This project fine-tunes the deepseek-ai/DeepSeek-R1-0528-Qwen3-8B model using a medical reasoning dataset (mamachang/medical-reasoning) with 4-bit quantization for memory-efficient training. 1. Load the Model and Tokenizer The script downloads the DeepSeek-R1-0528-Qwen3-8B model and applies 4-bit quantization with BitsAndBytesConfig for efficient memory usage. 2. Prepare the Dataset — The notebook uses mamachang/medical-reasoning. — It formats each example into an instruction-following prompt with step-by-step chain-of-thought reasoning. 3. Fine-tuning — Fine-tuning is set up with PEFT (LoRA / Adapter Tuning style) to modify a small subset of model parameters. — TRL (Transformer Reinforcement Learning) is used to fine-tune efficiently. 4. Push Fine-tuned Model — After training, the fine-tuned model and tokenizer are pushed back to your Hugging Face Hub. — GPU Required: Make sure you have access to 1X RTX4090. Get it from RunPod for an hours. Training took only 15 minutes. — Environment: The notebook expects an environment where NVIDIA CUDA drivers are available (nvidia-smi check is included). — Memory Efficiency: 4-bit loading greatly reduces memory footprint.
Модальности:
Генерация текста
Области применения:
Диалог / чат Медицина Логика и рассуждение
Задача: Генерация текста
Автор: kingabzpro
Теги: medical, deepseek-r1, qwen3, conversational, en, endpoints_compatible
Лайков: 4 | Загрузок: 0
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
