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

Llama-3.2-1B-Instruct-bnb-4bit (Indonesian Fine-tuned)

Overview

This is Llama-3.2-1B-Instruct, a fine-tuned model based on the Meta-Llama-3.2-1B-Instruct-bnb-4bit architecture. The model has been specifically trained on the Stanford Alpaca Instruction dataset, which has been translated into Indonesian. This fine-tuning enhances the model's ability to understand and generate responses in Indonesian, making it suitable for various language-specific tasks and instruction-following applications.

Features

Model Details

  • Trained using the Stanford Alpaca Instruction dataset translated into Indonesian.
  • Supports a wide range of text-generation and instruction-following tasks.
  • Designed with 4-bit quantization for efficient inference and deployment.
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Model size
1B params
Architecture
llama
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