Instructions to use Scale-or-Reason/Qwen2.5-1.5B-math-ift with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Scale-or-Reason/Qwen2.5-1.5B-math-ift with Transformers:

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

pipe = pipeline("text-generation", model="Scale-or-Reason/Qwen2.5-1.5B-math-ift")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Scale-or-Reason/Qwen2.5-1.5B-math-ift")
model = AutoModelForCausalLM.from_pretrained("Scale-or-Reason/Qwen2.5-1.5B-math-ift")
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 Scale-or-Reason/Qwen2.5-1.5B-math-ift with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Scale-or-Reason/Qwen2.5-1.5B-math-ift"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Scale-or-Reason/Qwen2.5-1.5B-math-ift",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Scale-or-Reason/Qwen2.5-1.5B-math-ift

SGLang

How to use Scale-or-Reason/Qwen2.5-1.5B-math-ift 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 "Scale-or-Reason/Qwen2.5-1.5B-math-ift" \
    --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": "Scale-or-Reason/Qwen2.5-1.5B-math-ift",
		"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 "Scale-or-Reason/Qwen2.5-1.5B-math-ift" \
        --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": "Scale-or-Reason/Qwen2.5-1.5B-math-ift",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Scale-or-Reason/Qwen2.5-1.5B-math-ift with Docker Model Runner:
```
docker model run hf.co/Scale-or-Reason/Qwen2.5-1.5B-math-ift
```

Qwen2.5-1.5B-math-ift

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---
library_name: transformers
tags:
- generated_from_trainer
datasets:
- When-Does-Reasoning-Matter/general-reasoning-ift-pairs
- When-Does-Reasoning-Matter/math-reasoning-ift-pairs
language:
- en
pipeline_tag: text-generation
---

# When Does Reasoning Matter?

<p align="left">
  <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/62be186a5f59ff2320e6e32b/GjJ15tY7-F4bqR96FN4pd.png" alt="Dataset Icon" width="180"/>
</p>

<p align="left">
<a href="https://arxiv.org/pdf/2509.22193" target="_blank" rel="noopener noreferrer">
  <img src="https://img.shields.io/badge/arXiv-2509.22193-b31b1b.svg?style=for-the-badge" alt="arXiv:2509.22193" />
  </a>
</p>


This model was trained as part of the paper [When Does Reasoning Matter?](https://arxiv.org/pdf/2509.22193)
It belongs to a collection of **General and Math-specific student models** distilled from Instruction-Fine-Tuned (IFT) or Reasoning answers generated by [Qwen/Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B).

<img src="https://huggingface.co/api/resolve-cache/models/When-Does-Reasoning-Matter/Qwen2.5-0.5B-ift/733797fee2fdd300e1a0453d368250327fe4cc44/results.png?%2FWhen-Does-Reasoning-Matter%2FQwen2.5-0.5B-ift%2Fresolve%2Fmain%2Fresults.png=&etag=%22d36dedfbca764a8ac9a7a5ebc043ca53f5ee4966%22" alt="results" width="600"/>

---

## Datasets

These models were trained on the **largest set of IFT and Reasoning answer pairs**:
- **General dataset**: [general-reasoning-ift-pairs](https://huggingface.co/datasets/When-Does-Reasoning-Matter/general-reasoning-ift-pairs)  
- **Math dataset**: [math-reasoning-ift-pairs](https://huggingface.co/datasets/When-Does-Reasoning-Matter/math-reasoning-ift-pairs)  

---

## Available Models

<table>
  <thead>
    <tr>
      <th colspan="2">General</th>
      <th colspan="2">Math</th>
    </tr>
    <tr>
      <th>IFT Models</th>
      <th>Reasoning Models</th>
      <th>IFT Models</th>
      <th>Reasoning Models</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td><a href="https://huggingface.co/When-Does-Reasoning-Matter/Qwen2.5-0.5B-ift">Qwen2.5-0.5B-ift</a></td>
      <td><a href="https://huggingface.co/When-Does-Reasoning-Matter/Qwen2.5-0.5B-reasoning">Qwen2.5-0.5B-reasoning</a></td>
      <td><a href="https://huggingface.co/When-Does-Reasoning-Matter/Qwen2.5-0.5B-math-ift">Qwen2.5-0.5B-math-ift</a></td>
      <td><a href="https://huggingface.co/When-Does-Reasoning-Matter/Qwen2.5-0.5B-math-reasoning">Qwen2.5-0.5B-math-reasoning</a></td>
    </tr>
    <tr>
      <td><a href="https://huggingface.co/When-Does-Reasoning-Matter/Qwen2.5-1.5B-ift">Qwen2.5-1.5B-ift</a></td>
      <td><a href="https://huggingface.co/When-Does-Reasoning-Matter/Qwen2.5-1.5B-reasoning">Qwen2.5-1.5B-reasoning</a></td>
      <td><a href="https://huggingface.co/When-Does-Reasoning-Matter/Qwen2.5-1.5B-math-ift">Qwen2.5-1.5B-math-ift</a></td>
      <td><a href="https://huggingface.co/When-Does-Reasoning-Matter/Qwen2.5-1.5B-math-reasoning">Qwen2.5-1.5B-math-reasoning</a></td>
    </tr>
    <tr>
      <td><a href="https://huggingface.co/When-Does-Reasoning-Matter/Qwen2.5-3B-ift">Qwen2.5-3B-ift</a></td>
      <td><a href="https://huggingface.co/When-Does-Reasoning-Matter/Qwen2.5-3B-reasoning">Qwen2.5-3B-reasoning</a></td>
      <td><a href="https://huggingface.co/When-Does-Reasoning-Matter/Qwen2.5-3B-math-ift">Qwen2.5-3B-math-ift</a></td>
      <td><a href="https://huggingface.co/When-Does-Reasoning-Matter/Qwen2.5-3B-math-reasoning">Qwen2.5-3B-math-reasoning</a></td>
    </tr>
    <tr>
      <td><a href="https://huggingface.co/When-Does-Reasoning-Matter/Qwen2.5-7B-ift">Qwen2.5-7B-ift</a></td>
      <td><a href="https://huggingface.co/When-Does-Reasoning-Matter/Qwen2.5-7B-reasoning">Qwen2.5-7B-reasoning</a></td>
      <td><a href="https://huggingface.co/When-Does-Reasoning-Matter/Qwen2.5-7B-math-ift">Qwen2.5-7B-math-ift</a></td>
      <td><a href="https://huggingface.co/When-Does-Reasoning-Matter/Qwen2.5-7B-math-reasoning">Qwen2.5-7B-math-reasoning</a></td>
    </tr>
    <tr>
      <td><a href="https://huggingface.co/When-Does-Reasoning-Matter/Qwen2.5-14B-ift">Qwen2.5-14B-ift</a></td>
      <td><a href="https://huggingface.co/When-Does-Reasoning-Matter/Qwen2.5-14B-reasoning">Qwen2.5-14B-reasoning</a></td>
      <td><a href="https://huggingface.co/When-Does-Reasoning-Matter/Qwen2.5-14B-math-ift">Qwen2.5-14B-math-ift</a></td>
      <td><a href="https://huggingface.co/When-Does-Reasoning-Matter/Qwen2.5-14B-math-reasoning">Qwen2.5-14B-math-reasoning</a></td>
    </tr>
  </tbody>
</table>

---

If you use this dataset in your work, please cite: **[When Does Reasoning Matter?](https://arxiv.org/pdf/2509.22193)**

```bibtex
@misc{boizard2025doesreasoningmattercontrolled,
      title={When Does Reasoning Matter? A Controlled Study of Reasoning's Contribution to Model Performance}, 
      author={Nicolas Boizard and Hippolyte Gisserot-Boukhlef and Kevin El-Haddad and Céline Hudelot and Pierre Colombo},
      year={2025},
      eprint={2509.22193},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.22193}, 
}
```