初始化项目，由ModelHub XC社区提供模型

Model: PraxySante/qwen3-0.6b-sft-asr-correction-v15-context-full
Source: Original Platform

README.md

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+---
+license: mit
+language: [fr]
+pipeline_tag: text-generation
+tags: [grammar-correction, medical, french, asr-correction, context-aware]
+---
+
+# Qwen3-0.6B-SFT-ASR-Correction-FR v15-context-full
+
+Modele final de fine-tuning SFT avec contexte pour la correction d'erreurs ASR medicales en francais.
+
+## Configuration
+
+- **Base model**: Qwen/Qwen3-0.6B
+- **Fine-tuning**: SFT (Supervised Fine-Tuning)
+- **Context**: Oui (3 champs: context, input errone, target corrige)
+- **Dataset**: merged_with_context.jsonl (~5.5M paires)
+- **GPUs**: 3x Tesla V100-SXM3-32GB
+- **Effective batch size**: 48 (3 GPUs x 4 batch x 4 grad accum)
+- **Learning rate**: 5e-6
+- **Max seq length**: 384
+- **Steps**: 79 836 (1 epoch)
+
+## Metriques finales
+
+- **Train loss**: 0.0726
+- **Best eval loss**: 0.073076
+- **Global step**: 79836
+
+## Courbe d'evaluation
+
+- step=1000: eval_loss=0.076286
+- step=11000: eval_loss=0.073498
+- step=21000: eval_loss=0.073254
+- step=31000: eval_loss=0.073145
+- step=41000: eval_loss=0.073099
+- step=51000: eval_loss=0.073082
+- step=61000: eval_loss=0.073087
+- step=71000: eval_loss=0.073086
+
+## Reprise d'entrainement
+
+Ce modele a ete repris depuis le checkpoint-31000 de v15-context-continued.