Instructions to use RedHatAI/Mistral-Nemo-Instruct-2407-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/Mistral-Nemo-Instruct-2407-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="RedHatAI/Mistral-Nemo-Instruct-2407-FP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RedHatAI/Mistral-Nemo-Instruct-2407-FP8")
model = AutoModelForCausalLM.from_pretrained("RedHatAI/Mistral-Nemo-Instruct-2407-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 Settings

vLLM

How to use RedHatAI/Mistral-Nemo-Instruct-2407-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/Mistral-Nemo-Instruct-2407-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/Mistral-Nemo-Instruct-2407-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RedHatAI/Mistral-Nemo-Instruct-2407-FP8

SGLang

How to use RedHatAI/Mistral-Nemo-Instruct-2407-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/Mistral-Nemo-Instruct-2407-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/Mistral-Nemo-Instruct-2407-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/Mistral-Nemo-Instruct-2407-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/Mistral-Nemo-Instruct-2407-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RedHatAI/Mistral-Nemo-Instruct-2407-FP8 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/Mistral-Nemo-Instruct-2407-FP8
```

Can not be inferenced with vllm openai server

by jjqsdq - opened Jul 19, 2024

Discussion

jjqsdq

Jul 19, 2024

•

edited Jul 19, 2024

export MODEL_DIR=/root/workspace/model/neuralmagic/Mistral-Nemo-Instruct-2407-FP8
export MODEL_NAME=neuralmagic/Mistral-Nemo-Instruct-2407-FP8
export MAX_MODEL_LEN=16384
CUDA_VISIBLE_DEVICES=0 python3 -m vllm.entrypoints.openai.api_server --tensor-parallel-size 1 --quantization="fp8" --host 0.0.0.0 --port 8080 --disable-log-requests --model $MODEL_DIR --served-model-name $MODEL_NAME --max-model-len $MAX_MODEL_LEN

WARNING 07-19 03:04:14 fp8.py:48] Detected fp8 checkpoint. Please note that the format is experimental and subject to change.
[rank0]: Traceback (most recent call last):
[rank0]:   File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
[rank0]:     return _run_code(code, main_globals, None,
[rank0]:   File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
[rank0]:     exec(code, run_globals)
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/api_server.py", line 196, in <module>
[rank0]:     engine = AsyncLLMEngine.from_engine_args(
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 395, in from_engine_args
[rank0]:     engine = cls(
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 349, in __init__
[rank0]:     self.engine = self._init_engine(*args, **kwargs)
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 470, in _init_engine
[rank0]:     return engine_class(*args, **kwargs)
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 223, in __init__
[rank0]:     self.model_executor = executor_class(
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/vllm/executor/executor_base.py", line 41, in __init__
[rank0]:     self._init_executor()
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/vllm/executor/gpu_executor.py", line 24, in _init_executor
[rank0]:     self.driver_worker.load_model()
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py", line 121, in load_model
[rank0]:     self.model_runner.load_model()
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 147, in load_model
[rank0]:     self.model = get_model(
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/model_loader/__init__.py", line 21, in get_model
[rank0]:     return loader.load_model(model_config=model_config,
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/model_loader/loader.py", line 249, in load_model
[rank0]:     model.load_weights(
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/llama.py", line 416, in load_weights
[rank0]:     weight_loader(param, loaded_weight, shard_id)
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/layers/linear.py", line 662, in weight_loader
[rank0]:     loaded_weight = loaded_weight.narrow(output_dim, start_idx,
[rank0]: RuntimeError: start (0) + length (1280) exceeds dimension size (1024).

robertgshaw2

Red Hat AI org Jul 19, 2024

Need to install vllm from source. The model is not supported in v0.5.2

robertgshaw2 changed discussion status to closed Jul 19, 2024

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