Instructions to use QuantFactory/Meta-Llama-3-120B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use QuantFactory/Meta-Llama-3-120B-Instruct-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 QuantFactory/Meta-Llama-3-120B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Meta-Llama-3-120B-Instruct-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 QuantFactory/Meta-Llama-3-120B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Meta-Llama-3-120B-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 QuantFactory/Meta-Llama-3-120B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Meta-Llama-3-120B-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 QuantFactory/Meta-Llama-3-120B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Meta-Llama-3-120B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Meta-Llama-3-120B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Meta-Llama-3-120B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Meta-Llama-3-120B-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": "QuantFactory/Meta-Llama-3-120B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Meta-Llama-3-120B-Instruct-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Meta-Llama-3-120B-Instruct-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Meta-Llama-3-120B-Instruct-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use QuantFactory/Meta-Llama-3-120B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Meta-Llama-3-120B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Meta-Llama-3-120B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Meta-Llama-3-120B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Meta-Llama-3-120B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Meta-Llama-3-120B-Instruct- GGUF

This is quantized version of mlabonne/Meta-Llama-3-120B-Instruct created using llama.cpp

Model Description

Meta-Llama-3-120B-Instruct is a self-merge with meta-llama/Meta-Llama-3-70B-Instruct.

It was inspired by large merges like alpindale/goliath-120b, nsfwthrowitaway69/Venus-120b-v1.0, cognitivecomputations/MegaDolphin-120b, and wolfram/miquliz-120b-v2.0.

No eval yet, but it is approved by Eric Hartford: https://twitter.com/erhartford/status/1787050962114207886

🧩 Configuration

slices:
- sources:
  - layer_range: [0, 20]
    model: meta-llama/Meta-Llama-3-70B-Instruct
- sources:
  - layer_range: [10, 30]
    model: meta-llama/Meta-Llama-3-70B-Instruct
- sources:
  - layer_range: [20, 40]
    model: meta-llama/Meta-Llama-3-70B-Instruct
- sources:
  - layer_range: [30, 50]
    model: meta-llama/Meta-Llama-3-70B-Instruct
- sources:
  - layer_range: [40, 60]
    model: meta-llama/Meta-Llama-3-70B-Instruct
- sources:
  - layer_range: [50, 70]
    model: meta-llama/Meta-Llama-3-70B-Instruct
- sources:
  - layer_range: [60, 80]
    model: meta-llama/Meta-Llama-3-70B-Instruct
merge_method: passthrough
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/Llama-3-120B"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])