FluidInference/phi-4-mini-instruct-fp16-ov-npu

🤗 Hugging Face 来源text-generationmit7.8 GBother✓ 5 个校验和今天更新
一条命令提交

在你的模型文件夹旁边运行它。它会制作种子、将文件与 Hugging Face 比对,然后提交。你只需开始做种,并粘贴你账户中的密钥。它只读取你的文件,绝不修改。如果愿意,可以先阅读脚本。

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo FluidInference/phi-4-mini-instruct-fp16-ov-npu ./model-folder
需要做种者 →

Phi-4-mini-instruct-fp16-ov

optimum-cli export openvino --model microsoft/phi-4-mini-instruct --task text-generation-with-past --weight-format fp16 --trust-remote-code phi-4-mini-instruct\FP16

Description

This is Phi-4-mini-instruct model converted to the OpenVINO™ IR (Intermediate Representation) format with weights compressed to FP16.

Compatibility

The provided OpenVINO™ IR model is compatible with:

  • OpenVINO version 2025.2.0 and higher
  • Optimum Intel 1.23.0 and higher

Running Model Inference with Optimum Intel

  1. Install packages required for using Optimum Intel integration with the OpenVINO backend:
pip install optimum[openvino]
  1. Run model inference:
from transformers import AutoTokenizer
from optimum.intel.openvino import OVModelForCausalLM
model_id = "OpenVINO/Phi-4-mini-instruct-fp16-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)

inputs = tokenizer("What is OpenVINO?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)

For more examples and possible optimizations, refer to the Inference with Optimum Intel.

Running Model Inference with OpenVINO GenAI

  1. Install packages required for using OpenVINO GenAI.
pip install -U openvino openvino-tokenizers openvino-genai
pip install huggingface_hub
  1. Download model from HuggingFace Hub
import huggingface_hub as hf_hub
model_id = "OpenVINO/Phi-4-mini-instruct-fp16-ov"
model_path = "Phi-4-mini-instruct-fp16-ov"
hf_hub.snapshot_download(model_id, local_dir=model_path)
  1. Run model inference:
import openvino_genai as ov_genai
device = "CPU"
pipe = ov_genai.LLMPipeline(model_path, device)
print(pipe.generate("What is OpenVINO?", max_length=200))

More GenAI usage examples can be found in OpenVINO GenAI library docs and samples

You can find more detaild usage examples in OpenVINO Notebooks:

Limitations

Check the original model card for original model card for limitations.

Legal information

The original model is distributed under mit license. More details can be found in original model card.

Disclaimer

Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See Intel’s Global Human Rights Principles. Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.