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

nielsr

Oct 1, 2025

This PR enhances the model card by:

Adding a link to the project page: https://huggingface.co/when-does-reasoning-matter.
Updating the text of the paper links in the content to the full paper title "When Does Reasoning Matter? A Controlled Study of Reasoning's Contribution to Model Performance" for improved clarity, while retaining the existing arXiv PDF URLs as per the instructions.

These changes provide more comprehensive and accurate information for users of the model.

Improve model card: Add project page link and full paper titleadac47f9

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