Instructions to use IntelligentEstate/MediMesh-Instruct-3B-Q_8-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use IntelligentEstate/MediMesh-Instruct-3B-Q_8-GGUF 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 IntelligentEstate/MediMesh-Instruct-3B-Q_8-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf IntelligentEstate/MediMesh-Instruct-3B-Q_8-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf IntelligentEstate/MediMesh-Instruct-3B-Q_8-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf IntelligentEstate/MediMesh-Instruct-3B-Q_8-GGUF:Q8_0
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 IntelligentEstate/MediMesh-Instruct-3B-Q_8-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf IntelligentEstate/MediMesh-Instruct-3B-Q_8-GGUF:Q8_0
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 IntelligentEstate/MediMesh-Instruct-3B-Q_8-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf IntelligentEstate/MediMesh-Instruct-3B-Q_8-GGUF:Q8_0
Use Docker
docker model run hf.co/IntelligentEstate/MediMesh-Instruct-3B-Q_8-GGUF:Q8_0
- LM Studio
- Jan
- Ollama
How to use IntelligentEstate/MediMesh-Instruct-3B-Q_8-GGUF with Ollama:
ollama run hf.co/IntelligentEstate/MediMesh-Instruct-3B-Q_8-GGUF:Q8_0
- Unsloth Studio
How to use IntelligentEstate/MediMesh-Instruct-3B-Q_8-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 IntelligentEstate/MediMesh-Instruct-3B-Q_8-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 IntelligentEstate/MediMesh-Instruct-3B-Q_8-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for IntelligentEstate/MediMesh-Instruct-3B-Q_8-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use IntelligentEstate/MediMesh-Instruct-3B-Q_8-GGUF with Docker Model Runner:
docker model run hf.co/IntelligentEstate/MediMesh-Instruct-3B-Q_8-GGUF:Q8_0
- Lemonade
How to use IntelligentEstate/MediMesh-Instruct-3B-Q_8-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull IntelligentEstate/MediMesh-Instruct-3B-Q_8-GGUF:Q8_0
Run and chat with the model
lemonade run user.MediMesh-Instruct-3B-Q_8-GGUF-Q8_0
List all available models
lemonade list
IntelligentEstate/MedIT-Mesh-3B-Instruct-Q8_0-GGUF Model for swar/edge use Multi check Quant training for the most Ideal and coherent model
This model was converted to GGUF format from meditsolutions/MedIT-Mesh-3B-Instruct using llama.cpp
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo IntelligentEstate/MedIT-Mesh-3B-Instruct-Q8_0-GGUF --hf-file medit-mesh-3b-instruct-q8_0.gguf -p "The meaning to life and the universe is"
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Hardware compatibility
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Model tree for IntelligentEstate/MediMesh-Instruct-3B-Q_8-GGUF
Base model
microsoft/Phi-3.5-mini-instruct Finetuned
meditsolutions/MedIT-Mesh-3B-Instruct