lora-output
This model is a fine-tuned version of
distilgpt2 on an unknown dataset.
Model description
This model is a parameter-efficient fine-tuned version of distilgpt2, trained using LoRA (Low-Rank Adaptation) on a small demonstration dataset inside a Google Colab Free Tier GPU environment.
The goal is to provide a lightweight, fast, reproducible, and beginner-friendly template for fine-tuning nano-scale language models.
The base model (distilgpt2) is a distilled version of GPT-2, making it significantly smaller and more efficient while retaining good generative capability.
LoRA makes training accessible on limited hardware by training only a small set of additional low-rank parameters.
Intended uses & limitations
This model is intended for:
Educational demonstration of nano-LLM fine-tuning
Research on lightweight parameter-efficient training
Small-scale text generation tasks
Custom FAQ or conversational agents
Prototyping ML workflows in Google Colab Free Tier
Not intended for:
High-risk decision-making
Medical, legal, financial, or political applications
Producing factual or authoritative information
Any use that requires accuracy beyond small toy datasets
Training and evaluation data
Hardware
Google Colab Free Tier
NVIDIA T4 GPU (or similar)
12–15GB RAM
Max runtime: <3 hours (safe for free tier limits)
Training Framework
The model was trained using:
Hugging Face Transformers (model / trainer)
Hugging Face Datasets (data loading)
PEFT (LoRA) for parameter-efficient fine-tuning
Accelerate (device handling)
Training Objective
Causal Language Modeling (next-token prediction), using the standard GPT-2 loss.
Hyperparameters
Epochs: 3
Batch size: 2 (gradient accumulation ×8)
Learning rate: 2e-4
Max sequence length: 512 tokens
Precision: fp32 (for Colab stability)
Optimizer: AdamW
Dataset
A small demonstration dataset was created in JSONL format for testing purposes.
Each example used a simple prompt → answer conversational style.
This dataset is only illustrative and should be replaced for real applications.
Example format:
Data Size
Very small (<10 samples in demo)
Not suitable for production
Only for demonstrating the workflow from data → fine-tuned model
Evaluation
No separate validation set was used due to the tiny dataset.
Evaluation strategy was set to "no" to reduce compute cost.
This model should not be evaluated as a general-purpose language model — it is a workflow demonstration.
Limitations
Limited training data → high risk of overfitting
Not instruction-tuned or alignment-tuned
Base model (distilgpt2) has known limitations inherited from GPT-2, including outdated knowledge
Demo dataset restricts conversational breadth
Not suitable for factual tasks
Potential Risks
May generate inaccurate or unsafe text if prompted incorrectly
May hallucinate or invent answers
Should not be used for impactful real-world decisions
Demo dataset may introduce unintended biases
Always supervise outputs when using in interactive environments.
Training procedure
How to Use
Load with LoRA adapter
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from peft import PeftModel
tokenizer = AutoTokenizer.from_pretrained("your-username/your-model")
base = AutoModelForCausalLM.from_pretrained("distilgpt2")
model = PeftModel.from_pretrained(base, "your-username/your-model")
generator = pipeline("text-generation",
model=model,
tokenizer=tokenizer)
generator("Q: Give a friendly greeting.\nA:", max_length=120)
Or use merged full model (if uploaded)
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
model = AutoModelForCausalLM.from_pretrained("your-username/your-model-full")
tokenizer = AutoTokenizer.from_pretrained("your-username/your-model-full")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
pipe("Hello, I am your assistant!", max_length=150)
Reproducibility
This model was built following the official Hugging Face training workflows and Colab notebook best practices.
More details can be found in the Hugging Face “Finetuning GPT-2” & “PEFT/LoRA” examples:
Transformers notebooks and tutorials
Trainer API documentation
PEFT (LoRA) docs and examples
Citation
If you use this model or training template, please cite the original libraries:
@misc{huggingface2023transformers,
title={Transformers: State-of-the-art Natural Language Processing},
author={The HuggingFace Team},
year={2023},
publisher={HuggingFace},
}
@misc{hu2021lora,
title={LoRA: Low-Rank Adaptation of Large Language Models},
author={Hu, Edward and others},
year={2021},
}
Model Creator
This model was prepared and fine-tuned by Abdur Rahman in a Google Colab environment with step-by-step guidance provided by ChatGPT.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Framework versions
- PEFT 0.17.1
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
