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jeremygracey-ai
/
FetchMerck_AI

Text Generation
llama-cpp-python
GGUF
English
rag
healthcare
clinical-decision-support
medical
merck-manual
retrieval-augmented-generation
mistral
Model card Files Files and versions
xet
Community

Instructions to use jeremygracey-ai/FetchMerck_AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • llama-cpp-python

    How to use jeremygracey-ai/FetchMerck_AI with llama-cpp-python:

    # !pip install llama-cpp-python
    
    from llama_cpp import Llama
    
    llm = Llama.from_pretrained(
    	repo_id="jeremygracey-ai/FetchMerck_AI",
    	filename="mistral-7b-instruct-v0.1.Q4_K_M.gguf",
    )
    
    output = llm(
    	"Once upon a time,",
    	max_tokens=512,
    	echo=True
    )
    print(output)
  • llama-cpp-python

    How to use jeremygracey-ai/FetchMerck_AI with llama-cpp-python:

    # !pip install llama-cpp-python
    
    from llama_cpp import Llama
    
    llm = Llama.from_pretrained(
    	repo_id="jeremygracey-ai/FetchMerck_AI",
    	filename="mistral-7b-instruct-v0.1.Q4_K_M.gguf",
    )
    
    output = llm(
    	"Once upon a time,",
    	max_tokens=512,
    	echo=True
    )
    print(output)
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • llama.cpp

    How to use jeremygracey-ai/FetchMerck_AI with llama.cpp:

    Install from brew
    brew install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf jeremygracey-ai/FetchMerck_AI:Q4_K_M
    # Run inference directly in the terminal:
    llama-cli -hf jeremygracey-ai/FetchMerck_AI:Q4_K_M
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf jeremygracey-ai/FetchMerck_AI:Q4_K_M
    # Run inference directly in the terminal:
    llama-cli -hf jeremygracey-ai/FetchMerck_AI:Q4_K_M
    Use pre-built binary
    # Download pre-built binary from:
    # https://github.com/ggerganov/llama.cpp/releases
    # Start a local OpenAI-compatible server with a web UI:
    ./llama-server -hf jeremygracey-ai/FetchMerck_AI:Q4_K_M
    # Run inference directly in the terminal:
    ./llama-cli -hf jeremygracey-ai/FetchMerck_AI:Q4_K_M
    Build from source code
    git clone https://github.com/ggerganov/llama.cpp.git
    cd llama.cpp
    cmake -B build
    cmake --build build -j --target llama-server llama-cli
    # Start a local OpenAI-compatible server with a web UI:
    ./build/bin/llama-server -hf jeremygracey-ai/FetchMerck_AI:Q4_K_M
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf jeremygracey-ai/FetchMerck_AI:Q4_K_M
    Use Docker
    docker model run hf.co/jeremygracey-ai/FetchMerck_AI:Q4_K_M
  • LM Studio
  • Jan
  • vLLM

    How to use jeremygracey-ai/FetchMerck_AI with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "jeremygracey-ai/FetchMerck_AI"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "jeremygracey-ai/FetchMerck_AI",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/jeremygracey-ai/FetchMerck_AI:Q4_K_M
  • Ollama

    How to use jeremygracey-ai/FetchMerck_AI with Ollama:

    ollama run hf.co/jeremygracey-ai/FetchMerck_AI:Q4_K_M
  • Unsloth Studio new

    How to use jeremygracey-ai/FetchMerck_AI with Unsloth Studio:

    Install Unsloth Studio (macOS, Linux, WSL)
    curl -fsSL https://unsloth.ai/install.sh | sh
    # Run unsloth studio
    unsloth studio -H 0.0.0.0 -p 8888
    # Then open http://localhost:8888 in your browser
    # Search for jeremygracey-ai/FetchMerck_AI to start chatting
    Install Unsloth Studio (Windows)
    irm https://unsloth.ai/install.ps1 | iex
    # Run unsloth studio
    unsloth studio -H 0.0.0.0 -p 8888
    # Then open http://localhost:8888 in your browser
    # Search for jeremygracey-ai/FetchMerck_AI to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for jeremygracey-ai/FetchMerck_AI to start chatting
  • Docker Model Runner

    How to use jeremygracey-ai/FetchMerck_AI with Docker Model Runner:

    docker model run hf.co/jeremygracey-ai/FetchMerck_AI:Q4_K_M
  • Lemonade

    How to use jeremygracey-ai/FetchMerck_AI with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull jeremygracey-ai/FetchMerck_AI:Q4_K_M
    Run and chat with the model
    lemonade run user.FetchMerck_AI-Q4_K_M
    List all available models
    lemonade list
FetchMerck_AI
4.82 GB
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  • 1 contributor
History: 10 commits
jeremygracey-ai's picture
jeremygracey-ai
Update README.md
684d69b verified 15 days ago
  • chroma_db
    Initial upload of chroma_db/ directory about 2 months ago
  • .gitattributes
    1.65 kB
    Initial upload of chroma_db/ directory about 2 months ago
  • Dockerfile
    1.38 kB
    Initial upload of Dockerfile about 2 months ago
  • README.md
    4.52 kB
    Update README.md 15 days ago
  • app_logic.py
    1.93 kB
    Initial upload of app_logic.py about 2 months ago
  • main.py
    1.45 kB
    Initial upload of main.py about 2 months ago
  • mistral-7b-instruct-v0.1.Q4_K_M.gguf
    4.37 GB
    xet
    Initial upload of mistral-7b-instruct-v0.1.Q4_K_M.gguf about 2 months ago