reddit-posts-summarization-grpo

YuvrajSingh9886/reddit-posts-summarization-grpo

reddit-posts-summarization-grpo is a summarization model from YuvrajSingh9886. Use it when you need a shorter version of a longer text. It is set up for mlx. The card lists the license as apache-2.0.

Checkpoints for two models fine-tuned with Group Relative Policy Optimization (GRPO) on the mlabonne/smoltldr Reddit summarization dataset, trained with smolcluster on an Apple Silicon Mac cluster.

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Updated Oct 7, 2026

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At a glance

Task: Summarization
Library: mlx
License: apache-2.0
Access: Public
Created: Apr 17, 2026
Updated: Oct 7, 2026
SHA: eb0853c8

Try a prompt

Base models

Datasets

mlabonne/smoltldr

Model card

From the Hugging Face model README

GRPO Summarization — Qwen2.5-0.5B-Instruct & LFM2.5-350M

Checkpoints for two models fine-tuned with Group Relative Policy Optimization (GRPO) on the mlabonne/smoltldr Reddit summarization dataset, trained with smolcluster on an Apple Silicon Mac cluster.

Each checkpoint sub-folder differs only in which automatic quality metric was added to the length reward during GRPO training.

Full rollouts, per-example scores, and paired significance tests: reddit-posts-summarization-grpo (dataset)

Checkpoints

Qwen2.5-0.5B-Instruct-bf16

Fine-tuned from Qwen/Qwen2.5-0.5B-Instruct. Checkpoints live under Qwen2.5-0.5B-Instruct-bf16/grpo-summarization-*/.

Subfolder	Reward	Faithfulness	Coverage	Conciseness	Clarity	Composite
`grpo-summarization-length-quality-bleu`	length + BLEU	0.680	0.399	0.577	0.744	2.400
`grpo-summarization-length-quality-rouge`	length + ROUGE	—	—	—	—	—
`grpo-summarization-length-quality-meteor`	length + METEOR	—	—	—	—	—
`grpo-summarization-length-quality-bleu-rouge`	length + BLEU + ROUGE	0.810	0.502	0.650	0.770	2.732
`grpo-summarization-length-quality-meteor-bleu`	length + METEOR + BLEU	0.792	0.468	0.648	0.756	2.664
`grpo-summarization-length-quality-meteor-rouge`	length + METEOR + ROUGE	0.832	0.511	0.659	0.767	2.769

Baseline (length-only, composite 2.416): not included as a checkpoint.

LFM2.5-350M-bf16

Fine-tuned from liquid-ai/LFM-2.5-350M. Checkpoints live under LFM-2.5-350M-bf16/grpo-summarization-*/.

Subfolder	Reward	Faithfulness	Coverage	Conciseness	Clarity	Composite
`grpo-summarization-length-only`	length only (baseline)	0.627	0.378	0.554	0.674	2.233
`grpo-summarization-length-quality-bleu`	length + BLEU	0.620	0.401	0.556	0.665	2.243
`grpo-summarization-length-quality-rouge`	length + ROUGE	0.642	0.414	0.575	0.646	2.278
`grpo-summarization-length-quality-meteor`	length + METEOR	0.689	0.433	0.595	0.641	2.358
`grpo-summarization-length-quality-bleu-rouge`	length + BLEU + ROUGE	0.696	0.443	0.606	0.643	2.387
`grpo-summarization-length-quality-meteor-bleu`	length + METEOR + BLEU	0.696	0.451	0.595	0.634	2.377
`grpo-summarization-length-quality-meteor-rouge`	length + METEOR + ROUGE	0.834	0.493	0.685	0.690	2.701

Composite = sum of four G-Eval metrics (max 4.0). Evaluated on 200 examples with gpt-5-mini-2025-08-07 as the LLM judge (5 rounds averaged).

Usage (MLX)

git clone https://huggingface.co/YuvrajSingh9886/reddit-posts-summarization-grpo
cd reddit-posts-summarization-grpo

Qwen2.5-0.5B — best run

from mlx_lm import load, generate

model, tokenizer = load("Qwen2.5-0.5B-Instruct-bf16/grpo-summarization-length-quality-meteor-rouge/latest")
messages = [
    {"role": "system", "content": "Summarize the following Reddit post in 2-3 sentences."},
    {"role": "user",   "content": "<paste your Reddit post here>"},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
output = generate(model, tokenizer, prompt=prompt, max_tokens=128, verbose=False)
print(output)

LFM2.5-350M — best run

from mlx_lm import load, generate

model, tokenizer = load("LFM-2.5-350M-bf16/grpo-summarization-length-quality-meteor-rouge/latest")
messages = [
    {"role": "system", "content": "Summarize the following Reddit post in 2-3 sentences."},
    {"role": "user",   "content": "<paste your Reddit post here>"},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
output = generate(model, tokenizer, prompt=prompt, max_tokens=128, verbose=False)
print(output)

Repository Structure

reddit-posts-summarization-grpo/
├── README.md
│
├── Qwen2.5-0.5B-Instruct-bf16/
│   ├── grpo-summarization-length-quality-bleu/latest/
│   │   ├── model.safetensors        # MLX bf16 weights (~940 MB)
│   │   ├── config.json
│   │   ├── tokenizer.json
│   │   ├── tokenizer_config.json
│   │   └── chat_template.jinja
│   ├── grpo-summarization-length-quality-bleu-rouge/latest/   └── ...
│   ├── grpo-summarization-length-quality-meteor/latest/       └── ...
│   ├── grpo-summarization-length-quality-meteor-bleu/latest/  └── ...
│   ├── grpo-summarization-length-quality-meteor-rouge/latest/ └── ...  ← best (2.769)
│   └── grpo-summarization-length-quality-rouge/latest/        └── ...
│
└── LFM-2.5-350M-bf16/
    ├── grpo-summarization-length-only/latest/
    │   ├── model.safetensors    # MLX bf16 weights (~709 MB)
    │   ├── config.json
    │   ├── tokenizer.json
    │   ├── tokenizer_config.json
    │   └── chat_template.jinja
    ├── grpo-summarization-length-quality-bleu/latest/        └── ...
    ├── grpo-summarization-length-quality-rouge/latest/       └── ...
    ├── grpo-summarization-length-quality-meteor/latest/      └── ...
    ├── grpo-summarization-length-quality-bleu-rouge/latest/  └── ...
    ├── grpo-summarization-length-quality-meteor-bleu/latest/ └── ...
    └── grpo-summarization-length-quality-meteor-rouge/latest/ └── ...  ← best (2.701)

Training Details

Setting	Qwen2.5-0.5B	LFM2.5-350M
Base model	Qwen/Qwen2.5-0.5B-Instruct	liquid-ai/LFM-2.5-350M
Algorithm	GRPO	GRPO
Dataset	`mlabonne/smoltldr` (train split)	`mlabonne/smoltldr` (train split)
Shared reward	Length penalty	Length penalty
Variable reward	BLEU / ROUGE / METEOR and combinations	BLEU / ROUGE / METEOR and combinations
Hardware	Apple Silicon Mac mini cluster	Apple Silicon Mac mini cluster
Framework	smolcluster (MLX)	smolcluster (MLX)
Weights format	MLX safetensors (bf16)	MLX safetensors (bf16)
Eval examples	200 (validation split)	200 (validation split)
Judge	`gpt-5-mini-2025-08-07` via DeepEval GEval	`gpt-5-mini-2025-08-07` via DeepEval GEval

Citation

These checkpoints were trained with smolcluster. If you use them in academic work, please cite:

@software{singh2026smolcluster,
  title     = {smolcluster: An Educational Distributed Training and Inference Library for Local Computing},
  author    = {Singh, Yuvraj},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.23196836},
  url       = {https://github.com/YuvrajSingh-mist/smolcluster}
}

Released freely — support more experiments like it:

More: https://huggingface.co/YuvrajSingh9886

Task: Summarization
Library: mlx
License: apache-2.0
Languages: en
Created: Apr 17, 2026
Updated: Oct 7, 2026