Instructions to use dipta007/GanitLLM-1.7B_SFT_CGRPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dipta007/GanitLLM-1.7B_SFT_CGRPO with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="dipta007/GanitLLM-1.7B_SFT_CGRPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dipta007/GanitLLM-1.7B_SFT_CGRPO")
model = AutoModelForCausalLM.from_pretrained("dipta007/GanitLLM-1.7B_SFT_CGRPO", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dipta007/GanitLLM-1.7B_SFT_CGRPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dipta007/GanitLLM-1.7B_SFT_CGRPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dipta007/GanitLLM-1.7B_SFT_CGRPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dipta007/GanitLLM-1.7B_SFT_CGRPO

SGLang

How to use dipta007/GanitLLM-1.7B_SFT_CGRPO with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "dipta007/GanitLLM-1.7B_SFT_CGRPO" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dipta007/GanitLLM-1.7B_SFT_CGRPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "dipta007/GanitLLM-1.7B_SFT_CGRPO" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dipta007/GanitLLM-1.7B_SFT_CGRPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dipta007/GanitLLM-1.7B_SFT_CGRPO with Docker Model Runner:
```
docker model run hf.co/dipta007/GanitLLM-1.7B_SFT_CGRPO
```

dipta007 commited on Apr 11

Commit

1d2b265

verified ·

1 Parent(s): d8e07b5

Update README.md

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@@ -18,9 +18,18 @@ datasets:
 # GanitLLM-1.7B_SFT_CGRPO
-[![Paper](https://img.shields.io/badge/arXiv-Paper-red)](https://arxiv.org/)
 [![Dataset](https://img.shields.io/badge/HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/dipta007/Ganit)
 [![Models](https://img.shields.io/badge/HuggingFace-Models-orange)](https://huggingface.co/collections/dipta007/ganitllm)
 ## Highlights
@@ -110,7 +119,16 @@ vllm serve dipta007/GanitLLM-1.7B_SFT_CGRPO --max-model-len 4096
 ## Citation
 ```bibtex
-will be updated
 ```
 ## License

 # GanitLLM-1.7B_SFT_CGRPO
+<p align="center">
+  <a href="https://arxiv.org/abs/2601.06767">
+    <img src="https://img.shields.io/badge/%F0%9F%94%A5_Accepted_at-ACL_2026_(Findings)_%F0%9F%94%A5-b12a00?style=for-the-badge&labelColor=ffb300" alt="Accepted at ACL 2026 (Findings)">
+  </a>
+</p>
+[![ACL 2026 (Findings)](https://img.shields.io/badge/ACL%202026-Findings-blue)](https://arxiv.org/abs/2601.06767)
+[![Paper](https://img.shields.io/badge/arXiv-2601.06767-red)](https://arxiv.org/abs/2601.06767)
+[![Project Page](https://img.shields.io/badge/Project-Page-green)](https://dipta007.github.io/GanitLLM/)
 [![Dataset](https://img.shields.io/badge/HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/dipta007/Ganit)
 [![Models](https://img.shields.io/badge/HuggingFace-Models-orange)](https://huggingface.co/collections/dipta007/ganitllm)
+[![GitHub](https://img.shields.io/badge/GitHub-Code-blue)](https://github.com/dipta007/GanitLLM)
 ## Highlights
 ## 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