phi-4-mini-instruct-int4-sym-npu-ov

Phi-4-mini-instruct INT4_SYM for Intel NPU

🎉 First NPU-optimized Phi-4-mini model with correct quantization for Intel NPU!

Model Description

This is microsoft/Phi-4-mini-instruct (2.6B parameters) converted to OpenVINO IR format with NPU-specific INT4 symmetric quantization.

Key Difference from Standard OpenVINO Models

Critical Discovery: Intel NPU requires INT4_SYM (symmetric, channel-wise) quantization, not the INT4_ASYM (asymmetric, grouped) used by standard OpenVINO pre-converted models.

Quantization Details

• Method: INT4_SYM (symmetric)
• Group size: -1 (channel-wise, not grouped)
• Calibration: AWQ + scale_estimation on wikitext2 dataset
• Distribution: 84% INT4_SYM (128 layers), 16% INT8_ASYM (1 layer)
• Size: 2.13 GB

Performance on Intel NPU

Tested on Intel Core Ultra 7 155H (NPU driver v32.0.100.4297):

• Speed: 6.8 tok/s
• Compilation: 68.5s
• Inference: Stable, production-ready

Comparison to other models on same hardware (Intel Core Ultra 7 155H):

• Qwen2.5-1.5B-Instruct (INT4_SYM): 10.7 tok/s (0.87 GB) - Baseline performance
• Phi-4-mini-instruct (INT4_SYM): 6.8 tok/s (2.13 GB) - 73% more parameters, reasoning capabilities
• Performance ratio: ~64% of Qwen speed, but significantly more capable model

Usage

Requirements

pip install openvino-genai huggingface-hub

Python API

from openvino_genai import LLMPipeline

# Load and run on Intel NPU
pipe = LLMPipeline("AhtnaGlen/phi-4-mini-instruct-int4-sym-npu-ov", device="NPU")

# Generate text
response = pipe.generate("Explain quantum computing:", max_new_tokens=100)
print(response)

Streaming

for token in pipe.generate("Write a story:", max_new_tokens=200, stream=True):
    print(token, end='', flush=True)

Why This Matters

Standard OpenVINO Phi-4 models (e.g., OpenVINO/Phi-4-mini-instruct-int4-ov) use INT4_ASYM quantization which fails NPU compilation with errors like:

[ERROR] Channels count of input tensor shape and filter shape must be the same: 0 != 48

This model uses the correct NPU-optimized quantization as specified in Intel's NPU documentation:

optimum-cli export openvino -m microsoft/Phi-4-mini-instruct \
    --weight-format int4 \
    --sym \                    # Symmetric (key for NPU!)
    --group-size -1 \          # Channel-wise (not grouped!)
    --awq --scale-estimation \
    --dataset wikitext2

Model Capabilities

• Instruction following: Fine-tuned for chat/instruction tasks
• Reasoning: Enhanced reasoning capabilities (Phi-4 series)
• Context length: 4096 tokens
• NPU acceleration: Full hardware offload to Intel NPU

Hardware Requirements

• Intel NPU: Core Ultra 7 155H (tested), or other NPU 3720/4000 series
• Driver: v32.0.100.4297 or newer
• OpenVINO: 2025.3.0 or newer
• Memory: ~3 GB for model + inference

Limitations

• NPU only: This model is quantized specifically for Intel NPU
• Speed trade-off: 6.8 tok/s vs Qwen2.5-1.5B @ 10.7 tok/s on Intel Core Ultra 7 155H
• Size vs capability: Larger model (2.13 GB) but enhanced reasoning and instruction-following
• Hardware specific: Performance validated on Intel Core Ultra 7 155H NPU

Citation

If you use this model, please cite:

@misc{phi4-mini-npu-optimized,
  title={Phi-4-mini-instruct INT4_SYM for Intel NPU},
  author={OpenVINO Community},
  year={2025},
  howpublished={\url{https://huggingface.co/AhtnaGlen/phi-4-mini-instruct-int4-sym-npu-ov}},
}

Acknowledgments

• Base model: Microsoft Phi-4-mini-instruct
• Framework: Intel OpenVINO
• Quantization: NNCF (Neural Network Compression Framework)
• Discovery: Community finding on NPU quantization requirements

License

MIT (following base model license)

Model Card Contact

For issues or questions about NPU compatibility, please open an issue on the model repository.

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Note: This model demonstrates the importance of quantization method selection for hardware-specific optimization. Always verify quantization parameters match target hardware requirements!