Instructions to use Scale-or-Reason/Qwen2.5-0.5B-ift with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Scale-or-Reason/Qwen2.5-0.5B-ift with Transformers:

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

pipe = pipeline("text-generation", model="Scale-or-Reason/Qwen2.5-0.5B-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/Qwen2.5-0.5B-ift")
model = AutoModelForCausalLM.from_pretrained("Scale-or-Reason/Qwen2.5-0.5B-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/Qwen2.5-0.5B-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/Qwen2.5-0.5B-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/Qwen2.5-0.5B-ift",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Scale-or-Reason/Qwen2.5-0.5B-ift

SGLang

How to use Scale-or-Reason/Qwen2.5-0.5B-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/Qwen2.5-0.5B-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/Qwen2.5-0.5B-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/Qwen2.5-0.5B-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/Qwen2.5-0.5B-ift",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Scale-or-Reason/Qwen2.5-0.5B-ift with Docker Model Runner:
```
docker model run hf.co/Scale-or-Reason/Qwen2.5-0.5B-ift
```

Improve model card: Add project page link and full paper title

by nielsr HF Staff - opened Oct 1, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-6

Files changed (1) hide show

README.md +7 -6

README.md CHANGED Viewed

@@ -1,13 +1,13 @@
 ---
-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?
@@ -22,8 +22,9 @@ pipeline_tag: text-generation
   </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"/>
@@ -89,7 +90,7 @@ These models were trained on the **largest set of IFT and Reasoning answer pairs
 ---
-If you use this dataset in your work, please cite: **[When Does Reasoning Matter?](https://arxiv.org/pdf/2509.22193)**
 ```bibtex
 @misc{boizard2025doesreasoningmattercontrolled,
@@ -101,4 +102,4 @@ If you use this dataset in your work, please cite: **[When Does Reasoning Matter
       primaryClass={cs.CL},
       url={https://arxiv.org/abs/2509.22193},
 }
-```

 ---
 datasets:
 - When-Does-Reasoning-Matter/general-reasoning-ift-pairs
 - When-Does-Reasoning-Matter/math-reasoning-ift-pairs
 language:
 - en
+library_name: transformers
 pipeline_tag: text-generation
+tags:
+- generated_from_trainer
 ---
 # When Does Reasoning Matter?
   </a>
 </p>
+Project Page: [https://huggingface.co/when-does-reasoning-matter](https://huggingface.co/when-does-reasoning-matter)
+This model was trained as part of the paper [When Does Reasoning Matter? A Controlled Study of Reasoning's Contribution to Model Performance](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"/>
 ---
+If you use this dataset in your work, please cite: **[When Does Reasoning Matter? A Controlled Study of Reasoning's Contribution to Model Performance](https://arxiv.org/pdf/2509.22193)**
 ```bibtex
 @misc{boizard2025doesreasoningmattercontrolled,
       primaryClass={cs.CL},
       url={https://arxiv.org/abs/2509.22193},
 }
+```