Instructions to use mlabonne/phixtral-2x2_8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlabonne/phixtral-2x2_8 with Transformers:

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

pipe = pipeline("text-generation", model="mlabonne/phixtral-2x2_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-2x2_8", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mlabonne/phixtral-2x2_8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlabonne/phixtral-2x2_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-2x2_8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mlabonne/phixtral-2x2_8

SGLang

How to use mlabonne/phixtral-2x2_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-2x2_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-2x2_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-2x2_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-2x2_8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mlabonne/phixtral-2x2_8 with Docker Model Runner:
```
docker model run hf.co/mlabonne/phixtral-2x2_8
```

mlabonne commited on Jan 9, 2024

Commit

4e11bdb

1 Parent(s): ba0a899

Update configuration_phi.py

Browse files

Files changed (1) hide show

configuration_phi.py +4 -0

configuration_phi.py CHANGED Viewed

@@ -27,6 +27,8 @@ class PhiConfig(PretrainedConfig):
         n_inner: Optional[int] = None,
         n_head: int = 16,
         n_head_kv: Optional[int] = None,
         rotary_dim: Optional[int] = 32,
         activation_function: Optional[str] = "gelu_new",
         flash_attn: bool = False,
@@ -48,6 +50,8 @@ class PhiConfig(PretrainedConfig):
         self.n_inner = n_inner
         self.n_head = n_head
         self.n_head_kv = n_head_kv
         self.rotary_dim = min(rotary_dim, n_embd // n_head)
         self.activation_function = activation_function
         self.flash_attn = flash_attn

         n_inner: Optional[int] = None,
         n_head: int = 16,
         n_head_kv: Optional[int] = None,
+        num_experts_per_tok: int = 2,
+        num_local_experts: int = 4,
         rotary_dim: Optional[int] = 32,
         activation_function: Optional[str] = "gelu_new",
         flash_attn: bool = False,
         self.n_inner = n_inner
         self.n_head = n_head
         self.n_head_kv = n_head_kv
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_local_experts = num_local_experts
         self.rotary_dim = min(rotary_dim, n_embd // n_head)
         self.activation_function = activation_function
         self.flash_attn = flash_attn