dipta007/dagger-12B_SFT

DAGGER-12B-SFT

Model Description

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
• 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

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"
)

USER_PROMPT_TEMPLATE = """You are an expert Bengali Math Reasoner. Your task is to solve mathematical problems by constructing a "Computational Graph".

### Graph Rules:
- `id`: Unique identifier (e.g., "n1", "n2").
- `val`: The raw number extracted from text (for input nodes).
- `op`: The operation (`add`, `sub`, `mul`, `div`, `round`, `sqrt`, `floor`, `sum`, `mean`, `ratio_split`). Use `const` for input numbers.
- `args`: List of input node IDs.
- `distractor`: Boolean (`true` / `false`). Set to `true` if the node is NOT used in the final calculation path.
- `label`: Label for the node.

### Available Operations:
- Input: `const` (Use this for all numbers found in text or constants).
- Arithmetic: `add`, `sub`, `mul`, `div`, `abs` (absolute difference).
- Logic/Stats: `sum`, `mean`, `min` (minimum), `max` (maximum).
- Rounding: `round` (nearest int), `floor` (round down), `ceil` (round up).
- Advanced: `sqrt`, `pow`, `mod` (remainder), `gcd`, `lcm`.
- Output: `identity` ("final_result" points to the answer node)

Only output a JSON graph representing the solution, nothing else. Nodes must be topologically sorted, and there must be exactly one "final_result" node that represents the final answer. One example is provided below.

### Example:
Question:
মিনার কাছে ১২২১৯৫ টা কলম আছে। রাজুর কাছে ২৫০৮৪ টা কলম আছে। মিনা রাজুর কাছে ১১২৬ টি কলম চাইল। রাজু ১০০০ টি কলম দিতে রাজি হল, কিন্তু পরে আর দিলেনা। প্রতিটি কলমের দাম ৪৫.৬ টাকা। মিনা যদি কলমগুলো বিক্রি করতে চায়, সে কত টাকা পাবে?

Output:
```json
{{
  "nodes": [
    {{"id": "n1", "op": "const", "val": 122195, "distractor": false, "label": "মিনার কলম"}},
    {{"id": "n2", "op": "const", "val": 25084, "distractor": true, "label": "রাজুর কলম"}},
    {{"id": "n3", "op": "const", "val": 1126, "distractor": true, "label": "মিনা রাজুর কাছে চাইল"}},
    {{"id": "n4", "op": "const", "val": 1000, "distractor": true, "label": "রাজু দিতে রাজি হল"}},
    {{"id": "n5", "op": "const", "val": 45.6, "distractor": false, "label": "প্রতিটি কলমের দাম"}},
    {{"id": "total_money", "op": "mul", "args": ["n1", "n5"], "distractor": false, "label": "মিনার মোট টাকা"}},
    {{"id": "final_result", "op": "identity", "args": ["total_money"], "distractor": false, "label": "চূড়ান্ত উত্তর"}}
  ]
}}```

### Your Task:

Question:
{question}

Output:
"""

question = "রজারের 5টি টেনিস বল আছে। সে আরও 2 ক্যান টেনিস বল কিনেছে। প্রতিটি ক্যানে 3টি করে টেনিস বল আছে। তার কাছে এখন কতগুলি টেনিস বল আছে?"
prompt = USER_PROMPT_TEMPLATE.format(question=question)

messages = [
  {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

# Generate
outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.7, top_p=0.8)
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	SFT → GRPO	69.4
dagger-12B_GRPO	Base → GRPO	69.4

Citation

@misc{nazi2026dagdaggerdistractorawaregraphgeneration,
      title={{\dag}DAGGER: Distractor-Aware Graph Generation for Executable Reasoning in Math Problems}, 
      author={Zabir Al Nazi and Shubhashis Roy Dipta and Sudipta Kar},
      year={2026},
      eprint={2601.06853},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2601.06853}, 
}