Instructions to use mlabonne/phixtral-4x2_8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlabonne/phixtral-4x2_8 with Transformers:

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

pipe = pipeline("text-generation", model="mlabonne/phixtral-4x2_8", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("mlabonne/phixtral-4x2_8", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mlabonne/phixtral-4x2_8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlabonne/phixtral-4x2_8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlabonne/phixtral-4x2_8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mlabonne/phixtral-4x2_8

SGLang

How to use mlabonne/phixtral-4x2_8 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 "mlabonne/phixtral-4x2_8" \
    --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": "mlabonne/phixtral-4x2_8",
		"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 "mlabonne/phixtral-4x2_8" \
        --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": "mlabonne/phixtral-4x2_8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mlabonne/phixtral-4x2_8 with Docker Model Runner:
```
docker model run hf.co/mlabonne/phixtral-4x2_8
```

Script for eval

by ewqr2130 - opened Jan 14, 2024

Discussion

ewqr2130

Jan 14, 2024

Hello mlabonne

I am huge fan and a big followers of all of your works! Thanks for these fantastic blogs and models!
Quick question, do you have script for offline evaluation? How did you evaluate your model on these different bench marks that you posted??

Thanks!

mlabonne

Owner Jan 14, 2024

Hi @ewqr2130 thanks! It's a good question, I should provide a link in the readme file.

I use a small tool I built called LLM AutoEval (https://github.com/mlabonne/llm-autoeval), it's super easy to use.

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