akhilfau/smollm2-360m-physics-gguf

smollm2-360m-physics-gguf

Author: Akhil Vallala
Base Model: 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
Benchmark: MMLU College Physics
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

# 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:

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

@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}},
}