Instructions to use mlabonne/Meta-Llama-3-225B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlabonne/Meta-Llama-3-225B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="mlabonne/Meta-Llama-3-225B-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-3-225B-Instruct")
model = AutoModelForCausalLM.from_pretrained("mlabonne/Meta-Llama-3-225B-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-3-225B-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-3-225B-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-3-225B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mlabonne/Meta-Llama-3-225B-Instruct

SGLang

How to use mlabonne/Meta-Llama-3-225B-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-3-225B-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-3-225B-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-3-225B-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-3-225B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mlabonne/Meta-Llama-3-225B-Instruct with Docker Model Runner:
```
docker model run hf.co/mlabonne/Meta-Llama-3-225B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Meta-Llama-3-225B-Instruct

Meta-Llama-3-225B-Instruct is a self-merge with meta-llama/Meta-Llama-3-70B-Instruct.

It was inspired by large merges like:

I don't recommend using it as it seems to break quite easily (but feel free to prove me wrong).

🧩 Configuration

slices:
- sources:
  - layer_range: [0, 20]
    model: mlabonne/Meta-Llama-3-120B-Instruct
- sources:
  - layer_range: [10, 30]
    model: mlabonne/Meta-Llama-3-120B-Instruct
- sources:
  - layer_range: [20, 40]
    model: mlabonne/Meta-Llama-3-120B-Instruct
- sources:
  - layer_range: [30, 50]
    model: mlabonne/Meta-Llama-3-120B-Instruct
- sources:
  - layer_range: [40, 60]
    model: mlabonne/Meta-Llama-3-120B-Instruct
- sources:
  - layer_range: [50, 70]
    model: mlabonne/Meta-Llama-3-120B-Instruct
- sources:
  - layer_range: [60, 80]
    model: mlabonne/Meta-Llama-3-120B-Instruct
- sources:
  - layer_range: [70, 90]
    model: mlabonne/Meta-Llama-3-120B-Instruct
- sources:
  - layer_range: [80, 100]
    model: mlabonne/Meta-Llama-3-120B-Instruct
- sources:
  - layer_range: [90, 110]
    model: mlabonne/Meta-Llama-3-120B-Instruct
- sources:
  - layer_range: [100, 120]
    model: mlabonne/Meta-Llama-3-120B-Instruct
- sources:
  - layer_range: [110, 130]
    model: mlabonne/Meta-Llama-3-120B-Instruct
- sources:
  - layer_range: [120, 140]
    model: mlabonne/Meta-Llama-3-120B-Instruct
merge_method: passthrough
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/Meta-Llama-3-220B-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"])

Downloads last month: 15

Model tree for mlabonne/Meta-Llama-3-225B-Instruct

Base model

meta-llama/Meta-Llama-3-70B

Finetuned

meta-llama/Meta-Llama-3-70B-Instruct

Finetuned

mlabonne/Meta-Llama-3-120B-Instruct

Finetuned

(1)

this model

Finetunes

1 model

Quantizations

1 model