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
```

TianHongZXY commited on Mar 2

Commit

9c623fe

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1 Parent(s): 6798041

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@@ -16,5 +16,5 @@ SFT alone accounts for the majority of performance gains across benchmarks. See
 | 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** |

 | 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** |