Instructions to use kanrishaurus/llama3-8b-sahabatai-v1-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kanrishaurus/llama3-8b-sahabatai-v1-instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="kanrishaurus/llama3-8b-sahabatai-v1-instruct-GGUF",
	filename="llama3-8b-cpt-sahabatai-v1-instruct-f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use kanrishaurus/llama3-8b-sahabatai-v1-instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf kanrishaurus/llama3-8b-sahabatai-v1-instruct-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf kanrishaurus/llama3-8b-sahabatai-v1-instruct-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf kanrishaurus/llama3-8b-sahabatai-v1-instruct-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf kanrishaurus/llama3-8b-sahabatai-v1-instruct-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 kanrishaurus/llama3-8b-sahabatai-v1-instruct-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf kanrishaurus/llama3-8b-sahabatai-v1-instruct-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 kanrishaurus/llama3-8b-sahabatai-v1-instruct-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kanrishaurus/llama3-8b-sahabatai-v1-instruct-GGUF:F16

Use Docker

docker model run hf.co/kanrishaurus/llama3-8b-sahabatai-v1-instruct-GGUF:F16

LM Studio
Jan

vLLM

How to use kanrishaurus/llama3-8b-sahabatai-v1-instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kanrishaurus/llama3-8b-sahabatai-v1-instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kanrishaurus/llama3-8b-sahabatai-v1-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kanrishaurus/llama3-8b-sahabatai-v1-instruct-GGUF:F16

Ollama
How to use kanrishaurus/llama3-8b-sahabatai-v1-instruct-GGUF with Ollama:
```
ollama run hf.co/kanrishaurus/llama3-8b-sahabatai-v1-instruct-GGUF:F16
```

Unsloth Studio

How to use kanrishaurus/llama3-8b-sahabatai-v1-instruct-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 kanrishaurus/llama3-8b-sahabatai-v1-instruct-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 kanrishaurus/llama3-8b-sahabatai-v1-instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kanrishaurus/llama3-8b-sahabatai-v1-instruct-GGUF to start chatting

Docker Model Runner
How to use kanrishaurus/llama3-8b-sahabatai-v1-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/kanrishaurus/llama3-8b-sahabatai-v1-instruct-GGUF:F16
```

Lemonade

How to use kanrishaurus/llama3-8b-sahabatai-v1-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kanrishaurus/llama3-8b-sahabatai-v1-instruct-GGUF:F16

Run and chat with the model

lemonade run user.llama3-8b-sahabatai-v1-instruct-GGUF-F16

List all available models

lemonade list

Llama 3 8B Sahabat-AI Instruct (GGUF Versions)

This repository contains GGUF converted and quantized versions of the Sahabat-AI/llama3-8b-cpt-sahabatai-v1-instruct model, converted using llama.cpp.

This model is an instruction-tuned variant, suitable for chat and following commands.

Available GGUF Files:

1. `llama3-8b-cpt-sahabatai-v1-instruct-f16.gguf`

Format: FP16 (Full Precision)
Size: ~16.1 GB
Description: This is the full-precision GGUF conversion. It offers the highest fidelity but requires significant VRAM (approx. 16 GB).

2. `llama3-8b-cpt-sahabatai-v1-instruct-q4km.gguf`

Format: Q4_K_M (4-bit Quantized)
Size: ~4.58 GB (approximate, actual size may vary slightly)
Description: This is a highly optimized 4-bit quantized version, suitable for devices with limited VRAM (e.g., 8GB GPU VRAM). It offers a good balance between model size, performance, and minimal quality loss.

Original Model:

Sahabat-AI/llama3-8b-cpt-sahabatai-v1-instruct

How to Use:

Download the desired .gguf file and use it with llama.cpp, LM Studio, Ollama, or any other GGUF-compatible inference tool.

For llama.cpp CLI, you might use:

./main -m llama3-8b-cpt-sahabatai-v1-instruct-q4km.gguf -p "Write a story about a dragon." -n 128

Downloads last month: 4

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

16-bit

View +1 variant