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  **Luth-0.6B-Instruct** is a French fine-tuned version of [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B), trained on the [Luth-SFT](https://huggingface.co/datasets/kurakurai/luth-sft) dataset. The model has drastically improved its French capabilities in instruction following, math, and general knowledge. Additionally, its English capabilities have remained stable and have even increased in some areas.
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  ## Model Details
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  Luth was trained using full fine-tuning on the Luth-SFT dataset with [Axolotl](https://github.com/axolotl-ai-cloud/axolotl). The resulting model was then merged with the base Qwen3-0.6B model. This process successfully retained the model's English capabilities while improving its performance on nearly all selected benchmarks in both French and English.
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  ## Benchmark Results
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  **French Evaluation:**
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  ![French Evaluation](media/french_evaluation.png)
 
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  **Luth-0.6B-Instruct** is a French fine-tuned version of [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B), trained on the [Luth-SFT](https://huggingface.co/datasets/kurakurai/luth-sft) dataset. The model has drastically improved its French capabilities in instruction following, math, and general knowledge. Additionally, its English capabilities have remained stable and have even increased in some areas.
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+ Our Evaluation, training and data scripts are available on [GitHub](https://github.com/kurakurai/Luth).
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  ## Model Details
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  Luth was trained using full fine-tuning on the Luth-SFT dataset with [Axolotl](https://github.com/axolotl-ai-cloud/axolotl). The resulting model was then merged with the base Qwen3-0.6B model. This process successfully retained the model's English capabilities while improving its performance on nearly all selected benchmarks in both French and English.
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  ## Benchmark Results
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+ We used LightEval for evaluation, with custom tasks for the French benchmarks. The models were evaluated with a `temperature=0`.
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  **French Evaluation:**
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  ![French Evaluation](media/french_evaluation.png)