Instructions to use meta-llama/Llama-3.3-70B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meta-llama/Llama-3.3-70B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="meta-llama/Llama-3.3-70B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.3-70B-Instruct")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.3-70B-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]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use meta-llama/Llama-3.3-70B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meta-llama/Llama-3.3-70B-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": "meta-llama/Llama-3.3-70B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/meta-llama/Llama-3.3-70B-Instruct

SGLang

How to use meta-llama/Llama-3.3-70B-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 "meta-llama/Llama-3.3-70B-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": "meta-llama/Llama-3.3-70B-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 "meta-llama/Llama-3.3-70B-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": "meta-llama/Llama-3.3-70B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use meta-llama/Llama-3.3-70B-Instruct with Docker Model Runner:
```
docker model run hf.co/meta-llama/Llama-3.3-70B-Instruct
```

request access: Error - Cannot access gated repo for url

#124

by zack-1234 - opened Jul 16, 2025

Discussion

zack-1234

Jul 16, 2025

Hi there,
I used to be able to access this. for personal testing PoC. But now I started to get an error, despite auth'ing about Meta LLaMA 3.3 being a gated model.
How do I request access please?

│ /usr/src/.venv/lib/python3.11/site-packages/huggingface_hub/utils/_http.py:4 │
│ 26 in hf_raise_for_status │
│ │
│ 423 │ │ │ message = ( │
│ 424 │ │ │ │ f"{response.status_code} Client Error." + "\n\n" + f"C │
│ 425 │ │ │ ) │
│ ❱ 426 │ │ │ raise _format(GatedRepoError, message, response) from e │
│ 427 │ │ │
│ 428 │ │ elif error_message == "Access to this resource is disabled.": │
│ 429 │ │ │ message = ( │
│ │
│ ╭───────────────────────────────── locals ─────────────────────────────────╮ │
│ │ endpoint_name = None │ │
│ │ error_code = 'GatedRepo' │ │
│ │ error_message = 'Access to model meta-llama/Llama-3.3-70B-Instruct is │ │
│ │ restricted. You must have a'+61 │ │
│ │ message = '401 Client Error.\n\nCannot access gated repo for url │ │
│ │ https://huggingface.co/meta-'+62 │ │
│ │ response = <Response [401]> │ │
│ ╰──────────────────────────────────────────────────────────────────────────╯ │
╰──────────────────────────────────────────────────────────────────────────────╯
GatedRepoError: 401 Client Error. (Request ID:
Root=1-6877bfc7-601ceeb328ab7b196ada7d78;29121faa-a5b3-4855-82da-d987fab2a5a2)

Cannot access gated repo for url
https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct/resolve/main/adapter_co
nfig.json.
Access to model meta-llama/Llama-3.3-70B-Instruct is restricted. You must have
access to it and be authenticated to access it. Please log in.
Error: DownloadError

xujfcn

Feb 24

Great discussion! For anyone wanting to quickly test this, Crazyrouter offers API access to this model. No infrastructure setup needed — just an API key and the standard OpenAI SDK.

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment