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nfaheem
/
Marcoroni-7b-DPO-Merge

Text Generation
Transformers
Safetensors
mistral
Merge
mergekit
lazymergekit
fblgit/UNA-TheBeagle-7b-v1
udkai/Turdus
text-generation-inference
Model card Files Files and versions
xet
Community
2

Instructions to use nfaheem/Marcoroni-7b-DPO-Merge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use nfaheem/Marcoroni-7b-DPO-Merge with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="nfaheem/Marcoroni-7b-DPO-Merge")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("nfaheem/Marcoroni-7b-DPO-Merge")
    model = AutoModelForCausalLM.from_pretrained("nfaheem/Marcoroni-7b-DPO-Merge")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use nfaheem/Marcoroni-7b-DPO-Merge with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "nfaheem/Marcoroni-7b-DPO-Merge"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "nfaheem/Marcoroni-7b-DPO-Merge",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/nfaheem/Marcoroni-7b-DPO-Merge
  • SGLang

    How to use nfaheem/Marcoroni-7b-DPO-Merge 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 "nfaheem/Marcoroni-7b-DPO-Merge" \
        --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": "nfaheem/Marcoroni-7b-DPO-Merge",
    		"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 "nfaheem/Marcoroni-7b-DPO-Merge" \
            --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": "nfaheem/Marcoroni-7b-DPO-Merge",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use nfaheem/Marcoroni-7b-DPO-Merge with Docker Model Runner:

    docker model run hf.co/nfaheem/Marcoroni-7b-DPO-Merge
Marcoroni-7b-DPO-Merge
14.5 GB
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  • 1 contributor
History: 7 commits
nfaheem's picture
nfaheem
Update README.md
5fc2a1c verified over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
    3.32 kB
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  • config.json
    651 Bytes
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  • mergekit_config.yml
    419 Bytes
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  • model-00001-of-00002.safetensors
    9.94 GB
    xet
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  • model-00002-of-00002.safetensors
    4.54 GB
    xet
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  • model.safetensors.index.json
    22.8 kB
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  • special_tokens_map.json
    487 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
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