Instructions to use Thinking-Space/UltraData-IF-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Thinking-Space/UltraData-IF-1.5B with Transformers:

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

pipe = pipeline("text-generation", model="Thinking-Space/UltraData-IF-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Thinking-Space/UltraData-IF-1.5B")
model = AutoModelForCausalLM.from_pretrained("Thinking-Space/UltraData-IF-1.5B", device_map="auto")
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 Thinking-Space/UltraData-IF-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Thinking-Space/UltraData-IF-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": "Thinking-Space/UltraData-IF-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Thinking-Space/UltraData-IF-1.5B

SGLang

How to use Thinking-Space/UltraData-IF-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 "Thinking-Space/UltraData-IF-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": "Thinking-Space/UltraData-IF-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 "Thinking-Space/UltraData-IF-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": "Thinking-Space/UltraData-IF-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Thinking-Space/UltraData-IF-1.5B with Docker Model Runner:
```
docker model run hf.co/Thinking-Space/UltraData-IF-1.5B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

UltraData-IF-1.5B

UltraData-IF-1.5B is an RL-trained instruction-following model initialized from DeepSeek-R1-Distill-Qwen-1.5B and trained on the instruction-following subset of openbmb/UltraData-SFT-2605.

This model is associated with the paper: Rethinking On-Policy Distillation of Large Language Models II: One Training Example

Model Description

UltraData-IF-1.5B serves as the teacher model for the instruction-following experiments in our paper. It is paired with DeepSeek-R1-Distill-Qwen-1.5B to study on-policy distillation in the instruction-following domain. The two models share the same architecture, tokenizer, and chat template, allowing token-level distillation without cross-model-family mismatch.

Key characteristics

Role in the paper: instruction-following teacher for OPD
Training method: GRPO
Base model: DeepSeek-R1-Distill-Qwen-1.5B （R1-Distill-1.5B）
Training data: instruction-following subset of openbmb/UltraData-SFT-2605
Paired student in OPD: DeepSeek-R1-Distill-Qwen-1.5B
Primary domain: Instruction following
Architecture: Qwen2ForCausalLM
Parameters: 1.78B
Precision: bfloat16
Vocabulary size: 151,936
Layers / hidden size: 28 / 1536
Attention heads: 12 query / 2 key-value (GQA)
Context length: 131,072 positions in config.json; tokenizer model_max_length is 16,384

Training Details

Item	Value
Training algorithm	GRPO
Training set	UltraData-SFT-2605
Reward	Fraction of satisfied instruction constraints
Prompt batch size	32
Rollouts per prompt	8
Rollouts per step	256
Learning rate	1e-6
Sampling temperature / top-p	0.9 / 0.95
Max prompt / response length	2,048 / 14,336
KL regularization	None
GRPO clipping	0.20 / 0.28
Entropy bonus	0.0
Loss aggregation	`token-mean`
Training steps	600
Training framework	veRL
Hardware	8 GPUs

Evaluation

Scores for this checkpoint as the instruction-following teacher, alongside the student it is distilled into. Multi-IF is averaged over its eight languages; IFBench follows the standard prompt-level and instruction-level, strict and loose breakdown.

Benchmark	DeepSeek-R1-Distill-Qwen-1.5B (Student)	UltraData-IF-1.5B (teacher)
Multi-IF, 8-lang, final turn	20.84	28.58
Multi-IF, 8-lang, 3-turn mean	28.56	40.39
Multi-IF, English, final turn	24.71	35.54
Multi-IF, English, 3-turn mean	30.51	45.05
IFBench, prompt-level strict	10.33	17.00
IFBench, prompt-level loose	15.00	22.00
IFBench, instruction-level strict	12.79	18.90
IFBench, instruction-level loose	19.19	24.71

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Thinking-Space/UltraData-IF-1.5B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [{"role": "user", "content": "Write a haiku about distillation. Use exactly three lines."}]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(inputs, max_new_tokens=2048)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

Citation

If you use this model, please consider citing:

@article{fu2026rethinking,
  title={Rethinking on-policy distillation of large language models ii: One training example},
  author={Fu, Zixuan and He, Bingxiang and Zuo, Yuxin and Huang, Haohuan and Zhang, Jinqian and Xiao, Ruhang and Qian, Cheng and Luo, Qinyu and Gao, Huan-ang and Wang, Yudong and others},
  journal={arXiv preprint arXiv:2609.04172},
  year={2026}
}