Instructions to use meta-llama/Llama-3.1-70B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meta-llama/Llama-3.1-70B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="meta-llama/Llama-3.1-70B-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-3.1-70B-Instruct")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-70B-Instruct", device_map="auto")
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
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use meta-llama/Llama-3.1-70B-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-3.1-70B-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-3.1-70B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/meta-llama/Llama-3.1-70B-Instruct

SGLang

How to use meta-llama/Llama-3.1-70B-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-3.1-70B-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-3.1-70B-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-3.1-70B-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-3.1-70B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use meta-llama/Llama-3.1-70B-Instruct with Docker Model Runner:
```
docker model run hf.co/meta-llama/Llama-3.1-70B-Instruct
```

Bug in config.json?

by dhruvmullick - opened Jul 23, 2024

Discussion

dhruvmullick

Jul 23, 2024

•

edited Jul 23, 2024

While loading the model, I'm getting a key error:

model = transformers.AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3.1-70B-Instruct", torch_dtype=torch.bfloat16, quantization_config = quant_config)

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py", line 564, in from_pretrained
    return model_class.from_pretrained(
  File "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py", line 3775, in from_pretrained
    model = cls(config, *model_args, **model_kwargs)
  File "/usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py", line 1066, in __init__
    self.model = LlamaModel(config)
  File "/usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py", line 845, in __init__
    [LlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
  File "/usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py", line 845, in <listcomp>
    [LlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
  File "/usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py", line 632, in __init__
    self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
  File "/usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py", line 306, in __init__
    self.rotary_emb = LlamaRotaryEmbedding(config=self.config)
  File "/usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py", line 110, in __init__
    self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling["type"])
KeyError: 'type'

This seems related to the latest config update
https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct/commit/25acb1b514688b222a02a89c6976a8d7ad0e017f

I'm using transformers==4.43

@ArthurZ @osanseviero