---
title: DeepSeek-R1T-Chimera
canonical_url: "https://www.modelscope.cn/models/unsloth/DeepSeek-R1T-Chimera"
md_url: "https://www.modelscope.cn/models/unsloth/DeepSeek-R1T-Chimera.md"
repository: unsloth/DeepSeek-R1T-Chimera
last_updated: 2025-05-18
license: mit
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - deepseek_v3
architectures:
  - DeepseekV3ForCausalLM
base_model:
  - tngtech/DeepSeek-R1T-Chimera
  - deepseek-ai/DeepSeek-R1
base_model_relation: quantized
parameters: 684.5B
tensor_type:
  - BF16
  - F8_E4M3
  - F32
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
inference_backends:
  - "deploy_task text/emb"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 200
stars: 0
tags:
  - unsloth
---

# DeepSeek-R1T-Chimera

> DeepSeek-R1T-Chimera - unsloth 在 ModelScope 开源的模型。Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

unsloth/DeepSeek-R1T-Chimera 是 ModelScope 魔搭社区上的 684.5B 参数text-generation模型，采用 mit 许可，基于 tngtech/DeepSeek-R1T-Chimera、deepseek-ai/DeepSeek-R1 构建，可用 deploy_task text/emb、sglang 0.5.2、vllm 0.9.2 部署。

- **Repository**: unsloth/DeepSeek-R1T-Chimera
- **License**: mit
- **Tasks**: text-generation
- **Parameters**: 684.5B
- **Base model**: tngtech/DeepSeek-R1T-Chimera, deepseek-ai/DeepSeek-R1
- **Inference backends**: deploy_task text/emb, sglang 0.5.2, vllm 0.9.2
- **Tags**: unsloth
- **Downloads**: 200
- **Stars**: 0
- **Last updated**: 2025-05-18

Source: https://www.modelscope.cn/models/unsloth/DeepSeek-R1T-Chimera

---

<div>
<p style="margin-top: 0;margin-bottom: 0;">
    <em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em>
  </p>
  <div style="display: flex; gap: 5px; align-items: center; ">
    <a href="https://github.com/unslothai/unsloth/">
      <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
    </a>
    <a href="https://discord.gg/unsloth">
      <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
    </a>
    <a href="https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune">
      <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
    </a>
  </div>
</div>

# DeepSeek-R1T-Chimera

<div align="center">
<img src="https://354918363417-runtime-assets.s3.eu-central-1.amazonaws.com/company_logo_light.svg"
     alt="TNG Logo" 
     width="400"
     style="display: inline-block; vertical-align: middle;"/>
</div>
<br>
<div align="center">
  <a href="LICENSE" style="margin: 2px;">
    <img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
  </a>
</div>
<br>
<div align="center">
  <a href="https://x.com/tngtech/status/1916284566127444468" style="margin: 2px;">
    <img alt="Benchmarks" src="R1T-Chimera_Benchmarks_20250427_V1.jpg" style="display: inline-block; vertical-align: middle;"/>
  </a>
</div>


**Model merge of DeepSeek-R1 and DeepSeek-V3 (0324)**

An open weights model combining the intelligence of R1 with the token efficiency of V3.

[Announcement on X](https://x.com/tngtech/status/1916284566127444468) | [LinkedIn post](https://www.linkedin.com/posts/tng-technology-consulting_on-the-weekend-we-released-deepseek-r1t-chimera-activity-7323008947236290560-Cf2m) | [Try it on OpenRouter](https://openrouter.ai/tngtech/deepseek-r1t-chimera:free)


## Model Details

- **Architecture**: DeepSeek-MoE Transformer-based language model
- **Combination Method**: Merged model weights from DeepSeek-R1 and DeepSeek-V3 (0324)
- **Release Date**: 2025-04-27

## Use, Out-of-scope Use, Limitations, Risks, Recommendations et al
Regarding R1T Chimera, we ask you to follow the careful guidelines that Microsoft has created for their "MAI-DS-R1" DeepSeek-based model. 

These guidelines are available [here on Hugging Face](https://huggingface.co/microsoft/MAI-DS-R1).

## Contact

- Email: research@tngtech.com
- X.com: @tngtech

## Citation

```
@misc{tng_technology_consulting_gmbh_2025,
	author       = { TNG Technology Consulting GmbH },
	title        = { DeepSeek-R1T-Chimera },
	year         = 2025,
    month        = {April},
	url          = { https://huggingface.co/tngtech/DeepSeek-R1T-Chimera },
	doi          = { 10.57967/hf/5330 },
	publisher    = { Hugging Face }
}

```