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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)
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • llama.cpp

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

    Install (macOS, Linux)
    curl -LsSf https://llama.app/install.sh | sh
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -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 serve -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/ggml-org/llama.cpp.git
    cd llama.cpp
    cmake -B build
    cmake --build build -j --target llama-app
    # Start a local OpenAI-compatible server with a web UI:
    ./build/bin/llama serve -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
  • Llama new
  • 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 Desktop
  • 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
  • Atomic Chat
FetchMerck_AI / chroma_db
449 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
jeremygracey-ai's picture
jeremygracey-ai
Initial upload of chroma_db/ directory
d1e02b7 verified 8 months ago
  • e4c42398-50ba-4b08-871a-cb9b619c61e9
    Initial upload of chroma_db/ directory 8 months ago
  • chroma.sqlite3
    334 MB
    xet
    Initial upload of chroma_db/ directory 8 months ago