Instructions to use RedHatAI/Meta-Llama-3.1-70B-Instruct-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/Meta-Llama-3.1-70B-Instruct-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="RedHatAI/Meta-Llama-3.1-70B-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-3.1-70B-Instruct-FP8")
model = AutoModelForCausalLM.from_pretrained("RedHatAI/Meta-Llama-3.1-70B-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 RedHatAI/Meta-Llama-3.1-70B-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-3.1-70B-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": "RedHatAI/Meta-Llama-3.1-70B-Instruct-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RedHatAI/Meta-Llama-3.1-70B-Instruct-FP8

SGLang

How to use RedHatAI/Meta-Llama-3.1-70B-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 "RedHatAI/Meta-Llama-3.1-70B-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": "RedHatAI/Meta-Llama-3.1-70B-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 "RedHatAI/Meta-Llama-3.1-70B-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": "RedHatAI/Meta-Llama-3.1-70B-Instruct-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RedHatAI/Meta-Llama-3.1-70B-Instruct-FP8 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/Meta-Llama-3.1-70B-Instruct-FP8
```

KeyError: 'model.layers.10.mlp.down_proj.input_scale'

by justinbahasa - opened Oct 17, 2024

Discussion

justinbahasa

Oct 17, 2024

got this error when using vllm, it seems that there's some mismatch keys on llama 3.1 by meta weights keys and this fp8 weights keys. How to fix it?

Traceback (most recent call last):
  File "~/miniconda3/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "~/miniconda3/lib/python3.12/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "~/miniconda3/lib/python3.12/site-packages/vllm/engine/multiprocessing/engine.py", line 388, in run_mp_engine
    engine = MQLLMEngine.from_engine_args(engine_args=engine_args,
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "~/miniconda3/lib/python3.12/site-packages/vllm/engine/multiprocessing/engine.py", line 138, in from_engine_args
    return cls(
           ^^^^
  File "~/miniconda3/lib/python3.12/site-packages/vllm/engine/multiprocessing/engine.py", line 78, in __init__
    self.engine = LLMEngine(*args,
                  ^^^^^^^^^^^^^^^^
  File "~/miniconda3/lib/python3.12/site-packages/vllm/engine/llm_engine.py", line 325, in __init__
    self.model_executor = executor_class(
                          ^^^^^^^^^^^^^^^
  File "~/miniconda3/lib/python3.12/site-packages/vllm/executor/distributed_gpu_executor.py", line 26, in __init__
    super().__init__(*args, **kwargs)
  File "~/miniconda3/lib/python3.12/site-packages/vllm/executor/executor_base.py", line 47, in __init__
    self._init_executor()
  File "~/miniconda3/lib/python3.12/site-packages/vllm/executor/multiproc_gpu_executor.py", line 111, in _init_executor
    self._run_workers("load_model",
  File "~/miniconda3/lib/python3.12/site-packages/vllm/executor/multiproc_gpu_executor.py", line 185, in _run_workers
    driver_worker_output = driver_worker_method(*args, **kwargs)
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "~/miniconda3/lib/python3.12/site-packages/vllm/worker/worker.py", line 183, in load_model
    self.model_runner.load_model()
  File "~/miniconda3/lib/python3.12/site-packages/vllm/worker/model_runner.py", line 1016, in load_model
    self.model = get_model(model_config=self.model_config,
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "~/miniconda3/lib/python3.12/site-packages/vllm/model_executor/model_loader/__init__.py", line 19, in get_model
    return loader.load_model(model_config=model_config,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "~/miniconda3/lib/python3.12/site-packages/vllm/model_executor/model_loader/loader.py", line 403, in load_model
    model.load_weights(self._get_all_weights(model_config, model))
  File "~/miniconda3/lib/python3.12/site-packages/vllm/model_executor/models/ultravox.py", line 502, in load_weights
    self.language_model.load_weights(weights_group["language_model"])
  File "~/miniconda3/lib/python3.12/site-packages/vllm/model_executor/models/llama.py", line 544, in load_weights
    param = params_dict[name]
            ~~~~~~~~~~~^^^^^^
KeyError: 'model.layers.10.mlp.down_proj.input_scale'

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