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bartowski
/
Hermes-2-Pro-Mistral-10.7B-exl2

Text Generation
Transformers
English
mergekit
Merge
Mistral
instruct
finetune
chatml
DPO
RLHF
gpt4
synthetic data
distillation
function calling
json mode
Model card Files Files and versions
xet
Community

Instructions to use bartowski/Hermes-2-Pro-Mistral-10.7B-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use bartowski/Hermes-2-Pro-Mistral-10.7B-exl2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="bartowski/Hermes-2-Pro-Mistral-10.7B-exl2")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("bartowski/Hermes-2-Pro-Mistral-10.7B-exl2", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use bartowski/Hermes-2-Pro-Mistral-10.7B-exl2 with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "bartowski/Hermes-2-Pro-Mistral-10.7B-exl2"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "bartowski/Hermes-2-Pro-Mistral-10.7B-exl2",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/bartowski/Hermes-2-Pro-Mistral-10.7B-exl2
  • SGLang

    How to use bartowski/Hermes-2-Pro-Mistral-10.7B-exl2 with SGLang:

    Install from pip and serve model
    # Install SGLang from pip:
    pip install sglang
    # Start the SGLang server:
    python3 -m sglang.launch_server \
        --model-path "bartowski/Hermes-2-Pro-Mistral-10.7B-exl2" \
        --host 0.0.0.0 \
        --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "bartowski/Hermes-2-Pro-Mistral-10.7B-exl2",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker images
    docker run --gpus all \
        --shm-size 32g \
        -p 30000:30000 \
        -v ~/.cache/huggingface:/root/.cache/huggingface \
        --env "HF_TOKEN=<secret>" \
        --ipc=host \
        lmsysorg/sglang:latest \
        python3 -m sglang.launch_server \
            --model-path "bartowski/Hermes-2-Pro-Mistral-10.7B-exl2" \
            --host 0.0.0.0 \
            --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "bartowski/Hermes-2-Pro-Mistral-10.7B-exl2",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use bartowski/Hermes-2-Pro-Mistral-10.7B-exl2 with Docker Model Runner:

    docker model run hf.co/bartowski/Hermes-2-Pro-Mistral-10.7B-exl2
Hermes-2-Pro-Mistral-10.7B-exl2
4.95 GB
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  • 1 contributor
History: 3 commits
bartowski's picture
bartowski
Quant for 3.5
b6a2061 verified over 2 years ago
  • .DS_Store
    8.2 kB
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  • .gitattributes
    1.52 kB
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  • README.md
    19.8 kB
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  • added_tokens.json
    51 Bytes
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  • config.json
    1.01 kB
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  • measurement.json
    2.68 MB
    measurement.json over 2 years ago
  • mergekit_config.yml
    475 Bytes
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  • model.safetensors.index.json
    34.1 kB
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  • original_repo_url.txt
    63 Bytes
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  • output.safetensors
    4.95 GB
    xet
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  • special_tokens_map.json
    557 Bytes
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  • tokenizer.json
    1.8 MB
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  • tokenizer.model
    493 kB
    xet
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  • tokenizer_config.json
    1.6 kB
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