Instructions to use dipta007/decomposeRL-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dipta007/decomposeRL-7b with Transformers:

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

pipe = pipeline("text-generation", model="dipta007/decomposeRL-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dipta007/decomposeRL-7b")
model = AutoModelForCausalLM.from_pretrained("dipta007/decomposeRL-7b")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dipta007/decomposeRL-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dipta007/decomposeRL-7b"
# 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/decomposeRL-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dipta007/decomposeRL-7b

SGLang

How to use dipta007/decomposeRL-7b 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/decomposeRL-7b" \
    --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/decomposeRL-7b",
		"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/decomposeRL-7b" \
        --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/decomposeRL-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dipta007/decomposeRL-7b with Docker Model Runner:
```
docker model run hf.co/dipta007/decomposeRL-7b
```

Update README: real arXiv link (2605.27858) + add citation

by dipta007 - opened 25 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+12

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README.md +12 -4

README.md CHANGED Viewed

@@ -18,12 +18,12 @@ tags:
 # DecomposeRL-7B
 <p align="center">
-  <a href="https://arxiv.org/abs/0000.00000">
-    <img src="https://img.shields.io/badge/%F0%9F%93%84_Paper-Coming_Soon-b12a00?style=for-the-badge&labelColor=ffb300" alt="Paper Coming Soon">
   </a>
 </p>
-[![Paper](https://img.shields.io/badge/arXiv-coming--soon-red)](https://arxiv.org/abs/0000.00000)
 [![Project Page](https://img.shields.io/badge/Project-Page-green)](https://dipta007.github.io/DecomposeRL/)
 [![Dataset](https://img.shields.io/badge/HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/dipta007/decomposeRL)
 [![Models](https://img.shields.io/badge/HuggingFace-Models-orange)](https://huggingface.co/collections/dipta007/decomposerl)
@@ -309,7 +309,15 @@ The model is trained to say *"I don't know"* when the evidence document is silen
 ## Citation
 ```bibtex
 ```
 ## License

 # DecomposeRL-7B
 <p align="center">
+  <a href="https://arxiv.org/abs/2605.27858v1">
+    <img src="https://img.shields.io/badge/%F0%9F%93%84_Paper-arXiv-b12a00?style=for-the-badge&labelColor=ffb300" alt="Paper">
   </a>
 </p>
+[![Paper](https://img.shields.io/badge/arXiv-2605.27858-red)](https://arxiv.org/abs/2605.27858v1)
 [![Project Page](https://img.shields.io/badge/Project-Page-green)](https://dipta007.github.io/DecomposeRL/)
 [![Dataset](https://img.shields.io/badge/HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/dipta007/decomposeRL)
 [![Models](https://img.shields.io/badge/HuggingFace-Models-orange)](https://huggingface.co/collections/dipta007/decomposerl)
 ## Citation
 ```bibtex
+@article{dipta2025decomposerl,
+  title={DecomposeRL: Learning to Ask Useful, Informative, and Diverse Questions for Semi-Supervised, Traceable Claim Verification},
+  author={Shubhashis Roy Dipta and Ankur Padia and Francis Ferraro},
+  year={2025},
+  eprint={2605.27858},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL},
+  url={https://arxiv.org/abs/2605.27858v1},
+}
 ```
 ## License