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README.md
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license: mit
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---
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license: mit
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tags:
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- tinyllama
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- sciq
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- multiple-choice
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- peft
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- lora
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- 4bit
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- quantization
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- instruction-tuning
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datasets:
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- allenai/sciq
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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---
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# 🧠 TinyLLaMA-1.1B LoRA Fine-tuned on SciQ Dataset
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This is a **TinyLLaMA-1.1B** model fine-tuned using **LoRA (Low-Rank Adaptation)** on the [SciQ](https://huggingface.co/datasets/allenai/sciq) multiple-choice question answering dataset. It uses **4-bit quantization** via `bitsandbytes` to reduce memory usage and improve inference efficiency.
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## 🧪 Use Cases
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This model is suitable for:
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- Educational QA bots
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- MCQ-style reasoning
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- Lightweight inference on constrained hardware (e.g., GPUs with <8GB VRAM)
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## 🛠️ Training Details
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- Base Model: `TinyLlama/TinyLlama-1.1B-Chat-v1.0`
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- Dataset: `allenai/sciq` (Science QA)
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- Method: Parameter-Efficient Fine-Tuning using LoRA
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- Quantization: 4-bit using `bitsandbytes`
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- Framework: 🤗 Transformers + PEFT + Datasets
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## 🧬 Model Architecture
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- Model: Causal Language Model
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- Fine-tuned layers: `q_proj`, `v_proj` (via LoRA)
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- Quantization: 4-bit (bnb config)
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## 📊 Evaluation
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- Accuracy: **100%** on a 1000-sample SciQ subset
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- Eval Loss: ~0.19
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## 💡 How to Use
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("your-username/tinyllama-sciq-lora")
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tokenizer = AutoTokenizer.from_pretrained("your-username/tinyllama-sciq-lora")
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prompt = """Question: What is the boiling point of water?\nChoices:\nA. 50°C\nB. 75°C\nC. 90°C\nD. 100°C\nAnswer:"""
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=20)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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## 🔐 License
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This model is released under the MIT License.
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## 🙌 Credits
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FineTuned By - [Uditanshu Pandey](https://huggingface.co/TechyCode)
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Based on - [TinyLLaMA-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)
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