Instructions to use RedHatAI/Meta-Llama-3-8B-Instruct-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/Meta-Llama-3-8B-Instruct-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="RedHatAI/Meta-Llama-3-8B-Instruct-FP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RedHatAI/Meta-Llama-3-8B-Instruct-FP8")
model = AutoModelForCausalLM.from_pretrained("RedHatAI/Meta-Llama-3-8B-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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use RedHatAI/Meta-Llama-3-8B-Instruct-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/Meta-Llama-3-8B-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": "RedHatAI/Meta-Llama-3-8B-Instruct-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RedHatAI/Meta-Llama-3-8B-Instruct-FP8

SGLang

How to use RedHatAI/Meta-Llama-3-8B-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 "RedHatAI/Meta-Llama-3-8B-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": "RedHatAI/Meta-Llama-3-8B-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 "RedHatAI/Meta-Llama-3-8B-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": "RedHatAI/Meta-Llama-3-8B-Instruct-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RedHatAI/Meta-Llama-3-8B-Instruct-FP8 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/Meta-Llama-3-8B-Instruct-FP8
```

Fails to run with nm-vllm

by clintonruairi - opened May 11, 2024

Discussion

clintonruairi

May 11, 2024

•

edited May 11, 2024

Hello,

python version: 3.11
os: WSL

Only other dependencies are those specified by nm-vllm. Followed the instructions here:

https://github.com/neuralmagic/nm-vllm

Ran the model as such, after installing all of the dependencies in a fresh conda env:

(nm-vllm) unix@rog-zephyrus:/code/structure$ pip install nm-vllm[sparse]
(nm-vllm) unix@rog-zephyrus:/code/structure$ python -m vllm.entrypoints.openai.api_server --model neuralmagic/Meta-Llama-3-8B-Instruct-FP8 --sparsity sparse_w16a16
INFO 05-11 23:41:26 api_server.py:149] vLLM API server version 0.2.0

warnings.warn(
Traceback (most recent call last):
File "", line 198, in _run_module_as_main
File "", line 88, in _run_code
File "/home/unix/miniconda3/envs/nm-vllm/lib/python3.11/site-packages/vllm/entrypoints/openai/api_server.py", line 157, in
engine = AsyncLLMEngine.from_engine_args(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/unix/miniconda3/envs/nm-vllm/lib/python3.11/site-packages/vllm/engine/async_llm_engine.py", line 331, in from_engine_args
engine_configs = engine_args.create_engine_configs()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/unix/miniconda3/envs/nm-vllm/lib/python3.11/site-packages/vllm/engine/arg_utils.py", line 405, in create_engine_configs
model_config = ModelConfig(
^^^^^^^^^^^^
File "/home/unix/miniconda3/envs/nm-vllm/lib/python3.11/site-packages/vllm/config.py", line 133, in init
self._verify_quantization()
File "/home/unix/miniconda3/envs/nm-vllm/lib/python3.11/site-packages/vllm/config.py", line 234, in _verify_quantization
raise ValueError(
ValueError: Unknown quantization method: fp8. Must be one of ['awq', 'gptq', 'squeezellm', 'marlin'].

am I doing something wrong here? I also tried to run the model in standard vllm, v0.4.2. Performance was great, but about 30% of responses were bizarre, lots of responses containing "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!".

robertgshaw2

Red Hat AI org May 11, 2024

Fp8 will be supported in our next release of nm-vllm

There is a bug in v0.4.2 for fp8 static quantization. It is resolved on the latest main and will be fixed in v0.4.3

So I would suggest installing vllm from source

mgoin changed discussion status to closed Jun 8, 2024

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