Instructions to use mlabonne/phixtral-4x2_8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
The difference of prompt template between base models
Hello, I found phixtral very interesting. While trying to conduct an experiment in this regard, I suddenly became curious about what effect it would have if the prompt templates of these models were different when combining several models. So, I would like to ask how you took these points into consideration when creating phixtral.
Additionally, I found NeuralMarcoro, which uses model-merging, very interesting, and I am also curious about the impact of differences in prompt templates between base models in terms of model merging rather than MoE.
try alpaca at first
Thanks! This is an interesting point. From experience, models trained on different templates tend to be good at following any of them.
For phixtral, I chose chatml because dolphin-2_6-phi-2 has been fine-tuned using this template (and it's my favorite one). I tested it and found that it works well.
For merges, it's quite similar: I'd choose the template used by the majority (in terms of weights) of the models. Chatml is quite popular now so it's often a good option. Like @cloudyu said, Alpaca or Llama Chat work well too.