Instructions to use Abrhaley/gpt2-tigrinya-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Abrhaley/gpt2-tigrinya-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("gpt2")
model = PeftModel.from_pretrained(base_model, "Abrhaley/gpt2-tigrinya-lora")

Transformers

How to use Abrhaley/gpt2-tigrinya-lora with Transformers:

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

pipe = pipeline("text-generation", model="Abrhaley/gpt2-tigrinya-lora")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Abrhaley/gpt2-tigrinya-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Abrhaley/gpt2-tigrinya-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Abrhaley/gpt2-tigrinya-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Abrhaley/gpt2-tigrinya-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Abrhaley/gpt2-tigrinya-lora

SGLang

How to use Abrhaley/gpt2-tigrinya-lora 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 "Abrhaley/gpt2-tigrinya-lora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Abrhaley/gpt2-tigrinya-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Abrhaley/gpt2-tigrinya-lora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Abrhaley/gpt2-tigrinya-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Abrhaley/gpt2-tigrinya-lora with Docker Model Runner:
```
docker model run hf.co/Abrhaley/gpt2-tigrinya-lora
```

GPT-2 Tigrinya (LoRA Fine-Tuned)

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 (MSc Student, Warsaw University of Technology)
Model type: Causal Language Model (decoder-only Transformer)
Languages (NLP): Tigrinya (ti)
License: MIT
Finetuned from model: gpt2
Framework versions: Transformers 4.x, PEFT 0.17.1, PyTorch 2.x

Model Sources

Repository (GitHub): abrhaley/gpt2-tigrinya-lora
Hugging Face Model Hub: abrhaley/gpt2-tigrinya-lora
Paper: N/A
Demo (Gradio): coming soon via Hugging Face Spaces

Uses

Direct Use

Generate Tigrinya text (stories, conversation, completions)
Chatbots and dialogue systems in Tigrinya
Creative text applications (poetry, narratives, cultural content)

Downstream Use

Fine-tuning for specialized domains (news, education, healthcare in Tigrinya)

Out-of-Scope Use

Generating factual or authoritative knowledge (may hallucinate)
Sensitive/critical applications (medical, legal, political decisions)
Harmful or offensive text generation

Bias, Risks, and Limitations

Dataset does not cover all Tigrinya dialects equally.
Model may generate biased, offensive, or incoherent outputs.
Not reliable for factual Q&A.
Small model (GPT-2 small) → may struggle with long contexts.

Recommendations

Users should carefully review outputs before use.
Avoid deploying the model in sensitive applications without human oversight.

How to Get Started with the Model

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", model=model, tokenizer=tokenizer)

prompt = "ኣብ ኣዲስ ኣበባ"
print(generator(prompt, max_length=100, do_sample=True))

## Training Details

### Training Data
- **Source**: Custom Tigrinya text corpus (news + literature + web text)  
- **Split**: Train/validation prepared manually  

### Training Procedure
- **Base model**: GPT-2 small (124M parameters)  
- **Fine-tuning method**: LoRA (PEFT) applied on attention layers  
- **LoRA config**: r=8, alpha=32, dropout=0.05  

### Hyperparameters
- **Batch size (effective)**: 8  
- **Learning rate**: 2e-4  
- **Optimizer**: AdamW  
- **Epochs**: 1 (demo training; extendable)  
- **Precision**: FP16 (when GPU available)  

---

## Evaluation

### Results
- **Training Loss**: 1.67  
- **Validation Loss**: 1.61  
- **Perplexity (PPL)**: ≈ 5.0  

### Metrics
- **Primary metric**: Perplexity (lower is better → more fluent text)  
- **Summary**: The model achieves ~5.0 PPL on validation and produces fluent/natural Tigrinya completions.  

---

## Environmental Impact
- **Hardware**: NVIDIA T4 (Google Colab)  
- **Training time**: ~5.5 hours  
- **Cloud Provider**: Google Cloud (via Colab)  
- **Carbon estimate**: <1kg CO₂eq (low emissions, small-scale run).  

---

## Technical Specifications
- **Architecture**: GPT-2 small (decoder-only Transformer)  
- **LoRA applied to**: attention layers (`c_attn`, `c_proj`)  
- **Framework**: Hugging Face Transformers + PEFT  
- **Precision**: FP16 mixed-precision (on GPU)  

## Citation

If you use this model, please cite it as:

**BibTeX:**
```bibtex
@misc{abrhaley2025gpt2tigrinya,
  title   = {GPT-2 Tigrinya LoRA Fine-Tuned},
  author  = {Abrhaley},
  year    = {2025},
  url     = {https://huggingface.co/abrhaley/gpt2-tigrinya-lora}
}

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