DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Paper • 2402.03300 • Published • 145
This model is a fine-tuned version of unsloth/Devstral-Small-2507-unsloth-bnb-4bit. It has been trained using TRL.
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
This model was trained with GRPO, a method introduced in DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.
Cite GRPO as:
@article{shao2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
Cite TRL as:
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
This model is published under the AI Act framework (Regulation EU 2024/1689).
| Field | Value |
|---|---|
| Provider | L'Électron Rare (clemsail) |
| Role under AI Act | GPAI provider |
| Adapter type | LoRA / PEFT — DAPO RL fine-tune adapter (on top of -sft) |
| Base model | mistralai/Devstral-Small-2-24B-Instruct-2512 |
| License | Apache-2.0 (this artefact); upstream Mistral licence applies separately |
| Intended use | Code generation across Python / Rust / TypeScript / C++ / SQL / shell, with stronger reasoning on engineering questions |
| Out of scope | Healthcare diagnosis, legal advice, autonomous safety-critical decisions, generation of malicious code or exploits |
| Risk classification | Limited risk — Article 50 transparency obligations apply |
| Copyright respect | Training data does not include scraped copyrighted material. Public engineering documentation under permissive licences plus internal synthetic distillation. |
| Full provenance | https://github.com/L-electron-Rare/eu-kiki/tree/main/docs/provenance |
| Contact | postmaster@saillant.cc |
⚠️ You are using an AI model. Outputs may be inaccurate, biased or fabricated. Do not act on them without independent verification, especially in regulated domains.
Run via lm-eval-harness v0.4.x against the FUSED checkpoint (base + this
adapter merged for inference). Strict-match where applicable.
| Task | Metric | Score |
|---|---|---|
| gsm8k | exact_match,strict-match |
0.844 |
| ifeval | prompt_level_strict_acc,none |
0.691 |
| bbh_cot_fewshot | exact_match,get-answer |
0.795 |
| bbh_cot_fewshot_boolean_expressions | exact_match,get-answer |
0.900 |
| bbh_cot_fewshot_causal_judgement | exact_match,get-answer |
0.600 |
| bbh_cot_fewshot_date_understanding | exact_match,get-answer |
0.933 |
| bbh_cot_fewshot_disambiguation_qa | exact_match,get-answer |
0.767 |
| bbh_cot_fewshot_dyck_languages | exact_match,get-answer |
0.100 |
| bbh_cot_fewshot_formal_fallacies | exact_match,get-answer |
0.600 |
| bbh_cot_fewshot_geometric_shapes | exact_match,get-answer |
0.367 |
| bbh_cot_fewshot_hyperbaton | exact_match,get-answer |
1.000 |
| bbh_cot_fewshot_logical_deduction_five_objects | exact_match,get-answer |
0.767 |
| bbh_cot_fewshot_logical_deduction_seven_objects | exact_match,get-answer |
0.533 |
| bbh_cot_fewshot_logical_deduction_three_objects | exact_match,get-answer |
0.900 |
| bbh_cot_fewshot_movie_recommendation | exact_match,get-answer |
0.833 |
| bbh_cot_fewshot_multistep_arithmetic_two | exact_match,get-answer |
0.867 |
| bbh_cot_fewshot_navigate | exact_match,get-answer |
0.967 |
| bbh_cot_fewshot_object_counting | exact_match,get-answer |
0.967 |
| bbh_cot_fewshot_penguins_in_a_table | exact_match,get-answer |
0.933 |
| bbh_cot_fewshot_reasoning_about_colored_objects | exact_match,get-answer |
0.967 |
| bbh_cot_fewshot_ruin_names | exact_match,get-answer |
0.667 |
| bbh_cot_fewshot_salient_translation_error_detection | exact_match,get-answer |
0.700 |
| bbh_cot_fewshot_snarks | exact_match,get-answer |
0.700 |
| bbh_cot_fewshot_sports_understanding | exact_match,get-answer |
0.900 |
| bbh_cot_fewshot_temporal_sequences | exact_match,get-answer |
0.967 |
| bbh_cot_fewshot_tracking_shuffled_objects_five_objects | exact_match,get-answer |
0.967 |
| bbh_cot_fewshot_tracking_shuffled_objects_seven_objects | exact_match,get-answer |
0.933 |
| bbh_cot_fewshot_tracking_shuffled_objects_three_objects | exact_match,get-answer |
0.967 |
| bbh_cot_fewshot_web_of_lies | exact_match,get-answer |
1.000 |
| bbh_cot_fewshot_word_sorting | exact_match,get-answer |
0.667 |
| mmlu_pro | exact_match,custom-extract |
0.619 |
| mmlu_pro_biology | exact_match,custom-extract |
0.768 |
| mmlu_pro_business | exact_match,custom-extract |
0.660 |
| mmlu_pro_chemistry | exact_match,custom-extract |
0.580 |
| mmlu_pro_computer_science | exact_match,custom-extract |
0.676 |
| mmlu_pro_economics | exact_match,custom-extract |
0.678 |
| mmlu_pro_engineering | exact_match,custom-extract |
0.448 |
| mmlu_pro_health | exact_match,custom-extract |
0.678 |
| mmlu_pro_history | exact_match,custom-extract |
0.575 |
| mmlu_pro_law | exact_match,custom-extract |
0.432 |
| mmlu_pro_math | exact_match,custom-extract |
0.678 |
| mmlu_pro_other | exact_match,custom-extract |
0.612 |
| mmlu_pro_philosophy | exact_match,custom-extract |
0.549 |
| mmlu_pro_physics | exact_match,custom-extract |
0.630 |
| mmlu_pro_psychology | exact_match,custom-extract |
0.704 |
| leaderboard_math_hard | exact_match,none |
0.341 |
| leaderboard_math_algebra_hard | exact_match,none |
0.570 |
| leaderboard_math_counting_and_prob_hard | exact_match,none |
0.252 |
| leaderboard_math_geometry_hard | exact_match,none |
0.182 |
| leaderboard_math_intermediate_algebra_hard | exact_match,none |
0.139 |
| leaderboard_math_num_theory_hard | exact_match,none |
0.416 |
| leaderboard_math_prealgebra_hard | exact_match,none |
0.523 |
| leaderboard_math_precalculus_hard | exact_match,none |
0.126 |
Raw results_*.json files are committed under evals/.
Base model
mistralai/Mistral-Small-3.1-24B-Base-2503