This is a TinyLLaMA-1.1B model fine-tuned using LoRA (Low-Rank Adaptation) on the SciQ multiple-choice question answering dataset. It uses 4-bit quantization via bitsandbytes to reduce memory usage and improve inference efficiency.

🧪 Use Cases

This model is suitable for:

Educational QA bots
MCQ-style reasoning
Lightweight inference on constrained hardware (e.g., GPUs with <8GB VRAM)

🛠️ Training Details

Base Model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
Dataset: allenai/sciq (Science QA)
Method: Parameter-Efficient Fine-Tuning using LoRA
Quantization: 4-bit using bitsandbytes
Framework: 🤗 Transformers + PEFT + Datasets

🧬 Model Architecture

Model: Causal Language Model
Fine-tuned layers: q_proj, v_proj (via LoRA)
Quantization: 4-bit (bnb config)

📊 Evaluation

Accuracy: 100% on a 1000-sample SciQ subset
Eval Loss: ~0.19

💡 How to Use

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("TechyCode/tinyllama-sciq-lora")
tokenizer = AutoTokenizer.from_pretrained("TechyCode/tinyllama-sciq-lora")

prompt = """Question: What is the boiling point of water?\nChoices:\nA. 50°C\nB. 75°C\nC. 90°C\nD. 100°C\nAnswer:"""
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🔐 License

This model is released under the MIT License.

🙌 Credits

FineTuned By - Uditanshu Pandey
Linkedin - UditanshuPandey
GitHub - UditanshuPandey
Based on - TinyLLaMA-1.1B-Chat-v1.0