Create README.md

README.md

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+smollm2-360m-physics-gguf
+
+**Author**: [Akhil Vallala](https://www.linkedin.com/in/akhil-fau)  
+**Base Model**: [`akhilfau/fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset`](https://huggingface.co/akhilfau/fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset)  
+**Architecture**: LLaMA (SmolLM2)  
+**Parameter count**: 362M  
+**Format**: GGUF (Q4_K_M, Q8_0, FP16)  
+**License**: Apache 2.0  
+**Model Type**: Instruction-tuned Small Language Model (SLM)  
+**Use Case**: Solving physics word problems on mobile devices  
+
+Model Overview
+
+This GGUF model is a quantized version of the **Tiny-Physics** model, based on SmolLM2-360M and fine-tuned for **physics word problem solving** using both real and synthetic datasets. It is designed to deliver accurate, low-latency performance on **mobile and edge devices**.
+
+Datasets Used
+
+- 📘 **camel-ai/physics**: Publicly available dataset with 20,000+ physics QA pairs  
+- 📘 **Seed dataset**: Extracted from *1000 Solved Problems in Classical Physics*  
+- 🧠 **Synthetic dataset**: 6,279 rigorously validated question-answer pairs generated using a GPT-4o-based multi-agent system
+
+These datasets were formatted for instruction tuning using structured prompt–response pairs.
+
+Training Details
+
+- **Model**: SmolLM2-360M  
+- **Fine-tuning**: Instruction fine-tuning with LoRA (Low-Rank Adaptation)  
+- **Libraries**: Hugging Face Transformers, TRL, Lighteval  
+- **Training Epochs**: 3 (best accuracy observed at 3–5 epochs)  
+- **Fine-tuning Objective**: Maximize performance on MMLU College Physics  
+- **Best Model Accuracy**: `24.51%` on MMLU College Physics  
+
+Evaluation
+
+**Evaluated with**: [Lighteval](https://github.com/huggingface/lighteval)  
+**Benchmark**: [MMLU College Physics](https://huggingface.co/datasets/hendrycks_test)  
+**Performance**:
+| Dataset                      | Accuracy (SmolLM2-360M-Instruct) |
+|-----------------------------|----------------------------------|
+| MMLU: College Physics       | 24.51%                           |
+| Instruction-Tuned camel-ai  | 25.49%                           |
+| Combined Instruction Dataset| 24.51%                           |
+
+GGUF Quantization
+
+Model is provided in multiple quantization formats:
+| Format   | Size   | Accuracy Retention | Inference Speed | RAM Usage     | Target Use                         |
+|----------|--------|--------------------|------------------|---------------|------------------------------------|
+| `Q4_K_M` | ~271MB | ~95–97%            | Fast             | ~600–800MB    | Ideal for mid-range mobile devices |
+| `Q8_0`   | ~386MB | ~99%               | Medium           | ~1–1.5GB      | Best for higher-end devices        |
+| `FP16`   | ~800MB | 100%               | Slow             | ~2GB+         | Reference use only                 |
+
+How to Use
+
+```bash
+# Using llama.cpp
+./main -m smollm2-360m-physics-gguf.Q4_K_M.gguf -p "What is the acceleration of a 2kg mass falling from 5 meters?"
+```
+
+Or via `llama-cpp-python`:
+
+```python
+from llama_cpp import Llama
+
+llm = Llama(model_path="smollm2-360m-physics-gguf.Q4_K_M.gguf")
+output = llm("What is the potential energy of a 3kg object at 10 meters?")
+```
+
+Intended Use
+
+- 📚 **Physics tutoring apps**
+- 📶 **Offline mobile inference**
+- 🧑‍🏫 **Educational tools for conceptual reasoning**
+- 🔋 **Low-power deployment scenarios**
+
+Limitations
+
+- Not trained on multiple-choice formatted data (MCQ output mismatch possible)
+- Topic imbalance in datasets may affect generalization
+- Not suitable for non-physics or open-domain tasks
+
+Carbon Footprint
+
+Training and fine-tuning consumed approx. **2.64 kg CO₂e**, equivalent to a ~7-mile car ride. This was achieved using local GPU resources (RTX A5500) and energy-efficient batch tuning with LoRA.
+
+
+
+Citation
+
+```bibtex
+@misc{vallala2025tinyphysics,
+  title={Tiny-Physics: A Compact Large Language Model for Physics Word Problems on Mobile Devices},
+  author={Akhil Vallala},
+  year={2025},
+  howpublished={\url{https://huggingface.co/akhilfau/smollm2-360m-physics-gguf}},
+}
+```
+