The article describes a successful implementation of fine-tuning a Qwen language model using the Open-R1 framework in Google Colab, overcoming initial challenges with the Accelerate library through a
custom training approach. The author achieved improved model performance over 3 epochs with minimal parameter increase through PEFT/LoRA, while gaining valuable insights into the fine-tuning process.
Reasons to Read -- Learn:
how to implement practical solutions for fine-tuning large language models in resource-constrained environments like Google Colab, with specific code examples and workarounds for common issues.
how to achieve efficient model fine-tuning using PEFT/LoRA, resulting in only 0.14% trainable parameters while still achieving significant performance improvements over 3 epochs.
implementing custom training loops with Open-R1's SFTTrainer, including specific techniques for handling data type compatibility issues and monitoring training progress.
6 min readauthor: Frank Morales Aguilera
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