| --- |
| license: apache-2.0 |
| tags: |
| - peft |
| - lora |
| - math |
| - reasoning |
| - gsm8k |
| - phi-2 |
| - transformers |
| library_name: peft |
| base_model: microsoft/phi-2 |
| model_type: causal-lm |
| --- |
| |
| # 🧠 Phi-2 LoRA Adapter for GSM8K (Math Word Problems) |
|
|
| This repository contains a parameter-efficient **LoRA fine-tuning** of [`microsoft/phi-2`](https://huggingface.co/microsoft/phi-2) on the **GSM8K** dataset, designed for solving grade-school arithmetic and reasoning problems in natural language. |
|
|
| > ✅ Adapter-only: This is a **LoRA adapter**, not a full model. You must load it on top of `microsoft/phi-2`. |
|
|
| --- |
|
|
| ## ✨ What's Inside |
|
|
| - **Base Model**: `microsoft/phi-2` (1.7B parameters) |
| - **Adapter Type**: LoRA (Low-Rank Adaptation via [PEFT](https://github.com/huggingface/peft)) |
| - **Task**: Grade-school math reasoning (multi-step logic and arithmetic) |
| - **Dataset**: [GSM8K](https://huggingface.co/datasets/gsm8k) |
|
|
| --- |
|
|
| ## 🚀 Quick Start |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| from peft import PeftModel |
| |
| # Load base model and tokenizer |
| base_model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2") |
| tokenizer = AutoTokenizer.from_pretrained("darshjoshi16/phi2-lora-math") |
| |
| # Load LoRA adapter |
| model = PeftModel.from_pretrained(base_model, "darshjoshi16/phi2-lora-math") |
| |
| # Inference |
| prompt = "Q: Julie read 12 pages yesterday and twice as many today. If she wants to read half of the remaining 84 pages tomorrow, how many pages should she read?\nA:" |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
| outputs = model.generate(**inputs, max_new_tokens=100) |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
| ``` |
|
|
| --- |
|
|
| ## 📊 Evaluation Results |
|
|
| | Task | Metric | Score | Samples | |
| |-------------|-----------------------------|--------|---------| |
| | GSM8K | Exact Match (strict) | 54.6% | 500 | |
| | ARC-Easy | Accuracy | 79.0% | 500 | |
| | HellaSwag | Accuracy (Normalized) | 61.0% | 500 | |
|
|
| > Benchmarks were run using [EleutherAI’s lm-eval-harness](https://github.com/EleutherAI/lm-eval-harness) |
|
|
| --- |
|
|
| ## ⚙️ Training Details |
|
|
| - **Method**: LoRA (rank=8, alpha=16, dropout=0.1) |
| - **Epochs**: 1 (proof of concept) |
| - **Batch size**: 4 per device |
| - **Precision**: FP16 |
| - **Platform**: Google Colab (T4 GPU) |
| - **Framework**: [🤗 Transformers](https://github.com/huggingface/transformers) + [PEFT](https://github.com/huggingface/peft) |
|
|
| --- |
|
|
| ## 🔍 Limitations |
|
|
| - Fine-tuned for math problems only (not general-purpose reasoning) |
| - Trained for 1 epoch — additional training may improve performance |
| - Adapter-only: base model (`microsoft/phi-2`) must be loaded alongside |
|
|
| --- |
|
|
| ## 📘 Citation & References |
|
|
| - [LoRA: Low-Rank Adaptation](https://arxiv.org/abs/2106.09685) |
| - [Phi-2 Model Card](https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/) |
| - [GSM8K Dataset](https://huggingface.co/datasets/gsm8k) |
| - [PEFT Library](https://github.com/huggingface/peft) |
| - [Transformers](https://huggingface.co/docs/transformers) |
|
|
| --- |
|
|
| ## 💬 Author |
|
|
| This model was fine-tuned and open-sourced by **[Darsh Joshi](https://huggingface.co/darshjoshi16)**. |
| Feel free to [reach out](mailto:contact@darshjoshi.com) or contribute. |
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