Create README.md

README.md

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+---
+library_name: transformers
+license: gemma
+license_link: https://ai.google.dev/gemma/terms
+pipeline_tag: text-generation
+tags:
+- math
+- reasoning
+- computational-graph
+- bangla
+- low-resource
+- distractor-aware
+- small-model
+base_model:
+- google/gemma-3-4b-it
+language:
+- bn
+- en
+datasets:
+- dipta007/dagger
+- dipta007/DistractMath-Bn
+---
+
+# DAGGER-4B-SFT-GRPO
+
+<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/dipta007/dagger" target="_blank">
+    <img alt="GitHub" src="https://img.shields.io/badge/GitHub-Code-black" style="display: inline-block; vertical-align: middle;"/>
+</a>
+
+## Model Description
+
+**DAGGER-4B-SFT-GRPO** is the smaller variant of DAGGER, trained with SFT followed by GRPO on Gemma-3-4B. While showing lower performance than the 12B variant, it demonstrates that the DAGGER framework can work with smaller models.
+
+## Highlights
+
+- **Lightweight**: 4B parameters for resource-constrained deployment
+- **SFT → GRPO training**: Full training pipeline
+- **Improved over baselines**: Still outperforms CoT on distractor robustness
+- **Capacity study**: Demonstrates model size requirements for graph generation
+
+## Model Overview
+
+| Attribute | Value |
+|-----------|-------|
+| Base Model | Gemma-3-4B-Instruct |
+| Training | SFT → GRPO |
+| Parameters | 4B |
+| LoRA Rank | 64 |
+
+## Performance
+
+| Dataset | Original | +Distractor | Drop |
+|---------|----------|-------------|------|
+| MGSM | 54.8 | 31.4 | 23.4 |
+| MSVAMP | 70.3 | 42.9 | 27.4 |
+| **Weighted Avg** | - | - | **47.3** |
+
+### Comparison with 12B Variant
+
+| Model | Params | Weighted Avg |
+|-------|--------|--------------|
+| dagger-4B_SFT_GRPO | 4B | 47.3 |
+| dagger-12B_SFT_GRPO | 12B | **69.4** (+22.1) |
+
+**Key Finding**: The 12B model provides +22 points improvement, suggesting a capacity threshold for effective computational graph generation.
+
+## Quickstart
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "dipta007/dagger-4B_SFT_GRPO"
+
+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।"
+
+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
+
+Same as 12B variant:
+
+| Parameter | Value |
+|-----------|-------|
+| LoRA Rank / Alpha | 64 / 128 |
+| SFT Batch Size | 256 |
+| GRPO Batch Size | 32 |
+| Generations per Prompt | 8 |
+| Epochs | 4 |
+
+## When to Use This Model
+
+- **Resource-constrained deployment**: When 12B is too large
+- **Capacity studies**: Research on model size vs. performance
+- **Edge deployment**: Smaller memory footprint
+- **Prototyping**: Faster iteration during development
+
+## Limitations
+
+- **Lower accuracy**: 22 points below 12B variant
+- **Reduced robustness**: Larger accuracy drop under distractors
+- **Capacity constraints**: May struggle with complex multi-step problems
+
+## Related Models
+
+| Model | Size | Weighted Avg |
+|-------|------|--------------|
+| **dagger-4B_SFT_GRPO** | 4B | 47.3 |
+| [dagger-4B_SFT](https://huggingface.co/dipta007/dagger-4B_SFT) | 4B | 44.3 |
+| [dagger-12B_SFT_GRPO](https://huggingface.co/dipta007/dagger-12B_SFT_GRPO) | 12B | **69.4** |
+
+## Citation
+
+```bibtex
+will be updated
+```