Instructions to use SkylarWhite/SahabatAI-MediChatIndo-8B-v1-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 SkylarWhite/SahabatAI-MediChatIndo-8B-v1-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 SkylarWhite/SahabatAI-MediChatIndo-8B-v1-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf SkylarWhite/SahabatAI-MediChatIndo-8B-v1-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf SkylarWhite/SahabatAI-MediChatIndo-8B-v1-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf SkylarWhite/SahabatAI-MediChatIndo-8B-v1-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 SkylarWhite/SahabatAI-MediChatIndo-8B-v1-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf SkylarWhite/SahabatAI-MediChatIndo-8B-v1-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 SkylarWhite/SahabatAI-MediChatIndo-8B-v1-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf SkylarWhite/SahabatAI-MediChatIndo-8B-v1-gguf:Q4_K_M
Use Docker
docker model run hf.co/SkylarWhite/SahabatAI-MediChatIndo-8B-v1-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use SkylarWhite/SahabatAI-MediChatIndo-8B-v1-gguf with Ollama:
ollama run hf.co/SkylarWhite/SahabatAI-MediChatIndo-8B-v1-gguf:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use SkylarWhite/SahabatAI-MediChatIndo-8B-v1-gguf with Docker Model Runner:
docker model run hf.co/SkylarWhite/SahabatAI-MediChatIndo-8B-v1-gguf:Q4_K_M
- Lemonade
How to use SkylarWhite/SahabatAI-MediChatIndo-8B-v1-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull SkylarWhite/SahabatAI-MediChatIndo-8B-v1-gguf:Q4_K_M
Run and chat with the model
lemonade run user.SahabatAI-MediChatIndo-8B-v1-gguf-Q4_K_M
List all available models
lemonade list
- Atomic Chat
SkylarWhite/SahabatAI-MediChatIndo-8B-v1-gguf
This is hosts various GGUF quantized versions of the gmonsoon/SahabatAI-MediChatIndo-8B-v1 model. This model is designed for medical and general-purpose conversational AI in Indonesian and is based on the LLaMA3 architecture. The GGUF format is optimized for efficient inference on low-resource devices and fast deployment.
Model Overview
SahabatAI-MediChatIndo-8B-v1 is a fine-tuned model created by merging:
It has been optimized for understanding and responding in medical and general Indonesian conversations.
GGUF Quantized Versions
The following GGUF quantized versions are available in this repository:
- 16-bit (F16): High-precision quantization for use cases requiring maximal accuracy.
- Q4_K_M: Balanced between speed and performance, ideal for most use cases.
- Q5_K_M: Improved precision over Q4 while maintaining efficient performance.
- Q8_0: Full precision for demanding tasks where accuracy is critical.
Feedback and Contributions
Feedback and contributions are welcome! Please open an issue or contact the model's author for further discussions.
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Base model
gmonsoon/SahabatAI-MediChatIndo-8B-v1