Update README.md

README.md

@@ -1,3 +1,134 @@
----
-license: cc-by-nc-4.0
----
+---
+license: cc-by-nc-4.0
+datasets:
+- openai/gsm8k
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-Math-1.5B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- math
+- qwen
+- lora
+- mathematics
+- gsm8k
+---
+
+# OpenMath  
+Fine-tuning a Small Language Model (SLM) for Step-by-Step Math Reasoning  
+
+## Overview  
+OpenMath is an open-source project focused on fine-tuning a small language model for math reasoning using QLoRA (4-bit LoRA).  
+
+This repository contains only a LoRA adapter trained on GSM8K. Users must load the base model separately and attach the adapter.  
+
+The latest version of this model was trained on an AMD MI300X GPU using ROCm, showing that modern non-NVIDIA accelerators can successfully support large-scale fine-tuning with Hugging Face and PyTorch.  
+
+---
+
+## Base Model  
+Qwen/Qwen2.5-Math-1.5B  
+
+This repository does not contain the base model weights — they must be loaded from Hugging Face.  
+
+---
+
+## Hardware Used (Latest Training Run)  
+
+GPU: AMD MI300X (ROCm 7.0)  
+VRAM: 192 GB  
+Operating System: Ubuntu 24.04  
+Framework: PyTorch + Hugging Face  
+Backend: ROCm  
+
+---
+
+## Dataset  
+
+GSM8K (Grade School Math 8K)  
+Training samples: 1,000  
+Evaluation: Full GSM8K test split (1,319 problems)  
+
+Only the solution portion of each example was used for loss computation through loss masking.  
+
+---
+
+## Training Configuration  
+
+Method: QLoRA (4-bit)  
+Quantization: NF4 with float16 compute  
+LoRA rank: 16  
+LoRA alpha: 32  
+LoRA dropout: 0.05  
+Target modules: q_proj, k_proj, v_proj, o_proj  
+Max sequence length: 1024  
+Batch size: 1  
+Gradient accumulation: 16  
+Effective batch size: 16  
+Learning rate: 1e-4  
+Optimizer: paged_adamw_8bit  
+Scheduler: cosine  
+Warmup: 5 percent  
+Epochs: 6  
+
+---
+
+## Results  
+
+GSM8K Accuracy (Full Test Set):  
+750 out of 1319 correct, which equals 56.86 percent accuracy.  
+
+This is significantly stronger than the earlier Colab T4 run and is a strong result for a 1.5B model trained with LoRA.  
+
+---
+
+## What This Repository Contains  
+
+adapter_model.safetensors — LoRA weights  
+adapter_config.json — LoRA configuration  
+chat_template.jinja — chat formatting template  
+tokenizer.json — tokenizer file  
+tokenizer_config.json — tokenizer settings  
+README.md — documentation  
+
+This repository does not include checkpoints, optimizer states, or full base model weights.  
+
+---
+
+## How to Use This Model  
+
+Load the base model Qwen/Qwen2.5-Math-1.5B from Hugging Face, then attach this LoRA adapter using PEFT. Generate answers using a prompt that includes an instruction, problem, and solution section.  
+
+---
+
+## Why This Matters  
+
+This project demonstrates that AMD MI300X can train modern language models with Hugging Face and QLoRA.  
+It shows that high-quality math reasoning is possible at 1.5B parameters using efficient fine-tuning.  
+It provides a lightweight adapter instead of requiring users to download a massive full model.  
+
+---
+
+## Limitations  
+
+The model can make reasoning mistakes.  
+It should not be used for exams, assignments, or professional decisions.  
+Performance depends heavily on prompt formatting.  
+
+---
+
+## Future Work  
+
+Train on 3,000 to 5,000 GSM8K samples.  
+Add SVAMP and ASDiv datasets.  
+Improve decoding to reduce repetition.  
+Experiment with multi-GPU scaling on MI300X.  
+Add a Streamlit demo for interactive use.  
+
+---
+
+## License  
+
+cc-by-nc-4.0