llama-3-8b
3. (alpaca_prompt =) Change the prompt format, this one is setup to meet llama-3-8b-instruct format, but match it to your specifications.
4. (dataset = load_dataset("Replete-AI/code-test-dataset", split = "train")) What dataset you are using from huggingface
5. (model.push_to_hub_merged("rombodawg/test_dataset_Codellama-3-8B", tokenizer, save_method = "merged_16bit", token = ""))
6. For the above you need to change "rombodawg" to your Hugginface name, "test_dataset_Codellama-3-8B" to the model name you want saved as, and in token = "" you need to put your huggingface write token so the model can be saved.
| [
"## GOOGLE COLAB IS A SCAM DO NOT USE THE PAID VERSION",
"## THEY WILL DISCONNECT YOUR RUNTIME BEFORE EVEN 24 HOURS\nURL\n_________________________________________________________________________________________",
"## PLEASE INSTEAD USE TENSORDOCK ITS CHEAPER AND DOESNT DISCONNECT YOU\nURL\n_________________________________________________________________________________________\n__________________________________________________________________________________________________________________________________________________________________________________\n_________________________________________________________________________________________\n_________________________________________________________________________________________\n_________________________________________________________________________________________\n_________________________________________________________________________________________\n_________________________________________________________________________________________\n_________________________________________________________________________________________\n_________________________________________________________________________________________\n_________________________________________________________________________________________\n_________________________________________________________________________________________\n_________________________________________________________________________________________\nThis is unsloth/llama-3-8b-Instruct trained on the Replete-AI/code-test-dataset using the code bellow with unsloth and google colab with under 15gb of vram. This training was complete in about 40 minutes total.\n\n__________________________________________________________________________\nColab doc if you dont want to copy the code by hand:\n- URL\n__________________________________________________________________________\nCopy from my announcement in my discord:\n\n\nFor anyone that is new to coding and training Ai, all your really have to edit is\n\n1. (max_seq_length = 8192) To match the max tokens of the dataset or model you are using\n2. (model_name = \"unsloth/llama-3-8b-Instruct\",) Change what model you are finetuning, this setup is specifically for llama-3-8b\n3. (alpaca_prompt =) Change the prompt format, this one is setup to meet llama-3-8b-instruct format, but match it to your specifications. \n4. (dataset = load_dataset(\"Replete-AI/code-test-dataset\", split = \"train\")) What dataset you are using from huggingface\n5. (model.push_to_hub_merged(\"rombodawg/test_dataset_Codellama-3-8B\", tokenizer, save_method = \"merged_16bit\", token = \"\"))\n6. For the above you need to change \"rombodawg\" to your Hugginface name, \"test_dataset_Codellama-3-8B\" to the model name you want saved as, and in token = \"\" you need to put your huggingface write token so the model can be saved."
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #en #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #has_space \n",
"## GOOGLE COLAB IS A SCAM DO NOT USE THE PAID VERSION",
"## THEY WILL DISCONNECT YOUR RUNTIME BEFORE EVEN 24 HOURS\nURL\n_________________________________________________________________________________________",
"## PLEASE INSTEAD USE TENSORDOCK ITS CHEAPER AND DOESNT DISCONNECT YOU\nURL\n_________________________________________________________________________________________\n__________________________________________________________________________________________________________________________________________________________________________________\n_________________________________________________________________________________________\n_________________________________________________________________________________________\n_________________________________________________________________________________________\n_________________________________________________________________________________________\n_________________________________________________________________________________________\n_________________________________________________________________________________________\n_________________________________________________________________________________________\n_________________________________________________________________________________________\n_________________________________________________________________________________________\n_________________________________________________________________________________________\nThis is unsloth/llama-3-8b-Instruct trained on the Replete-AI/code-test-dataset using the code bellow with unsloth and google colab with under 15gb of vram. This training was complete in about 40 minutes total.\n\n__________________________________________________________________________\nColab doc if you dont want to copy the code by hand:\n- URL\n__________________________________________________________________________\nCopy from my announcement in my discord:\n\n\nFor anyone that is new to coding and training Ai, all your really have to edit is\n\n1. (max_seq_length = 8192) To match the max tokens of the dataset or model you are using\n2. (model_name = \"unsloth/llama-3-8b-Instruct\",) Change what model you are finetuning, this setup is specifically for llama-3-8b\n3. (alpaca_prompt =) Change the prompt format, this one is setup to meet llama-3-8b-instruct format, but match it to your specifications. \n4. (dataset = load_dataset(\"Replete-AI/code-test-dataset\", split = \"train\")) What dataset you are using from huggingface\n5. (model.push_to_hub_merged(\"rombodawg/test_dataset_Codellama-3-8B\", tokenizer, save_method = \"merged_16bit\", token = \"\"))\n6. For the above you need to change \"rombodawg\" to your Hugginface name, \"test_dataset_Codellama-3-8B\" to the model name you want saved as, and in token = \"\" you need to put your huggingface write token so the model can be saved."
] |
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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[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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#### Preprocessing [optional]
[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. -->
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#### 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]
### 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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/30c00x4 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T01:58:27+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 |
# stablelm-2-zephyr-1.6b-dareties2
stablelm-2-zephyr-1.6b-dareties2 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [aipib/stablelm-2-zephyr-1.6b-slerpx13](https://huggingface.co/aipib/stablelm-2-zephyr-1.6b-slerpx13)
* [stabilityai/stablelm-2-1_6b](https://huggingface.co/stabilityai/stablelm-2-1_6b)
* [stabilityai/stablelm-2-zephyr-1_6b](https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b)
## 🧩 Configuration
```yaml
slices:
- sources:
- layer_range: [0, 24]
model: aipib/stablelm-2-zephyr-1.6b-slerpx13
parameters:
density: [1, 0.7, 0.1]
weight: 1.0
- layer_range: [0, 24]
model: stabilityai/stablelm-2-1_6b
parameters:
density: 0.53
weight:
- filter: mlp
value: 0.5
- value: 0
- layer_range: [0, 24]
model: stabilityai/stablelm-2-zephyr-1_6b
parameters:
density: 0.53
weight:
- filter: mlp
value: 0.5
- value: 0
merge_method: dare_ties
base_model: aipib/stablelm-2-zephyr-1.6b-slerpx13
parameters:
normalize: true
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "aipib/stablelm-2-zephyr-1.6b-dareties2"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "aipib/stablelm-2-zephyr-1.6b-slerpx13", "stabilityai/stablelm-2-1_6b", "stabilityai/stablelm-2-zephyr-1_6b"], "base_model": ["aipib/stablelm-2-zephyr-1.6b-slerpx13", "stabilityai/stablelm-2-1_6b", "stabilityai/stablelm-2-zephyr-1_6b"]} | aipib/stablelm-2-zephyr-1.6b-dareties2 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"aipib/stablelm-2-zephyr-1.6b-slerpx13",
"stabilityai/stablelm-2-1_6b",
"stabilityai/stablelm-2-zephyr-1_6b",
"conversational",
"base_model:aipib/stablelm-2-zephyr-1.6b-slerpx13",
"base_model:stabilityai/stablelm-2-1_6b",
"base_model:stabilityai/stablelm-2-zephyr-1_6b",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T02:00:18+00:00 | [] | [] | TAGS
#transformers #safetensors #stablelm #text-generation #merge #mergekit #lazymergekit #aipib/stablelm-2-zephyr-1.6b-slerpx13 #stabilityai/stablelm-2-1_6b #stabilityai/stablelm-2-zephyr-1_6b #conversational #base_model-aipib/stablelm-2-zephyr-1.6b-slerpx13 #base_model-stabilityai/stablelm-2-1_6b #base_model-stabilityai/stablelm-2-zephyr-1_6b #autotrain_compatible #endpoints_compatible #region-us
|
# stablelm-2-zephyr-1.6b-dareties2
stablelm-2-zephyr-1.6b-dareties2 is a merge of the following models using LazyMergekit:
* aipib/stablelm-2-zephyr-1.6b-slerpx13
* stabilityai/stablelm-2-1_6b
* stabilityai/stablelm-2-zephyr-1_6b
## Configuration
## Usage
| [
"# stablelm-2-zephyr-1.6b-dareties2\n\nstablelm-2-zephyr-1.6b-dareties2 is a merge of the following models using LazyMergekit:\n* aipib/stablelm-2-zephyr-1.6b-slerpx13\n* stabilityai/stablelm-2-1_6b\n* stabilityai/stablelm-2-zephyr-1_6b",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #stablelm #text-generation #merge #mergekit #lazymergekit #aipib/stablelm-2-zephyr-1.6b-slerpx13 #stabilityai/stablelm-2-1_6b #stabilityai/stablelm-2-zephyr-1_6b #conversational #base_model-aipib/stablelm-2-zephyr-1.6b-slerpx13 #base_model-stabilityai/stablelm-2-1_6b #base_model-stabilityai/stablelm-2-zephyr-1_6b #autotrain_compatible #endpoints_compatible #region-us \n",
"# stablelm-2-zephyr-1.6b-dareties2\n\nstablelm-2-zephyr-1.6b-dareties2 is a merge of the following models using LazyMergekit:\n* aipib/stablelm-2-zephyr-1.6b-slerpx13\n* stabilityai/stablelm-2-1_6b\n* stabilityai/stablelm-2-zephyr-1_6b",
"## Configuration",
"## Usage"
] |
text-generation | transformers |
# 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. -->
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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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
### Results
[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).
- **Hardware Type:** [More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/wpwlrj4 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T02:03:27+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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## Uses
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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
Use the code below to get started with the model.
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### 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",
"### Downstream Use [optional]",
"### 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",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
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"## Technical Specifications [optional]",
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"### Compute Infrastructure",
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"## 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",
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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",
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"## Training Details",
"### Training Data",
"### Training Procedure",
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"#### Training Hyperparameters\n\n- Training regime:",
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"#### Factors",
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"### Results",
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"## Technical Specifications [optional]",
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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-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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### 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.
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[More Information Needed]
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[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. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### 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]
- **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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[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]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/x8p3c85 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T02:03:33+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]:",
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"### 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"
] | [
"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]:",
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"## Training Details",
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"## 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]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## 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
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[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
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[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. -->
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[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/9a7qxs2 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T02:03:38+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]:
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- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
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- 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",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
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"## Model Card Authors [optional]",
"## Model Card Contact"
] |
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. -->
# Whisper-small-speechocean
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6821
## 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: 0.001
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5214 | 1.0 | 417 | 1.3231 |
| 0.6304 | 2.0 | 834 | 0.6180 |
| 0.532 | 3.0 | 1251 | 0.5340 |
| 0.4258 | 4.0 | 1668 | 0.5058 |
| 0.3192 | 5.0 | 2085 | 0.5050 |
| 0.288 | 6.0 | 2502 | 0.4952 |
| 0.2097 | 7.0 | 2919 | 0.5252 |
| 0.1986 | 8.0 | 3336 | 0.5281 |
| 0.1185 | 9.0 | 3753 | 0.5534 |
| 0.091 | 10.0 | 4170 | 0.5695 |
| 0.0548 | 11.0 | 4587 | 0.5935 |
| 0.0423 | 12.0 | 5004 | 0.6130 |
| 0.031 | 13.0 | 5421 | 0.6170 |
| 0.0169 | 14.0 | 5838 | 0.6234 |
| 0.0193 | 15.0 | 6255 | 0.6416 |
| 0.0125 | 16.0 | 6672 | 0.6478 |
| 0.0055 | 17.0 | 7089 | 0.6602 |
| 0.0064 | 18.0 | 7506 | 0.6736 |
| 0.004 | 19.0 | 7923 | 0.6785 |
| 0.003 | 20.0 | 8340 | 0.6821 |
### Framework versions
- PEFT 0.8.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper-small-speechocean", "results": []}]} | nrshoudi/Whisper-small-speechocean | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:openai/whisper-small",
"license:apache-2.0",
"region:us"
] | null | 2024-04-28T02:06:16+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-openai/whisper-small #license-apache-2.0 #region-us
| Whisper-small-speechocean
=========================
This model is a fine-tuned version of openai/whisper-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6821
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: 0.001
* train\_batch\_size: 6
* eval\_batch\_size: 6
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 50
* num\_epochs: 20
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* PEFT 0.8.0
* Transformers 4.40.0
* Pytorch 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.19.1
| [
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"### Training results",
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] |
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]
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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 -->
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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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#### 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. -->
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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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[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": []} | Elhassnaoui-2001/mistral_7b-instruct-ploty | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T02:06:18+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
| [
"# 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",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
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"## Model Card Contact"
] | [
"TAGS\n#transformers #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]:",
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"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
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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:",
"#### 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",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
image-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. -->
# Boya1_RMSProp_1-e5_10Epoch_swinv2-base-patch4_fold5
This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window12-192-22k](https://huggingface.co/microsoft/swinv2-base-patch4-window12-192-22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2529
- Accuracy: 0.6758
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1907 | 1.0 | 924 | 1.1805 | 0.6048 |
| 1.0225 | 2.0 | 1848 | 0.9957 | 0.6503 |
| 0.8132 | 3.0 | 2772 | 0.9354 | 0.6774 |
| 0.5715 | 4.0 | 3696 | 1.0093 | 0.6725 |
| 0.6964 | 5.0 | 4620 | 0.9993 | 0.6820 |
| 0.6956 | 6.0 | 5544 | 1.0237 | 0.6809 |
| 0.4499 | 7.0 | 6468 | 1.0986 | 0.6807 |
| 0.4383 | 8.0 | 7392 | 1.2146 | 0.6736 |
| 0.278 | 9.0 | 8316 | 1.2342 | 0.6753 |
| 0.2251 | 10.0 | 9240 | 1.2529 | 0.6758 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swinv2-base-patch4-window12-192-22k", "model-index": [{"name": "Boya1_RMSProp_1-e5_10Epoch_swinv2-base-patch4_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.675792897804283, "name": "Accuracy"}]}]}]} | onizukal/Boya1_RMSProp_1-e5_10Epoch_swinv2-base-patch4_fold5 | null | [
"transformers",
"safetensors",
"swinv2",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swinv2-base-patch4-window12-192-22k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T02:06:35+00:00 | [] | [] | TAGS
#transformers #safetensors #swinv2 #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swinv2-base-patch4-window12-192-22k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Boya1\_RMSProp\_1-e5\_10Epoch\_swinv2-base-patch4\_fold5
========================================================
This model is a fine-tuned version of microsoft/swinv2-base-patch4-window12-192-22k on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2529
* Accuracy: 0.6758
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.1.0
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #safetensors #swinv2 #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swinv2-base-patch4-window12-192-22k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] |
object-detection | 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": []} | Spatiallysaying/detr-finetuned-runwaymarkings-Horizontal-v1 | null | [
"transformers",
"safetensors",
"detr",
"object-detection",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T02:14:25+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #detr #object-detection #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 #safetensors #detr #object-detection #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"
] |
null | mlx |
# mlx-community/dolphin-2.9-llama3-8b-256-4bit
This model was converted to MLX format from [`cognitivecomputations/dolphin-2.9-llama3-8b-256k`]() using mlx-lm version **0.12.0**.
Refer to the [original model card](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b-256k) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/dolphin-2.9-llama3-8b-256-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"license": "llama3", "tags": ["mlx"]} | mlx-community/dolphin-2.9-llama3-8b-256-4bit | null | [
"mlx",
"safetensors",
"llama",
"license:llama3",
"region:us"
] | null | 2024-04-28T02:14:55+00:00 | [] | [] | TAGS
#mlx #safetensors #llama #license-llama3 #region-us
|
# mlx-community/dolphin-2.9-llama3-8b-256-4bit
This model was converted to MLX format from ['cognitivecomputations/dolphin-2.9-llama3-8b-256k']() using mlx-lm version 0.12.0.
Refer to the original model card for more details on the model.
## Use with mlx
| [
"# mlx-community/dolphin-2.9-llama3-8b-256-4bit\nThis model was converted to MLX format from ['cognitivecomputations/dolphin-2.9-llama3-8b-256k']() using mlx-lm version 0.12.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] | [
"TAGS\n#mlx #safetensors #llama #license-llama3 #region-us \n",
"# mlx-community/dolphin-2.9-llama3-8b-256-4bit\nThis model was converted to MLX format from ['cognitivecomputations/dolphin-2.9-llama3-8b-256k']() using mlx-lm version 0.12.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] |
text-to-image | null |
# SDXS Onnx
Converted from [IDKiro/sdxs-512-0.9](https://huggingface.co/IDKiro/sdxs-512-0.9) (i.e. the original one, without dreamshaper) through this command:
```
optimum-cli export onnx -m <local absolute path to original model> --task stable-diffusion ./mysdxs
```
Notice that I replaced the `/vae` folder in the local copy of the repo with `/vae_large` in that same repo, and updated the model config at the repo root. This is because the Onnx converter doesn't currently seem mature enough to handle nonstandard pipeline so we're effectively using the original, ordinary autoencoder.
For actual inference, you can test with something like:
```py
from optimum.onnxruntime import ORTStableDiffusionPipeline
pipeline = ORTStableDiffusionPipeline.from_pretrained("/local/absolute/path/to/repo")
prompt = "Sailing ship in storm by Leonardo da Vinci"
image = pipeline(prompt, num_inference_steps=1, guidance_scale=0).images[0]
image.save("hello.png", "PNG")
```
## Using with TAESD
(Not tested yet)
Consider using the Onnx converted model of TAESD at [deinferno/taesd-onnx](https://huggingface.co/deinferno/taesd-onnx) (Original model at [madebyollin/taesd](https://huggingface.co/madebyollin/taesd) )
Combined inference code:
```py
from huggingface_hub import snapshot_download
from diffusers.pipelines import OnnxRuntimeModel
from optimum.onnxruntime import ORTStableDiffusionPipeline
taesd_dir = snapshot_download(repo_id="deinferno/taesd-onnx")
pipeline = ORTStableDiffusionPipeline.from_pretrained(
"lemonteaa/sdxs-onnx",
vae_decoder_session = OnnxRuntimeModel.from_pretrained(f"{taesd_dir}/vae_decoder"),
vae_encoder_session = OnnxRuntimeModel.from_pretrained(f"{taesd_dir}/vae_encoder"),
revision="onnx")
prompt = "Sailing ship in storm by Leonardo da Vinci"
image = pipeline(prompt, num_inference_steps=1, guidance_scale=0).images[0]
image.save("hello.png", "PNG")
```
| {"pipeline_tag": "text-to-image"} | lemonteaa/sdxs-onnx | null | [
"onnx",
"text-to-image",
"region:us"
] | null | 2024-04-28T02:15:04+00:00 | [] | [] | TAGS
#onnx #text-to-image #region-us
|
# SDXS Onnx
Converted from IDKiro/sdxs-512-0.9 (i.e. the original one, without dreamshaper) through this command:
Notice that I replaced the '/vae' folder in the local copy of the repo with '/vae_large' in that same repo, and updated the model config at the repo root. This is because the Onnx converter doesn't currently seem mature enough to handle nonstandard pipeline so we're effectively using the original, ordinary autoencoder.
For actual inference, you can test with something like:
## Using with TAESD
(Not tested yet)
Consider using the Onnx converted model of TAESD at deinferno/taesd-onnx (Original model at madebyollin/taesd )
Combined inference code:
| [
"# SDXS Onnx\n\nConverted from IDKiro/sdxs-512-0.9 (i.e. the original one, without dreamshaper) through this command:\n\n\n\nNotice that I replaced the '/vae' folder in the local copy of the repo with '/vae_large' in that same repo, and updated the model config at the repo root. This is because the Onnx converter doesn't currently seem mature enough to handle nonstandard pipeline so we're effectively using the original, ordinary autoencoder.\n\nFor actual inference, you can test with something like:",
"## Using with TAESD\n\n(Not tested yet)\n\nConsider using the Onnx converted model of TAESD at deinferno/taesd-onnx (Original model at madebyollin/taesd )\n\nCombined inference code:"
] | [
"TAGS\n#onnx #text-to-image #region-us \n",
"# SDXS Onnx\n\nConverted from IDKiro/sdxs-512-0.9 (i.e. the original one, without dreamshaper) through this command:\n\n\n\nNotice that I replaced the '/vae' folder in the local copy of the repo with '/vae_large' in that same repo, and updated the model config at the repo root. This is because the Onnx converter doesn't currently seem mature enough to handle nonstandard pipeline so we're effectively using the original, ordinary autoencoder.\n\nFor actual inference, you can test with something like:",
"## Using with TAESD\n\n(Not tested yet)\n\nConsider using the Onnx converted model of TAESD at deinferno/taesd-onnx (Original model at madebyollin/taesd )\n\nCombined inference code:"
] |
text-generation | transformers | # llama-3-experiment-v1-9B
This is an experimental merge, replicating additional layers to the model without post-merge healing.
There is damage to the model, but it appears to be tolerable as is; the performance difference in benchmarks from the original 8B Instruct model does not appear to be significant.
The resulting impact on narrative text completion may also be of interest.
Light testing performed with instruct prompting and the following sampler settings:
- temp=1 and minP=0.02
- temp=1 and smoothing factor=0.33
Full weights: [grimjim/llama-3-experiment-v1-9B](https://huggingface.co/grimjim/llama-3-experiment-v1-9B)
GGUF quants: [grimjim/llama-3-experiment-v1-9B-GGUF](https://huggingface.co/grimjim/llama-3-experiment-v1-9B-GGUF)
This is a merge of pre-trained language model meta-llama/Meta-Llama-3-8B-Instruct created using [mergekit](https://github.com/cg123/mergekit).
Built with Meta Llama 3.
## Merge Details
### Merge Method
This model was merged using the passthrough merge method.
### Models Merged
The following models were included in the merge:
* meta-llama/Meta-Llama-3-8B-Instruct
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: meta-llama/Meta-Llama-3-8B-Instruct
layer_range: [0, 12]
- sources:
- model: meta-llama/Meta-Llama-3-8B-Instruct
layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16
```
| {"language": ["en"], "license": "llama3", "library_name": "transformers", "tags": ["meta", "llama-3", "pytorch", "mergekit", "merge"], "base_model": ["meta-llama/Meta-Llama-3-8B-Instruct"], "license_link": "LICENSE", "pipeline_tag": "text-generation", "widget": [{"example_title": "Hello", "messages": [{"role": "user", "content": "Hey my name is Corwin! How are you?"}]}, {"example_title": "Hellriding out of Amber", "messages": [{"role": "system", "content": "You are a helpful and honest assistant. Please, respond concisely and truthfully."}, {"role": "user", "content": "Can you recommend a good destination for a hellride out of Amber?"}]}], "inference": {"parameters": {"max_new_tokens": 300, "stop": ["<|end_of_text|>", "<|eot_id|>"]}}, "model-index": [{"name": "grimjim/grimjim/llama-3-experiment-v1-9B", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 66.41, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=grimjim/grimjim/llama-3-experiment-v1-9B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HellaSwag (10-Shot)", "type": "hellaswag", "split": "validation", "args": {"num_few_shot": 10}}, "metrics": [{"type": "acc_norm", "value": 78.56, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=grimjim/llama-3-experiment-v1-9B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "MMLU (5-Shot)", "type": "cais/mmlu", "config": "all", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 66.71, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=grimjim/llama-3-experiment-v1-9B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "TruthfulQA (0-shot)", "type": "truthful_qa", "config": "multiple_choice", "split": "validation", "args": {"num_few_shot": 0}}, "metrics": [{"type": "mc2", "value": 50.7}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=grimjim/llama-3-experiment-v1-9B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Winogrande (5-shot)", "type": "winogrande", "config": "winogrande_xl", "split": "validation", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 75.93, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=grimjim/llama-3-experiment-v1-9B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "GSM8k (5-shot)", "type": "gsm8k", "config": "main", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 65.88, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=grimjim/llama-3-experiment-v1-9B", "name": "Open LLM Leaderboard"}}]}]} | grimjim/llama-3-experiment-v1-9B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"meta",
"llama-3",
"pytorch",
"mergekit",
"merge",
"conversational",
"en",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T02:15:41+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #meta #llama-3 #pytorch #mergekit #merge #conversational #en #base_model-meta-llama/Meta-Llama-3-8B-Instruct #license-llama3 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # llama-3-experiment-v1-9B
This is an experimental merge, replicating additional layers to the model without post-merge healing.
There is damage to the model, but it appears to be tolerable as is; the performance difference in benchmarks from the original 8B Instruct model does not appear to be significant.
The resulting impact on narrative text completion may also be of interest.
Light testing performed with instruct prompting and the following sampler settings:
- temp=1 and minP=0.02
- temp=1 and smoothing factor=0.33
Full weights: grimjim/llama-3-experiment-v1-9B
GGUF quants: grimjim/llama-3-experiment-v1-9B-GGUF
This is a merge of pre-trained language model meta-llama/Meta-Llama-3-8B-Instruct created using mergekit.
Built with Meta Llama 3.
## Merge Details
### Merge Method
This model was merged using the passthrough merge method.
### Models Merged
The following models were included in the merge:
* meta-llama/Meta-Llama-3-8B-Instruct
### Configuration
The following YAML configuration was used to produce this model:
| [
"# llama-3-experiment-v1-9B\n\nThis is an experimental merge, replicating additional layers to the model without post-merge healing.\nThere is damage to the model, but it appears to be tolerable as is; the performance difference in benchmarks from the original 8B Instruct model does not appear to be significant.\nThe resulting impact on narrative text completion may also be of interest.\n\nLight testing performed with instruct prompting and the following sampler settings:\n- temp=1 and minP=0.02\n- temp=1 and smoothing factor=0.33\n\nFull weights: grimjim/llama-3-experiment-v1-9B\n\nGGUF quants: grimjim/llama-3-experiment-v1-9B-GGUF\n\nThis is a merge of pre-trained language model meta-llama/Meta-Llama-3-8B-Instruct created using mergekit.\n\nBuilt with Meta Llama 3.",
"## 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* meta-llama/Meta-Llama-3-8B-Instruct",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #meta #llama-3 #pytorch #mergekit #merge #conversational #en #base_model-meta-llama/Meta-Llama-3-8B-Instruct #license-llama3 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# llama-3-experiment-v1-9B\n\nThis is an experimental merge, replicating additional layers to the model without post-merge healing.\nThere is damage to the model, but it appears to be tolerable as is; the performance difference in benchmarks from the original 8B Instruct model does not appear to be significant.\nThe resulting impact on narrative text completion may also be of interest.\n\nLight testing performed with instruct prompting and the following sampler settings:\n- temp=1 and minP=0.02\n- temp=1 and smoothing factor=0.33\n\nFull weights: grimjim/llama-3-experiment-v1-9B\n\nGGUF quants: grimjim/llama-3-experiment-v1-9B-GGUF\n\nThis is a merge of pre-trained language model meta-llama/Meta-Llama-3-8B-Instruct created using mergekit.\n\nBuilt with Meta Llama 3.",
"## 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* meta-llama/Meta-Llama-3-8B-Instruct",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
text-generation | transformers | # llama-3-experiment-v1-9B-GGUF
This is an experimental merge, replicating additional layers to the model without post-merge healing. There is damage to the model, but it appears to be tolerable as is. The resulting impact on narrative text completion may be of interest.
Light testing performed with instruct prompting and the following sampler settings:
- temp=1 and minP=0.02
- temp=1 and smoothing factor=0.33
Full weights: [grimjim/llama-3-experiment-v1-9B](https://huggingface.co/grimjim/llama-3-experiment-v1-9B)
GGUF quants: [grimjim/llama-3-experiment-v1-9B-GGUF](https://huggingface.co/grimjim/llama-3-experiment-v1-9B-GGUF)
This is a merge of pre-trained language model meta-llama/Meta-Llama-3-8B-Instruct created using [mergekit](https://github.com/cg123/mergekit).
Built with Meta Llama 3.
## Merge Details
### Merge Method
This model was merged using the passthrough merge method.
### Models Merged
The following models were included in the merge:
* meta-llama/Meta-Llama-3-8B-Instruct
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: meta-llama/Meta-Llama-3-8B-Instruct
layer_range: [0, 12]
- sources:
- model: meta-llama/Meta-Llama-3-8B-Instruct
layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16
```
| {"language": ["en"], "license": "llama3", "library_name": "transformers", "tags": ["meta", "llama-3", "pytorch", "mergekit", "merge"], "base_model": ["meta-llama/Meta-Llama-3-8B-Instruct"], "license_link": "LICENSE", "pipeline_tag": "text-generation", "widget": [{"example_title": "Hello", "messages": [{"role": "user", "content": "Hey my name is Corwin! How are you?"}]}, {"example_title": "Hellriding out of Amber", "messages": [{"role": "system", "content": "You are a helpful and honest assistant. Please, respond concisely and truthfully."}, {"role": "user", "content": "Can you recommend a good destination for a hellride out of Amber?"}]}], "inference": {"parameters": {"max_new_tokens": 300, "stop": ["<|end_of_text|>", "<|eot_id|>"]}}} | grimjim/llama-3-experiment-v1-9B-GGUF | null | [
"transformers",
"gguf",
"llama",
"text-generation",
"meta",
"llama-3",
"pytorch",
"mergekit",
"merge",
"en",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T02:16:29+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #llama #text-generation #meta #llama-3 #pytorch #mergekit #merge #en #base_model-meta-llama/Meta-Llama-3-8B-Instruct #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # llama-3-experiment-v1-9B-GGUF
This is an experimental merge, replicating additional layers to the model without post-merge healing. There is damage to the model, but it appears to be tolerable as is. The resulting impact on narrative text completion may be of interest.
Light testing performed with instruct prompting and the following sampler settings:
- temp=1 and minP=0.02
- temp=1 and smoothing factor=0.33
Full weights: grimjim/llama-3-experiment-v1-9B
GGUF quants: grimjim/llama-3-experiment-v1-9B-GGUF
This is a merge of pre-trained language model meta-llama/Meta-Llama-3-8B-Instruct created using mergekit.
Built with Meta Llama 3.
## Merge Details
### Merge Method
This model was merged using the passthrough merge method.
### Models Merged
The following models were included in the merge:
* meta-llama/Meta-Llama-3-8B-Instruct
### Configuration
The following YAML configuration was used to produce this model:
| [
"# llama-3-experiment-v1-9B-GGUF\n\nThis is an experimental merge, replicating additional layers to the model without post-merge healing. There is damage to the model, but it appears to be tolerable as is. The resulting impact on narrative text completion may be of interest.\n\nLight testing performed with instruct prompting and the following sampler settings:\n- temp=1 and minP=0.02\n- temp=1 and smoothing factor=0.33\n\nFull weights: grimjim/llama-3-experiment-v1-9B\n\nGGUF quants: grimjim/llama-3-experiment-v1-9B-GGUF\n\nThis is a merge of pre-trained language model meta-llama/Meta-Llama-3-8B-Instruct created using mergekit.\n\nBuilt with Meta Llama 3.",
"## 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* meta-llama/Meta-Llama-3-8B-Instruct",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #gguf #llama #text-generation #meta #llama-3 #pytorch #mergekit #merge #en #base_model-meta-llama/Meta-Llama-3-8B-Instruct #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# llama-3-experiment-v1-9B-GGUF\n\nThis is an experimental merge, replicating additional layers to the model without post-merge healing. There is damage to the model, but it appears to be tolerable as is. The resulting impact on narrative text completion may be of interest.\n\nLight testing performed with instruct prompting and the following sampler settings:\n- temp=1 and minP=0.02\n- temp=1 and smoothing factor=0.33\n\nFull weights: grimjim/llama-3-experiment-v1-9B\n\nGGUF quants: grimjim/llama-3-experiment-v1-9B-GGUF\n\nThis is a merge of pre-trained language model meta-llama/Meta-Llama-3-8B-Instruct created using mergekit.\n\nBuilt with Meta Llama 3.",
"## 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* meta-llama/Meta-Llama-3-8B-Instruct",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/FPHam/Marvin_TheGrumpyOldAssistant_13B-HF
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Marvin_TheGrumpyOldAssistant_13B-HF-GGUF/resolve/main/Marvin_TheGrumpyOldAssistant_13B-HF.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/Marvin_TheGrumpyOldAssistant_13B-HF-GGUF/resolve/main/Marvin_TheGrumpyOldAssistant_13B-HF.IQ3_XS.gguf) | IQ3_XS | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Marvin_TheGrumpyOldAssistant_13B-HF-GGUF/resolve/main/Marvin_TheGrumpyOldAssistant_13B-HF.IQ3_S.gguf) | IQ3_S | 5.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Marvin_TheGrumpyOldAssistant_13B-HF-GGUF/resolve/main/Marvin_TheGrumpyOldAssistant_13B-HF.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Marvin_TheGrumpyOldAssistant_13B-HF-GGUF/resolve/main/Marvin_TheGrumpyOldAssistant_13B-HF.IQ3_M.gguf) | IQ3_M | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/Marvin_TheGrumpyOldAssistant_13B-HF-GGUF/resolve/main/Marvin_TheGrumpyOldAssistant_13B-HF.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Marvin_TheGrumpyOldAssistant_13B-HF-GGUF/resolve/main/Marvin_TheGrumpyOldAssistant_13B-HF.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/Marvin_TheGrumpyOldAssistant_13B-HF-GGUF/resolve/main/Marvin_TheGrumpyOldAssistant_13B-HF.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/Marvin_TheGrumpyOldAssistant_13B-HF-GGUF/resolve/main/Marvin_TheGrumpyOldAssistant_13B-HF.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Marvin_TheGrumpyOldAssistant_13B-HF-GGUF/resolve/main/Marvin_TheGrumpyOldAssistant_13B-HF.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Marvin_TheGrumpyOldAssistant_13B-HF-GGUF/resolve/main/Marvin_TheGrumpyOldAssistant_13B-HF.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/Marvin_TheGrumpyOldAssistant_13B-HF-GGUF/resolve/main/Marvin_TheGrumpyOldAssistant_13B-HF.Q5_K_M.gguf) | Q5_K_M | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/Marvin_TheGrumpyOldAssistant_13B-HF-GGUF/resolve/main/Marvin_TheGrumpyOldAssistant_13B-HF.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Marvin_TheGrumpyOldAssistant_13B-HF-GGUF/resolve/main/Marvin_TheGrumpyOldAssistant_13B-HF.Q8_0.gguf) | Q8_0 | 13.9 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "library_name": "transformers", "tags": ["llm", "llama2", "marvin", "funny", "model"], "base_model": "FPHam/Marvin_TheGrumpyOldAssistant_13B-HF", "quantized_by": "mradermacher"} | mradermacher/Marvin_TheGrumpyOldAssistant_13B-HF-GGUF | null | [
"transformers",
"gguf",
"llm",
"llama2",
"marvin",
"funny",
"model",
"en",
"base_model:FPHam/Marvin_TheGrumpyOldAssistant_13B-HF",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T02:18:25+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #llm #llama2 #marvin #funny #model #en #base_model-FPHam/Marvin_TheGrumpyOldAssistant_13B-HF #endpoints_compatible #region-us
| About
-----
static quants of URL
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
-----
If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs for
more details, including on how to concatenate multi-part files.
Provided Quants
---------------
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #llm #llama2 #marvin #funny #model #en #base_model-FPHam/Marvin_TheGrumpyOldAssistant_13B-HF #endpoints_compatible #region-us \n"
] |
null | mlx |
# mlx-community/dolphin-2.9-llama3-8b-256-8bit
This model was converted to MLX format from [`cognitivecomputations/dolphin-2.9-llama3-8b-256k`]() using mlx-lm version **0.12.0**.
Refer to the [original model card](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b-256k) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/dolphin-2.9-llama3-8b-256-8bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"license": "llama3", "tags": ["mlx"]} | mlx-community/dolphin-2.9-llama3-8b-256-8bit | null | [
"mlx",
"safetensors",
"llama",
"license:llama3",
"region:us"
] | null | 2024-04-28T02:20:13+00:00 | [] | [] | TAGS
#mlx #safetensors #llama #license-llama3 #region-us
|
# mlx-community/dolphin-2.9-llama3-8b-256-8bit
This model was converted to MLX format from ['cognitivecomputations/dolphin-2.9-llama3-8b-256k']() using mlx-lm version 0.12.0.
Refer to the original model card for more details on the model.
## Use with mlx
| [
"# mlx-community/dolphin-2.9-llama3-8b-256-8bit\nThis model was converted to MLX format from ['cognitivecomputations/dolphin-2.9-llama3-8b-256k']() using mlx-lm version 0.12.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] | [
"TAGS\n#mlx #safetensors #llama #license-llama3 #region-us \n",
"# mlx-community/dolphin-2.9-llama3-8b-256-8bit\nThis model was converted to MLX format from ['cognitivecomputations/dolphin-2.9-llama3-8b-256k']() using mlx-lm version 0.12.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] |
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. -->
[<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: LlamaForCausalLM
tokenizer_type: AutoTokenizer
strict: false
# dataset
datasets:
- path: BEE-spoke-data/bees-internal
type: completion # format from earlier
field: text # Optional[str] default: text, field to use for completion data
val_set_size: 0.05
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
train_on_inputs: false
group_by_length: false
# WANDB
wandb_project: llama3-8bee
wandb_entity: pszemraj
wandb_watch: gradients
wandb_name: llama3-8bee-8192
hub_model_id: pszemraj/Meta-Llama-3-8Bee
hub_strategy: every_save
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 1
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 2e-5
load_in_8bit: false
load_in_4bit: false
bf16: auto
fp16:
tf32: true
torch_compile: true # requires >= torch 2.0, may sometimes cause problems
torch_compile_backend: inductor # Optional[str]
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
logging_steps: 10
xformers_attention:
flash_attention: true
warmup_steps: 25
# hyperparams for freq of evals, saving, etc
evals_per_epoch: 3
saves_per_epoch: 3
save_safetensors: true
save_total_limit: 1 # Checkpoints saved at a time
output_dir: ./output-axolotl/output-model-gamma
resume_from_checkpoint:
deepspeed:
weight_decay: 0.0
special_tokens:
pad_token: <|end_of_text|>
```
</details><br>
# Meta-Llama-3-8Bee
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3319
## 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: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 25
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.0 | 1 | 2.5339 |
| 2.3719 | 0.33 | 232 | 2.3658 |
| 2.2914 | 0.67 | 464 | 2.3319 |
### Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.3.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0 | {"language": ["en"], "license": "llama3", "tags": ["axolotl", "generated_from_trainer"], "datasets": ["BEE-spoke-data/bees-internal"], "base_model": "meta-llama/Meta-Llama-3-8B", "pipeline_tag": "text-generation", "model-index": [{"name": "Meta-Llama-3-8Bee", "results": []}]} | BEE-spoke-data/Meta-Llama-3-8Bee | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"axolotl",
"generated_from_trainer",
"en",
"dataset:BEE-spoke-data/bees-internal",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T02:21:05+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #axolotl #generated_from_trainer #en #dataset-BEE-spoke-data/bees-internal #base_model-meta-llama/Meta-Llama-3-8B #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| <img src="URL alt="Built with Axolotl" width="200" height="32"/>
See axolotl config
axolotl version: '0.4.0'
Meta-Llama-3-8Bee
=================
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3319
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: 1
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 25
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.40.0.dev0
* Pytorch 2.3.0+cu118
* Datasets 2.15.0
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 25\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0.dev0\n* Pytorch 2.3.0+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #axolotl #generated_from_trainer #en #dataset-BEE-spoke-data/bees-internal #base_model-meta-llama/Meta-Llama-3-8B #license-llama3 #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: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 25\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0.dev0\n* Pytorch 2.3.0+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
] |
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/6eb20ojg)
# facebook-detr-resnet-50-finetuned-10k-cppe5-auto-pad
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 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: 1
- 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
### 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": "facebook/detr-resnet-50", "model-index": [{"name": "facebook-detr-resnet-50-finetuned-10k-cppe5-auto-pad", "results": []}]} | qubvel-hf/facebook-detr-resnet-50-finetuned-10k-cppe5-auto-pad | null | [
"transformers",
"safetensors",
"detr",
"object-detection",
"vision",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T02:23:41+00:00 | [] | [] | TAGS
#transformers #safetensors #detr #object-detection #vision #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us
|
<img src="URL alt="Visualize in Weights & Biases" width="200" height="32"/>
# facebook-detr-resnet-50-finetuned-10k-cppe5-auto-pad
This model is a fine-tuned version of facebook/detr-resnet-50 on the cppe-5 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: 1
- 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
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.18.0
- Tokenizers 0.19.0
| [
"# facebook-detr-resnet-50-finetuned-10k-cppe5-auto-pad\n\nThis model is a fine-tuned version of facebook/detr-resnet-50 on the cppe-5 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: 1\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",
"### Framework versions\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 #detr #object-detection #vision #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# facebook-detr-resnet-50-finetuned-10k-cppe5-auto-pad\n\nThis model is a fine-tuned version of facebook/detr-resnet-50 on the cppe-5 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: 1\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",
"### Framework versions\n\n- Transformers 4.41.0.dev0\n- Pytorch 1.13.0+cu117\n- Datasets 2.18.0\n- Tokenizers 0.19.0"
] |
null | null | ---
license: apache-2.0
---
# Model url: https://huggingface.co/TimeMobius/Mobius-RWKV-r5-chat-12B-8k
Considering the long context required for training from scratch, we decided to retrain the r5 12B model from 8k.
This model exhibits lower diversity compared to its predecessor, but it excels in following instructions and logical understanding. It is possible to utilize both models simultaneously as multi-agents, each performing a different task.
# Mobius RWKV r5 chat 12B 8k
Mobius is a RWKV v5.2 arch chat model, benifit from [Matrix-Valued States and Dynamic Recurrence](https://arxiv.org/abs/2404.05892)
## Introduction
Mobius is a RWKV v5.2 arch model, a state based RNN+CNN+Transformer Mixed language model pretrained on a certain amount of data.
In comparison with the previous released Mobius, the improvements include:
* Only 24G Vram to run this model locally with fp16;
* Significant performance improvement;
* Multilingual support ;
* Stable support of 128K context length.
* Base model [Mobius-mega-12B-128k-base](https://huggingface.co/TimeMobius/Moibus-mega-12B-128k-base)
## Usage
We encourage you use few shots to use this model, Desipte Directly use User: xxxx\n\nAssistant: xxx\n\n is really good too, Can boost all potential ability.
Recommend Temp and topp: 0.7 0.6/1 0.3/1.5 0.3/0.2 0.8
## More details
Mobius 12B 128k based on RWKV v5.2 arch, which is leading state based RNN+CNN+Transformer Mixed large language model which focus opensouce community
* 10~100 trainning/inference cost reduce;
* state based,selected memory, which mean good at grok;
* community support.
## requirements
24G vram to run fp16, 12G for int8, 6G for nf4 with Ai00 server.
* [RWKV Runner](https://github.com/josStorer/RWKV-Runner)
* [Ai00 server](https://github.com/cgisky1980/ai00_rwkv_server)
## future plan
If you need a HF version let us know
[Mobius-Chat-12B-128k](https://huggingface.co/TimeMobius/Mobius-Chat-12B-128k)
| {"license": "apache-2.0"} | xiaol/Mobius-RWKV-r5-chat-12B-8k | null | [
"arxiv:2404.05892",
"license:apache-2.0",
"region:us"
] | null | 2024-04-28T02:24:13+00:00 | [
"2404.05892"
] | [] | TAGS
#arxiv-2404.05892 #license-apache-2.0 #region-us
| ---
license: apache-2.0
---
# Model url: URL
Considering the long context required for training from scratch, we decided to retrain the r5 12B model from 8k.
This model exhibits lower diversity compared to its predecessor, but it excels in following instructions and logical understanding. It is possible to utilize both models simultaneously as multi-agents, each performing a different task.
# Mobius RWKV r5 chat 12B 8k
Mobius is a RWKV v5.2 arch chat model, benifit from Matrix-Valued States and Dynamic Recurrence
## Introduction
Mobius is a RWKV v5.2 arch model, a state based RNN+CNN+Transformer Mixed language model pretrained on a certain amount of data.
In comparison with the previous released Mobius, the improvements include:
* Only 24G Vram to run this model locally with fp16;
* Significant performance improvement;
* Multilingual support ;
* Stable support of 128K context length.
* Base model Mobius-mega-12B-128k-base
## Usage
We encourage you use few shots to use this model, Desipte Directly use User: xxxx\n\nAssistant: xxx\n\n is really good too, Can boost all potential ability.
Recommend Temp and topp: 0.7 0.6/1 0.3/1.5 0.3/0.2 0.8
## More details
Mobius 12B 128k based on RWKV v5.2 arch, which is leading state based RNN+CNN+Transformer Mixed large language model which focus opensouce community
* 10~100 trainning/inference cost reduce;
* state based,selected memory, which mean good at grok;
* community support.
## requirements
24G vram to run fp16, 12G for int8, 6G for nf4 with Ai00 server.
* RWKV Runner
* Ai00 server
## future plan
If you need a HF version let us know
Mobius-Chat-12B-128k
| [
"# Model url: URL\nConsidering the long context required for training from scratch, we decided to retrain the r5 12B model from 8k.\nThis model exhibits lower diversity compared to its predecessor, but it excels in following instructions and logical understanding. It is possible to utilize both models simultaneously as multi-agents, each performing a different task.",
"# Mobius RWKV r5 chat 12B 8k\nMobius is a RWKV v5.2 arch chat model, benifit from Matrix-Valued States and Dynamic Recurrence",
"## Introduction\n\nMobius is a RWKV v5.2 arch model, a state based RNN+CNN+Transformer Mixed language model pretrained on a certain amount of data.\nIn comparison with the previous released Mobius, the improvements include:\n\n* Only 24G Vram to run this model locally with fp16;\n* Significant performance improvement;\n* Multilingual support ;\n* Stable support of 128K context length.\n* Base model Mobius-mega-12B-128k-base",
"## Usage\nWe encourage you use few shots to use this model, Desipte Directly use User: xxxx\\n\\nAssistant: xxx\\n\\n is really good too, Can boost all potential ability. \n\nRecommend Temp and topp: 0.7 0.6/1 0.3/1.5 0.3/0.2 0.8",
"## More details\nMobius 12B 128k based on RWKV v5.2 arch, which is leading state based RNN+CNN+Transformer Mixed large language model which focus opensouce community\n* 10~100 trainning/inference cost reduce;\n* state based,selected memory, which mean good at grok;\n* community support.",
"## requirements\n24G vram to run fp16, 12G for int8, 6G for nf4 with Ai00 server.\n\n* RWKV Runner\n* Ai00 server",
"## future plan\nIf you need a HF version let us know\n\nMobius-Chat-12B-128k"
] | [
"TAGS\n#arxiv-2404.05892 #license-apache-2.0 #region-us \n",
"# Model url: URL\nConsidering the long context required for training from scratch, we decided to retrain the r5 12B model from 8k.\nThis model exhibits lower diversity compared to its predecessor, but it excels in following instructions and logical understanding. It is possible to utilize both models simultaneously as multi-agents, each performing a different task.",
"# Mobius RWKV r5 chat 12B 8k\nMobius is a RWKV v5.2 arch chat model, benifit from Matrix-Valued States and Dynamic Recurrence",
"## Introduction\n\nMobius is a RWKV v5.2 arch model, a state based RNN+CNN+Transformer Mixed language model pretrained on a certain amount of data.\nIn comparison with the previous released Mobius, the improvements include:\n\n* Only 24G Vram to run this model locally with fp16;\n* Significant performance improvement;\n* Multilingual support ;\n* Stable support of 128K context length.\n* Base model Mobius-mega-12B-128k-base",
"## Usage\nWe encourage you use few shots to use this model, Desipte Directly use User: xxxx\\n\\nAssistant: xxx\\n\\n is really good too, Can boost all potential ability. \n\nRecommend Temp and topp: 0.7 0.6/1 0.3/1.5 0.3/0.2 0.8",
"## More details\nMobius 12B 128k based on RWKV v5.2 arch, which is leading state based RNN+CNN+Transformer Mixed large language model which focus opensouce community\n* 10~100 trainning/inference cost reduce;\n* state based,selected memory, which mean good at grok;\n* community support.",
"## requirements\n24G vram to run fp16, 12G for int8, 6G for nf4 with Ai00 server.\n\n* RWKV Runner\n* Ai00 server",
"## future plan\nIf you need a HF version let us know\n\nMobius-Chat-12B-128k"
] |
null | peft | ## Training procedure
### Framework versions
- PEFT 0.4.0
| {"library_name": "peft"} | yuzhang/llava-prumerge-vicuna-13b-v1.5-lora | null | [
"peft",
"llava",
"region:us"
] | null | 2024-04-28T02:24:51+00:00 | [] | [] | TAGS
#peft #llava #region-us
| ## Training procedure
### Framework versions
- PEFT 0.4.0
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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]
- **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": []} | shallow6414/3bzox14 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T02:24:59+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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"## Model Card Contact"
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"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
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"## Model Details",
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"## 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. -->
# CS505_COQE_viT5_total_Instruction0_PAOSL_v1_h1
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: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- 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_total_Instruction0_PAOSL_v1_h1", "results": []}]} | ThuyNT/CS505_COQE_viT5_total_Instruction0_PAOSL_v1_h1 | 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 | 2024-04-28T02:25:32+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_total_Instruction0_PAOSL_v1_h1
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: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- 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_total_Instruction0_PAOSL_v1_h1\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: 8\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: 25\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-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# CS505_COQE_viT5_total_Instruction0_PAOSL_v1_h1\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: 8\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: 25\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 | null | <p align="center">
<img src="../assets/lumina-logo.png" width="30%"/>
<br>
</p>
# Lumina-T2I
Lumina-T2I is a model that generates images based on text conditions, supporting various text encoders and models of different parameter sizes. With minimal training costs, it achieves high-quality image generation by training from scratch. Additionally, it offers usage through CLI console programs and Web Demo displays.
Our generative model has `LargeDiT` as the backbone, the text encoder is the `LLaMa` 7B model, and the VAE uses a version of `sdxl` fine-tuned by stabilityai.
- Generation Model: Large-DiT
- Text Encoder: LLaMa-7B
- VAE: stabilityai/sd-vae-ft-sdxl
## 📰 News
- [2024-4-1] 🚀🚀🚀 We release the initial version of Lumina-T2I for text-to-image generation
## 🎮 Model Zoo
More checkpoints of our model will be released soon~
| Resolution | Flag-DiT Parameter| Text Encoder | Prediction | Download URL |
| ---------- | ----------------------- | ------------ | -----------|-------------- |
| 1024 | 5B | LLaMa-7B | Rectified Flow | [hugging face](https://huggingface.co/Alpha-VLLM/Lumina-T2I) |
## Installation
Before installation, ensure that you have a working ``nvcc``
```bash
# The command should work and show the same version number as in our case. (12.1 in our case).
nvcc --version
```
On some outdated distros (e.g., CentOS 7), you may also want to check that a late enough version of
``gcc`` is available
```bash
# The command should work and show a version of at least 6.0.
# If not, consult distro-specific tutorials to obtain a newer version or build manually.
gcc --version
```
Downloading Lumina-T2X repo from github:
```bash
git clone https://github.com/Alpha-VLLM/Lumina-T2X
```
### 1. Create a conda environment and install PyTorch
Note: You may want to adjust the CUDA version [according to your driver version](https://docs.nvidia.com/deploy/cuda-compatibility/#default-to-minor-version).
```bash
conda create -n Lumina_T2X -y
conda activate Lumina_T2X
conda install python=3.11 pytorch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 pytorch-cuda=12.1 -c pytorch -c nvidia -y
```
### 2. Install dependencies
```bash
pip install diffusers fairscale accelerate tensorboard transformers gradio torchdiffeq click
```
or you can use
```bash
cd lumina-t2i
pip install -r requirements.txt
```
### 3. Install ``flash-attn``
```bash
pip install flash-attn --no-build-isolation
```
### 4. Install [nvidia apex](https://github.com/nvidia/apex) (optional)
>[!Warning]
> While Apex can improve efficiency, it is *not* a must to make Lumina-T2X work.
>
> Note that Lumina-T2X works smoothly with either:
> + Apex not installed at all; OR
> + Apex successfully installed with CUDA and C++ extensions.
>
> However, it will fail when:
> + A Python-only build of Apex is installed.
>
> If the error `No module named 'fused_layer_norm_cuda'` appears, it typically means you are using a Python-only build of Apex. To resolve this, please run `pip uninstall apex`, and Lumina-T2X should then function correctly.
You can clone the repo and install following the official guidelines (note that we expect a full
build, i.e., with CUDA and C++ extensions)
```bash
pip install ninja
git clone https://github.com/NVIDIA/apex
cd apex
# if pip >= 23.1 (ref: https://pip.pypa.io/en/stable/news/#v23-1) which supports multiple `--config-settings` with the same key...
pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./
# otherwise
pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --global-option="--cpp_ext" --global-option="--cuda_ext" ./
```
## Inference
To ensure that our generative model is ready to use immediately, we provide a user-friendly CLI program and a locally deployable Web Demo site.
### CLI
1. Install Lumina-T2I
```bash
pip install -e .
```
2. Prepare the pre-trained model
⭐⭐ (Recommended) you can use huggingface_cli to download our model:
```bash
huggingface-cli download --resume-download Alpha-VLLM/Lumina-T2I --local-dir /path/to/ckpt
```
or using git for cloning the model you want to use:
```bash
git clone https://huggingface.co/Alpha-VLLM/Lumina-T2I
```
1. Setting your personal inference configuration
Update your own personal inference settings to generate different styles of images, checking `config/infer/config.yaml` for detailed settings. Detailed config structure:
> `/path/to/ckpt` should be a directory containing `consolidated*.pth` and `model_args.pth`
```yaml
- settings:
model:
ckpt: "/path/to/ckpt" # if ckpt is "", you should use `--ckpt` for passing model path when using `lumina` cli.
ckpt_lm: "" # if ckpt is "", you should use `--ckpt_lm` for passing model path when using `lumina` cli.
token: "" # if LLM is a huggingface gated repo, you should input your access token from huggingface and when token is "", you should `--token` for accessing the model.
transport:
path_type: "Linear" # option: ["Linear", "GVP", "VP"]
prediction: "velocity" # option: ["velocity", "score", "noise"]
loss_weight: "velocity" # option: [None, "velocity", "likelihood"]
sample_eps: 0.1
train_eps: 0.2
ode:
atol: 1e-6 # Absolute tolerance
rtol: 1e-3 # Relative tolerance
reverse: false # option: true or false
likelihood: false # option: true or false
sde:
sampling_method: "Euler" # option: ["Euler", "Heun"]
diffusion_form: "sigma" # option: ["constant", "SBDM", "sigma", "linear", "decreasing", "increasing-decreasing"]
diffusion_norm: 1.0 # range: 0-1
last_step: Mean # option: [None, "Mean", "Tweedie", "Euler"]
last_step_size: 0.04
infer:
resolution: "1024x1024" # option: ["1024x1024", "512x2048", "2048x512", "(Extrapolation) 1664x1664", "(Extrapolation) 1024x2048", "(Extrapolation) 2048x1024"]
num_sampling_steps: 60 # range: 1-1000
cfg_scale: 4. # range: 1-20
solver: "euler" # option: ["euler", "dopri5", "dopri8"]
t_shift: 4 # range: 1-20 (int only)
ntk_scaling: true # option: true or false
proportional_attn: true # option: true or false
seed: 0 # rnage: any number
```
- model:
- `ckpt`: lumina-t2i checkpoint path from [huggingface repo](https://huggingface.co/Alpha-VLLM/Lumina-T2I) containing `consolidated*.pth` and `model_args.pth`.
- `ckpt_lm`: LLM checkpoint.
- `token`: huggingface access token for accessing gated repo.
- transport:
- `path_type`: the type of path for transport: 'Linear', 'GVP' (Geodesic Vector Pursuit), or 'VP' (Vector Pursuit).
- `prediction`: the prediction model for the transport dynamics.
- `loss_weight`: the weighting of different components in the loss function, can be 'velocity' for dynamic modeling, 'likelihood' for statistical consistency, or None for no weighting
- `sample_eps`: sampling in the transport model.
- `train_eps`: training to stabilize the learning process.
- ode:
- `atol`: Absolute tolerance for the ODE solver. (options: ["Linear", "GVP", "VP"])
- `rtol`: Relative tolerance for the ODE solver. (option: ["velocity", "score", "noise"])
- `reverse`: run the ODE solver in reverse. (option: [None, "velocity", "likelihood"])
- `likelihood`: Enable calculation of likelihood during the ODE solving process.
- sde
- `sampling-method`: the numerical method used for sampling the stochastic differential equation: 'Euler' for simplicity or 'Heun' for improved accuracy.
- `diffusion-form`: form of diffusion coefficient in the SDE
- `diffusion-norm`: Normalizes the diffusion coefficient, affecting the scale of the stochastic component.
- `last-step`: form of last step taken in the SDE
- `last-step-size`: size of the last step taken
- infer
- `resolution`: generated image resolution.
- `num_sampling_steps`: sampling step for generating image.
- `cfg_scale`: classifier-free guide scaling factor
- `solver`: solver for image generation.
- `t_shift`: time shift factor.
- `ntk_scaling`: ntk rope scaling factor.
- `proportional_attn`: Whether to use proportional attention.
- `seed`: random initialization seeds.
1. Run with CLI
inference command:
```bash
lumina infer -c <config_path> <caption_here> <output_dir>
```
e.g. Demo command:
```bash
cd lumina-t2i
lumina infer -c "config/infer/settings.yaml" "a snow man of ..." "./outputs"
```
### Web Demo
To host a local gradio demo for interactive inference, run the following command:
```bash
# `/path/to/ckpt` should be a directory containing `consolidated*.pth` and `model_args.pth`
# default
python -u demo.py --ckpt "/path/to/ckpt"
# the demo by default uses bf16 precision. to switch to fp32:
python -u demo.py --ckpt "/path/to/ckpt" --precision fp32
# use ema model
python -u demo.py --ckpt "/path/to/ckpt" --ema
```
| {} | Alpha-VLLM/Lumina-T2I | null | [
"region:us"
] | null | 2024-04-28T02:27:46+00:00 | [] | [] | TAGS
#region-us
|
Lumina-T2I
==========
Lumina-T2I is a model that generates images based on text conditions, supporting various text encoders and models of different parameter sizes. With minimal training costs, it achieves high-quality image generation by training from scratch. Additionally, it offers usage through CLI console programs and Web Demo displays.
Our generative model has 'LargeDiT' as the backbone, the text encoder is the 'LLaMa' 7B model, and the VAE uses a version of 'sdxl' fine-tuned by stabilityai.
* Generation Model: Large-DiT
* Text Encoder: LLaMa-7B
* VAE: stabilityai/sd-vae-ft-sdxl
News
----
* [2024-4-1] We release the initial version of Lumina-T2I for text-to-image generation
Model Zoo
---------
More checkpoints of our model will be released soon~
Installation
------------
Before installation, ensure that you have a working ''nvcc''
On some outdated distros (e.g., CentOS 7), you may also want to check that a late enough version of
''gcc'' is available
Downloading Lumina-T2X repo from github:
### 1. Create a conda environment and install PyTorch
Note: You may want to adjust the CUDA version according to your driver version.
### 2. Install dependencies
or you can use
### 3. Install ''flash-attn''
### 4. Install nvidia apex (optional)
>
> [!Warning]
> While Apex can improve efficiency, it is *not* a must to make Lumina-T2X work.
>
>
> Note that Lumina-T2X works smoothly with either:
>
>
> * Apex not installed at all; OR
> * Apex successfully installed with CUDA and C++ extensions.
>
>
> However, it will fail when:
>
>
> * A Python-only build of Apex is installed.
>
>
> If the error 'No module named 'fused\_layer\_norm\_cuda'' appears, it typically means you are using a Python-only build of Apex. To resolve this, please run 'pip uninstall apex', and Lumina-T2X should then function correctly.
>
>
>
You can clone the repo and install following the official guidelines (note that we expect a full
build, i.e., with CUDA and C++ extensions)
Inference
---------
To ensure that our generative model is ready to use immediately, we provide a user-friendly CLI program and a locally deployable Web Demo site.
### CLI
1. Install Lumina-T2I
2. Prepare the pre-trained model
⭐⭐ (Recommended) you can use huggingface\_cli to download our model:
or using git for cloning the model you want to use:
1. Setting your personal inference configuration
Update your own personal inference settings to generate different styles of images, checking 'config/infer/URL' for detailed settings. Detailed config structure:
>
> '/path/to/ckpt' should be a directory containing 'consolidated\*.pth' and 'model\_args.pth'
>
>
>
* model:
+ 'ckpt': lumina-t2i checkpoint path from huggingface repo containing 'consolidated\*.pth' and 'model\_args.pth'.
+ 'ckpt\_lm': LLM checkpoint.
+ 'token': huggingface access token for accessing gated repo.
* transport:
+ 'path\_type': the type of path for transport: 'Linear', 'GVP' (Geodesic Vector Pursuit), or 'VP' (Vector Pursuit).
+ 'prediction': the prediction model for the transport dynamics.
+ 'loss\_weight': the weighting of different components in the loss function, can be 'velocity' for dynamic modeling, 'likelihood' for statistical consistency, or None for no weighting
+ 'sample\_eps': sampling in the transport model.
+ 'train\_eps': training to stabilize the learning process.
* ode:
+ 'atol': Absolute tolerance for the ODE solver. (options: ["Linear", "GVP", "VP"])
+ 'rtol': Relative tolerance for the ODE solver. (option: ["velocity", "score", "noise"])
+ 'reverse': run the ODE solver in reverse. (option: [None, "velocity", "likelihood"])
+ 'likelihood': Enable calculation of likelihood during the ODE solving process.
* sde
+ 'sampling-method': the numerical method used for sampling the stochastic differential equation: 'Euler' for simplicity or 'Heun' for improved accuracy.
+ 'diffusion-form': form of diffusion coefficient in the SDE
+ 'diffusion-norm': Normalizes the diffusion coefficient, affecting the scale of the stochastic component.
+ 'last-step': form of last step taken in the SDE
+ 'last-step-size': size of the last step taken
* infer
+ 'resolution': generated image resolution.
+ 'num\_sampling\_steps': sampling step for generating image.
+ 'cfg\_scale': classifier-free guide scaling factor
+ 'solver': solver for image generation.
+ 't\_shift': time shift factor.
+ 'ntk\_scaling': ntk rope scaling factor.
+ 'proportional\_attn': Whether to use proportional attention.
+ 'seed': random initialization seeds.
1. Run with CLI
inference command:
e.g. Demo command:
### Web Demo
To host a local gradio demo for interactive inference, run the following command:
| [
"### 1. Create a conda environment and install PyTorch\n\n\nNote: You may want to adjust the CUDA version according to your driver version.",
"### 2. Install dependencies\n\n\nor you can use",
"### 3. Install ''flash-attn''",
"### 4. Install nvidia apex (optional)\n\n\n\n> \n> [!Warning]\n> While Apex can improve efficiency, it is *not* a must to make Lumina-T2X work.\n> \n> \n> Note that Lumina-T2X works smoothly with either:\n> \n> \n> * Apex not installed at all; OR\n> * Apex successfully installed with CUDA and C++ extensions.\n> \n> \n> However, it will fail when:\n> \n> \n> * A Python-only build of Apex is installed.\n> \n> \n> If the error 'No module named 'fused\\_layer\\_norm\\_cuda'' appears, it typically means you are using a Python-only build of Apex. To resolve this, please run 'pip uninstall apex', and Lumina-T2X should then function correctly.\n> \n> \n> \n\n\nYou can clone the repo and install following the official guidelines (note that we expect a full\nbuild, i.e., with CUDA and C++ extensions)\n\n\nInference\n---------\n\n\nTo ensure that our generative model is ready to use immediately, we provide a user-friendly CLI program and a locally deployable Web Demo site.",
"### CLI\n\n\n1. Install Lumina-T2I\n2. Prepare the pre-trained model\n\n\n⭐⭐ (Recommended) you can use huggingface\\_cli to download our model:\n\n\nor using git for cloning the model you want to use:\n\n\n1. Setting your personal inference configuration\n\n\nUpdate your own personal inference settings to generate different styles of images, checking 'config/infer/URL' for detailed settings. Detailed config structure:\n\n\n\n> \n> '/path/to/ckpt' should be a directory containing 'consolidated\\*.pth' and 'model\\_args.pth'\n> \n> \n> \n\n\n* model:\n\t+ 'ckpt': lumina-t2i checkpoint path from huggingface repo containing 'consolidated\\*.pth' and 'model\\_args.pth'.\n\t+ 'ckpt\\_lm': LLM checkpoint.\n\t+ 'token': huggingface access token for accessing gated repo.\n* transport:\n\t+ 'path\\_type': the type of path for transport: 'Linear', 'GVP' (Geodesic Vector Pursuit), or 'VP' (Vector Pursuit).\n\t+ 'prediction': the prediction model for the transport dynamics.\n\t+ 'loss\\_weight': the weighting of different components in the loss function, can be 'velocity' for dynamic modeling, 'likelihood' for statistical consistency, or None for no weighting\n\t+ 'sample\\_eps': sampling in the transport model.\n\t+ 'train\\_eps': training to stabilize the learning process.\n* ode:\n\t+ 'atol': Absolute tolerance for the ODE solver. (options: [\"Linear\", \"GVP\", \"VP\"])\n\t+ 'rtol': Relative tolerance for the ODE solver. (option: [\"velocity\", \"score\", \"noise\"])\n\t+ 'reverse': run the ODE solver in reverse. (option: [None, \"velocity\", \"likelihood\"])\n\t+ 'likelihood': Enable calculation of likelihood during the ODE solving process.\n* sde\n\t+ 'sampling-method': the numerical method used for sampling the stochastic differential equation: 'Euler' for simplicity or 'Heun' for improved accuracy.\n\t+ 'diffusion-form': form of diffusion coefficient in the SDE\n\t+ 'diffusion-norm': Normalizes the diffusion coefficient, affecting the scale of the stochastic component.\n\t+ 'last-step': form of last step taken in the SDE\n\t+ 'last-step-size': size of the last step taken\n* infer\n\t+ 'resolution': generated image resolution.\n\t+ 'num\\_sampling\\_steps': sampling step for generating image.\n\t+ 'cfg\\_scale': classifier-free guide scaling factor\n\t+ 'solver': solver for image generation.\n\t+ 't\\_shift': time shift factor.\n\t+ 'ntk\\_scaling': ntk rope scaling factor.\n\t+ 'proportional\\_attn': Whether to use proportional attention.\n\t+ 'seed': random initialization seeds.\n\n\n1. Run with CLI\n\n\ninference command:\n\n\ne.g. Demo command:",
"### Web Demo\n\n\nTo host a local gradio demo for interactive inference, run the following command:"
] | [
"TAGS\n#region-us \n",
"### 1. Create a conda environment and install PyTorch\n\n\nNote: You may want to adjust the CUDA version according to your driver version.",
"### 2. Install dependencies\n\n\nor you can use",
"### 3. Install ''flash-attn''",
"### 4. Install nvidia apex (optional)\n\n\n\n> \n> [!Warning]\n> While Apex can improve efficiency, it is *not* a must to make Lumina-T2X work.\n> \n> \n> Note that Lumina-T2X works smoothly with either:\n> \n> \n> * Apex not installed at all; OR\n> * Apex successfully installed with CUDA and C++ extensions.\n> \n> \n> However, it will fail when:\n> \n> \n> * A Python-only build of Apex is installed.\n> \n> \n> If the error 'No module named 'fused\\_layer\\_norm\\_cuda'' appears, it typically means you are using a Python-only build of Apex. To resolve this, please run 'pip uninstall apex', and Lumina-T2X should then function correctly.\n> \n> \n> \n\n\nYou can clone the repo and install following the official guidelines (note that we expect a full\nbuild, i.e., with CUDA and C++ extensions)\n\n\nInference\n---------\n\n\nTo ensure that our generative model is ready to use immediately, we provide a user-friendly CLI program and a locally deployable Web Demo site.",
"### CLI\n\n\n1. Install Lumina-T2I\n2. Prepare the pre-trained model\n\n\n⭐⭐ (Recommended) you can use huggingface\\_cli to download our model:\n\n\nor using git for cloning the model you want to use:\n\n\n1. Setting your personal inference configuration\n\n\nUpdate your own personal inference settings to generate different styles of images, checking 'config/infer/URL' for detailed settings. Detailed config structure:\n\n\n\n> \n> '/path/to/ckpt' should be a directory containing 'consolidated\\*.pth' and 'model\\_args.pth'\n> \n> \n> \n\n\n* model:\n\t+ 'ckpt': lumina-t2i checkpoint path from huggingface repo containing 'consolidated\\*.pth' and 'model\\_args.pth'.\n\t+ 'ckpt\\_lm': LLM checkpoint.\n\t+ 'token': huggingface access token for accessing gated repo.\n* transport:\n\t+ 'path\\_type': the type of path for transport: 'Linear', 'GVP' (Geodesic Vector Pursuit), or 'VP' (Vector Pursuit).\n\t+ 'prediction': the prediction model for the transport dynamics.\n\t+ 'loss\\_weight': the weighting of different components in the loss function, can be 'velocity' for dynamic modeling, 'likelihood' for statistical consistency, or None for no weighting\n\t+ 'sample\\_eps': sampling in the transport model.\n\t+ 'train\\_eps': training to stabilize the learning process.\n* ode:\n\t+ 'atol': Absolute tolerance for the ODE solver. (options: [\"Linear\", \"GVP\", \"VP\"])\n\t+ 'rtol': Relative tolerance for the ODE solver. (option: [\"velocity\", \"score\", \"noise\"])\n\t+ 'reverse': run the ODE solver in reverse. (option: [None, \"velocity\", \"likelihood\"])\n\t+ 'likelihood': Enable calculation of likelihood during the ODE solving process.\n* sde\n\t+ 'sampling-method': the numerical method used for sampling the stochastic differential equation: 'Euler' for simplicity or 'Heun' for improved accuracy.\n\t+ 'diffusion-form': form of diffusion coefficient in the SDE\n\t+ 'diffusion-norm': Normalizes the diffusion coefficient, affecting the scale of the stochastic component.\n\t+ 'last-step': form of last step taken in the SDE\n\t+ 'last-step-size': size of the last step taken\n* infer\n\t+ 'resolution': generated image resolution.\n\t+ 'num\\_sampling\\_steps': sampling step for generating image.\n\t+ 'cfg\\_scale': classifier-free guide scaling factor\n\t+ 'solver': solver for image generation.\n\t+ 't\\_shift': time shift factor.\n\t+ 'ntk\\_scaling': ntk rope scaling factor.\n\t+ 'proportional\\_attn': Whether to use proportional attention.\n\t+ 'seed': random initialization seeds.\n\n\n1. Run with CLI\n\n\ninference command:\n\n\ne.g. Demo command:",
"### Web Demo\n\n\nTo host a local gradio demo for interactive inference, run the following command:"
] |
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-sigoIAspirantes-Orca-oass-500-gpu
This model is a fine-tuned version of [NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v2](https://huggingface.co/NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1371
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.6909 | 0.3521 | 25 | 1.2135 |
| 1.026 | 0.7042 | 50 | 0.8374 |
| 0.7146 | 1.0563 | 75 | 0.6276 |
| 0.5094 | 1.4085 | 100 | 0.4925 |
| 0.3916 | 1.7606 | 125 | 0.3939 |
| 0.3408 | 2.1127 | 150 | 0.3084 |
| 0.1724 | 2.4648 | 175 | 0.2717 |
| 0.2586 | 2.8169 | 200 | 0.2026 |
| 0.1434 | 3.1690 | 225 | 0.1940 |
| 0.1253 | 3.5211 | 250 | 0.1579 |
| 0.1197 | 3.8732 | 275 | 0.1526 |
| 0.0792 | 4.2254 | 300 | 0.1582 |
| 0.0937 | 4.5775 | 325 | 0.1579 |
| 0.0898 | 4.9296 | 350 | 0.1381 |
| 0.0717 | 5.2817 | 375 | 0.1386 |
| 0.0665 | 5.6338 | 400 | 0.1350 |
| 0.0773 | 5.9859 | 425 | 0.1325 |
| 0.0602 | 6.3380 | 450 | 0.1394 |
| 0.055 | 6.6901 | 475 | 0.1377 |
| 0.0602 | 7.0423 | 500 | 0.1371 |
### Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.41.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v2", "model-index": [{"name": "mistral-sigoIAspirantes-Orca-oass-500-gpu", "results": []}]} | fergos80/mistral-sigoIAspirantes-Orca-oass-500-gpu | null | [
"peft",
"safetensors",
"mistral",
"generated_from_trainer",
"base_model:NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v2",
"license:apache-2.0",
"4-bit",
"region:us"
] | null | 2024-04-28T02:27:56+00:00 | [] | [] | TAGS
#peft #safetensors #mistral #generated_from_trainer #base_model-NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v2 #license-apache-2.0 #4-bit #region-us
| mistral-sigoIAspirantes-Orca-oass-500-gpu
=========================================
This model is a fine-tuned version of NickyNicky/Mistral-7B-OpenOrca-oasst\_top1\_2023-08-25-v2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1371
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2.5e-05
* train\_batch\_size: 2
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1
* training\_steps: 500
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* PEFT 0.10.1.dev0
* Transformers 4.41.0.dev0
* Pytorch 2.3.0+cu121
* Datasets 2.19.0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.5e-05\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: linear\n* lr\\_scheduler\\_warmup\\_steps: 1\n* training\\_steps: 500\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.1.dev0\n* Transformers 4.41.0.dev0\n* Pytorch 2.3.0+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1"
] | [
"TAGS\n#peft #safetensors #mistral #generated_from_trainer #base_model-NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v2 #license-apache-2.0 #4-bit #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.5e-05\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: linear\n* lr\\_scheduler\\_warmup\\_steps: 1\n* training\\_steps: 500\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.1.dev0\n* Transformers 4.41.0.dev0\n* Pytorch 2.3.0+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1"
] |
text-generation | transformers |
# Llama-3-Ko-Instruct
## Methodology
https://huggingface.co/blog/maywell/llm-feature-transfer
Paper Soon
### Model Used
[meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
[meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
[beomi/Llama-3-Open-Ko-8B](https://huggingface.co/beomi/Llama-3-Open-Ko-8B)
## Benchmark
### Kobest
| Task | beomi/Llama-3-Open-Ko-8B-Instruct | maywell/Llama-3-Ko-8B-Instruct |
| --- | --- | --- |
| kobest overall | 0.6220 ± 0.0070 | 0.6852 ± 0.0066 |
| kobest_boolq| 0.6254 ± 0.0129| 0.7208 ± 0.0120
| kobest_copa| 0.7110 ± 0.0143| 0.7650 ± 0.0134
| kobest_hellaswag| 0.3840 ± 0.0218| 0.4440 ± 0.0222
| kobest_sentineg| 0.8388 ± 0.0185| 0.9194 ± 0.0137
| kobest_wic| 0.5738 ± 0.0139| 0.6040 ± 0.0138
# Original Model Card by Beomi
> Update @ 2024.04.24: Release Llama-3-Open-Ko-8B model & [Llama-3-Open-Ko-8B-Instruct-preview](https://huggingface.co/beomi/Llama-3-Open-Ko-8B-Instruct-preview)
## Model Details
**Llama-3-Open-Ko-8B**
Llama-3-Open-Ko-8B model is continued pretrained language model based on Llama-3-8B.
This model is trained fully with publicily available resource, with 60GB+ of deduplicated texts.
With the new Llama-3 tokenizer, the pretraining conducted with 17.7B+ tokens, which slightly more than Korean tokenizer(Llama-2-Ko tokenizer).
The train was done on TPUv5e-256, with the warm support from TRC program by Google.
**Note for [Llama-3-Open-Ko-8B-Instruct-preview](https://huggingface.co/beomi/Llama-3-Open-Ko-8B-Instruct-preview)**
With applying the idea from [Chat Vector paper](https://arxiv.org/abs/2310.04799), I released Instruction model named [Llama-3-Open-Ko-8B-Instruct-preview](https://huggingface.co/beomi/Llama-3-Open-Ko-8B-Instruct-preview).
Since it is NOT finetuned with any Korean instruction set(indeed `preview`), but it would be great starting point for creating new Chat/Instruct models.
**Meta Llama-3**
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Junbum Lee (Beomi)
**Variations** Llama-3-Open-Ko comes in one size — 8B.
**Input** Models input text only.
**Output** Models generate text and code only.
**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="2" >Llama-3-Open-Ko
</td>
<td rowspan="2" >Same as *Open-Solar-Ko Dataset
</td>
<td>8B
</td>
<td>8k
</td>
<td>Yes
</td>
<td rowspan="2" >17.7B+
</td>
<td>Jun, 2023
</td>
</tr>
</table>
*You can find dataset list here: https://huggingface.co/beomi/OPEN-SOLAR-KO-10.7B/tree/main/corpus
**Model Release Date** 2024.04.24.
**Status** This is a static model trained on an offline dataset.
**License** Llama3 License: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
## How to use
TBD
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. 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/).
## Ethical Considerations and Limitations
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide)
## Citation instructions
**Llama-3-Open-Ko**
```
@article{llama3openko,
title={Llama-3-Open-Ko},
author={L, Junbum},
year={2024},
url={https://huggingface.co/beomi/Llama-3-Open-Ko-8B}
}
```
**Original Llama-3**
```
@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", "ko"], "license": "other", "tags": ["facebook", "meta", "pytorch", "llama", "llama-3", "llama-3-ko"], "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE"} | maywell/Llama-3-Ko-8B-Instruct | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"llama-3-ko",
"conversational",
"en",
"ko",
"arxiv:2310.04799",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T02:30:58+00:00 | [
"2310.04799"
] | [
"en",
"ko"
] | TAGS
#transformers #safetensors #llama #text-generation #facebook #meta #pytorch #llama-3 #llama-3-ko #conversational #en #ko #arxiv-2310.04799 #license-other #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Llama-3-Ko-Instruct
===================
Methodology
-----------
URL
Paper Soon
### Model Used
meta-llama/Meta-Llama-3-8B-Instruct
meta-llama/Meta-Llama-3-8B
beomi/Llama-3-Open-Ko-8B
Benchmark
---------
### Kobest
Task: kobest overall, beomi/Llama-3-Open-Ko-8B-Instruct: 0.6220 ± 0.0070, maywell/Llama-3-Ko-8B-Instruct: 0.6852 ± 0.0066
Task: kobest\_boolq, beomi/Llama-3-Open-Ko-8B-Instruct: 0.6254 ± 0.0129, maywell/Llama-3-Ko-8B-Instruct: 0.7208 ± 0.0120
Task: kobest\_copa, beomi/Llama-3-Open-Ko-8B-Instruct: 0.7110 ± 0.0143, maywell/Llama-3-Ko-8B-Instruct: 0.7650 ± 0.0134
Task: kobest\_hellaswag, beomi/Llama-3-Open-Ko-8B-Instruct: 0.3840 ± 0.0218, maywell/Llama-3-Ko-8B-Instruct: 0.4440 ± 0.0222
Task: kobest\_sentineg, beomi/Llama-3-Open-Ko-8B-Instruct: 0.8388 ± 0.0185, maywell/Llama-3-Ko-8B-Instruct: 0.9194 ± 0.0137
Task: kobest\_wic, beomi/Llama-3-Open-Ko-8B-Instruct: 0.5738 ± 0.0139, maywell/Llama-3-Ko-8B-Instruct: 0.6040 ± 0.0138
Original Model Card by Beomi
============================
>
> Update @ 2024.04.24: Release Llama-3-Open-Ko-8B model & Llama-3-Open-Ko-8B-Instruct-preview
>
>
>
Model Details
-------------
Llama-3-Open-Ko-8B
Llama-3-Open-Ko-8B model is continued pretrained language model based on Llama-3-8B.
This model is trained fully with publicily available resource, with 60GB+ of deduplicated texts.
With the new Llama-3 tokenizer, the pretraining conducted with 17.7B+ tokens, which slightly more than Korean tokenizer(Llama-2-Ko tokenizer).
The train was done on TPUv5e-256, with the warm support from TRC program by Google.
Note for Llama-3-Open-Ko-8B-Instruct-preview
With applying the idea from Chat Vector paper, I released Instruction model named Llama-3-Open-Ko-8B-Instruct-preview.
Since it is NOT finetuned with any Korean instruction set(indeed 'preview'), but it would be great starting point for creating new Chat/Instruct models.
Meta Llama-3
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
Model developers Junbum Lee (Beomi)
Variations Llama-3-Open-Ko comes in one size — 8B.
Input Models input text only.
Output Models generate text and code only.
Model Architecture Llama 3 is an auto-regressive language model that uses an optimized transformer architecture.
\*You can find dataset list here: URL
Model Release Date 2024.04.24.
Status This is a static model trained on an offline dataset.
License Llama3 License: URL
Intended Use
------------
Intended Use Cases Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English.
Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
How to use
----------
TBD
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our Responsible Use Guide to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including Meta Llama Guard 2 and Code Shield safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a reference implementation to get you started.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at URL
Ethical Considerations and Limitations
--------------------------------------
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating Purple Llama solutions into your workflows and specifically Llama Guard which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at URL
instructions
Llama-3-Open-Ko
Original Llama-3
| [
"### Model Used\n\n\nmeta-llama/Meta-Llama-3-8B-Instruct\n\n\nmeta-llama/Meta-Llama-3-8B\n\n\nbeomi/Llama-3-Open-Ko-8B\n\n\nBenchmark\n---------",
"### Kobest\n\n\nTask: kobest overall, beomi/Llama-3-Open-Ko-8B-Instruct: 0.6220 ± 0.0070, maywell/Llama-3-Ko-8B-Instruct: 0.6852 ± 0.0066\nTask: kobest\\_boolq, beomi/Llama-3-Open-Ko-8B-Instruct: 0.6254 ± 0.0129, maywell/Llama-3-Ko-8B-Instruct: 0.7208 ± 0.0120\nTask: kobest\\_copa, beomi/Llama-3-Open-Ko-8B-Instruct: 0.7110 ± 0.0143, maywell/Llama-3-Ko-8B-Instruct: 0.7650 ± 0.0134\nTask: kobest\\_hellaswag, beomi/Llama-3-Open-Ko-8B-Instruct: 0.3840 ± 0.0218, maywell/Llama-3-Ko-8B-Instruct: 0.4440 ± 0.0222\nTask: kobest\\_sentineg, beomi/Llama-3-Open-Ko-8B-Instruct: 0.8388 ± 0.0185, maywell/Llama-3-Ko-8B-Instruct: 0.9194 ± 0.0137\nTask: kobest\\_wic, beomi/Llama-3-Open-Ko-8B-Instruct: 0.5738 ± 0.0139, maywell/Llama-3-Ko-8B-Instruct: 0.6040 ± 0.0138\n\n\nOriginal Model Card by Beomi\n============================\n\n\n\n> \n> Update @ 2024.04.24: Release Llama-3-Open-Ko-8B model & Llama-3-Open-Ko-8B-Instruct-preview\n> \n> \n> \n\n\nModel Details\n-------------\n\n\nLlama-3-Open-Ko-8B\n\n\nLlama-3-Open-Ko-8B model is continued pretrained language model based on Llama-3-8B.\n\n\nThis model is trained fully with publicily available resource, with 60GB+ of deduplicated texts.\n\n\nWith the new Llama-3 tokenizer, the pretraining conducted with 17.7B+ tokens, which slightly more than Korean tokenizer(Llama-2-Ko tokenizer).\n\n\nThe train was done on TPUv5e-256, with the warm support from TRC program by Google.\n\n\nNote for Llama-3-Open-Ko-8B-Instruct-preview\n\n\nWith applying the idea from Chat Vector paper, I released Instruction model named Llama-3-Open-Ko-8B-Instruct-preview.\n\n\nSince it is NOT finetuned with any Korean instruction set(indeed 'preview'), but it would be great starting point for creating new Chat/Instruct models.\n\n\nMeta Llama-3\n\n\nMeta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.\n\n\nModel developers Junbum Lee (Beomi)\n\n\nVariations Llama-3-Open-Ko comes in one size — 8B.\n\n\nInput Models input text only.\n\n\nOutput Models generate text and code only.\n\n\nModel Architecture Llama 3 is an auto-regressive language model that uses an optimized transformer architecture.\n\n\n\n\\*You can find dataset list here: URL\n\n\nModel Release Date 2024.04.24.\n\n\nStatus This is a static model trained on an offline dataset.\n\n\nLicense Llama3 License: URL\n\n\nIntended Use\n------------\n\n\nIntended Use Cases Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.\n\n\nOut-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English.\n\n\nNote: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.\n\n\nHow to use\n----------\n\n\nTBD",
"### Responsibility & Safety\n\n\nWe believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.\n\n\nFoundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.\n\n\nRather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.\n\n\nAs part of the Llama 3 release, we updated our Responsible Use Guide to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including Meta Llama Guard 2 and Code Shield safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a reference implementation to get you started.",
"#### Responsible release\n\n\nIn addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.\n\n\nMisuse\n\n\nIf you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at URL\n\n\nEthical Considerations and Limitations\n--------------------------------------\n\n\nThe core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.\n\n\nBut Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating Purple Llama solutions into your workflows and specifically Llama Guard which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.\n\n\nPlease see the Responsible Use Guide available at URL\n\n\ninstructions\n\n\nLlama-3-Open-Ko\n\n\nOriginal Llama-3"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #facebook #meta #pytorch #llama-3 #llama-3-ko #conversational #en #ko #arxiv-2310.04799 #license-other #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Model Used\n\n\nmeta-llama/Meta-Llama-3-8B-Instruct\n\n\nmeta-llama/Meta-Llama-3-8B\n\n\nbeomi/Llama-3-Open-Ko-8B\n\n\nBenchmark\n---------",
"### Kobest\n\n\nTask: kobest overall, beomi/Llama-3-Open-Ko-8B-Instruct: 0.6220 ± 0.0070, maywell/Llama-3-Ko-8B-Instruct: 0.6852 ± 0.0066\nTask: kobest\\_boolq, beomi/Llama-3-Open-Ko-8B-Instruct: 0.6254 ± 0.0129, maywell/Llama-3-Ko-8B-Instruct: 0.7208 ± 0.0120\nTask: kobest\\_copa, beomi/Llama-3-Open-Ko-8B-Instruct: 0.7110 ± 0.0143, maywell/Llama-3-Ko-8B-Instruct: 0.7650 ± 0.0134\nTask: kobest\\_hellaswag, beomi/Llama-3-Open-Ko-8B-Instruct: 0.3840 ± 0.0218, maywell/Llama-3-Ko-8B-Instruct: 0.4440 ± 0.0222\nTask: kobest\\_sentineg, beomi/Llama-3-Open-Ko-8B-Instruct: 0.8388 ± 0.0185, maywell/Llama-3-Ko-8B-Instruct: 0.9194 ± 0.0137\nTask: kobest\\_wic, beomi/Llama-3-Open-Ko-8B-Instruct: 0.5738 ± 0.0139, maywell/Llama-3-Ko-8B-Instruct: 0.6040 ± 0.0138\n\n\nOriginal Model Card by Beomi\n============================\n\n\n\n> \n> Update @ 2024.04.24: Release Llama-3-Open-Ko-8B model & Llama-3-Open-Ko-8B-Instruct-preview\n> \n> \n> \n\n\nModel Details\n-------------\n\n\nLlama-3-Open-Ko-8B\n\n\nLlama-3-Open-Ko-8B model is continued pretrained language model based on Llama-3-8B.\n\n\nThis model is trained fully with publicily available resource, with 60GB+ of deduplicated texts.\n\n\nWith the new Llama-3 tokenizer, the pretraining conducted with 17.7B+ tokens, which slightly more than Korean tokenizer(Llama-2-Ko tokenizer).\n\n\nThe train was done on TPUv5e-256, with the warm support from TRC program by Google.\n\n\nNote for Llama-3-Open-Ko-8B-Instruct-preview\n\n\nWith applying the idea from Chat Vector paper, I released Instruction model named Llama-3-Open-Ko-8B-Instruct-preview.\n\n\nSince it is NOT finetuned with any Korean instruction set(indeed 'preview'), but it would be great starting point for creating new Chat/Instruct models.\n\n\nMeta Llama-3\n\n\nMeta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.\n\n\nModel developers Junbum Lee (Beomi)\n\n\nVariations Llama-3-Open-Ko comes in one size — 8B.\n\n\nInput Models input text only.\n\n\nOutput Models generate text and code only.\n\n\nModel Architecture Llama 3 is an auto-regressive language model that uses an optimized transformer architecture.\n\n\n\n\\*You can find dataset list here: URL\n\n\nModel Release Date 2024.04.24.\n\n\nStatus This is a static model trained on an offline dataset.\n\n\nLicense Llama3 License: URL\n\n\nIntended Use\n------------\n\n\nIntended Use Cases Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.\n\n\nOut-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English.\n\n\nNote: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.\n\n\nHow to use\n----------\n\n\nTBD",
"### Responsibility & Safety\n\n\nWe believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.\n\n\nFoundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.\n\n\nRather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.\n\n\nAs part of the Llama 3 release, we updated our Responsible Use Guide to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including Meta Llama Guard 2 and Code Shield safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a reference implementation to get you started.",
"#### Responsible release\n\n\nIn addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.\n\n\nMisuse\n\n\nIf you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at URL\n\n\nEthical Considerations and Limitations\n--------------------------------------\n\n\nThe core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.\n\n\nBut Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating Purple Llama solutions into your workflows and specifically Llama Guard which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.\n\n\nPlease see the Responsible Use Guide available at URL\n\n\ninstructions\n\n\nLlama-3-Open-Ko\n\n\nOriginal Llama-3"
] |
null | transformers |
# DavidAU/D_AU-Orac-13B-Tiefighter-slerp-Q6_K-GGUF
This model was converted to GGUF format from [`DavidAU/D_AU-Orac-13B-Tiefighter-slerp`](https://huggingface.co/DavidAU/D_AU-Orac-13B-Tiefighter-slerp) 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/DavidAU/D_AU-Orac-13B-Tiefighter-slerp) 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 DavidAU/D_AU-Orac-13B-Tiefighter-slerp-Q6_K-GGUF --model d_au-orac-13b-tiefighter-slerp.Q6_K.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/D_AU-Orac-13B-Tiefighter-slerp-Q6_K-GGUF --model d_au-orac-13b-tiefighter-slerp.Q6_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 d_au-orac-13b-tiefighter-slerp.Q6_K.gguf -n 128
```
| {"library_name": "transformers", "tags": ["mergekit", "merge", "llama-cpp", "gguf-my-repo"], "base_model": ["microsoft/Orca-2-13b", "KoboldAI/LLaMA2-13B-Tiefighter"]} | DavidAU/D_AU-Orac-13B-Tiefighter-slerp-Q6_K-GGUF | null | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:microsoft/Orca-2-13b",
"base_model:KoboldAI/LLaMA2-13B-Tiefighter",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T02:31:25+00:00 | [] | [] | TAGS
#transformers #gguf #mergekit #merge #llama-cpp #gguf-my-repo #base_model-microsoft/Orca-2-13b #base_model-KoboldAI/LLaMA2-13B-Tiefighter #endpoints_compatible #region-us
|
# DavidAU/D_AU-Orac-13B-Tiefighter-slerp-Q6_K-GGUF
This model was converted to GGUF format from 'DavidAU/D_AU-Orac-13B-Tiefighter-slerp' 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.
| [
"# DavidAU/D_AU-Orac-13B-Tiefighter-slerp-Q6_K-GGUF\nThis model was converted to GGUF format from 'DavidAU/D_AU-Orac-13B-Tiefighter-slerp' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] | [
"TAGS\n#transformers #gguf #mergekit #merge #llama-cpp #gguf-my-repo #base_model-microsoft/Orca-2-13b #base_model-KoboldAI/LLaMA2-13B-Tiefighter #endpoints_compatible #region-us \n",
"# DavidAU/D_AU-Orac-13B-Tiefighter-slerp-Q6_K-GGUF\nThis model was converted to GGUF format from 'DavidAU/D_AU-Orac-13B-Tiefighter-slerp' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] |
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": []} | AmirlyPhd/v2_bert-text-classification-model | null | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T02:32:18+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:
- 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 #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.",
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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]
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### 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
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[More Information Needed]
## Training Details
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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. -->
### 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
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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]
- **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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## 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. -->
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[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/6gi0xkw | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T02:32:45+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
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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
### 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": []} | golf2248/2de9rij | null | [
"transformers",
"safetensors",
"stablelm",
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"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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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:**
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## Glossary [optional]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/xqin00o | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
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"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
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"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]",
"## 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 #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.",
"## 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 | ## モデル
- ベースモデル：[llm-jp/llm-jp-1.3b-v1.0](https://huggingface.co/llm-jp/llm-jp-1.3b-v1.0)
- 学習データセット：[llm-jp/databricks-dolly-15k-ja](https://huggingface.co/datasets/llm-jp/databricks-dolly-15k-ja)
- 学習方式：フルパラメータチューニング
## サンプル
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained(
"ryota39/llm-jp-1b-sft-15k"
)
pad_token_id = tokenizer.pad_token_id
model = AutoModelForCausalLM.from_pretrained(
"ryota39/llm-jp-1b-sft-15k",
device_map="auto",
)
text = "###Input: 東京の観光名所を教えてください。\n###Output: "
tokenized_input = tokenizer.encode(
text,
add_special_tokens=False,
return_tensors="pt"
).to(model.device)
attention_mask = torch.ones_like(tokenized_input)
attention_mask[tokenized_input == pad_token_id] = 0
with torch.no_grad():
output = model.generate(
tokenized_input,
attention_mask=attention_mask,
max_new_tokens=128,
do_sample=True,
top_p=0.95,
temperature=0.8,
repetition_penalty=1.0
)[0]
print(tokenizer.decode(output))
```
## 出力例
```
###Input: 東京の観光名所を教えてください。
###Output: 東京には多くの観光名所がある：
1.皇居
2.江戸東京博物館
3.東京タワー
4.東京スカイツリー
5.芝公園
6.東京タワー、増上寺、増上寺宝物館
7.浜離宮恩賜庭園
8.東京都庁
9.増上寺
10.新宿御苑
11.浅草寺
12.上野公園
13.お台場
14.明治神宮
15.上野動物園
16.東京国立博物館
17.浅草寺、浅草寺仲見
```
## 謝辞
本成果は【LOCAL AI HACKATHON #001】240時間ハッカソンの成果です。
運営の方々に深く御礼申し上げます。
- 【メタデータラボ株式会社】様
- 【AI声づくり技術研究会】
- サーバー主：やなぎ(Yanagi)様
- 【ローカルLLMに向き合う会】
- サーバー主：saldra(サルドラ)様
[メタデータラボ、日本最大規模のAIハッカソン「LOCAL AI HACKATHON #001」~ AIの民主化 ~を開催、本日より出場チームの募集を開始](https://prtimes.jp/main/html/rd/p/000000008.000056944.html)
| {"language": ["ja"], "license": "apache-2.0", "library_name": "transformers", "datasets": ["llm-jp/databricks-dolly-15k-ja"]} | ryota39/llm-jp-1b-sft-15k | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"ja",
"dataset:llm-jp/databricks-dolly-15k-ja",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T02:33:39+00:00 | [] | [
"ja"
] | TAGS
#transformers #safetensors #gpt2 #text-generation #ja #dataset-llm-jp/databricks-dolly-15k-ja #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## モデル
- ベースモデル：llm-jp/llm-jp-1.3b-v1.0
- 学習データセット：llm-jp/databricks-dolly-15k-ja
- 学習方式：フルパラメータチューニング
## サンプル
## 出力例
## 謝辞
本成果は【LOCAL AI HACKATHON #001】240時間ハッカソンの成果です。
運営の方々に深く御礼申し上げます。
- 【メタデータラボ株式会社】様
- 【AI声づくり技術研究会】
- サーバー主：やなぎ(Yanagi)様
- 【ローカルLLMに向き合う会】
- サーバー主：saldra(サルドラ)様
メタデータラボ、日本最大規模のAIハッカソン「LOCAL AI HACKATHON #001」~ AIの民主化 ~を開催、本日より出場チームの募集を開始
| [
"## モデル\n\n- ベースモデル：llm-jp/llm-jp-1.3b-v1.0\n- 学習データセット：llm-jp/databricks-dolly-15k-ja\n- 学習方式：フルパラメータチューニング",
"## サンプル",
"## 出力例",
"## 謝辞\n\n本成果は【LOCAL AI HACKATHON #001】240時間ハッカソンの成果です。\n運営の方々に深く御礼申し上げます。\n\n- 【メタデータラボ株式会社】様\n- 【AI声づくり技術研究会】\n - サーバー主：やなぎ(Yanagi)様\n- 【ローカルLLMに向き合う会】\n - サーバー主：saldra(サルドラ)様\n\nメタデータラボ、日本最大規模のAIハッカソン「LOCAL AI HACKATHON #001」~ AIの民主化 ~を開催、本日より出場チームの募集を開始"
] | [
"TAGS\n#transformers #safetensors #gpt2 #text-generation #ja #dataset-llm-jp/databricks-dolly-15k-ja #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## モデル\n\n- ベースモデル：llm-jp/llm-jp-1.3b-v1.0\n- 学習データセット：llm-jp/databricks-dolly-15k-ja\n- 学習方式：フルパラメータチューニング",
"## サンプル",
"## 出力例",
"## 謝辞\n\n本成果は【LOCAL AI HACKATHON #001】240時間ハッカソンの成果です。\n運営の方々に深く御礼申し上げます。\n\n- 【メタデータラボ株式会社】様\n- 【AI声づくり技術研究会】\n - サーバー主：やなぎ(Yanagi)様\n- 【ローカルLLMに向き合う会】\n - サーバー主：saldra(サルドラ)様\n\nメタデータラボ、日本最大規模のAIハッカソン「LOCAL AI HACKATHON #001」~ AIの民主化 ~を開催、本日より出場チームの募集を開始"
] |
null | transformers |
# DavidAU/D_AU-Orac-13B-Tiefighter-slerp-Q5_K_M-GGUF
This model was converted to GGUF format from [`DavidAU/D_AU-Orac-13B-Tiefighter-slerp`](https://huggingface.co/DavidAU/D_AU-Orac-13B-Tiefighter-slerp) 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/DavidAU/D_AU-Orac-13B-Tiefighter-slerp) 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 DavidAU/D_AU-Orac-13B-Tiefighter-slerp-Q5_K_M-GGUF --model d_au-orac-13b-tiefighter-slerp.Q5_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/D_AU-Orac-13B-Tiefighter-slerp-Q5_K_M-GGUF --model d_au-orac-13b-tiefighter-slerp.Q5_K_M.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 d_au-orac-13b-tiefighter-slerp.Q5_K_M.gguf -n 128
```
| {"library_name": "transformers", "tags": ["mergekit", "merge", "llama-cpp", "gguf-my-repo"], "base_model": ["microsoft/Orca-2-13b", "KoboldAI/LLaMA2-13B-Tiefighter"]} | DavidAU/D_AU-Orac-13B-Tiefighter-slerp-Q5_K_M-GGUF | null | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:microsoft/Orca-2-13b",
"base_model:KoboldAI/LLaMA2-13B-Tiefighter",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T02:34:32+00:00 | [] | [] | TAGS
#transformers #gguf #mergekit #merge #llama-cpp #gguf-my-repo #base_model-microsoft/Orca-2-13b #base_model-KoboldAI/LLaMA2-13B-Tiefighter #endpoints_compatible #region-us
|
# DavidAU/D_AU-Orac-13B-Tiefighter-slerp-Q5_K_M-GGUF
This model was converted to GGUF format from 'DavidAU/D_AU-Orac-13B-Tiefighter-slerp' 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.
| [
"# DavidAU/D_AU-Orac-13B-Tiefighter-slerp-Q5_K_M-GGUF\nThis model was converted to GGUF format from 'DavidAU/D_AU-Orac-13B-Tiefighter-slerp' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] | [
"TAGS\n#transformers #gguf #mergekit #merge #llama-cpp #gguf-my-repo #base_model-microsoft/Orca-2-13b #base_model-KoboldAI/LLaMA2-13B-Tiefighter #endpoints_compatible #region-us \n",
"# DavidAU/D_AU-Orac-13B-Tiefighter-slerp-Q5_K_M-GGUF\nThis model was converted to GGUF format from 'DavidAU/D_AU-Orac-13B-Tiefighter-slerp' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] |
text-generation | transformers |
## モデル
- ベースモデル：[ryota39/llm-jp-1b-sft-100k-LoRA](https://huggingface.co/ryota39/llm-jp-1b-sft-100k-LoRA)
- 学習データセット：[llm-jp/hh-rlhf-12k-ja](https://huggingface.co/datasets/llm-jp/hh-rlhf-12k-ja)
- 学習方式：フルパラメータチューニング
## サンプル
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained(
"ryota39/llm-jp-1b-sft-100k-LoRA-dpo-12k"
)
pad_token_id = tokenizer.pad_token_id
model = AutoModelForCausalLM.from_pretrained(
"ryota39/llm-jp-1b-sft-100k-LoRA-dpo-12k",
device_map="auto",
)
text = "###Input: 東京の観光名所を教えてください。\n###Output: "
tokenized_input = tokenizer.encode(
text,
add_special_tokens=False,
return_tensors="pt"
).to(model.device)
attention_mask = torch.ones_like(tokenized_input)
attention_mask[tokenized_input == pad_token_id] = 0
with torch.no_grad():
output = model.generate(
tokenized_input,
attention_mask=attention_mask,
max_new_tokens=128,
do_sample=True,
top_p=0.95,
temperature=0.8,
repetition_penalty=1.10
)[0]
print(tokenizer.decode(output))
```
## 出力例
```
###Input: 東京の観光名所を教えてください。
###Output: 20枚の観光スポット写真がランダムに出される。写真はどこでもよい。
10枚以上がベストだが、10枚以下でも可。1枚につき「観光地」と「街歩き」の2種類の選択肢があるが、この時には「観光地」しか選ばないこと。
写真は5秒以内に撮らせること。1人ずつ順番に写真を撮る。最後に写真から観光名所1枚を選び、その写真に対して###Output: 大阪の観光名所を教えてください。
###Output: 30
```
## 謝辞
本成果は【LOCAL AI HACKATHON #001】240時間ハッカソンの成果です。
運営の方々に深く御礼申し上げます。
- 【メタデータラボ株式会社】様
- 【AI声づくり技術研究会】
- サーバー主：やなぎ(Yanagi)様
- 【ローカルLLMに向き合う会】
- サーバー主：saldra(サルドラ)様
[メタデータラボ、日本最大規模のAIハッカソン「LOCAL AI HACKATHON #001」~ AIの民主化 ~を開催、本日より出場チームの募集を開始](https://prtimes.jp/main/html/rd/p/000000008.000056944.html) | {"language": ["ja"], "license": "cc", "library_name": "transformers", "tags": ["dpo"], "datasets": ["llm-jp/hh-rlhf-12k-ja"]} | ryota39/llm-jp-1b-sft-100k-LoRA-dpo-12k | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"dpo",
"ja",
"dataset:llm-jp/hh-rlhf-12k-ja",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T02:39:33+00:00 | [] | [
"ja"
] | TAGS
#transformers #safetensors #gpt2 #text-generation #dpo #ja #dataset-llm-jp/hh-rlhf-12k-ja #license-cc #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## モデル
- ベースモデル：ryota39/llm-jp-1b-sft-100k-LoRA
- 学習データセット：llm-jp/hh-rlhf-12k-ja
- 学習方式：フルパラメータチューニング
## サンプル
## 出力例
## 謝辞
本成果は【LOCAL AI HACKATHON #001】240時間ハッカソンの成果です。
運営の方々に深く御礼申し上げます。
- 【メタデータラボ株式会社】様
- 【AI声づくり技術研究会】
- サーバー主：やなぎ(Yanagi)様
- 【ローカルLLMに向き合う会】
- サーバー主：saldra(サルドラ)様
メタデータラボ、日本最大規模のAIハッカソン「LOCAL AI HACKATHON #001」~ AIの民主化 ~を開催、本日より出場チームの募集を開始 | [
"## モデル\n\n- ベースモデル：ryota39/llm-jp-1b-sft-100k-LoRA\n- 学習データセット：llm-jp/hh-rlhf-12k-ja\n- 学習方式：フルパラメータチューニング",
"## サンプル",
"## 出力例",
"## 謝辞\n\n本成果は【LOCAL AI HACKATHON #001】240時間ハッカソンの成果です。\n運営の方々に深く御礼申し上げます。\n\n- 【メタデータラボ株式会社】様\n- 【AI声づくり技術研究会】\n - サーバー主：やなぎ(Yanagi)様\n- 【ローカルLLMに向き合う会】\n - サーバー主：saldra(サルドラ)様\n\nメタデータラボ、日本最大規模のAIハッカソン「LOCAL AI HACKATHON #001」~ AIの民主化 ~を開催、本日より出場チームの募集を開始"
] | [
"TAGS\n#transformers #safetensors #gpt2 #text-generation #dpo #ja #dataset-llm-jp/hh-rlhf-12k-ja #license-cc #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## モデル\n\n- ベースモデル：ryota39/llm-jp-1b-sft-100k-LoRA\n- 学習データセット：llm-jp/hh-rlhf-12k-ja\n- 学習方式：フルパラメータチューニング",
"## サンプル",
"## 出力例",
"## 謝辞\n\n本成果は【LOCAL AI HACKATHON #001】240時間ハッカソンの成果です。\n運営の方々に深く御礼申し上げます。\n\n- 【メタデータラボ株式会社】様\n- 【AI声づくり技術研究会】\n - サーバー主：やなぎ(Yanagi)様\n- 【ローカルLLMに向き合う会】\n - サーバー主：saldra(サルドラ)様\n\nメタデータラボ、日本最大規模のAIハッカソン「LOCAL AI HACKATHON #001」~ AIの民主化 ~を開催、本日より出場チームの募集を開始"
] |
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-7b-Finetuned-Extented-MBPP-Dataset-Synthetic
This model is a fine-tuned version of [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2069
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9825 | 0.24 | 500 | 1.5994 |
| 0.8528 | 0.47 | 1000 | 1.2043 |
| 0.4851 | 0.71 | 1500 | 1.1762 |
| 0.5511 | 0.94 | 2000 | 1.1914 |
| 0.4916 | 1.18 | 2500 | 1.2069 |
### Framework versions
- PEFT 0.10.1.dev0
- 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": ["trl", "sft", "generated_from_trainer"], "base_model": "tiiuae/falcon-7b-instruct", "model-index": [{"name": "Falcon-7b-Finetuned-Extented-MBPP-Dataset-Synthetic", "results": []}]} | MUsama100/Falcon-7b-Finetuned-Extented-MBPP-Dataset-Synthetic | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:tiiuae/falcon-7b-instruct",
"license:apache-2.0",
"region:us"
] | null | 2024-04-28T02:47:50+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-tiiuae/falcon-7b-instruct #license-apache-2.0 #region-us
| Falcon-7b-Finetuned-Extented-MBPP-Dataset-Synthetic
===================================================
This model is a fine-tuned version of tiiuae/falcon-7b-instruct on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2069
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-06
* train\_batch\_size: 1
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 4
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* PEFT 0.10.1.dev0
* 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: 1e-06\n* train\\_batch\\_size: 1\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: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.1.dev0\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 #trl #sft #generated_from_trainer #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: 1e-06\n* train\\_batch\\_size: 1\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: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.1.dev0\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="HoldenT/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | HoldenT/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2024-04-28T02:47:54+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] |
object-detection | transformers | # Model Card for Model ID
This finetuned YOLOv5 model is developed to aid businesses in automating the inspection of returned goods. It utilizes advanced computer vision techniques to detect, classify, and assess the condition of items from images, determining whether returns are genuine or potentially fraudulent. The model is tailored to recognize various product conditions and features that align with common return reasons, enabling quick and efficient processing within return workflows.
## Model Details
### Model Description
The finetuned YOLOv5 model is designed specifically for use in retail and ecommerce environments to assist with the assessment of returned merchandise. It uses deep learning algorithms to analyze images of returned items, identifying specific product features, damages, or discrepancies that may indicate misuse or fraud. This model has been trained on a diverse dataset of product images, capturing a wide range of conditions, from new to heavily used items.
The model's capabilities include detecting subtle signs of wear and tear, modifications, or missing components that are often overlooked in manual inspections. By automating the inspection process, the model helps streamline return operations, reduce human error, and prevent fraudulent returns, thereby protecting revenue and improving customer service efficiency.
This YOLOv5 model variant has been optimized to perform well under various lighting conditions and camera angles, making it robust and reliable for deployment in varied operational settings where returns are processed. It integrates seamlessly with existing computer vision pipelines and can be further connected to APIs like OpenAI's GPT for enhanced decision-making about the item's return eligibility based on visual assessment.
- **Developed by:** Cody Liu, Arjun Dabir
- **Model type:** YOLOv5 (You Only Look Once version 5), Fine-tuned Object Detection Model
- **Language(s) (NLP):** Python
- **License:** Apache License 2.0
- **Finetuned from model:** YOLOv5
## Uses
### Direct Use
This finetuned YOLOv5 model is designed to detect and classify objects in images for return verification processes. It's intended for businesses to automate the inspection of returned goods, determining their condition and authenticity. The primary users are retail companies and online marketplaces aiming to streamline return operations and reduce fraudulent activities.
### Out-of-Scope Use
The model is not intended for applications beyond visual inspection tasks, such as medical image analysis, autonomous driving, or any environment where its object detection capabilities may not apply directly. It should not be used as a standalone decision-maker without human oversight due to the potential for misclassification. Misuse includes any application involving sensitive personal data or scenarios where a misclassification could lead to safety risks.
## Bias, Risks, and Limitations
This model, a fine-tuned version of YOLOv5 for object detection, is integrated with a GPT-based API to assess the condition of returned items. While this setup aims to automate the evaluation of returned goods, several biases, risks, and limitations are inherent in the technology:
Bias in Training Data: The object detection model's performance is contingent on the diversity and representativeness of its training dataset. If the training data lacks variety in terms of item conditions, environments, or object types, the model may exhibit biased or underperformative behavior against unrepresented categories.
Risk of Hallucination in LLM: The use of a language model (GPT) for interpreting object detection results introduces a risk of "hallucinations" or generating incorrect or misleading information based on the detected items. These inaccuracies can lead to incorrect assessments of item conditions, potentially categorizing non-fraudulent returns as fraudulent.
Limitations in Detection Capabilities: While YOLOv5 is robust in detecting objects within diverse and complex scenes, its accuracy can be compromised under conditions of poor lighting, occlusion, or unusual item orientations. These factors can lead to false negatives or false positives in identifying items and their conditions.
Sociotechnical Implications: Relying on automated systems for assessing returns could have implications for consumer trust and satisfaction. Incorrect assessments due to model limitations or errors can lead to customer dissatisfaction and potential loss of business, particularly if customers feel their returns are unjustly categorized.
Out-of-Scope Use: The model is not designed for and should not be used in scenarios involving sensitive or regulated items, such as pharmaceuticals, where specialized detection and assessment systems are required. Misuse in such contexts could lead to serious safety and compliance issues.
Acknowledging these limitations is crucial for deploying the model in a manner that minimizes risks and ensures fairness and accuracy in its applications. Further, continuous monitoring and updating of both the object detection and language processing components are recommended to address emergent biases or inaccuracies.
### Recommendations
Given the identified biases, risks, and limitations associated with the combined use of the YOLOv5 object detection model and the GPT language model in the returns assessment pipeline, the following recommendations are proposed to mitigate potential issues and enhance overall system effectiveness:
Enhance Dataset Diversity: Regularly update and expand the training datasets for the YOLOv5 model to include a wider range of items, conditions, and environmental factors. This will help reduce bias and improve the model's accuracy across diverse real-world scenarios.
Improve Error Handling: Develop robust error-handling and verification protocols to address and mitigate the risks of hallucinations from the GPT model. This could include cross-verifications with additional data sources or manual reviews in cases of uncertainty or high-risk assessments.
Conduct Regular Model Audits: Perform periodic audits of both the YOLOv5 and GPT models to assess and improve their performance and fairness. This includes testing the models against new and varied datasets to identify any potential drifts or biases in model behavior.
Increase Transparency: Provide clear documentation and transparency regarding the model's capabilities, limitations, and the basis of its decisions. This could involve detailed logs of decision pathways and the factors influencing model assessments, accessible to both customers and regulatory bodies.
User Education: Educate users and stakeholders about the capabilities, general workings, and limitations of the AI system. This helps set realistic expectations and promotes more informed and cautious use of the technology.
Develop Contingency Plans: Establish contingency plans including manual oversight and customer service interventions to handle disputes or failures in the automated system effectively. This will help maintain customer trust and mitigate negative impacts from potential model failures.
Ethical and Compliance Checks: Ensure that the deployment and ongoing use of the model comply with relevant laws and ethical guidelines, particularly those concerning consumer rights and data protection.
Implementing these recommendations will help in responsibly leveraging AI capabilities to enhance business processes while maintaining trust and compliance.
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.
```python
import torch
import intel_extension_for_pytorch as ipex
from models.common import DetectMultiBackend
from utils.general import non_max_suppression, scale_boxes
from utils.torch_utils import select_device
from utils.dataloaders import LoadImages
from pathlib import Path
def run_inference(weights, source, imgsz=(640, 640), conf_thres=0.25, iou_thres=0.45):
# Initialize device and model
device = select_device('')
model = DetectMultiBackend(weights, device=device, dnn=False)
model = ipex.optimize(model, dtype=torch.float32) # Optimize model
# Load image
dataset = LoadImages(source, img_size=imgsz, stride=model.stride, auto=model.pt)
path, img, im0s, _ = next(iter(dataset))
# Inference
img = torch.from_numpy(img).to(device)
img = img.float() # uint8 to fp32
img /= 255 # 0 - 255 to 0.0 - 1.0
if len(img.shape) == 3:
img = img[None] # expand for batch dim
with torch.cpu.amp.autocast(): # Enable mixed precision
pred = model(img, augment=False, visualize=False)
# Apply non-max suppression
pred = non_max_suppression(pred, conf_thres, iou_thres)
# Scale boxes to original image size and display or save
for i, det in enumerate(pred): # detections per image
if len(det):
det[:, :4] = scale_boxes(img.shape[2:], det[:, :4], im0s.shape).round()
return det # Return detections
if __name__ == '__main__':
weights_path = 'path/to/yolov5s.pt'
image_path = 'path/to/image.jpg'
detections = run_inference(weights_path, image_path)
print(f'Detections: {detections}')
```
### Key Modifications:
1. **Intel IPEX Optimization:** The model is wrapped with `ipex.optimize()` right after its instantiation to apply Intel-specific optimizations. You can specify the data type (`torch.float32` or `torch.bfloat16`) based on your preference for precision and performance.
2. **Mixed Precision:** Utilizes `torch.cpu.amp.autocast()` for mixed precision during inference, which can provide a boost in performance with minimal impact on accuracy when running on CPUs that support vector neural network instructions (VNNI).
## Training Details
### Training Data
https://huggingface.co/datasets/imagenet-1k
The ImageNet-1K dataset, available on Hugging Face, provides access to a subset of the larger ImageNet database, specifically the ILSVRC 2012 configuration. It includes 1,281,167 training images, 50,000 validation images, and 100,000 test images across 1,000 different object classes. This dataset is a fundamental resource for training deep learning models in various computer vision tasks due to its extensive range of high-quality, human-annotated images.
### Training Procedure
The YOLOv5 model was fine-tuned using the Intel® Extension for PyTorch*, which significantly optimized its performance on Intel architectures. This extension allows for more efficient computation and resource utilization, especially by enhancing the utilization of CPU capabilities, which are often less emphasized in typical GPU-centric training processes.
Technical Integration:
Intel® Extension for PyTorch: This extension optimizes PyTorch operations on Intel CPUs, leveraging Intel's oneDNN primitives to improve both training and inference speeds.
Intel® Deep Learning Boost (VNNI): This was employed to accelerate integer operations, common in convolutional networks like YOLOv5, enhancing model throughput during training.
BFloat16 Training: The use of BFloat16 data types supported by Intel CPUs allowed the model to train with larger batch sizes and faster epoch times with minimal impact on precision.
Parallel Training: The model used Intel's oneAPI Collective Communications Library (oneCCL) for efficient distributed training across Intel CPUs, enhancing scalability and reducing training times.
Performance Improvements:
The optimizations led to a noticeable increase in training speed and efficiency compared to traditional training setups on similar hardware.
Energy efficiency was also prioritized, with adjustments during training phases resulting in reduced power consumption.
Tools and Libraries:
Intel VTune™ Profiler: This tool was utilized to analyze the model's performance during training, helping to identify computational bottlenecks and optimize processing.
Intel® Advisor: This tool provided recommendations for vectorization and threading improvements, crucial for maximizing the multi-core capabilities of Intel CPUs.
These enhancements facilitated by Intel’s tools not only shortened the training cycle but also improved the overall efficiency of the YOLOv5 model, making it highly suitable for integration into computer vision pipelines that assess product returns.
#### Training Hyperparameters
- **Training regime:** bf16 mixed precision
### Results
[More Information Needed]
#### Summary
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:** Small VM - Intel® Xeon 4th Gen ® Scalable processor
- **Cloud Provider:** Intel® Developer Cloud
- **Compute Region:** us-region-1
## Citation
https://zenodo.org/records/7347926 | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["computer-vision", "object-detection", "fraud-detection", "yolov5"], "datasets": ["imagenet-1k"], "metrics": ["accuracy", "precision", "recall"]} | CodyLiu/checkThat_YOLOv5 | null | [
"transformers",
"computer-vision",
"object-detection",
"fraud-detection",
"yolov5",
"en",
"dataset:imagenet-1k",
"arxiv:1910.09700",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T02:48:44+00:00 | [
"1910.09700"
] | [
"en"
] | TAGS
#transformers #computer-vision #object-detection #fraud-detection #yolov5 #en #dataset-imagenet-1k #arxiv-1910.09700 #license-apache-2.0 #endpoints_compatible #region-us
| # Model Card for Model ID
This finetuned YOLOv5 model is developed to aid businesses in automating the inspection of returned goods. It utilizes advanced computer vision techniques to detect, classify, and assess the condition of items from images, determining whether returns are genuine or potentially fraudulent. The model is tailored to recognize various product conditions and features that align with common return reasons, enabling quick and efficient processing within return workflows.
## Model Details
### Model Description
The finetuned YOLOv5 model is designed specifically for use in retail and ecommerce environments to assist with the assessment of returned merchandise. It uses deep learning algorithms to analyze images of returned items, identifying specific product features, damages, or discrepancies that may indicate misuse or fraud. This model has been trained on a diverse dataset of product images, capturing a wide range of conditions, from new to heavily used items.
The model's capabilities include detecting subtle signs of wear and tear, modifications, or missing components that are often overlooked in manual inspections. By automating the inspection process, the model helps streamline return operations, reduce human error, and prevent fraudulent returns, thereby protecting revenue and improving customer service efficiency.
This YOLOv5 model variant has been optimized to perform well under various lighting conditions and camera angles, making it robust and reliable for deployment in varied operational settings where returns are processed. It integrates seamlessly with existing computer vision pipelines and can be further connected to APIs like OpenAI's GPT for enhanced decision-making about the item's return eligibility based on visual assessment.
- Developed by: Cody Liu, Arjun Dabir
- Model type: YOLOv5 (You Only Look Once version 5), Fine-tuned Object Detection Model
- Language(s) (NLP): Python
- License: Apache License 2.0
- Finetuned from model: YOLOv5
## Uses
### Direct Use
This finetuned YOLOv5 model is designed to detect and classify objects in images for return verification processes. It's intended for businesses to automate the inspection of returned goods, determining their condition and authenticity. The primary users are retail companies and online marketplaces aiming to streamline return operations and reduce fraudulent activities.
### Out-of-Scope Use
The model is not intended for applications beyond visual inspection tasks, such as medical image analysis, autonomous driving, or any environment where its object detection capabilities may not apply directly. It should not be used as a standalone decision-maker without human oversight due to the potential for misclassification. Misuse includes any application involving sensitive personal data or scenarios where a misclassification could lead to safety risks.
## Bias, Risks, and Limitations
This model, a fine-tuned version of YOLOv5 for object detection, is integrated with a GPT-based API to assess the condition of returned items. While this setup aims to automate the evaluation of returned goods, several biases, risks, and limitations are inherent in the technology:
Bias in Training Data: The object detection model's performance is contingent on the diversity and representativeness of its training dataset. If the training data lacks variety in terms of item conditions, environments, or object types, the model may exhibit biased or underperformative behavior against unrepresented categories.
Risk of Hallucination in LLM: The use of a language model (GPT) for interpreting object detection results introduces a risk of "hallucinations" or generating incorrect or misleading information based on the detected items. These inaccuracies can lead to incorrect assessments of item conditions, potentially categorizing non-fraudulent returns as fraudulent.
Limitations in Detection Capabilities: While YOLOv5 is robust in detecting objects within diverse and complex scenes, its accuracy can be compromised under conditions of poor lighting, occlusion, or unusual item orientations. These factors can lead to false negatives or false positives in identifying items and their conditions.
Sociotechnical Implications: Relying on automated systems for assessing returns could have implications for consumer trust and satisfaction. Incorrect assessments due to model limitations or errors can lead to customer dissatisfaction and potential loss of business, particularly if customers feel their returns are unjustly categorized.
Out-of-Scope Use: The model is not designed for and should not be used in scenarios involving sensitive or regulated items, such as pharmaceuticals, where specialized detection and assessment systems are required. Misuse in such contexts could lead to serious safety and compliance issues.
Acknowledging these limitations is crucial for deploying the model in a manner that minimizes risks and ensures fairness and accuracy in its applications. Further, continuous monitoring and updating of both the object detection and language processing components are recommended to address emergent biases or inaccuracies.
### Recommendations
Given the identified biases, risks, and limitations associated with the combined use of the YOLOv5 object detection model and the GPT language model in the returns assessment pipeline, the following recommendations are proposed to mitigate potential issues and enhance overall system effectiveness:
Enhance Dataset Diversity: Regularly update and expand the training datasets for the YOLOv5 model to include a wider range of items, conditions, and environmental factors. This will help reduce bias and improve the model's accuracy across diverse real-world scenarios.
Improve Error Handling: Develop robust error-handling and verification protocols to address and mitigate the risks of hallucinations from the GPT model. This could include cross-verifications with additional data sources or manual reviews in cases of uncertainty or high-risk assessments.
Conduct Regular Model Audits: Perform periodic audits of both the YOLOv5 and GPT models to assess and improve their performance and fairness. This includes testing the models against new and varied datasets to identify any potential drifts or biases in model behavior.
Increase Transparency: Provide clear documentation and transparency regarding the model's capabilities, limitations, and the basis of its decisions. This could involve detailed logs of decision pathways and the factors influencing model assessments, accessible to both customers and regulatory bodies.
User Education: Educate users and stakeholders about the capabilities, general workings, and limitations of the AI system. This helps set realistic expectations and promotes more informed and cautious use of the technology.
Develop Contingency Plans: Establish contingency plans including manual oversight and customer service interventions to handle disputes or failures in the automated system effectively. This will help maintain customer trust and mitigate negative impacts from potential model failures.
Ethical and Compliance Checks: Ensure that the deployment and ongoing use of the model comply with relevant laws and ethical guidelines, particularly those concerning consumer rights and data protection.
Implementing these recommendations will help in responsibly leveraging AI capabilities to enhance business processes while maintaining trust and compliance.
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.
### Key Modifications:
1. Intel IPEX Optimization: The model is wrapped with 'ipex.optimize()' right after its instantiation to apply Intel-specific optimizations. You can specify the data type ('torch.float32' or 'torch.bfloat16') based on your preference for precision and performance.
2. Mixed Precision: Utilizes 'URL.autocast()' for mixed precision during inference, which can provide a boost in performance with minimal impact on accuracy when running on CPUs that support vector neural network instructions (VNNI).
## Training Details
### Training Data
URL
The ImageNet-1K dataset, available on Hugging Face, provides access to a subset of the larger ImageNet database, specifically the ILSVRC 2012 configuration. It includes 1,281,167 training images, 50,000 validation images, and 100,000 test images across 1,000 different object classes. This dataset is a fundamental resource for training deep learning models in various computer vision tasks due to its extensive range of high-quality, human-annotated images.
### Training Procedure
The YOLOv5 model was fine-tuned using the Intel® Extension for PyTorch*, which significantly optimized its performance on Intel architectures. This extension allows for more efficient computation and resource utilization, especially by enhancing the utilization of CPU capabilities, which are often less emphasized in typical GPU-centric training processes.
Technical Integration:
Intel® Extension for PyTorch: This extension optimizes PyTorch operations on Intel CPUs, leveraging Intel's oneDNN primitives to improve both training and inference speeds.
Intel® Deep Learning Boost (VNNI): This was employed to accelerate integer operations, common in convolutional networks like YOLOv5, enhancing model throughput during training.
BFloat16 Training: The use of BFloat16 data types supported by Intel CPUs allowed the model to train with larger batch sizes and faster epoch times with minimal impact on precision.
Parallel Training: The model used Intel's oneAPI Collective Communications Library (oneCCL) for efficient distributed training across Intel CPUs, enhancing scalability and reducing training times.
Performance Improvements:
The optimizations led to a noticeable increase in training speed and efficiency compared to traditional training setups on similar hardware.
Energy efficiency was also prioritized, with adjustments during training phases resulting in reduced power consumption.
Tools and Libraries:
Intel VTune™ Profiler: This tool was utilized to analyze the model's performance during training, helping to identify computational bottlenecks and optimize processing.
Intel® Advisor: This tool provided recommendations for vectorization and threading improvements, crucial for maximizing the multi-core capabilities of Intel CPUs.
These enhancements facilitated by Intel’s tools not only shortened the training cycle but also improved the overall efficiency of the YOLOv5 model, making it highly suitable for integration into computer vision pipelines that assess product returns.
#### Training Hyperparameters
- Training regime: bf16 mixed precision
### Results
#### Summary
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: Small VM - Intel® Xeon 4th Gen ® Scalable processor
- Cloud Provider: Intel® Developer Cloud
- Compute Region: us-region-1
URL | [
"# Model Card for Model ID\n\nThis finetuned YOLOv5 model is developed to aid businesses in automating the inspection of returned goods. It utilizes advanced computer vision techniques to detect, classify, and assess the condition of items from images, determining whether returns are genuine or potentially fraudulent. The model is tailored to recognize various product conditions and features that align with common return reasons, enabling quick and efficient processing within return workflows.",
"## Model Details",
"### Model Description\n\nThe finetuned YOLOv5 model is designed specifically for use in retail and ecommerce environments to assist with the assessment of returned merchandise. It uses deep learning algorithms to analyze images of returned items, identifying specific product features, damages, or discrepancies that may indicate misuse or fraud. This model has been trained on a diverse dataset of product images, capturing a wide range of conditions, from new to heavily used items.\n\nThe model's capabilities include detecting subtle signs of wear and tear, modifications, or missing components that are often overlooked in manual inspections. By automating the inspection process, the model helps streamline return operations, reduce human error, and prevent fraudulent returns, thereby protecting revenue and improving customer service efficiency.\n\nThis YOLOv5 model variant has been optimized to perform well under various lighting conditions and camera angles, making it robust and reliable for deployment in varied operational settings where returns are processed. It integrates seamlessly with existing computer vision pipelines and can be further connected to APIs like OpenAI's GPT for enhanced decision-making about the item's return eligibility based on visual assessment.\n\n- Developed by: Cody Liu, Arjun Dabir\n- Model type: YOLOv5 (You Only Look Once version 5), Fine-tuned Object Detection Model\n- Language(s) (NLP): Python\n- License: Apache License 2.0\n- Finetuned from model: YOLOv5",
"## Uses",
"### Direct Use\n\nThis finetuned YOLOv5 model is designed to detect and classify objects in images for return verification processes. It's intended for businesses to automate the inspection of returned goods, determining their condition and authenticity. The primary users are retail companies and online marketplaces aiming to streamline return operations and reduce fraudulent activities.",
"### Out-of-Scope Use\n\nThe model is not intended for applications beyond visual inspection tasks, such as medical image analysis, autonomous driving, or any environment where its object detection capabilities may not apply directly. It should not be used as a standalone decision-maker without human oversight due to the potential for misclassification. Misuse includes any application involving sensitive personal data or scenarios where a misclassification could lead to safety risks.",
"## Bias, Risks, and Limitations\n\nThis model, a fine-tuned version of YOLOv5 for object detection, is integrated with a GPT-based API to assess the condition of returned items. While this setup aims to automate the evaluation of returned goods, several biases, risks, and limitations are inherent in the technology:\n\nBias in Training Data: The object detection model's performance is contingent on the diversity and representativeness of its training dataset. If the training data lacks variety in terms of item conditions, environments, or object types, the model may exhibit biased or underperformative behavior against unrepresented categories.\nRisk of Hallucination in LLM: The use of a language model (GPT) for interpreting object detection results introduces a risk of \"hallucinations\" or generating incorrect or misleading information based on the detected items. These inaccuracies can lead to incorrect assessments of item conditions, potentially categorizing non-fraudulent returns as fraudulent.\nLimitations in Detection Capabilities: While YOLOv5 is robust in detecting objects within diverse and complex scenes, its accuracy can be compromised under conditions of poor lighting, occlusion, or unusual item orientations. These factors can lead to false negatives or false positives in identifying items and their conditions.\nSociotechnical Implications: Relying on automated systems for assessing returns could have implications for consumer trust and satisfaction. Incorrect assessments due to model limitations or errors can lead to customer dissatisfaction and potential loss of business, particularly if customers feel their returns are unjustly categorized.\nOut-of-Scope Use: The model is not designed for and should not be used in scenarios involving sensitive or regulated items, such as pharmaceuticals, where specialized detection and assessment systems are required. Misuse in such contexts could lead to serious safety and compliance issues.\n\nAcknowledging these limitations is crucial for deploying the model in a manner that minimizes risks and ensures fairness and accuracy in its applications. Further, continuous monitoring and updating of both the object detection and language processing components are recommended to address emergent biases or inaccuracies.",
"### Recommendations\n\nGiven the identified biases, risks, and limitations associated with the combined use of the YOLOv5 object detection model and the GPT language model in the returns assessment pipeline, the following recommendations are proposed to mitigate potential issues and enhance overall system effectiveness:\n\nEnhance Dataset Diversity: Regularly update and expand the training datasets for the YOLOv5 model to include a wider range of items, conditions, and environmental factors. This will help reduce bias and improve the model's accuracy across diverse real-world scenarios.\nImprove Error Handling: Develop robust error-handling and verification protocols to address and mitigate the risks of hallucinations from the GPT model. This could include cross-verifications with additional data sources or manual reviews in cases of uncertainty or high-risk assessments.\nConduct Regular Model Audits: Perform periodic audits of both the YOLOv5 and GPT models to assess and improve their performance and fairness. This includes testing the models against new and varied datasets to identify any potential drifts or biases in model behavior.\nIncrease Transparency: Provide clear documentation and transparency regarding the model's capabilities, limitations, and the basis of its decisions. This could involve detailed logs of decision pathways and the factors influencing model assessments, accessible to both customers and regulatory bodies.\nUser Education: Educate users and stakeholders about the capabilities, general workings, and limitations of the AI system. This helps set realistic expectations and promotes more informed and cautious use of the technology.\nDevelop Contingency Plans: Establish contingency plans including manual oversight and customer service interventions to handle disputes or failures in the automated system effectively. This will help maintain customer trust and mitigate negative impacts from potential model failures.\nEthical and Compliance Checks: Ensure that the deployment and ongoing use of the model comply with relevant laws and ethical guidelines, particularly those concerning consumer rights and data protection.\nImplementing these recommendations will help in responsibly leveraging AI capabilities to enhance business processes while maintaining trust and compliance.\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.",
"### Key Modifications:\n\n1. Intel IPEX Optimization: The model is wrapped with 'ipex.optimize()' right after its instantiation to apply Intel-specific optimizations. You can specify the data type ('torch.float32' or 'torch.bfloat16') based on your preference for precision and performance.\n2. Mixed Precision: Utilizes 'URL.autocast()' for mixed precision during inference, which can provide a boost in performance with minimal impact on accuracy when running on CPUs that support vector neural network instructions (VNNI).",
"## Training Details",
"### Training Data\n\nURL\n\nThe ImageNet-1K dataset, available on Hugging Face, provides access to a subset of the larger ImageNet database, specifically the ILSVRC 2012 configuration. It includes 1,281,167 training images, 50,000 validation images, and 100,000 test images across 1,000 different object classes. This dataset is a fundamental resource for training deep learning models in various computer vision tasks due to its extensive range of high-quality, human-annotated images.",
"### Training Procedure\n\nThe YOLOv5 model was fine-tuned using the Intel® Extension for PyTorch*, which significantly optimized its performance on Intel architectures. This extension allows for more efficient computation and resource utilization, especially by enhancing the utilization of CPU capabilities, which are often less emphasized in typical GPU-centric training processes.\n\nTechnical Integration:\nIntel® Extension for PyTorch: This extension optimizes PyTorch operations on Intel CPUs, leveraging Intel's oneDNN primitives to improve both training and inference speeds.\nIntel® Deep Learning Boost (VNNI): This was employed to accelerate integer operations, common in convolutional networks like YOLOv5, enhancing model throughput during training.\nBFloat16 Training: The use of BFloat16 data types supported by Intel CPUs allowed the model to train with larger batch sizes and faster epoch times with minimal impact on precision.\nParallel Training: The model used Intel's oneAPI Collective Communications Library (oneCCL) for efficient distributed training across Intel CPUs, enhancing scalability and reducing training times.\n\nPerformance Improvements:\nThe optimizations led to a noticeable increase in training speed and efficiency compared to traditional training setups on similar hardware.\nEnergy efficiency was also prioritized, with adjustments during training phases resulting in reduced power consumption.\n\nTools and Libraries:\nIntel VTune™ Profiler: This tool was utilized to analyze the model's performance during training, helping to identify computational bottlenecks and optimize processing.\nIntel® Advisor: This tool provided recommendations for vectorization and threading improvements, crucial for maximizing the multi-core capabilities of Intel CPUs.\n\nThese enhancements facilitated by Intel’s tools not only shortened the training cycle but also improved the overall efficiency of the YOLOv5 model, making it highly suitable for integration into computer vision pipelines that assess product returns.",
"#### Training Hyperparameters\n\n- Training regime: bf16 mixed precision",
"### Results",
"#### Summary\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: Small VM - Intel® Xeon 4th Gen ® Scalable processor\n- Cloud Provider: Intel® Developer Cloud\n- Compute Region: us-region-1\n\nURL"
] | [
"TAGS\n#transformers #computer-vision #object-detection #fraud-detection #yolov5 #en #dataset-imagenet-1k #arxiv-1910.09700 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Model Card for Model ID\n\nThis finetuned YOLOv5 model is developed to aid businesses in automating the inspection of returned goods. It utilizes advanced computer vision techniques to detect, classify, and assess the condition of items from images, determining whether returns are genuine or potentially fraudulent. The model is tailored to recognize various product conditions and features that align with common return reasons, enabling quick and efficient processing within return workflows.",
"## Model Details",
"### Model Description\n\nThe finetuned YOLOv5 model is designed specifically for use in retail and ecommerce environments to assist with the assessment of returned merchandise. It uses deep learning algorithms to analyze images of returned items, identifying specific product features, damages, or discrepancies that may indicate misuse or fraud. This model has been trained on a diverse dataset of product images, capturing a wide range of conditions, from new to heavily used items.\n\nThe model's capabilities include detecting subtle signs of wear and tear, modifications, or missing components that are often overlooked in manual inspections. By automating the inspection process, the model helps streamline return operations, reduce human error, and prevent fraudulent returns, thereby protecting revenue and improving customer service efficiency.\n\nThis YOLOv5 model variant has been optimized to perform well under various lighting conditions and camera angles, making it robust and reliable for deployment in varied operational settings where returns are processed. It integrates seamlessly with existing computer vision pipelines and can be further connected to APIs like OpenAI's GPT for enhanced decision-making about the item's return eligibility based on visual assessment.\n\n- Developed by: Cody Liu, Arjun Dabir\n- Model type: YOLOv5 (You Only Look Once version 5), Fine-tuned Object Detection Model\n- Language(s) (NLP): Python\n- License: Apache License 2.0\n- Finetuned from model: YOLOv5",
"## Uses",
"### Direct Use\n\nThis finetuned YOLOv5 model is designed to detect and classify objects in images for return verification processes. It's intended for businesses to automate the inspection of returned goods, determining their condition and authenticity. The primary users are retail companies and online marketplaces aiming to streamline return operations and reduce fraudulent activities.",
"### Out-of-Scope Use\n\nThe model is not intended for applications beyond visual inspection tasks, such as medical image analysis, autonomous driving, or any environment where its object detection capabilities may not apply directly. It should not be used as a standalone decision-maker without human oversight due to the potential for misclassification. Misuse includes any application involving sensitive personal data or scenarios where a misclassification could lead to safety risks.",
"## Bias, Risks, and Limitations\n\nThis model, a fine-tuned version of YOLOv5 for object detection, is integrated with a GPT-based API to assess the condition of returned items. While this setup aims to automate the evaluation of returned goods, several biases, risks, and limitations are inherent in the technology:\n\nBias in Training Data: The object detection model's performance is contingent on the diversity and representativeness of its training dataset. If the training data lacks variety in terms of item conditions, environments, or object types, the model may exhibit biased or underperformative behavior against unrepresented categories.\nRisk of Hallucination in LLM: The use of a language model (GPT) for interpreting object detection results introduces a risk of \"hallucinations\" or generating incorrect or misleading information based on the detected items. These inaccuracies can lead to incorrect assessments of item conditions, potentially categorizing non-fraudulent returns as fraudulent.\nLimitations in Detection Capabilities: While YOLOv5 is robust in detecting objects within diverse and complex scenes, its accuracy can be compromised under conditions of poor lighting, occlusion, or unusual item orientations. These factors can lead to false negatives or false positives in identifying items and their conditions.\nSociotechnical Implications: Relying on automated systems for assessing returns could have implications for consumer trust and satisfaction. Incorrect assessments due to model limitations or errors can lead to customer dissatisfaction and potential loss of business, particularly if customers feel their returns are unjustly categorized.\nOut-of-Scope Use: The model is not designed for and should not be used in scenarios involving sensitive or regulated items, such as pharmaceuticals, where specialized detection and assessment systems are required. Misuse in such contexts could lead to serious safety and compliance issues.\n\nAcknowledging these limitations is crucial for deploying the model in a manner that minimizes risks and ensures fairness and accuracy in its applications. Further, continuous monitoring and updating of both the object detection and language processing components are recommended to address emergent biases or inaccuracies.",
"### Recommendations\n\nGiven the identified biases, risks, and limitations associated with the combined use of the YOLOv5 object detection model and the GPT language model in the returns assessment pipeline, the following recommendations are proposed to mitigate potential issues and enhance overall system effectiveness:\n\nEnhance Dataset Diversity: Regularly update and expand the training datasets for the YOLOv5 model to include a wider range of items, conditions, and environmental factors. This will help reduce bias and improve the model's accuracy across diverse real-world scenarios.\nImprove Error Handling: Develop robust error-handling and verification protocols to address and mitigate the risks of hallucinations from the GPT model. This could include cross-verifications with additional data sources or manual reviews in cases of uncertainty or high-risk assessments.\nConduct Regular Model Audits: Perform periodic audits of both the YOLOv5 and GPT models to assess and improve their performance and fairness. This includes testing the models against new and varied datasets to identify any potential drifts or biases in model behavior.\nIncrease Transparency: Provide clear documentation and transparency regarding the model's capabilities, limitations, and the basis of its decisions. This could involve detailed logs of decision pathways and the factors influencing model assessments, accessible to both customers and regulatory bodies.\nUser Education: Educate users and stakeholders about the capabilities, general workings, and limitations of the AI system. This helps set realistic expectations and promotes more informed and cautious use of the technology.\nDevelop Contingency Plans: Establish contingency plans including manual oversight and customer service interventions to handle disputes or failures in the automated system effectively. This will help maintain customer trust and mitigate negative impacts from potential model failures.\nEthical and Compliance Checks: Ensure that the deployment and ongoing use of the model comply with relevant laws and ethical guidelines, particularly those concerning consumer rights and data protection.\nImplementing these recommendations will help in responsibly leveraging AI capabilities to enhance business processes while maintaining trust and compliance.\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.",
"### Key Modifications:\n\n1. Intel IPEX Optimization: The model is wrapped with 'ipex.optimize()' right after its instantiation to apply Intel-specific optimizations. You can specify the data type ('torch.float32' or 'torch.bfloat16') based on your preference for precision and performance.\n2. Mixed Precision: Utilizes 'URL.autocast()' for mixed precision during inference, which can provide a boost in performance with minimal impact on accuracy when running on CPUs that support vector neural network instructions (VNNI).",
"## Training Details",
"### Training Data\n\nURL\n\nThe ImageNet-1K dataset, available on Hugging Face, provides access to a subset of the larger ImageNet database, specifically the ILSVRC 2012 configuration. It includes 1,281,167 training images, 50,000 validation images, and 100,000 test images across 1,000 different object classes. This dataset is a fundamental resource for training deep learning models in various computer vision tasks due to its extensive range of high-quality, human-annotated images.",
"### Training Procedure\n\nThe YOLOv5 model was fine-tuned using the Intel® Extension for PyTorch*, which significantly optimized its performance on Intel architectures. This extension allows for more efficient computation and resource utilization, especially by enhancing the utilization of CPU capabilities, which are often less emphasized in typical GPU-centric training processes.\n\nTechnical Integration:\nIntel® Extension for PyTorch: This extension optimizes PyTorch operations on Intel CPUs, leveraging Intel's oneDNN primitives to improve both training and inference speeds.\nIntel® Deep Learning Boost (VNNI): This was employed to accelerate integer operations, common in convolutional networks like YOLOv5, enhancing model throughput during training.\nBFloat16 Training: The use of BFloat16 data types supported by Intel CPUs allowed the model to train with larger batch sizes and faster epoch times with minimal impact on precision.\nParallel Training: The model used Intel's oneAPI Collective Communications Library (oneCCL) for efficient distributed training across Intel CPUs, enhancing scalability and reducing training times.\n\nPerformance Improvements:\nThe optimizations led to a noticeable increase in training speed and efficiency compared to traditional training setups on similar hardware.\nEnergy efficiency was also prioritized, with adjustments during training phases resulting in reduced power consumption.\n\nTools and Libraries:\nIntel VTune™ Profiler: This tool was utilized to analyze the model's performance during training, helping to identify computational bottlenecks and optimize processing.\nIntel® Advisor: This tool provided recommendations for vectorization and threading improvements, crucial for maximizing the multi-core capabilities of Intel CPUs.\n\nThese enhancements facilitated by Intel’s tools not only shortened the training cycle but also improved the overall efficiency of the YOLOv5 model, making it highly suitable for integration into computer vision pipelines that assess product returns.",
"#### Training Hyperparameters\n\n- Training regime: bf16 mixed precision",
"### Results",
"#### Summary\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: Small VM - Intel® Xeon 4th Gen ® Scalable processor\n- Cloud Provider: Intel® Developer Cloud\n- Compute Region: us-region-1\n\nURL"
] |
text-classification | 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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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": []} | MohammadKarami/medium-roberta | null | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T02:50:25+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #roberta #text-classification #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):
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## Uses
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### Out-of-Scope Use
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### 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
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- Hardware Type:
- Hours used:
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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
| [
"# 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 #roberta #text-classification #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.",
"## 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]
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## 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": []} | shallow6414/4dheple | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T02:51:05+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 |
# Model Card for free-solar-evo-v0.13
## Developed by : [Freewheelin](https://freewheelin-recruit.oopy.io/) AI Technical Team
## Method
- We were inspired by this [Sakana project](https://sakana.ai/evolutionary-model-merge/)
## Base Model
- free-solar-evo-model | {"language": ["ko", "en"], "license": "mit"} | freewheelin/free-solar-evo-v0.13 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"ko",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T02:58:08+00:00 | [] | [
"ko",
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #ko #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for free-solar-evo-v0.13
## Developed by : Freewheelin AI Technical Team
## Method
- We were inspired by this Sakana project
## Base Model
- free-solar-evo-model | [
"# Model Card for free-solar-evo-v0.13",
"## Developed by : Freewheelin AI Technical Team",
"## Method\n- We were inspired by this Sakana project",
"## Base Model \n- free-solar-evo-model"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #ko #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for free-solar-evo-v0.13",
"## Developed by : Freewheelin AI Technical Team",
"## Method\n- We were inspired by this Sakana project",
"## Base Model \n- free-solar-evo-model"
] |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Epoching/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget"]} | Epoching/ppo-SnowballTarget | null | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | null | 2024-04-28T02:58:44+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us
|
# ppo Agent playing SnowballTarget
This is a trained model of a ppo agent playing SnowballTarget
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: Epoching/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\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: Epoching/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us \n",
"# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\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: Epoching/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Yuma42/KangalKhan-NeoRuby-7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/KangalKhan-NeoRuby-7B-GGUF/resolve/main/KangalKhan-NeoRuby-7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/KangalKhan-NeoRuby-7B-GGUF/resolve/main/KangalKhan-NeoRuby-7B.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/KangalKhan-NeoRuby-7B-GGUF/resolve/main/KangalKhan-NeoRuby-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/KangalKhan-NeoRuby-7B-GGUF/resolve/main/KangalKhan-NeoRuby-7B.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/KangalKhan-NeoRuby-7B-GGUF/resolve/main/KangalKhan-NeoRuby-7B.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/KangalKhan-NeoRuby-7B-GGUF/resolve/main/KangalKhan-NeoRuby-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/KangalKhan-NeoRuby-7B-GGUF/resolve/main/KangalKhan-NeoRuby-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/KangalKhan-NeoRuby-7B-GGUF/resolve/main/KangalKhan-NeoRuby-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/KangalKhan-NeoRuby-7B-GGUF/resolve/main/KangalKhan-NeoRuby-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/KangalKhan-NeoRuby-7B-GGUF/resolve/main/KangalKhan-NeoRuby-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/KangalKhan-NeoRuby-7B-GGUF/resolve/main/KangalKhan-NeoRuby-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/KangalKhan-NeoRuby-7B-GGUF/resolve/main/KangalKhan-NeoRuby-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/KangalKhan-NeoRuby-7B-GGUF/resolve/main/KangalKhan-NeoRuby-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/KangalKhan-NeoRuby-7B-GGUF/resolve/main/KangalKhan-NeoRuby-7B.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/KangalKhan-NeoRuby-7B-GGUF/resolve/main/KangalKhan-NeoRuby-7B.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["merge", "mergekit", "lazymergekit", "argilla/CapybaraHermes-2.5-Mistral-7B", "argilla/distilabeled-OpenHermes-2.5-Mistral-7B"], "base_model": "Yuma42/KangalKhan-NeoRuby-7B", "quantized_by": "mradermacher"} | mradermacher/KangalKhan-NeoRuby-7B-GGUF | null | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"argilla/CapybaraHermes-2.5-Mistral-7B",
"argilla/distilabeled-OpenHermes-2.5-Mistral-7B",
"en",
"base_model:Yuma42/KangalKhan-NeoRuby-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T03:01:29+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #merge #mergekit #lazymergekit #argilla/CapybaraHermes-2.5-Mistral-7B #argilla/distilabeled-OpenHermes-2.5-Mistral-7B #en #base_model-Yuma42/KangalKhan-NeoRuby-7B #license-apache-2.0 #endpoints_compatible #region-us
| About
-----
static quants of URL
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
-----
If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs for
more details, including on how to concatenate multi-part files.
Provided Quants
---------------
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #merge #mergekit #lazymergekit #argilla/CapybaraHermes-2.5-Mistral-7B #argilla/distilabeled-OpenHermes-2.5-Mistral-7B #en #base_model-Yuma42/KangalKhan-NeoRuby-7B #license-apache-2.0 #endpoints_compatible #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
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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": []} | quickstep3621/pzk6o2u | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T03:01:31+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):
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## Uses
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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## Technical Specifications [optional]
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### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
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## 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
### 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 when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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### Out-of-Scope Use
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<!-- Relevant interpretability work for the model goes here -->
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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).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/4v6ph3k | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
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"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T03:01:36+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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- Model type:
- Language(s) (NLP):
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## Uses
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### 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
### 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
### 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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<!-- 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
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[More Information Needed]
## Training Details
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### Training Procedure
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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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#### 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]
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[More Information Needed]
#### Hardware
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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] | {"library_name": "transformers", "tags": []} | quickstep3621/nfo51od | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
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"endpoints_compatible",
"region:us"
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"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.
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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]
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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
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BibTeX:
APA:
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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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<!-- 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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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": []} | HC-85/distilbert-lora-32r-arxiv-multilabel-b16 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T03:01:57+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #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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### Compute Infrastructure
#### Hardware
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[optional]
BibTeX:
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## 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
### 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 when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- 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]
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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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## Technical Specifications [optional]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Ynir/llama-3-8b-test-v2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T03:02:49+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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"## Model Details",
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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. -->
# plm-nsp-1000
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5839
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6962 | 1.0 | 16 | 0.6919 |
| 0.6653 | 2.0 | 32 | 0.5959 |
| 0.6127 | 3.0 | 48 | 0.6141 |
| 0.6327 | 4.0 | 64 | 0.5738 |
| 0.5968 | 5.0 | 80 | 0.5760 |
| 0.5922 | 6.0 | 96 | 0.5758 |
| 0.5895 | 7.0 | 112 | 0.5839 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "roberta-base", "model-index": [{"name": "plm-nsp-1000", "results": []}]} | mhr2004/plm-nsp-1000 | null | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T03:07:59+00:00 | [] | [] | TAGS
#transformers #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
| plm-nsp-1000
============
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5839
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: 128
* eval\_batch\_size: 128
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
### Training results
### Framework versions
* Transformers 4.40.1
* Pytorch 2.3.0+cu121
* 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
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<!-- 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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- **License:** [More Information Needed]
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<!-- 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
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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]
- **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": "mistralai/Mistral-7B-v0.1"} | cgihlstorf/NEW_finetuned_Mistral-7B32_1_0.0003_alternate_no_output | null | [
"peft",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"region:us"
] | null | 2024-04-28T03:08:36+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-v0.1 #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-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 1.4913766384124756
f1_macro: 0.28164367547346275
f1_micro: 0.64
f1_weighted: 0.5917376665887304
precision_macro: 0.2705775014459225
precision_micro: 0.64
precision_weighted: 0.5802396761133604
recall_macro: 0.3324350649350649
recall_micro: 0.64
recall_weighted: 0.64
accuracy: 0.64
| {"tags": ["autotrain", "text-classification"], "datasets": ["autotrain-9c20u-twasm/autotrain-data"], "widget": [{"text": "I love AutoTrain"}]} | dhananjay2912/deberta_aci_bench_medical_section_classifier | null | [
"transformers",
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"text-classification",
"autotrain",
"dataset:autotrain-9c20u-twasm/autotrain-data",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T03:13:54+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #deberta-v2 #text-classification #autotrain #dataset-autotrain-9c20u-twasm/autotrain-data #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 1.4913766384124756
f1_macro: 0.28164367547346275
f1_micro: 0.64
f1_weighted: 0.5917376665887304
precision_macro: 0.2705775014459225
precision_micro: 0.64
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recall_macro: 0.3324350649350649
recall_micro: 0.64
recall_weighted: 0.64
accuracy: 0.64
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] |
text-generation | null |
# MoMonir/Meta-Llama-Guard-2-8B-GGUF
This model was converted to GGUF format from [`meta-llama/Meta-Llama-Guard-2-8B`](https://huggingface.co/meta-llama/Meta-Llama-Guard-2-8B) 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/meta-llama/Meta-Llama-Guard-2-8B) for more details on the model.
<!-- README_GGUF.md-about-gguf start -->
### About GGUF ([TheBloke](https://huggingface.co/TheBloke) Description)
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 -->
## 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 MoMonir/Meta-Llama-Guard-2-8B-GGUF --model meta-llama-guard-2-8b.Q5_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo MoMonir/Meta-Llama-Guard-2-8B-GGUF --model meta-llama-guard-2-8b.Q5_K_M.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 meta-llama-guard-2-8b.Q5_K_M.gguf -n 128
```
| {"language": ["en"], "license": "other", "tags": ["facebook", "meta", "pytorch", "llama", "llama-3", "llama-cpp", "gguf-my-repo"], "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"} | MoMonir/Meta-Llama-Guard-2-8B-GGUF | null | [
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"license:other",
"region:us"
] | null | 2024-04-28T03:15:34+00:00 | [] | [
"en"
] | TAGS
#gguf #facebook #meta #pytorch #llama #llama-3 #llama-cpp #gguf-my-repo #text-generation #en #license-other #region-us
|
# MoMonir/Meta-Llama-Guard-2-8B-GGUF
This model was converted to GGUF format from 'meta-llama/Meta-Llama-Guard-2-8B' using URL via the URL's GGUF-my-repo space.
Refer to the original model card for more details on the model.
### About GGUF (TheBloke Description)
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.
## 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.
| [
"# MoMonir/Meta-Llama-Guard-2-8B-GGUF\nThis model was converted to GGUF format from 'meta-llama/Meta-Llama-Guard-2-8B' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"### About GGUF (TheBloke Description)\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.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] | [
"TAGS\n#gguf #facebook #meta #pytorch #llama #llama-3 #llama-cpp #gguf-my-repo #text-generation #en #license-other #region-us \n",
"# MoMonir/Meta-Llama-Guard-2-8B-GGUF\nThis model was converted to GGUF format from 'meta-llama/Meta-Llama-Guard-2-8B' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"### About GGUF (TheBloke Description)\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.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] |
text-generation | transformers | # Model Card for Model ID
This is the finetuned instruct AI model from Mistral AI.
### Model Description
Mistral 7b Instruct v.1.0 is an AI model developed by Mistral AI, based on the original model: Mistral 7b. It was finetuned for conversational discussions and was trained on a variety of public data.
- **Developed by:** Mistral AI
- **Model type:** Text Generation/Conversational
- **License:** Apache 2.0
- **Finetuned from model:** Mistral 7b
## Uses
This model was designed to be used for conversational discussions with the model.
## Bias, Risks, and Limitations
The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
[More Information Needed]
| {"license": "apache-2.0", "pipeline_tag": "text-generation"} | orionai/mistral-7b-instruct-v.1.0 | null | [
"transformers",
"pytorch",
"safetensors",
"mistral",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T03:16:28+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #mistral #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Model Card for Model ID
This is the finetuned instruct AI model from Mistral AI.
### Model Description
Mistral 7b Instruct v.1.0 is an AI model developed by Mistral AI, based on the original model: Mistral 7b. It was finetuned for conversational discussions and was trained on a variety of public data.
- Developed by: Mistral AI
- Model type: Text Generation/Conversational
- License: Apache 2.0
- Finetuned from model: Mistral 7b
## Uses
This model was designed to be used for conversational discussions with the model.
## Bias, Risks, and Limitations
The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
| [
"# Model Card for Model ID\n\n\nThis is the finetuned instruct AI model from Mistral AI.",
"### Model Description\n\nMistral 7b Instruct v.1.0 is an AI model developed by Mistral AI, based on the original model: Mistral 7b. It was finetuned for conversational discussions and was trained on a variety of public data.\n\n\n\n- Developed by: Mistral AI\n- Model type: Text Generation/Conversational\n- License: Apache 2.0\n- Finetuned from model: Mistral 7b",
"## Uses\n\nThis model was designed to be used for conversational discussions with the model.",
"## Bias, Risks, and Limitations\n\nThe Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs."
] | [
"TAGS\n#transformers #pytorch #safetensors #mistral #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID\n\n\nThis is the finetuned instruct AI model from Mistral AI.",
"### Model Description\n\nMistral 7b Instruct v.1.0 is an AI model developed by Mistral AI, based on the original model: Mistral 7b. It was finetuned for conversational discussions and was trained on a variety of public data.\n\n\n\n- Developed by: Mistral AI\n- Model type: Text Generation/Conversational\n- License: Apache 2.0\n- Finetuned from model: Mistral 7b",
"## Uses\n\nThis model was designed to be used for conversational discussions with the model.",
"## Bias, Risks, and Limitations\n\nThe Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs."
] |
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