Instructions to use QwenPilot/FIPO_32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QwenPilot/FIPO_32B with Transformers:

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

pipe = pipeline("text-generation", model="QwenPilot/FIPO_32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("QwenPilot/FIPO_32B")
model = AutoModelForCausalLM.from_pretrained("QwenPilot/FIPO_32B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use QwenPilot/FIPO_32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QwenPilot/FIPO_32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QwenPilot/FIPO_32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QwenPilot/FIPO_32B

SGLang

How to use QwenPilot/FIPO_32B 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 "QwenPilot/FIPO_32B" \
    --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": "QwenPilot/FIPO_32B",
		"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 "QwenPilot/FIPO_32B" \
        --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": "QwenPilot/FIPO_32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use QwenPilot/FIPO_32B with Docker Model Runner:
```
docker model run hf.co/QwenPilot/FIPO_32B
```

Add library_name and pipeline_tag

by nielsr HF Staff - opened Apr 2

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+16

-10

Files changed (1) hide show

README.md +16 -10

README.md CHANGED Viewed

@@ -1,11 +1,13 @@
 ---
-license: apache-2.0
 datasets:
 - BytedTsinghua-SIA/DAPO-Math-17k
 language:
 - en
-base_model:
-- Qwen/Qwen2.5-32B
 ---
 # FIPO: Eliciting Deep Reasoning with Future-KL Influenced Policy Optimization
@@ -70,10 +72,14 @@ The FIPO objective yields longer responses and a stronger AIME 2024 peak than th
 ## 🎈 Citation
 ```bibtex
-@misc{FIPO,
-  title = {FIPO: Eliciting Deep Reasoning with Future-KL Influenced Policy Optimization},
-  url = {[https://qwen-pilot.notion.site/fipo](https://qwen-pilot.notion.site/fipo)},
-  author = {Chiyu Ma and Shuo Yang and Kexin Huang and Jinda Lu and Haoming Meng and Shangshang Wang and Bolin Ding and Soroush Vosoughi and Guoyin Wang and Jingren Zhou},
-  year = {2026},
-  month = {March},
-}

 ---
+base_model:
+- Qwen/Qwen2.5-32B
 datasets:
 - BytedTsinghua-SIA/DAPO-Math-17k
 language:
 - en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
 ---
 # FIPO: Eliciting Deep Reasoning with Future-KL Influenced Policy Optimization
 ## 🎈 Citation
 ```bibtex
+@article{ma2026fipo,
+  title={FIPO: Eliciting Deep Reasoning with Future-KL Influenced Policy Optimization},
+  author={Ma, Chiyu and Yang, Shuo and Huang, Kexin and Lu, Jinda and Meng, Haoming and Shangshang Wang and Bolin Ding and Soroush Vosoughi and Guoyin Wang and Jingren Zhou},
+  journal={arXiv preprint arXiv:2603.19835},
+  year={2026}
+}
+```
+## 🌻 Acknowledgement
+This project builds on top of the [VeRL](https://github.com/volcengine/verl) training framework and follows the practical recipe structure introduced by [DAPO](https://github.com/Bytedance-Research/DAPO).