Update README.md

README.md

@@ -1,21 +1,131 @@
 ---
-base_model: unsloth/gemma-3-12b-it
+library_name: transformers
+license: gemma
+license_link: https://ai.google.dev/gemma/terms
+pipeline_tag: text-generation
 tags:
-- text-generation-inference
-- transformers
-- unsloth
-- gemma3
-license: apache-2.0
+- math
+- reasoning
+- computational-graph
+- bangla
+- low-resource
+- distractor-aware
+- sft
+base_model:
+- google/gemma-3-12b-it
 language:
+- bn
 - en
+datasets:
+- dipta007/dagger
+- dipta007/DistractMath-Bn
 ---
 
-# Uploaded finetuned  model
+# DAGGER-12B-SFT
 
-- **Developed by:** dipta007
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/gemma-3-12b-it
+<a href="https://arxiv.org/abs/XXXX.XXXXX" target="_blank">
+    <img alt="arXiv" src="https://img.shields.io/badge/arXiv-XXXX.XXXXX-b31b1b" style="display: inline-block; vertical-align: middle;"/>
+</a>
+<a href="https://github.com/your-username/dagger" target="_blank">
+    <img alt="GitHub" src="https://img.shields.io/badge/GitHub-Code-black" style="display: inline-block; vertical-align: middle;"/>
+</a>
 
-This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+## Model Description
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+**DAGGER-12B-SFT** is a supervised fine-tuned model for computational graph generation in Bangla mathematical reasoning. This is the SFT-only variant, serving as both a standalone model and initialization for GRPO training.
+
+## Highlights
+
+- **SFT-only training** on 3,000 verified computational graph examples
+- **Strong baseline performance** for distractor-aware reasoning
+- **Foundation for GRPO**: Used as initialization for [dagger-12B_SFT_GRPO](https://huggingface.co/dipta007/dagger-12B_SFT_GRPO)
+- **Efficient inference**: ~400 tokens per problem
+
+## Model Overview
+
+| Attribute | Value |
+|-----------|-------|
+| Base Model | Gemma-3-12B-Instruct |
+| Training | Supervised Fine-Tuning |
+| Parameters | 12B |
+| LoRA Rank | 64 |
+| Max Sequence Length | 4096 |
+
+## Performance
+
+| Dataset | Original | +Distractor | Drop |
+|---------|----------|-------------|------|
+| MGSM | 70.0 | 56.8 | 13.2 |
+| MSVAMP | 76.8 | 65.4 | 11.5 |
+| **Weighted Avg** | - | - | **66.7** |
+
+### Comparison with GRPO
+
+| Model | Weighted Avg Accuracy |
+|-------|----------------------|
+| dagger-12B_SFT | 66.7 |
+| dagger-12B_SFT_GRPO | **69.4** (+2.7) |
+
+GRPO provides +2.7 points improvement over SFT alone.
+
+## Quickstart
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "dipta007/dagger-12B_SFT"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+question = "মিনার কাছে ১০০টি কলম আছে। প্রতিটি কলমের দাম ৫ টাকা। মিনা সব কলম বিক্রি করলে কত টাকা পাবে?"
+
+messages = [
+    {"role": "system", "content": "You are an expert Bangla Math Reasoner. Solve by constructing a Computational Graph."},
+    {"role": "user", "content": question}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=1024)
+response = tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
+print(response)
+```
+
+## Training Configuration
+
+| Parameter | Value |
+|-----------|-------|
+| LoRA Rank / Alpha | 64 / 128 |
+| Global Batch Size | 256 |
+| Epochs | 4 |
+| Learning Rate | 1e-5 → 1e-6 |
+| Optimizer | AdamW |
+| Weight Decay | 0.001 |
+| Precision | BF16 |
+
+## When to Use This Model
+
+- **As a baseline**: Compare against GRPO-enhanced variants
+- **For GRPO initialization**: Use as starting point for policy optimization
+- **Resource-constrained settings**: When GRPO training is not feasible
+- **Research**: Studying the effect of SFT vs. GRPO on graph generation
+
+## Related Models
+
+| Model | Training | Performance |
+|-------|----------|-------------|
+| **dagger-12B_SFT** | SFT | 66.7 |
+| [dagger-12B_SFT_GRPO](https://huggingface.co/dipta007/dagger-12B_SFT_GRPO) | SFT → GRPO | **69.4** |
+| [dagger-12B_GRPO](https://huggingface.co/dipta007/dagger-12B_GRPO) | Base → GRPO | 69.4 |
+
+## Citation
+
+```bibtex
+will be updated
+```