Text Generation
llama-cpp-python
GGUF
English
rag
healthcare
clinical-decision-support
medical
merck-manual
retrieval-augmented-generation
mistral
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
Download chroma_db/chroma.sqlite3 from jeremygracey-ai/FetchMerck_AI: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/jeremygracey-ai/FetchMerck_AI/resolve/main/chroma_db/chroma.sqlite3
- Command line
-
hf download hf://jeremygracey-ai/FetchMerck_AI/chroma_db/chroma.sqlite3
-
curl -L -o chroma.sqlite3 https://huggingface.co/jeremygracey-ai/FetchMerck_AI/resolve/main/chroma_db/chroma.sqlite3
334 MB
- Xet hash:
- 49fd71c5f4e183c6a7351c8cba1fcd0168eb6c15b4f760175a73aafe70a31236
- Size of remote file:
- 334 MB
- SHA256:
- b771b88b60e51d47b950720148cc821acd2baf0eff9dcfa335087b68045e103f
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