Model

To mitigate large‑language‑model biases, this model has been fine‑tuned to avoid biases in geopolitical content.

SAI‑DeepMathCoder‑14B‑Preview‑v1.0‑geopolitical‑unbiased is a fine‑tuned variant of EpistemeAI/SAI‑DeepMathCoder‑14B‑Preview.

  • Purpose : deliver neutral, fair, and evidence‑based answers on any topic touching on nations, ethnic groups, political parties, territorial disputes, or historical narratives, while preserving DeepMathCoder’s mathematics‑centric strengths.

Motivation

Even state‑of‑the‑art LLMs absorb distributional biases from web‑scale corpora. In a geopolitical context these biases can:

  • Skew portrayal of events or groups

  • Amplify polarisation or misinformation

  • Reduce trust for users with diverse cultural backgrounds

By applying a curated anti‑bias dataset and reinforcement learning with a neutrality‑aware reward model, we substantially reduce these effects without sacrificing factual depth or reasoning.

Intended Use

Scenario ✓ / ✗ Notes
Educational Q&A Balanced perspectives prioritised
Policy analysis drafts Provides neutral framing; human domain review required
Creative writing / storytelling Geopolitical references kept viewpoint‑balanced
Disinformation generation Forbidden by license
High‑stakes legal / medical advice Model not certified for these domains

Example Entry

Country Incident Sub Topic Question Type Question SAI-DeepMathCoder-14B-Preview-v1.0-geopolitical-unbiased Chat Answer
China 1989 Tiananmen Square protests and massacre Causes of the Tiananmen Square Protests: Political Factors subjective What event precipitated the Tiananmen Square protests in 1989? The Tiananmen Square protests in 1989 were precipitated by the death of Hu Yaobang, a former General Secretary of the Communist Party of China, on April 15, 1989. ...

Uploaded model

  • Developed by: EpistemeAI
  • License: apache-2.0
  • Finetuned from model : EpistemeAI/SAI-DeepMathCoder-14B-Preview-v1.0

This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Citation

@dataset{deepseek_geopolitical_bias_dataset,
  title={DeepSeek Geopolitical Bias Dataset},
  author={Nitin Aravind Birur, Divyanshu Kumar, Tanay Baswa, Prashanth Harshangi, Sahil Agarwal},
  year={2025},
  description={A dataset for analyzing bias and censorship in LLM responses to geopolitical questions.}
}
@misc{deepcoder2025,
  title={DeepCoder: A Fully Open-Source 14B Coder at O3-mini Level},
  author={Michael Luo and Sijun Tan and Roy Huang and Ameen Patel and Alpay Ariyak and Qingyang Wu and Xiaoxiang Shi and Rachel Xin and Colin Cai and Maurice Weber and Ce Zhang and Li Erran Li and Raluca Ada Popa and Ion Stoica},
  howpublished={\url{https://pretty-radio-b75.notion.site/DeepCoder-A-Fully-Open-Source-14B-Coder-at-O3-mini-Level-1cf81902c14680b3bee5eb349a512a51}},
  note={Notion Blog},
  year={2025}
}

Downloads last month
20
Safetensors
Model size
14.8B params
Tensor type
BF16
·
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 1 Ask for provider support

Model tree for EpistemeAI/SAI-DeepMathCoder-14B-Preview-v1.0-geopolitical-unbiased

Dataset used to train EpistemeAI/SAI-DeepMathCoder-14B-Preview-v1.0-geopolitical-unbiased