Instructions to use uygarkurt/Llama-3.1-8B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use uygarkurt/Llama-3.1-8B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="uygarkurt/Llama-3.1-8B-Instruct-GGUF",
	filename="Llama-3.1-8B-Instruct.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 uygarkurt/Llama-3.1-8B-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 uygarkurt/Llama-3.1-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf uygarkurt/Llama-3.1-8B-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf uygarkurt/Llama-3.1-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf uygarkurt/Llama-3.1-8B-Instruct-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 uygarkurt/Llama-3.1-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf uygarkurt/Llama-3.1-8B-Instruct-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 uygarkurt/Llama-3.1-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf uygarkurt/Llama-3.1-8B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/uygarkurt/Llama-3.1-8B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use uygarkurt/Llama-3.1-8B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "uygarkurt/Llama-3.1-8B-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": "uygarkurt/Llama-3.1-8B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/uygarkurt/Llama-3.1-8B-Instruct-GGUF:Q4_K_M

Ollama
How to use uygarkurt/Llama-3.1-8B-Instruct-GGUF with Ollama:
```
ollama run hf.co/uygarkurt/Llama-3.1-8B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use uygarkurt/Llama-3.1-8B-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 uygarkurt/Llama-3.1-8B-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 uygarkurt/Llama-3.1-8B-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 uygarkurt/Llama-3.1-8B-Instruct-GGUF to start chatting

How to use uygarkurt/Llama-3.1-8B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf uygarkurt/Llama-3.1-8B-Instruct-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "uygarkurt/Llama-3.1-8B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use uygarkurt/Llama-3.1-8B-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf uygarkurt/Llama-3.1-8B-Instruct-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default uygarkurt/Llama-3.1-8B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use uygarkurt/Llama-3.1-8B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/uygarkurt/Llama-3.1-8B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use uygarkurt/Llama-3.1-8B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull uygarkurt/Llama-3.1-8B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.1-8B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Llama-3.1-8B-Instruct (GGUF) — Unified llama.cpp Quantization Benchmarks

This repository provides a set of GGUF quantizations of Llama-3.1-8B-Instruct, produced with the official llama.cpp toolchain and evaluated under a single, consistent experimental protocol. The goal is to make it practical to choose a GGUF quantization format based on measured trade-offs among quality, model size, and CPU throughput. For the full benchmark evaluation, refer to the paper.

Paper: Which Quantization Should I Use? A Unified Evaluation of llama.cpp Quantization on Llama-3.1-8B-Instruct

What’s in this repo

All files are GGUF versions of the same base checkpoint:

Reference: FP16 GGUF baseline (used as the evaluation reference)
Legacy formats: Q4_0, Q4_1, Q5_0, Q5_1, Q8_0
K-quant formats: Q3_K_S, Q3_K_M, Q3_K_L, Q4_K_S, Q4_K_M, Q5_K_S, Q5_K_M, Q6_K

These are the same families evaluated in the paper’s unified benchmark sweep.

How the quantizations were produced

All quantized models were generated from the same FP16 GGUF input using the official llama.cpp quantizer, varying only the scheme identifier:

./llama-quantize <f16.gguf> <out.gguf> <SCHEME>

Citation

@article{kurt2026quantization,
  title={Which Quantization Should I Use? A Unified Evaluation of llama. cpp Quantization on Llama-3.1-8B-Instruct},
  author={Kurt, Uygar},
  journal={arXiv preprint arXiv:2601.14277},
  year={2026}
}

Downloads last month: 108

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

View +1 variant

Model tree for uygarkurt/Llama-3.1-8B-Instruct-GGUF

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Quantized

(644)

this model

Paper for uygarkurt/Llama-3.1-8B-Instruct-GGUF

Which Quantization Should I Use? A Unified Evaluation of llama.cpp Quantization on Llama-3.1-8B-Instruct

Paper • 2601.14277 • Published Jan 11