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Date	Provider	Score	Summary
03 Sep 2026, 8:00 AM	Hugging Face Blog	7.5	Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps A fully open recipe for fine-tuning LiquidAI's LFM2.5-350M model with GRPO via the TRL library to improve structured-output compliance, evaluated on the IFStruct benchmark. The training runs in ~500 samples and 100 steps on a free-tier Colab or Kaggle GPU, lifting IFStruct scores from 22.6% to 29.7%. Evaluation is done locally via llama.cpp on a MacBook Pro M5 Max with 36GB unified memory. Why: If you ship LLM-powered pipelines that depend on schema-valid JSON or structured output, this shows you can cheaply fine-tune a 350M-parameter model on free-tier GPUs rather than paying for a large model API, with a concrete benchmark delta (22.6% to 29.7%) to set expectations. The entire pipeline is reproducible on GitHub and runnable without paid infrastructure. fine-tuning structured-output grpo trl small-models llama-cpp

Date

Provider

Score

Summary

03 Sep 2026, 8:00 AM

Hugging Face Blog

7.5

Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps

A fully open recipe for fine-tuning LiquidAI's LFM2.5-350M model with GRPO via the TRL library to improve structured-output compliance, evaluated on the IFStruct benchmark. The training runs in ~500 samples and 100 steps on a free-tier Colab or Kaggle GPU, lifting IFStruct scores from 22.6% to 29.7%. Evaluation is done locally via llama.cpp on a MacBook Pro M5 Max with 36GB unified memory.

Why: If you ship LLM-powered pipelines that depend on schema-valid JSON or structured output, this shows you can cheaply fine-tune a 350M-parameter model on free-tier GPUs rather than paying for a large model API, with a concrete benchmark delta (22.6% to 29.7%) to set expectations. The entire pipeline is reproducible on GitHub and runnable without paid infrastructure.