Instructions to use hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4 with Transformers:

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

pipe = pipeline("text-generation", model="hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4")
model = AutoModelForMultimodalLM.from_pretrained("hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4")
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 Settings

vLLM

How to use hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4

SGLang

How to use hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4 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 "hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4" \
    --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": "hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4",
		"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 "hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4" \
        --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": "hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4 with Docker Model Runner:
```
docker model run hf.co/hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4
```

Cuda failure 1 'invalid argument'

by JulianGerhard - opened Jul 25, 2024

Discussion

JulianGerhard

Jul 25, 2024

Hi all,

I tried to run the given model on the following host:

H100x8
Ubuntu 22.04
CPU x128
RAM x1.76 TB
Accelerators: ConnectX-7 x8, Hopper H100 (80 GB GPU memory) x8, NVSwitch Hopper x4
CUDA: 12.2
CUDA Docker Toolkit: properly installed

with the command:

docker run --gpus all --shm-size 1g -ti -p 8080:80 \
  -v hf_cache:/data \
  -e MODEL_ID=hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4 \
  -e NUM_SHARD=8 \
  -e QUANTIZE=awq \
  -e HF_TOKEN=$(cat ~/.cache/huggingface/token) \
  -e MAX_INPUT_LENGTH=4000 \
  -e MAX_TOTAL_TOKENS=4096 \
  ghcr.io/huggingface/text-generation-inference:2.2.0

Everything works as expected, but after trying to start the sharding, I always receive:

NCCL WARN Failed to execute operation Connect from rank 1, retcode 3
Cuda failure 1 'invalid argument'

Now I tried a different host yesterday with 8 x A100 which actually worked out of the box without this error, leading me to the question if someone experienced the same error on a system working with H100 or if it might be something host specific?

Best
Julian

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