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A minimal code example for Supervised Fine-Tuning (SFT) to convert a base language model into a conversational chat model.

5 starsPython

SFT to convert a base language model into a conversational chat model

by onurkanbkrc·Mar 4, 2026·1 point·0 comments
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AI Analysis

●MidShip It

Tutorial code for SFT pipeline, but dozens of identical examples exist on GitHub.

Strengths
  • •Clean, minimal SFT implementation with LoRA and quantization—good teaching reference.
  • •Uses production techniques (4-bit quant, cosine scheduler, parameter efficiency).
Weaknesses
  • •Zero novelty: OpenAssistant SFT tutorials dominate GitHub; no unique architecture or insight.
  • •No evaluation metrics, no trained model artifact, no comparison to baselines—incomplete as a project.
Category
AI/ML
Target Audience

ML engineers, researchers learning LLM fine-tuning techniques

Similar To

Hugging Face transformers examples · Alpaca fine-tuning repos · Mistral fine-tuning templates

Similar Projects

AI/ML●Mid

Minisft – from base model to chat model

sft.py packages a complete, small-footprint SFT flow — 4-bit quantized Llama-2-7b, LoRA adapters, OpenAssistant data, plus inference and push-to-HF helpers — so you can run an experiment with a few commands. It's a pragmatic, well-scoped starter for anyone learning parameter-efficient fine-tuning, but it doesn't claim new research and lacks deeper evaluation, recipe tuning guidance, or large-scale training validation.

Niche GemShip It
onurkanbkrc
103mo ago