knatware/knat

A sshleifer/tiny-gpt2 model fine-tuned with the Parameterised Efficiency (PEFT / LoRA) method, generated by the PASTA fine-tuning workflow.

Model Description

This model was produced with the Parameterised Efficiency (PEFT / LoRA) method (PASTA framework) starting from the base model sshleifer/tiny-gpt2, fine-tuned on a sample of the stanfordnlp/imdb dataset.

• Task type: causal
• Library: peft
• Generated by: the PASTA fine-tuning Colab notebook generator

Intended Uses & Limitations

This model was fine-tuned on a small sample for demonstration purposes. It has not been evaluated at scale and should not be used in production or safety-critical settings without further training, evaluation, and review. Behaviour is inherited from the base model and the (small) fine-tuning sample, and may reflect biases present in either.

Training Procedure

Hyperparameters

Hyperparameter	Value
Method	Parameterised Efficiency (PEFT / LoRA)
Base model	sshleifer/tiny-gpt2
Dataset	stanfordnlp/imdb (train[:200])
Epochs	1
Batch size	4
Learning rate	0.0005
Max steps	20
LoRA rank	4
LoRA alpha	8

Framework versions

See the !pip install cell in the training notebook for the exact package set used.

How to Get Started

from transformers import pipeline

gen = pipeline("text-generation", model="knatware/knat")
gen("Your prompt here")

Testing

Before being pushed, this model was tested locally with a sample inference call, and was re-loaded and tested again directly from the Hub after pushing to confirm the upload was complete and usable.

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© Knatware Technology UK. Developed by Kayode Okosi.