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dipta007/
GanitLLM-0.6B_SFT_CGRPO

Name: GanitLLM-0.6B_SFT_CGRPO
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GanitLLM-0.6B_SFT_CGRPO

Highlights

GanitLLM-0.6B_SFT_CGRPO is our smallest Bengali mathematical reasoning model trained using the novel Curriculum-GRPO approach. Ideal for resource-constrained deployments. Key improvements over the base Qwen3-0.6B model:

+20.0 accuracy on Bn-MGSM benchmark (8.4 → 28.4)
+40.2 accuracy on Bn-MSVAMP benchmark (12.2 → 52.4)
88.50% Bengali reasoning (vs 12.43% for base model)
80.4% fewer tokens in generated solutions (1265 → 248 words)

Model Overview

Property	Value
Model Type	Causal Language Model
Base Model	Qwen/Qwen3-0.6B
Parameters	0.6B
Training	SFT + Curriculum-GRPO
Context Length	4,096 tokens
Language	Bengali, English

Training Details

This model was trained using our multi-stage pipeline:

Supervised Fine-Tuning (SFT): Trained on GANIT-SFT (~11k examples) to ground reasoning in Bengali
Curriculum-GRPO: Reinforcement learning with difficulty-aware sampling on GANIT-RLVR (~7.3k examples)

Reward Functions

Format Reward: Validates <think> and <answer> tag structure
Correctness Reward: +2.0 for Bengali answer match, +1.0 for English match
Bengali Reasoning Reward: Ensures >80% Bengali text in reasoning

Quickstart

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "dipta007/GanitLLM-0.6B_SFT_CGRPO"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

problem = "একটি দোকানে ১২টি আপেল আছে। যদি ৫টি আপেল বিক্রি হয়, তাহলে কতটি আপেল বাকি থাকবে?"

prompt = f"""A conversation takes place between the user and the assistant. The user asks a question, and the assistant solves the problem. Please reason step by step in Bengali, and put your final answer in the <answer> </answer> tags.

Question: {problem}"""

messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(**model_inputs, max_new_tokens=2048, temperature=0.7)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
response = tokenizer.decode(output_ids, skip_special_tokens=True)
print(response)

Using vLLM

vllm serve dipta007/GanitLLM-0.6B_SFT_CGRPO --max-model-len 4096

Performance

Model	Bn-MGSM	Bn-MSVAMP	Avg. Words	Bengali %
Qwen3-0.6B (base)	8.40	12.20	1265	12.43%
GanitLLM-0.6B_SFT_CGRPO	28.40	52.40	248	88.50%

Related Models

Model	Parameters	Training	Link
GanitLLM-4B_SFT_CGRPO	4B	SFT + CGRPO	Link
GanitLLM-1.7B_SFT_CGRPO	1.7B	SFT + CGRPO	Link
GanitLLM-0.6B_SFT_CGRPO	0.6B	SFT + CGRPO	Link
GanitLLM-0.6B_SFT_GRPO	0.6B	SFT + GRPO	Link
GanitLLM-0.6B_CGRPO	0.6B	CGRPO	Link

Citation

bibtex

@inproceedings{dipta2026ganitllm,
  title={GanitLLM: Difficulty-Aware Bengali Mathematical Reasoning through Curriculum-GRPO},
  author={Shubhashis Roy Dipta and Khairul Mahbub and Nadia Najjar},
  booktitle={Findings of the Association for Computational Linguistics: ACL 2026},
  year={2026},
  eprint={2601.06767},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2601.06767},
}

License

This model is released under the Apache 2.0 License.