---
title: MiniCPM5-2B-DSpark
canonical_url: "https://www.modelscope.cn/models/OpenBMB/MiniCPM5-2B-DSpark"
md_url: "https://www.modelscope.cn/models/OpenBMB/MiniCPM5-2B-DSpark.md"
repository: OpenBMB/MiniCPM5-2B-DSpark
last_updated: 2026-09-07
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - qwen3
architectures:
  - Qwen3DSparkModel
parameters: 323.8M
tensor_type:
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
  - zh
downloads: 227
stars: 2
tags:
  - minicpm
  - minicpm5
  - llama
  - text-generation
  - long-context
  - tool-calling
  - on-device
  - edge-ai
---

# MiniCPM5-2B-DSpark

> MiniCPM5-2B-DSpark - OpenBMB 在 ModelScope 开源的模型。MiniCPM Tech Report | MiniCPM Wiki(Chinese) | GitHub Repo | UltraData | Online Demo

OpenBMB/MiniCPM5-2B-DSpark 是 ModelScope 魔搭社区上的 323.8M 参数text-generation模型，采用 apache-2.0 许可。

- **Repository**: OpenBMB/MiniCPM5-2B-DSpark
- **License**: apache-2.0
- **Tasks**: text-generation
- **Parameters**: 323.8M
- **Tags**: minicpm, minicpm5, llama, text-generation, long-context, tool-calling, on-device, edge-ai
- **Downloads**: 227
- **Stars**: 2
- **Last updated**: 2026-09-07

Source: https://www.modelscope.cn/models/OpenBMB/MiniCPM5-2B-DSpark

---

<div align="center">
<img src="https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm_logo.png" width="500em" />
</div>

<p align="center">
<a href="https://arxiv.org/pdf/2506.07900" target="_blank">MiniCPM Tech Report</a> |
<a href="https://modelbest.feishu.cn/wiki/UtWxwcERfiRIpIkBOjuc3h9tn1D" target="_blank">MiniCPM Wiki(Chinese)</a> |
<a href="https://github.com/OpenBMB/MiniCPM" target="_blank">GitHub Repo</a> |
<a href="https://ultradata.openbmb.cn/" target="_blank">UltraData</a> |
<a href="https://huggingface.co/spaces/openbmb/MiniCPM5-2B-Demo" target="_blank">Online Demo</a>
</p>

<p align="center">
English |
<a href="https://huggingface.co/openbmb/MiniCPM5-2B/blob/main/README-cn.md" target="_blank">中文</a>
</p>

# MiniCPM5-2B-DSpark

`MiniCPM5-2B-DSpark` is a [DSpark](https://arxiv.org/abs/2607.05147)
draft checkpoint trained for exact pairing with [MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B) and its
tokenizer.

## Model Specification

| Property | Value |
|---|---|
| Target model | [MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B) |
| Draft layers | 5 |
| Draft parameters | 323,776,001 |
| Draft tokens per forward pass | 7 |
| Target hidden-state layers | `[1, 10, 20, 30, 39]` |
| Checkpoint precision | BF16 |

## Training Recipe

Training responses were generated by [MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B) from a mixture of
general-domain, mathematics, and code prompts.

| Setting | Value |
|---|---|
| Training sequences | 1,959,525 |
| Training tokens | 7,054,154,509 |
| Epochs | 6 |
| Maximum sequence length | 12,288 |
| Optimizer | AdamW |
| Objective | CE + L1 + confidence loss |

## Evaluation

Acceptance length is defined as the total number of completion tokens divided
by the total number of speculative verification steps. Evaluation uses natural
EOS termination and `max_new_tokens=4096`.

| Domain | `T=0` | `T=1.0` |
|---|---:|---:|
| Math | 6.0496 | 4.6050 |
| Code | 6.1106 | 4.4381 |
| General | 4.1585 | 3.0997 |
| Aggregate | **5.5174** | **4.0514** |

## Usage with SGLang

```bash
python -m sglang.launch_server \
  --model-path <target-model-path> \
  --trust-remote-code \
  --speculative-algorithm DSPARK \
  --speculative-draft-model-path <draft-model-path> \
  --speculative-dspark-block-size 7
```


## Limitations and Disclaimer

This model has no autonomous intent or legal personhood; its outputs are text generated from statistical patterns and may be inaccurate, biased, or offensive, and may be manipulated by carefully crafted prompts ("jailbreaks") into producing unintended content. Its responses on sensitive topics such as politics, health, finance, and law are not reviewed by experts and should not be treated as professional advice.

This model is provided "**AS IS**", without warranty of any kind, express or implied, and the developers are not liable for any damages arising from its use. Users must employ the model only for lawful, compliant, and ethical purposes, configure their own safeguards, and label AI-generated content where required; deliberate jailbreaking, injection attacks, or inducing harmful output is prohibited, and any such testing is at the user's own risk.

## License

This repository and MiniCPM model weights are released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License.

## Citation

Please cite our paper if you find our work valuable:

```bibtex
@article{minicpm4,
  title={Minicpm4: Ultra-efficient llms on end devices},
  author={MiniCPM, Team},
  journal={arXiv preprint arXiv:2506.07900},
  year={2025}
}
```