Instructions to use Abdurrahmanesc/finetuning-infinite-workflow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Abdurrahmanesc/finetuning-infinite-workflow with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("gpt2")
model = PeftModel.from_pretrained(base_model, "Abdurrahmanesc/finetuning-infinite-workflow")

Transformers

How to use Abdurrahmanesc/finetuning-infinite-workflow with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Abdurrahmanesc/finetuning-infinite-workflow")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Abdurrahmanesc/finetuning-infinite-workflow", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Abdurrahmanesc/finetuning-infinite-workflow with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Abdurrahmanesc/finetuning-infinite-workflow"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Abdurrahmanesc/finetuning-infinite-workflow",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Abdurrahmanesc/finetuning-infinite-workflow

SGLang

How to use Abdurrahmanesc/finetuning-infinite-workflow with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Abdurrahmanesc/finetuning-infinite-workflow" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Abdurrahmanesc/finetuning-infinite-workflow",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Abdurrahmanesc/finetuning-infinite-workflow" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Abdurrahmanesc/finetuning-infinite-workflow",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Abdurrahmanesc/finetuning-infinite-workflow with Docker Model Runner:
```
docker model run hf.co/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

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