| --- |
| base_model: gpt2 |
| library_name: peft |
| pipeline_tag: text-generation |
| tags: |
| - base_model:adapter:gpt2 |
| - lora |
| - transformers |
| license: apache-2.0 |
| datasets: |
| - Abdurrahmanesc/textgen-synthetic |
| language: |
| - en |
| metrics: |
| - rouge |
| - perplexity |
| - bleu |
| - bertscore |
| --- |
| |
| # Model Card for Model ID |
|
|
| This repository contains a LoRA-fine-tuned version of a base language model trained on a custom dataset focused on improving response coherence, text quality, and task-specific alignment. |
|
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| The fine-tuning process was optimized for low-resource environments (CPU/TPU-friendly) while maintaining efficient training and strong post-training evaluation. |
|
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| This project is part of a broader effort to build an open-source AI fine-tuning tool offering full customization, dataset controls, and multi-platform support. |
|
|
|
|
| ### Model Description |
|
|
| | Property | Details | |
| | ---------------------- | ------------------------------------------------- | |
| | **Base Model** | (Your Base Model Name Here) | |
| | **Fine-Tuning Method** | LoRA / QLoRA | |
| | **Dataset** | Custom curated dataset (JSONL) | |
| | **Task Type** | Instruction following / text generation | |
| | **Intended Use** | Experimentation, research, downstream fine-tuning | |
|
|
| ## Goals of This Fine-Tuning |
|
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| Improve language generation quality |
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| Reduce perplexity |
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| Enhance alignment on user-style tasks |
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| Maintain generalization while improving dataset-specific behavior |
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| Validate training pipeline for the upcoming Open-Source Fine-Tuning Suite |
|
|
| ### Model Sources [optional] |
| ``` |
| yaml |
| === TRAIN METRICS (BEFORE vs AFTER) === |
| |
| ROUGE-L: |
| Before : 0.2726 |
| After : 0.2726 |
| Change : +0.0000 |
| |
| BLEU: |
| Before : 19.9785 |
| After : 19.9744 |
| Change : -0.0041 |
| |
| Perplexity: |
| Before : 23.67 |
| After : 3.02 |
| Change : -20.65 (major improvement) |
| |
| (Additional metrics shown in your logs) |
| ``` |
|
|
| ## Summary |
|
|
| ROUGE-L → Stable |
|
|
| BLEU → No significant change |
|
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| Perplexity → Massive improvement, indicating better fluency and internal consistency |
|
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| Other metrics followed similar minor/no-change trends, indicating: |
|
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| Minimal overfitting |
|
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| Stable behavior |
|
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| Improved confidence in generation |
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|
|
| ### Visualization |
|
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| The repository includes: |
|
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| Before/after metric graphs |
|
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| Automatic metric logs |
|
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| Training configuration dumps |
|
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| These help track performance over time and compare fine-tuning strategies. |
|
|
| ### Train Configuration |
|
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| LoRA Rank: r= (fill) |
|
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| LoRA Alpha: (fill) |
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| Target Modules: (fill) |
|
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| Batch Size: (fill) |
|
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| Gradient Accumulation: (fill) |
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| Max Seq Length: (fill) |
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| Optimizer: (fill) |
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| Learning Rate: (fill) |
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| Eval Strategy: Before/After automated benchmark |
|
|
| ### Repository Structure |
| ``` |
| ├── adapter_model.bin |
| ├── adapter_config.json |
| ├── training_args.json |
| ├── eval_before.json |
| ├── eval_after.json |
| ├── plots/ |
| │ ├── before_after_graph.png |
| │ └── (others) |
| └── README.md |
| ``` |
|
|
| ## Limitations |
|
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| Not suitable for safety-critical applications |
|
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| Fine-tuning dataset may shape generation style |
|
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| Further RLHF or SFT may be required for production-level behavior |
|
|
| ### Acknowledgements |
|
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| Thanks to the HuggingFace Transformers, PEFT, and the open-source community for enabling lightweight fine-tuning on low-compute environments. |
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|
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| ### Framework versions |
|
|
| - PEFT 0.18.0 |
