Text Generation
Transformers
Safetensors
phi
text-generation-inference
4-bit precision
intel/auto-round
Instructions to use INC4AI/phi-2-int4-inc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use INC4AI/phi-2-int4-inc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="INC4AI/phi-2-int4-inc")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("INC4AI/phi-2-int4-inc") model = AutoModelForCausalLM.from_pretrained("INC4AI/phi-2-int4-inc") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use INC4AI/phi-2-int4-inc with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "INC4AI/phi-2-int4-inc" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "INC4AI/phi-2-int4-inc", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/INC4AI/phi-2-int4-inc
- SGLang
How to use INC4AI/phi-2-int4-inc 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 "INC4AI/phi-2-int4-inc" \ --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": "INC4AI/phi-2-int4-inc", "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 "INC4AI/phi-2-int4-inc" \ --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": "INC4AI/phi-2-int4-inc", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use INC4AI/phi-2-int4-inc with Docker Model Runner:
docker model run hf.co/INC4AI/phi-2-int4-inc
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README.md
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---
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## Model Details
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This model is an int4 model with group_size128 and sym quantization of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) generated by [intel/auto-round](https://github.com/intel/auto-round).
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### How To Use
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### INT4 Inference with ITREX on CPU
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Install the latest [intel-extension-for-transformers](
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https://github.com/intel/intel-extension-for-transformers)
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```python
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from intel_extension_for_transformers.transformers import AutoModelForCausalLM
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from transformers import AutoTokenizer
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quantized_model_dir = "Intel/phi-2-int4-inc"
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model = AutoModelForCausalLM.from_pretrained(quantized_model_dir,
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device_map="auto",
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trust_remote_code=False,
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use_neural_speed=False,
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## revision="5973e3a" ##AutoGPTQ format
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tokenizer = AutoTokenizer.from_pretrained(quantized_model_dir, use_fast=True)
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print(tokenizer.decode(model.generate(**tokenizer("There is a girl who likes adventure,", return_tensors="pt").to(model.device),max_new_tokens=50)[0]))
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"""
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There is a girl who likes adventure,
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She loves to explore and to venture.
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She travels to faraway lands,
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And meets people from different lands.
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She learns new languages and cultures,
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And makes friends with all kinds of people.
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She is curious and brave and
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"""
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```
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### INT4 Inference
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---
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## Model Details
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This model is an int4 model with group_size128 and sym quantization of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) generated by [intel/auto-round](https://github.com/intel/auto-round). If you need AutoGPTQ format, please load the model with revision 5973e3a
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### How To Use
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### INT4 Inference
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