Instructions to use prithivMLmods/Crux-Qwen3_OpenThinking-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Crux-Qwen3_OpenThinking-4B with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Crux-Qwen3_OpenThinking-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Crux-Qwen3_OpenThinking-4B")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Crux-Qwen3_OpenThinking-4B", 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 prithivMLmods/Crux-Qwen3_OpenThinking-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Crux-Qwen3_OpenThinking-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Crux-Qwen3_OpenThinking-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Crux-Qwen3_OpenThinking-4B

SGLang

How to use prithivMLmods/Crux-Qwen3_OpenThinking-4B 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 "prithivMLmods/Crux-Qwen3_OpenThinking-4B" \
    --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": "prithivMLmods/Crux-Qwen3_OpenThinking-4B",
		"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 "prithivMLmods/Crux-Qwen3_OpenThinking-4B" \
        --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": "prithivMLmods/Crux-Qwen3_OpenThinking-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Crux-Qwen3_OpenThinking-4B with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Crux-Qwen3_OpenThinking-4B
```

prithivMLmods commited on May 23, 2025

Commit

accd468

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@@ -17,6 +17,8 @@ tags:
 - code
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 # Crux-Qwen3\_OpenThinking-4B
 > **Crux-Qwen3\_OpenThinking-4B** is fine-tuned on the **Qwen3-4B** architecture, optimized for advanced **open thinking**, **mathematical reasoning**, and **logical problem solving**. This model is trained on the traces of **sk1.1**, which include 1,000 entries from the **Gemini thinking trajectory**, combined with fine-tuning on 100k tokens of **open math reasoning** data. This makes it highly effective for nuanced reasoning, educational tasks, and complex problem-solving requiring clear thought processes.
@@ -106,4 +108,4 @@ print(response)
 ## References
-1. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071)

 - code
 ---
+![zdfbdccf.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/4XCMQEsE0mv2s5rx-YdIK.png)
 # Crux-Qwen3\_OpenThinking-4B
 > **Crux-Qwen3\_OpenThinking-4B** is fine-tuned on the **Qwen3-4B** architecture, optimized for advanced **open thinking**, **mathematical reasoning**, and **logical problem solving**. This model is trained on the traces of **sk1.1**, which include 1,000 entries from the **Gemini thinking trajectory**, combined with fine-tuning on 100k tokens of **open math reasoning** data. This makes it highly effective for nuanced reasoning, educational tasks, and complex problem-solving requiring clear thought processes.
 ## References
+1. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071)