---
title: Phi-4-mini-FastDraft-120M-int8-ov
canonical_url: "https://www.modelscope.ai/models/OpenVINO/Phi-4-mini-FastDraft-120M-int8-ov"
md_url: "https://www.modelscope.ai/models/OpenVINO/Phi-4-mini-FastDraft-120M-int8-ov.md"
repository: OpenVINO/Phi-4-mini-FastDraft-120M-int8-ov
last_updated: 2026-10-21
license: intel-research
model_type:
  - phi3
architectures:
  - Phi3ForCausalLM
library_name:
  - openvino
  - pytorch
frameworks:
  - pytorch
downloads: 251
stars: 0
---

# Phi-4-mini-FastDraft-120M-int8-ov

> Phi-4-mini-FastDraft-120M-int8-ov - An open-source model by OpenVINO on ModelScope. Phi-4-mini-FastDraft-120M-int8-ov

OpenVINO/Phi-4-mini-FastDraft-120M-int8-ov is a machine learning model on ModelScope. licensed under intel-research.

- **Repository**: OpenVINO/Phi-4-mini-FastDraft-120M-int8-ov
- **License**: intel-research
- **Downloads**: 251
- **Stars**: 0
- **Last updated**: 2026-10-21

Source: https://www.modelscope.ai/models/OpenVINO/Phi-4-mini-FastDraft-120M-int8-ov

---

# Phi-4-mini-FastDraft-120M-int8-ov

## Description

FastDraft is a novel and efficient approach for pre-training and aligning a draft model to any LLM to be used with speculative decoding, by incorporating efficient pre-training followed by fine-tuning over synthetic datasets generated by the target model.
FastDraft was presented in https://arxiv.org/abs/2411.11055 at ENLSP@NeurIPS24 by Intel Labs.
 
This is a draft model that was trained with FastDraft to accompany [Phi-4-mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct).
This is Phi-4-mini-FastDraft-120M model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2025/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT8 by [Optimum](https://github.com/huggingface/optimum).

## Quantization Parameters

Weight compression was performed using `nncf.compress_weights` with the following parameters:

* mode: **INT8_ASYM**

For more information on quantization, check the [OpenVINO model optimization guide](https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/weight-compression.html).


## Compatibility

The provided OpenVINO™ IR model is compatible with:

* OpenVINO version **2025.1** and higher
* Optimum Intel **1.23.0** and higher

## Running Model Inference with OpenVINO GenAI


1. Install packages required for using [OpenVINO GenAI](https://github.com/openvinotoolkit/openvino.genai) with Speculative decoding: 

```
pip install openvino-genai huggingface_hub
```

2. Download models from HuggingFace Hub
```
import huggingface_hub as hf_hub
 
main_model_id = "OpenVINO/Phi-4-mini-instruct-int4-ov"
draft_model_id = "OpenVINO/Phi-4-mini-FastDraft-120M-int8-ov"
 
main_model_path = "main"
draft_model_path = "draft"
 
hf_hub.snapshot_download(main_model_id, local_dir=main_model_path)
hf_hub.snapshot_download(draft_model_id, local_dir=draft_model_path)
```
3. Run model inference using the speculative decoding and specify the pipeline parameters:
```
import openvino_genai
 
prompt = “What is OpenVINO?”
 
config = openvino_genai.GenerationConfig()
config.num_assistant_tokens = 3
config.max_new_tokens = 128
 
def streamer(subword):
    print(subword, end='', flush=True)
    return False
 
main_device = "CPU"
draft_device = "CPU"
 
draft_model = openvino_genai.draft_model(draft_model_path, draft_device)

pipe = openvino_genai.LLMPipeline(args.model_dir, main_device, draft_model=draft_model)
 
pipe.generate(prompt, config, streamer)
```

More GenAI usage examples can be found in OpenVINO GenAI library [docs](https://github.com/openvinotoolkit/openvino.genai/blob/master/src/README.md) and [samples](https://github.com/openvinotoolkit/openvino.genai/tree/master/samples)

## Legal Information

The model is distributed under the [Intel Research Use License Agreement](https://huggingface.co/OpenVINO/Phi-4-mini-FastDraft-120M-int8-ov/blob/main/LICENSE.md).

## 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](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). 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.