A reasoning-focused fine-tune of Qwen/Qwen3.5-9B by Empero AI, trained to produce detailed chain-of-thought reasoning inside tags before providing its final answer. This model was distilled from Claude Opus 4.6 and Qwen3.5 reasoning traces, teaching the model to think step-by-step through problems before responding. GGUF quantizations available at: empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF The model outputs its reasoning process inside tags, followed by a clear final answer: — Base model: Qwen/Qwen3.5-9B — Technique: QLoRA 4-bit (rank 64, alpha 128) — Supervised Fine-Tuning — Epochs: 3 (best checkpoint selected at epoch 2) — Hardware: RTX 5090 (32GB VRAM) — Attention: SDPA — Optimizer: Paged AdamW 8-bit — Learning rate: 1e-4 with cosine schedule — Effective batch size: 8 — Max s We build open-source AI tools and models focused on advancing reasoning capabilities in smaller, efficient language models.
Модальности:
Генерация текста
Области применения:
Логика и рассуждение Диалог / чат
Задача: Генерация текста
Автор: empero-ai
Теги: qwen3_5_text, qwen, reasoning, claude, opus, distill, distillation, empero
Лайков: 4 | Загрузок: 117
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
