---
base_model: unsloth/Devstral-Small-2507-unsloth-bnb-4bit
library_name: peft
model_name: devstral-v3-dapo
tags:
- base_model:adapter:unsloth/Devstral-Small-2507-unsloth-bnb-4bit
- grpo
- lora
- transformers
- trl
- unsloth
licence: license
pipeline_tag: text-generation
---

# Model Card for devstral-v3-dapo

This model is a fine-tuned version of [unsloth/Devstral-Small-2507-unsloth-bnb-4bit](https://huggingface.co/unsloth/Devstral-Small-2507-unsloth-bnb-4bit).
It has been trained using [TRL](https://github.com/huggingface/trl).

## Quick start

```python
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"])
```

## Training procedure

 


This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).

### Framework versions

- PEFT 0.18.1
- TRL: 0.24.0
- Transformers: 5.5.0
- Pytorch: 2.10.0
- Datasets: 4.3.0
- Tokenizers: 0.22.2

## Citations

Cite GRPO as:

```bibtex
@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:
    
```bibtex
@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}}
}
```

# devstral-v3-dapo


## 🇪🇺 EU AI Act transparency

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.

## Benchmarks

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/`.