Instructions to use QuantFactory/BioMistral-MedMNX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuantFactory/BioMistral-MedMNX-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QuantFactory/BioMistral-MedMNX-GGUF", dtype="auto") - llama-cpp-python
How to use QuantFactory/BioMistral-MedMNX-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/BioMistral-MedMNX-GGUF", filename="BioMistral-MedMNX.Q2_K.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use QuantFactory/BioMistral-MedMNX-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/BioMistral-MedMNX-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/BioMistral-MedMNX-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/BioMistral-MedMNX-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/BioMistral-MedMNX-GGUF: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 QuantFactory/BioMistral-MedMNX-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/BioMistral-MedMNX-GGUF: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 QuantFactory/BioMistral-MedMNX-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/BioMistral-MedMNX-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/BioMistral-MedMNX-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use QuantFactory/BioMistral-MedMNX-GGUF with Ollama:
ollama run hf.co/QuantFactory/BioMistral-MedMNX-GGUF:Q4_K_M
- Unsloth Studio new
How to use QuantFactory/BioMistral-MedMNX-GGUF 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 QuantFactory/BioMistral-MedMNX-GGUF 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 QuantFactory/BioMistral-MedMNX-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for QuantFactory/BioMistral-MedMNX-GGUF to start chatting
- Docker Model Runner
How to use QuantFactory/BioMistral-MedMNX-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/BioMistral-MedMNX-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/BioMistral-MedMNX-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/BioMistral-MedMNX-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.BioMistral-MedMNX-GGUF-Q4_K_M
List all available models
lemonade list
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/BioMistral-MedMNX-GGUF:# Run inference directly in the terminal:
llama-cli -hf QuantFactory/BioMistral-MedMNX-GGUF: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 QuantFactory/BioMistral-MedMNX-GGUF:# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/BioMistral-MedMNX-GGUF: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 QuantFactory/BioMistral-MedMNX-GGUF:# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/BioMistral-MedMNX-GGUF:Use Docker
docker model run hf.co/QuantFactory/BioMistral-MedMNX-GGUF:QuantFactory/BioMistral-MedMNX-GGUF
This is quantized version of BioMistral/BioMistral-MedMNX created using llama.cpp
Original Model Card
BioMistral-MedMNX
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the DARE TIES merge method using johnsnowlabs/JSL-MedMNX-7B as a base.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
models:
- model: johnsnowlabs/JSL-MedMNX-7B
parameters:
density: 0.53
weight: 0.4
- model: BioMistral/BioMistral-7B-DARE
parameters:
density: 0.53
weight: 0.3
merge_method: dare_ties
tokenizer_source: union
base_model: johnsnowlabs/JSL-MedMNX-7B
parameters:
int8_mask: true
dtype: bfloat16
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Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/BioMistral-MedMNX-GGUF:# Run inference directly in the terminal: llama-cli -hf QuantFactory/BioMistral-MedMNX-GGUF: