132 lines
4.1 KiB
Markdown
132 lines
4.1 KiB
Markdown
---
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license: apache-2.0
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language:
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- en
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tags:
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- text-generation
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- grammar-correction
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- grammatical-error-correction
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- gec
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- style-preserving
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- dpo
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- sft
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- smollm
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- small-language-model
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- writing-assistant
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- proofreading
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- nlp
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base_model: HuggingFaceTB/SmolLM-135M
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datasets:
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- jfleg
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pipeline_tag: text-generation
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model-index:
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- name: SmolLM-135M-GEC-SFT-DPO
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results:
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- task:
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type: text-generation
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name: Grammar Correction
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metrics:
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- type: bleu
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value: 0.50
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name: BLEU
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---
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# SmolLM-135M-GEC-SFT-DPO
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A **style-preserving grammar correction** model based on SmolLM-135M, trained with SFT + DPO to make minimal, targeted corrections while preserving your original writing style.
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## Why This Model?
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Unlike large language models (GPT, Claude, etc.) that tend to rewrite entire sentences, this model makes **minimal, targeted corrections** - fixing only grammatical errors while preserving your vocabulary, tone, and voice. Perfect for:
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- **Legal documents**: Maintain precise legal terminology
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- **Academic writing**: Preserve scholarly tone
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- **ESL/EFL education**: Help learners without changing their ideas
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- **Professional communications**: Keep your authentic voice
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## Key Features
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- **Minimal corrections**: Fixes only grammatical errors, doesn't rewrite your sentences
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- **Style preservation**: Maintains your vocabulary, tone, and voice
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- **Small & efficient**: Only 135M parameters (~500MB) - runs on CPU!
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- **BLEU score**: ~0.50 on grammar correction benchmarks
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## Quick Start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("DanJZY/SmolLM-135M-GEC-SFT-DPO")
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tokenizer = AutoTokenizer.from_pretrained("DanJZY/SmolLM-135M-GEC-SFT-DPO")
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text = "As the number of people grows, the need of habitable environment is essential."
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inputs = tokenizer(f"Fix grammar: {text}", return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=100)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Example: Style-Preserving vs Over-Correction
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```
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Original (with error):
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"As the number of people grows, the need of habitable environment is essential."
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✅ Our Model (Style-Preserving):
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"As the number of people grows, the need for a habitable environment is essential."
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↑
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Only fixes "of" → "for a"
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❌ Typical Model (Over-Correction):
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"As population growth continues, the necessity for a habitable environment becomes essential."
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↑
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Completely rewrites: changes vocabulary, structure, and tone
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```
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## Training Details
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| Parameter | Value |
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|-----------|-------|
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| Base model | [SmolLM-135M](https://huggingface.co/HuggingFaceTB/SmolLM-135M) |
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| Training method | SFT + DPO (Direct Preference Optimization) |
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| Preference pairs | ~19,000 (generated using edit distance) |
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| Total experiments | 28 (22 SFT + 6 DPO/IPO) |
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| Hardware | 8x RTX 3090 |
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| Training time | ~3 hours |
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## Resources
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| Resource | Link |
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|----------|------|
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| GitHub Repository | [ZhuoyuanJiang/SmolLM-GEC-SFT-DPO](https://github.com/ZhuoyuanJiang/SmolLM-GEC-SFT-DPO) |
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| Full Experiment Checkpoints | [Google Drive](https://drive.google.com/drive/folders/1fxd_yi4r4crp7UK3tJVKxVPHo9gRcw4z) (~68GB) |
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| Training Notebooks | [GitHub notebooks/](https://github.com/ZhuoyuanJiang/SmolLM-GEC-SFT-DPO/tree/main/notebook) |
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## Intended Use
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- Grammar correction for English text
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- Writing assistance that preserves author's voice
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- Educational tools for language learners
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- Proofreading applications
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## Limitations
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- English only
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- Best for sentence-level corrections
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- Not designed for stylistic improvements (only grammar)
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## Citation
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```bibtex
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@misc{smollm_gec_sft_dpo_2025,
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title={SmolLM-135M-GEC-SFT-DPO: Style-Preserving Grammar Correction with Direct Preference Optimization},
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author={Zhuoyuan Jiang},
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year={2025},
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url={https://huggingface.co/DanJZY/SmolLM-135M-GEC-SFT-DPO},
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note={Fine-tuned SmolLM-135M for minimal, style-preserving grammatical error correction}
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}
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```
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## Acknowledgments
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Special thanks to **Nima Tajbakhsh** (Nvidia) for guidance on efficient training methods.
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