Abrhaley
/

gpt2-tigrinya-lora

@@ -3,205 +3,148 @@ base_model: gpt2
 library_name: peft
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:gpt2
-- lora
-- transformers
 ---
-# Model Card for Model ID
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 ## Model Details
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-### Framework versions
-- PEFT 0.17.1

 library_name: peft
 pipeline_tag: text-generation
 tags:
+  - base_model:adapter:gpt2
+  - lora
+  - transformers
+  - tigrinya
+datasets:
+  - custom
+metrics:
+  - perplexity
+language:
+  - ti
+license: mit
 ---
+# 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](https://huggingface.co/gpt2)
+- **Framework versions:** Transformers 4.x, PEFT 0.17.1, PyTorch 2.x
+---
+## Model Sources
+- **Repository (GitHub):** [abrhaley/gpt2-tigrinya-lora](https://github.com/abrhaleyarefaine1997/gpt2-tigrinya-lora)
+- **Hugging Face Model Hub:** [abrhaley/gpt2-tigrinya-lora](https://huggingface.co/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
+```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", 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}
+}