Instructions to use yolay/RAIF-DeepScaleR-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yolay/RAIF-DeepScaleR-1.5B with Transformers:

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

pipe = pipeline("text-generation", model="yolay/RAIF-DeepScaleR-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yolay/RAIF-DeepScaleR-1.5B")
model = AutoModelForCausalLM.from_pretrained("yolay/RAIF-DeepScaleR-1.5B")
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

vLLM

How to use yolay/RAIF-DeepScaleR-1.5B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/yolay/RAIF-DeepScaleR-1.5B

SGLang

How to use yolay/RAIF-DeepScaleR-1.5B 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 "yolay/RAIF-DeepScaleR-1.5B" \
    --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": "yolay/RAIF-DeepScaleR-1.5B",
		"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 "yolay/RAIF-DeepScaleR-1.5B" \
        --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": "yolay/RAIF-DeepScaleR-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use yolay/RAIF-DeepScaleR-1.5B with Docker Model Runner:
```
docker model run hf.co/yolay/RAIF-DeepScaleR-1.5B
```

Improve model card with full abstract, additional metrics, and sample usage

by nielsr HF Staff - opened Jul 30, 2025

base: refs/heads/main

←

from: refs/pr/3

Discussion Files changed

+100

-8

nielsr

Jul 30, 2025

Hi! This PR significantly improves the model card for yolay/RAIF-DeepScaleR-1.5B by adding:

Comprehensive metadata tags including license, language, and metrics for better discoverability.
The full abstract of the paper "Incentivizing Reasoning for Advanced Instruction-Following of Large Language Models".
A high-level overview of the RAIF method from the project's GitHub README.
The detailed "Table 2" performance metrics on ComplexBench, which was previously missing.
A "Sample Usage" section with Python code snippets using the transformers library, demonstrating both basic text generation and chat completion with the model's specific chat template.
A link to the related Hugging Face collection.

These updates aim to provide users with a more complete understanding of the model, its capabilities, and how to use it effectively.

Improve model card with full abstract, additional metrics, and sample usage9a1bfbaf

yolay changed pull request status to merged Jul 31, 2025

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