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 Settings
- 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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## 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). We found there is a large accuracy drop of asym kernel for this model.
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### Evaluate the model
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pip install lm-eval==0.4.
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```bash
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auto-round --eval --model Intel/phi-2-int4-inc --device cuda:0 --tasks lambada_openai,hellaswag,piqa,winogrande,truthfulqa_mc1,openbookqa,boolq,arc_easy,arc_challenge,mmlu --batch_size 16
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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). We found there is a large accuracy drop of asym kernel for this model. If you need AutoGPTQ format, please load the model with revision 5973e3a
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### Evaluate the model
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pip install lm-eval==0.4.4
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```bash
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auto-round --eval --model Intel/phi-2-int4-inc --device cuda:0 --tasks lambada_openai,hellaswag,piqa,winogrande,truthfulqa_mc1,openbookqa,boolq,arc_easy,arc_challenge,mmlu --batch_size 16
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