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

Libraries

How to use mlabonne/Meta-Llama-3-120B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mlabonne/Meta-Llama-3-120B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mlabonne/Meta-Llama-3-120B-Instruct")
model = AutoModelForCausalLM.from_pretrained("mlabonne/Meta-Llama-3-120B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlabonne/Meta-Llama-3-120B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlabonne/Meta-Llama-3-120B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mlabonne/Meta-Llama-3-120B-Instruct

SGLang

How to use mlabonne/Meta-Llama-3-120B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mlabonne/Meta-Llama-3-120B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlabonne/Meta-Llama-3-120B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mlabonne/Meta-Llama-3-120B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlabonne/Meta-Llama-3-120B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mlabonne/Meta-Llama-3-120B-Instruct with Docker Model Runner:
```
docker model run hf.co/mlabonne/Meta-Llama-3-120B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Meta-Llama-3-120B-Instruct

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

It was inspired by large merges like:

Special thanks to Eric Hartford for both inspiring and evaluating this model and to Charles Goddard for creating MergeKit.

🔍 Applications

I recommend using this model for creative writing. It uses the Llama 3 chat template with a default context window of 8K (can be extended with rope theta).

Check the examples in the evaluation section to get an idea of its performance. The model is generally quite unhinged but has a good writing style. It sometimes outputs typos and is a big fan of uppercase.

⚡ Quantized models

Thanks to Bartowski, elinas, the mlx-community and others for providing these models.

🏆 Evaluation

This model is great for creative writing but struggles in other tasks. I'd say use it with caution and don't expect it to outperform GPT-4 outside of some very specific use cases.

X thread by Eric Hartford (creative writing): https://twitter.com/erhartford/status/1787050962114207886
X thread by Daniel Kaiser (creative writing): https://twitter.com/spectate_or/status/1787257261309518101
X thread by Simon (reasoning): https://twitter.com/NewDigitalEdu/status/1787403266894020893
r/LocalLLaMa: https://www.reddit.com/r/LocalLLaMA/comments/1cl525q/goliath_lovers_where_is_the_feedback_about/

Creative Writing

Thanks to Sam Paech for evaluating this model and sending me his outputs!

🧩 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/Meta-Llama-3-120B-Instruct"
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"])