Provides SFT training patterns and Unsloth optimizations for LLM instruction-tuning workflows.
npx skills add https://github.com/atrawog/overthink-plugins --skill sft
Please help me install this Agent Skill. Skill: sft Source: https://github.com/atrawog/overthink-plugins/tree/main/overthink-jupyter/skills/sft Command: npx skills add https://github.com/atrawog/overthink-plugins --skill sft
SFT helps tailor pre-trained LLMs to follow instructions by training on high-quality instruction-response data, reducing misalignment and improving task performance.
Fine-tune an instruction-following model by configuring SFTTrainer with your dataset and a LoRA setup, then run training.
High-intent search queries and answers about installing and using this skill.
SFT instruction-tuning requires formatting datasets into instruction-response pairs and applying correct chat template formatting. Proper dataset preparation ensures your instruction-following data aligns with SFTTrainer configuration requirements.
Applying Unsloth optimizations is the best way to accelerate LoRA fine-tuning while lowering memory usage. It integrates directly with SFTConfig and SFTTrainer to enable faster, memory-efficient training for large language models.
Yes, SFTTrainer supports Qwen3-Thinking models through specific thinking-model patterns. These patterns encourage internal reasoning demonstrations during evaluation and generation across instruction-following tasks.
Yes, TRL is required to configure SFTConfig and SFTTrainer for instruction-tuning workflows. These components manage the training configuration and execution needed to successfully fine-tune large language models.
Use SFT when you need to tailor pre-trained LLMs to follow instructions using high-quality instruction-response data. It reduces misalignment and improves task performance by training models directly on targeted demonstrations.
markpittGenerate synthetic fine-tuning datasets in ChatML JSONL format for language models.
ihatesea69Evaluate code generation models with pass@k metrics across multiple benchmarks.
JeffallanAutomate fine-tuning workflows for large language models using LoRA/QLoRA and PEFT methods.
mwolff328-stackProvision ephemeral Neon databases and run Playwright tests in GitHub Actions.
