Instructions to use FriendliAI/Meta-Llama-3-70B-Instruct-fp8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FriendliAI/Meta-Llama-3-70B-Instruct-fp8 with Transformers:

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

pipe = pipeline("text-generation", model="FriendliAI/Meta-Llama-3-70B-Instruct-fp8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FriendliAI/Meta-Llama-3-70B-Instruct-fp8")
model = AutoModelForCausalLM.from_pretrained("FriendliAI/Meta-Llama-3-70B-Instruct-fp8")
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 FriendliAI/Meta-Llama-3-70B-Instruct-fp8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FriendliAI/Meta-Llama-3-70B-Instruct-fp8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FriendliAI/Meta-Llama-3-70B-Instruct-fp8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FriendliAI/Meta-Llama-3-70B-Instruct-fp8

SGLang

How to use FriendliAI/Meta-Llama-3-70B-Instruct-fp8 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 "FriendliAI/Meta-Llama-3-70B-Instruct-fp8" \
    --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": "FriendliAI/Meta-Llama-3-70B-Instruct-fp8",
		"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 "FriendliAI/Meta-Llama-3-70B-Instruct-fp8" \
        --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": "FriendliAI/Meta-Llama-3-70B-Instruct-fp8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FriendliAI/Meta-Llama-3-70B-Instruct-fp8 with Docker Model Runner:
```
docker model run hf.co/FriendliAI/Meta-Llama-3-70B-Instruct-fp8
```

Cannot load model after downloading all safetensors

by glli - opened Apr 24, 2024

Discussion

glli

Apr 24, 2024

It seems that you forgot adding metadata to the safetensors, and it is impossible to load them with transformers. Could you check it?

taebum93

FriendliAI org Apr 24, 2024

•

edited Apr 24, 2024

@glli
Thank you for downloading our checkpoint.
This model checkpoint is not executable with transformers library. You can run this model using Friendli Container with a proper GPU (e.g., NVIDIA H100 or L4 GPU).
For details, please refer to our model card.

glli

Apr 25, 2024

Thanks for your information. Could you please add this notification to the model card? I have spent hours downloading the checkpoints. And found it cannot run with transformers, very disappointing.

glli changed discussion status to closed Apr 25, 2024

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