GPT-2 Tigrinya LoRA Fine-Tuned Model
---
base_model: gpt2
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:gpt2
- lora
- transformers
datasets:
- custom
metrics:
- perplexity
language:
- ti
new_version: 1.0
---
# Low-Resource Tigrinya Language Modeling with LoRA-Fine-Tuned GPT-2
## Model Details
### Model Description
This model is a **GPT-2 small** (124M parameters) fine-tuned using **LoRA (Low-Rank Adaptation)** on a custom Tigrinya dataset.
It is designed for **Tigrinya text generation**, including chatbot/dialogue, storytelling, and text continuation.
- **Developed by:** Abrhaley (Warsaw University of Technology, MSc student)
- **Funded by [optional]:** N/A
- **Shared by [optional]:** Abrhaley
- **Model type:** Causal Language Model (decoder-only Transformer)
- **Language(s) (NLP):** Tigrinya (`ti`)
- **License:** MIT
- **Finetuned from model [optional]:** gpt2
- **Framework versions:** Transformers + PEFT 0.17.1
### Model Sources
- **Repository:** GitHub
- **Paper [optional]:** N/A
- **Demo [optional]:** N/A
---
## Uses
### Direct Use
- Tigrinya text generation
- Chatbot / dialogue systems
- Story or content generation
### Downstream Use [optional]
- Fine-tuning for domain-specific Tigrinya applications (news, education, cultural storytelling)
### Out-of-Scope Use
- Generating harmful, offensive, or misleading content
- Critical decision-making without human supervision
---
## Bias, Risks, and Limitations
- Dataset may not cover all Tigrinya dialects
- May generate biased, offensive, or incoherent outputs
- Not suitable for factual QA tasks
### Recommendations
Users should verify outputs before real-world use and avoid sensitive applications.
---
## How to Get Started with the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
model_id = "abrhaley/gpt2-tigrinya-lora"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
generator = pipeline("text-generation", …