Instructions to use meta-llama/Meta-Llama-3-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meta-llama/Meta-Llama-3-8B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="meta-llama/Meta-Llama-3-8B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-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/Meta-Llama-3-8B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meta-llama/Meta-Llama-3-8B-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/Meta-Llama-3-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/meta-llama/Meta-Llama-3-8B-Instruct

SGLang

How to use meta-llama/Meta-Llama-3-8B-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/Meta-Llama-3-8B-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/Meta-Llama-3-8B-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/Meta-Llama-3-8B-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/Meta-Llama-3-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use meta-llama/Meta-Llama-3-8B-Instruct with Docker Model Runner:
```
docker model run hf.co/meta-llama/Meta-Llama-3-8B-Instruct
```

*** OSError: meta-llama/Meta-Llama-3-8B-Instruct does not appear to have a file named pytorch_model.bin, tf_model.h5, model.ckpt or flax_model.msgpack.

#131

by akjagadish - opened May 29, 2024

Discussion

akjagadish

May 29, 2024

OSError: meta-llama/Meta-Llama-3-8B-Instruct does not appear to have a file named pytorch_model.bin, tf_model.h5, model.ckpt or flax_model.msgpack.

akjagadish changed discussion title from trying to use LLama-3-8B-Instruct model to self.model = AutoModel.from_pretrained(engine, device_map="auto", torch_dtype=torch.bfloat16, use_auth_token=hf_key) May 29, 2024

akjagadish changed discussion title from self.model = AutoModel.from_pretrained(engine, device_map="auto", torch_dtype=torch.bfloat16, use_auth_token=hf_key) to *** OSError: meta-llama/Meta-Llama-3-8B-Instruct does not appear to have a file named pytorch_model.bin, tf_model.h5, model.ckpt or flax_model.msgpack. May 29, 2024

zhangliyi97

Jun 16, 2024

•

edited Jun 16, 2024

I had the same error message with meta llama3 8b instruct. After I downgraded to torch==2.1.0 and transformers==4.40.0, it worked for me.

MichaelKang

Jul 2, 2024

I was bothered by this for quite a while as well. You probably solved this problem, but I hope others who see this do not have to go through the same tedious thing I went through again.

You are seeing this because you need to convert the original downloaded file to 'hf'(or that the converting did not go completely well), see here: https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py

Remember 3 things:

specify the llama version
make sure you have enough RAM for it(more than 16 GB). If when running it it shows 'killed', this is why
if there is dependency issues: download transformers from here: pip install git+https://github.com/huggingface/transformers

Then it should work
And when loading the tokenizer if something is wrong, use 'AutoTokenizer'

SayfAl-deen

Apr 13, 2025

This almost broke me. This thread definitely helped me figure out my issue.
as @MichaelKang mentioned, this error ( and others depend if from_tf=True) are due to failure to download the model.
For me the issue was related to request package. This solution fixed it for me:
https://stackoverflow.com/a/76218591/13695652

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