---
title: DeepSeek-R1-Distill-Qwen-1.5B-int4-ov
canonical_url: "https://www.modelscope.ai/models/OpenVINO/DeepSeek-R1-Distill-Qwen-1.5B-int4-ov"
md_url: "https://www.modelscope.ai/models/OpenVINO/DeepSeek-R1-Distill-Qwen-1.5B-int4-ov.md"
repository: OpenVINO/DeepSeek-R1-Distill-Qwen-1.5B-int4-ov
last_updated: 2025-12-03
license: mit
model_type:
  - qwen2
architectures:
  - Qwen2ForCausalLM
base_model:
  - deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
base_model_relation: quantized
library_name:
  - transformer
  - openvino
  - pytorch
frameworks:
  - pytorch
downloads: 130
stars: 0
---

# DeepSeek-R1-Distill-Qwen-1.5B-int4-ov

> DeepSeek-R1-Distill-Qwen-1.5B-int4-ov - An open-source model by OpenVINO on ModelScope. DeepSeek-R1-Distill-Qwen-1.5B-int4-ov Model creator: DeepSeek Original model: DeepSeek-R1-Distill-Qwen-1.5B

OpenVINO/DeepSeek-R1-Distill-Qwen-1.5B-int4-ov is a machine learning model on ModelScope. licensed under mit. derived from deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B.

- **Repository**: OpenVINO/DeepSeek-R1-Distill-Qwen-1.5B-int4-ov
- **License**: mit
- **Base model**: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
- **Downloads**: 130
- **Stars**: 0
- **Last updated**: 2025-12-03

Source: https://www.modelscope.ai/models/OpenVINO/DeepSeek-R1-Distill-Qwen-1.5B-int4-ov

---

# DeepSeek-R1-Distill-Qwen-1.5B-int4-ov
* Model creator: [DeepSeek](https://huggingface.co/deepseek-ai)
* Original model: [DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B)

## Description

This is [DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2024/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT4 by [NNCF](https://github.com/openvinotoolkit/nncf).

## Quantization Parameters

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

* mode: **INT4_ASYM**
* ratio: **0.7**
* group_size: **32**
* awq: **True**
* scale_estimation: **True**
* dataset: [wikitext2](https://huggingface.co/datasets/mindchain/wikitext2)

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 2025.1.0 and higher
* Optimum Intel 1.22.0 and higher

## Running Model Inference with [Optimum Intel](https://huggingface.co/docs/optimum/intel/index)


1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend:

```
pip install optimum[openvino]
```

2. Run model inference:

```
from transformers import AutoTokenizer
from optimum.intel.openvino import OVModelForCausalLM

model_id = "OpenVINO/DeepSeek-R1-Distill-Qwen-1.5B-int4-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForCausalLM.from_pretrained(model_id)

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 [OpenVINO Large Language Model Inference Guide](https://docs.openvino.ai/2025/learn-openvino/llm_inference_guide.html).

## Running Model Inference with [OpenVINO GenAI](https://github.com/openvinotoolkit/openvino.genai)

1. Install packages required for using OpenVINO GenAI.
```
pip install -U --pre openvino openvino-tokenizers openvino-genai --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/pre-release

pip install huggingface_hub
```

2. Download model from HuggingFace Hub
   
```
import huggingface_hub as hf_hub

model_id = "OpenVINO/DeepSeek-R1-Distill-Qwen-1.5B-int4-ov"
model_path = "DeepSeek-R1-Distill-Qwen-1.5B-int4-ov"

hf_hub.snapshot_download(model_id, local_dir=model_path)

```

3. 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](https://github.com/openvinotoolkit/openvino.genai/blob/master/src/README.md) and [samples](https://github.com/openvinotoolkit/openvino.genai?tab=readme-ov-file#openvino-genai-samples)

## Limitations

Check the original model card for [original model card](https://huggingface.codeepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) for limitations.

## Legal information

The original model is distributed under [mit](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md) license. More details can be found in [original model card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B).

## 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.