How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mohammad2928git/medical_v4_gguf:F16
# Run inference directly in the terminal:
llama-cli -hf mohammad2928git/medical_v4_gguf:F16
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mohammad2928git/medical_v4_gguf:F16
# Run inference directly in the terminal:
llama-cli -hf mohammad2928git/medical_v4_gguf:F16
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 mohammad2928git/medical_v4_gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf mohammad2928git/medical_v4_gguf:F16
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 mohammad2928git/medical_v4_gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mohammad2928git/medical_v4_gguf:F16
Use Docker
docker model run hf.co/mohammad2928git/medical_v4_gguf:F16
Quick Links

Uploaded model

  • Developed by: mohammad2928git
  • License: apache-2.0
  • Finetuned from model : ruslanmv/Medical-Llama3-8B

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month
4
GGUF
Model size
8B params
Architecture
llama
Hardware compatibility
Log In to add your hardware

16-bit

Inference Providers NEW
This model isn't deployed by any Inference Provider. ๐Ÿ™‹ Ask for provider support

Model tree for mohammad2928git/medical_v4_gguf

Quantized
(11)
this model