---
title: Luth-2-Post-Training-SFT
canonical_url: "https://www.modelscope.cn/datasets/kurakurai/Luth-2-Post-Training-SFT"
md_url: "https://www.modelscope.cn/datasets/kurakurai/Luth-2-Post-Training-SFT.md"
repository: kurakurai/Luth-2-Post-Training-SFT
last_updated: 2026-08-11
license: cc-by-4.0
downloads: 305
stars: 0
---

# Luth-2-Post-Training-SFT

> Luth-2-Post-Training-SFT - kurakurai 在 ModelScope 开源的数据集。Luth-2-Post-Training-SFT

kurakurai/Luth-2-Post-Training-SFT 是 ModelScope 魔搭社区上的数据集，采用 cc-by-4.0 许可。

- **Repository**: kurakurai/Luth-2-Post-Training-SFT
- **License**: cc-by-4.0
- **Downloads**: 305
- **Stars**: 0
- **Last updated**: 2026-08-11

Source: https://www.modelscope.cn/datasets/kurakurai/Luth-2-Post-Training-SFT

---

![luth-2-banner](https://cdn-uploads.huggingface.co/production/uploads/66beff523ae330ae8b698ee9/wso_h0n6syZf9E8YDKWYA.png)

# Luth-2-Post-Training-SFT

**Luth-2-Post-Training-SFT** is the French supervised fine-tuning mixture used to train [Luth-2-0.8B](https://huggingface.co/kurakurai/Luth-2-0.8B) and [Luth-2-2B](https://huggingface.co/kurakurai/Luth-2-2B). It spans math, code, knowledge, instruction following and tool calling in a single schema, with **1,969,768 examples** and **3.12B training tokens**.

- 📄 **Blog**: [Luth-2: Pushing the French Capabilities of SLMs with MOPD](https://huggingface.co/blog/MaxLSB/luth-2)
- 🤗 **Models**: [Luth-2-0.8B](https://huggingface.co/kurakurai/Luth-2-0.8B) · [Luth-2-2B](https://huggingface.co/kurakurai/Luth-2-2B)
- 📊 **Datasets**: [SFT](https://huggingface.co/datasets/kurakurai/Luth-2-Post-Training-SFT) · [RL](https://huggingface.co/datasets/kurakurai/Luth-2-Post-Training-RL)
- 💻 **Code**: [GitHub](https://github.com/kurakurai/Luth-2)
- 🏆 **Leaderboard**: [French LLM Leaderboard](https://huggingface.co/spaces/kurakurai/llm_leaderboard_fr)

## Composition

| Domain                | Examples      |     % | Tokens         |     % |
| --------------------- | ------------: | ----: | -------------: | ----: |
| Knowledge             |       595,187 | 30.2% |         870.5M | 27.9% |
| Code                  |       548,432 | 27.8% |         693.0M | 22.2% |
| Math                  |       428,031 | 21.7% |       1,161.1M | 37.2% |
| Instruction following |       211,942 | 10.8% |         202.3M |  6.5% |
| Tool calling          |       186,176 |  9.5% |         196.7M |  6.3% |
| **Total**             | **1,969,768** |  100% |     **3.12B** |  100% |

> [!NOTE]
> **Decontamination**: 8-gram token overlap against our evaluation benchmark suite. A sample is dropped when more than 50% of any benchmark item's 8-grams appear in it.


## Provenance

Prompts were translated to French where the source was English, and assistant turns were **regenerated** with a strong open-source teacher rather than translated. `source` names where the prompt came from, `generator` corresponds to the model that wrote the answer.

| Source                                              |    Rows | Generator                    |
| --------------------------------------------------- | ------: | ---------------------------- |
| `nvidia/Nemotron-Post-Training-Dataset-v2`          | 934,387 | `Qwen/Qwen3.6-35B-A3B`       |
| `allenai/Dolci-Instruct-SFT`                        | 702,570 | `Qwen/Qwen3-30B-A3B`         |
| `allenai/Dolci-Instruct-SFT-Tool-Use`               | 186,176 | `google/gemma-4-31B-it`      |
| `HuggingFaceTB/smoltalk2` (French rows)             |  50,396 | --         |
| `HuggingFaceTB/smoltalk2` (multi-turn IF)           |  28,182 | `google/gemma-4-26b-a4b-it`  |
| `allenai/tulu-3-sft-personas-math`                  |  47,440 | `Qwen/Qwen3-32B`             |
| `allenai/tulu-3-sft-personas-instruction-following` |  20,617 | `Qwen/Qwen3-32B`             |

> [!IMPORTANT]
> The dataset is CC-BY-4.0. However, StackOverflow-derived rows from Nemotron remain CC-BY-SA-4.0.

## SFT results
 
Full fine-tuning on top of Qwen3.5-0.8B and Qwen3.5-2B for 1 epoch, `lr=3e-5` cosine, `max_seq_length=16384`, global batch of ~500K tokens, with FlashAttention-3 and sequence packing. Evaluations can be reproduced using our [GitHub](https://github.com/kurakurai/Luth-2) repository.

The benchmarks are French subsets or verified translations, scored with `temperature=0.6, top_p=0.95, top_k=20`, thinking disabled, averaged over 10 runs.
 
|                  | Qwen3.5-0.8B |               |   Qwen3.5-2B |               |
| ---------------- | -----------: | ------------: | -----------: | ------------: |
| **Stage**        | **Baseline** | **After SFT** | **Baseline** | **After SFT** |
| MGSM-rev2        |        35.20 |     **66.12** |        64.60 |     **84.12** |
| AIME 24          |         1.00 |      **3.00** |        11.67 |     **11.67** |
| AIME 25          |         0.33 |      **2.00** |         6.67 |     **12.00** |
| Math-500         |        27.46 |     **47.04** |        65.06 |     **72.70** |
| Global-MMLU-Lite |        44.00 |     **50.20** |        58.00 |     **62.05** |
| MMLU-ProX-Lite   |        27.60 |     **35.63** |        47.20 |     **52.70** |
| GPQA-Diamond     |        23.80 |     **27.27** |        36.80 |     **39.39** |
| IFEval           |        44.47 |     **59.57** |        61.91 |     **64.64** |
| Multi-IF         |        32.72 |     **45.59** |        45.38 |     **58.66** |
| HumanEval+       |        10.87 |     **36.75** |        37.56 |     **58.56** |
| MBPP+            |        18.20 |     **36.85** |        42.12 |     **54.58** |
| BFCL v2          |        51.49 |     **63.20** |        51.78 |     **67.73** |

 ## Contact

Questions or feedback? Reach us on LinkedIn: [Maxence Lasbordes](https://www.linkedin.com/in/maxence-lasbordes/) and [Guillaume Pradel](https://www.linkedin.com/in/guillaume-pradel/).


## Citation

```bibtex
@misc{luth2,
  title  = {Luth-2: Pushing the French Capabilities of SLMs with MOPD},
  author = {Maxence Lasbordes and Guillaume Pradel},
  year   = {2026},
  url    = {https://huggingface.co/blog/MaxLSB/luth-2}
}
```