Peft Fine Tuning

**Judgement:** Strong, production-grade PEFT/LoRA toolkit built around HuggingFace’s official `peft` library. Excellent choice whenever you need to fine-tune large LLMs under tight GPU memory or want to manage multiple task-specific adapters from a single base model. **What it’s best for** - Fine-tuning 7B–70B models on a single GPU (24–80 GB), especially with QLoRA. - Training and managing lightweight adapters (<1% parameters) instead of full model checkpoints. - Integrating PEFT with existing HF/TRL/Axolotl/vLLM-based workflows. **Key strengths** - Very rich method coverage (LoRA, QLoRA, IA3, prefix/prompt tuning, etc.). - Concrete, copy-pastable examples for standard LoRA, QLoRA, multi-adapter serving, merging, and troubleshooting. - Good performance and memory characteristics, with realistic benchmark numbers and best-practice hyperparameter guidance. **Risks / limitations** - **Environment fragility:** Depends on CUDA, bitsandbytes, and transformers versions; users may hit installation or GPU/driver compatibility issues, especially on non-standard setups. - **Operational complexity:** Still requires solid understanding of training loops, data preprocessing, and evaluation; misconfigured ranks/targets can silently degrade quality. - **No built-in safety controls:** The tool focuses purely on fine-tuning; content safety, red-teaming, and dataset governance must be handled externally. **Recommended scenarios** - You have a base LLM (e.g., Llama/Qwen/Mistral) and want to adapt it to a new task or domain without retraining all weights. - You need to run fine-tuning on constrained hardware but still need near–full fine-tuning quality. - You plan to host multiple fine-tuned variants (adapters) behind one base model and switch between them at inference time.