Update README.md

README.md

@@ -1,22 +1,84 @@
 ---
-base_model: unsloth/Qwen2.5-7B-Instruct
+# 🦄 Model Card
+base_model: unsloth/Qwen2.5-3B-Instruct
 tags:
 - text-generation-inference
 - transformers
 - unsloth
 - qwen2
-- gguf
+- trl
+- grpo   # Gradient Reward Policy Optimization
 license: apache-2.0
 language:
 - en
 ---
 
-# Uploaded  model
+# 📦 Uploaded Model  
 
-- **Developed by:** bhaviktheslider
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/Qwen2.5-7B-Instruct
-
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+| **Field**             | **Value**                                  |
+|-----------------------|--------------------------------------------|
+| **Developed by**      | **bhaviktheslider**                        |
+| **License**           | Apache 2.0                                 |
+| **Finetuned from**    | `unsloth/Qwen2.5-3B-Instruct`              |
+| **Training Framework**| [Unsloth](https://github.com/unslothai/unsloth) × Hugging Face TRL |
 
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+
+---
+
+## 🚀 What’s New?
+
+> **TL;DR** – Think of this model as the beefed-up, protein-shake-powered sequel to **MasterControlAIML/DeepSeek-R1-Qwen2.5-1.5b-SFT-R1-JSON-Unstructured-To-Structured** … except we ditched the SFT and let a squad of reward functions do the coaching.
+
+### Key Upgrades
+1. **Larger Backbone** – We jumped from a 1.5 B parameter model to a 3 B parameter **Qwen 2.5** variant for more representational oomph.  
+2. **No SFT, All 🍬 Rewards** – Instead of supervised fine-tuning, training relied solely on reward-based optimization (GRPO).  
+   - **LM-as-Judge**: A language model scored candidate outputs for task quality.  
+   - **Auxiliary Rewards**: Style, length, and JSON-validity rewards kept the model on its best behavior.  
+3. **2× Faster Training** – Courtesy of Unsloth’s memory-efficient tricks (flash attention + fused optimizers).  
+
+---
+
+## 🛠️ Intended Use
+
+- Converts messy, free-form text into structured JSON—exactly like its 1.5 B predecessor, but with a deeper knowledge reservoir and reinforcement-tuned precision.  
+- Drop-in replacement for any pipeline already using the DeepSeek-R1 model. Just swap checkpoints and enjoy the headroom.
+
+---
+
+## 🏋️ Training Details
+
+| Item | Value |
+|------|-------|
+| **Base Model** | `unsloth/Qwen2.5-3B-Instruct` |
+| **Batching** | Gradient Accumulation 8, bfloat16 |
+| **Optimizer** | AdamW (fused) |
+| **Algorithm** | GRPO (policy ≈ LM; reward model ≈ separate LM judge) |
+| **Epochs** | 3 (effective) |
+| **Speed** | ~2× faster vs. vanilla PyTorch thanks to Unsloth |
+
+---
+
+## 📊 Evaluation (Coming Soon)
+
+We’re benchmarking against:
+- Exact-match JSON accuracy  
+- Structural F1  
+- Valid-JSON rate  
+
+…stay tuned—numbers arriving faster than you can say “schema validation.”
+
+---
+
+## 🤝 Citation
+
+If you build something cool with this model, a shout-out would be lovely:
+
+```bibtex
+@misc{bhaviktheslider_2025_unsloth_qwen2.5_3b_grpo,
+  title  = {An Unsloth-accelerated GRPO-trained Qwen 2.5 3B for JSON structuring},
+  author = {Bhaviktheslider},
+  year   = {2025},
+  howpublished = {Hugging Face},
+  note   = {https://huggingface.co/bhaviktheslider/<repo>}
+}