Instructions to use microsoft/phi-1_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/phi-1_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="microsoft/phi-1_5")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1_5") model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use microsoft/phi-1_5 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "microsoft/phi-1_5" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "microsoft/phi-1_5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/microsoft/phi-1_5
- SGLang
How to use microsoft/phi-1_5 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 "microsoft/phi-1_5" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "microsoft/phi-1_5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "microsoft/phi-1_5" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "microsoft/phi-1_5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use microsoft/phi-1_5 with Docker Model Runner:
docker model run hf.co/microsoft/phi-1_5
Enables to toggle fused_dense, flash_rotary and attn_pdrop in the configuration.
Browse files
configuration_mixformer_sequential.py
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@@ -30,6 +30,9 @@ class MixFormerSequentialConfig(PretrainedConfig):
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n_head_kv: Optional[int] = None,
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rotary_dim: Optional[int] = 32,
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activation_function: Optional[str] = "gelu_new",
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embd_pdrop: Optional[float] = 0.0,
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resid_pdrop: Optional[float] = 0.0,
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layer_norm_epsilon: Optional[float] = 1e-5,
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@@ -47,6 +50,9 @@ class MixFormerSequentialConfig(PretrainedConfig):
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self.n_head_kv = n_head_kv
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self.rotary_dim = min(rotary_dim, n_embd // n_head)
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self.activation_function = activation_function
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self.embd_pdrop = embd_pdrop
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self.resid_pdrop = resid_pdrop
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self.layer_norm_epsilon = layer_norm_epsilon
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n_head_kv: Optional[int] = None,
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rotary_dim: Optional[int] = 32,
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activation_function: Optional[str] = "gelu_new",
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flash_rotary: bool = False,
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fused_dense: bool = False,
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attn_pdrop: Optional[float] = 0.0,
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embd_pdrop: Optional[float] = 0.0,
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resid_pdrop: Optional[float] = 0.0,
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layer_norm_epsilon: Optional[float] = 1e-5,
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self.n_head_kv = n_head_kv
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self.rotary_dim = min(rotary_dim, n_embd // n_head)
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self.activation_function = activation_function
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self.flash_rotary = flash_rotary
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self.fused_dense = fused_dense
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self.attn_pdrop = attn_pdrop
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self.embd_pdrop = embd_pdrop
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self.resid_pdrop = resid_pdrop
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self.layer_norm_epsilon = layer_norm_epsilon
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