Instructions to use TianHongZXY/CHIMERA-4B-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TianHongZXY/CHIMERA-4B-SFT with Transformers:

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

pipe = pipeline("text-generation", model="TianHongZXY/CHIMERA-4B-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TianHongZXY/CHIMERA-4B-SFT")
model = AutoModelForCausalLM.from_pretrained("TianHongZXY/CHIMERA-4B-SFT", 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 TianHongZXY/CHIMERA-4B-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TianHongZXY/CHIMERA-4B-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TianHongZXY/CHIMERA-4B-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TianHongZXY/CHIMERA-4B-SFT

SGLang

How to use TianHongZXY/CHIMERA-4B-SFT 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 "TianHongZXY/CHIMERA-4B-SFT" \
    --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": "TianHongZXY/CHIMERA-4B-SFT",
		"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 "TianHongZXY/CHIMERA-4B-SFT" \
        --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": "TianHongZXY/CHIMERA-4B-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TianHongZXY/CHIMERA-4B-SFT with Docker Model Runner:
```
docker model run hf.co/TianHongZXY/CHIMERA-4B-SFT
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

CHIMERA-4B-SFT

CHIMERA-4B-SFT is Qwen3-4B-Thinking-2507 fine-tuned on the CHIMERA dataset via supervised fine-tuning (SFT).

This model was introduced in the paper CHIMERA: Compact Synthetic Data for Generalizable LLM Reasoning.

Model Description

CHIMERA is a compact synthetic reasoning dataset comprising 9K samples for generalizable cross-domain reasoning. It features rich, long Chain-of-Thought (CoT) reasoning trajectories spanning 8 major scientific disciplines. Despite its modest size, the resulting 4B model achieves strong performance on challenging reasoning benchmarks, approaching or matching the performance of substantially larger models such as DeepSeek-R1.

Results

SFT alone accounts for the majority of performance gains across benchmarks. See TianHongZXY/CHIMERA-4B-RL for the further RL-trained model.

Model	GPQA-D	AIME 24	AIME 25	AIME 26	HMMT Feb 25	HMMT Nov 25	HLE
Qwen3-4B-Thinking-2507	65.8	81.6	81.0	80.8	59.2	57.3	7.3
CHIMERA-4B-SFT	68.8	86.5	79.8	80.3	63.1	66.3	9.0
CHIMERA-4B-RL	70.1	86.9	80.7	82.7	65.7	67.0	9.0

Citation

@article{zhu2026chimera,
  title={CHIMERA: Compact Synthetic Data for Generalizable LLM Reasoning},
  author={Zhu, Xinyu and Feng, Yihao and Sun, Yanchao and Du, Xianzhi and Li, Pingzhi and Saarikivi, Olli and Zhu, Yun and Meng, Yu},
  journal={arXiv preprint arXiv:2603.00889},
  year={2026}
}