GanitLLM-0.6B_CGRPO

Highlights

GanitLLM-0.6B_CGRPO is a Bengali mathematical reasoning model trained with Curriculum-GRPO directly on the base model (without SFT). This variant shows limited improvement at this scale. Key results:

+8.8 accuracy on Bn-MGSM benchmark (8.4 → 17.2)
+23.0 accuracy on Bn-MSVAMP benchmark (12.2 → 35.2)
11.67% Bengali reasoning (similar to base model)
34.9% fewer tokens in generated solutions (1265 → 824 words)

Note: This model shows limited gains at the 0.6B scale. For better performance, use GanitLLM-0.6B_SFT_CGRPO or consider larger models.

Model Overview

Property	Value
Model Type	Causal Language Model
Base Model	Qwen/Qwen3-0.6B
Parameters	0.6B
Training	Curriculum-GRPO (no SFT)
Context Length	4,096 tokens
Language	Bengali, English

Training Details

This model was trained with a single-stage pipeline:

Curriculum-GRPO: Reinforcement learning with difficulty-aware sampling directly on the base model using 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

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "dipta007/GanitLLM-0.6B_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_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_CGRPO	17.20	35.20	824	11.67%

Related Models

Model	Parameters	Training	Link
GanitLLM-4B_CGRPO	4B	CGRPO	Link
GanitLLM-1.7B_CGRPO	1.7B	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