Abdurrahmanesc/finetuning-infinite-workflow

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.

The fine-tuning process was optimized for low-resource environments (CPU/TPU-friendly) while maintaining efficient training and strong post-training evaluation.

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

Improve language generation quality

Reduce perplexity

Enhance alignment on user-style tasks

Maintain generalization while improving dataset-specific behavior

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

Perplexity → Massive improvement, indicating better fluency and internal consistency

Other metrics followed similar minor/no-change trends, indicating:

Minimal overfitting

Stable behavior

Improved confidence in generation

Visualization

The repository includes:

Before/after metric graphs

Automatic metric logs

Training configuration dumps

These help track performance over time and compare fine-tuning strategies.

Train Configuration

LoRA Rank: r= (fill)

LoRA Alpha: (fill)

Target Modules: (fill)

Batch Size: (fill)

Gradient Accumulation: (fill)

Max Seq Length: (fill)

Optimizer: (fill)

Learning Rate: (fill)

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

Not suitable for safety-critical applications

Fine-tuning dataset may shape generation style

Further RLHF or SFT may be required for production-level behavior

Acknowledgements

Thanks to the HuggingFace Transformers, PEFT, and the open-source community for enabling lightweight fine-tuning on low-compute environments.

Framework versions

• PEFT 0.18.0