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Model: DanJZY/SmolLM-135M-GEC-SFT-DPO
Source: Original Platform

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README.md

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

added_tokens.json

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config.json

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+  "torch_dtype": "float32",
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+  "use_cache": true,
+  "vocab_size": 49154
+}

generation_config.json

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+}

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