Instructions to use OpenPipe/Deductive-Reasoning-Qwen-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenPipe/Deductive-Reasoning-Qwen-32B with Transformers:

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

pipe = pipeline("text-generation", model="OpenPipe/Deductive-Reasoning-Qwen-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenPipe/Deductive-Reasoning-Qwen-32B")
model = AutoModelForCausalLM.from_pretrained("OpenPipe/Deductive-Reasoning-Qwen-32B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenPipe/Deductive-Reasoning-Qwen-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenPipe/Deductive-Reasoning-Qwen-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenPipe/Deductive-Reasoning-Qwen-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OpenPipe/Deductive-Reasoning-Qwen-32B

SGLang

How to use OpenPipe/Deductive-Reasoning-Qwen-32B 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 "OpenPipe/Deductive-Reasoning-Qwen-32B" \
    --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": "OpenPipe/Deductive-Reasoning-Qwen-32B",
		"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 "OpenPipe/Deductive-Reasoning-Qwen-32B" \
        --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": "OpenPipe/Deductive-Reasoning-Qwen-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OpenPipe/Deductive-Reasoning-Qwen-32B with Docker Model Runner:
```
docker model run hf.co/OpenPipe/Deductive-Reasoning-Qwen-32B
```

Not a 'thinking' model if anyone is wondering

by Mdubbya - opened Mar 7, 2025

Discussion

Mdubbya

Mar 7, 2025

•

edited Mar 7, 2025

I finished quantizing the model to 8bit in GPTQ and am putting it through paces right now. Out of the gate, I was thinking this would be a 'thinking' model but that is not the case. I share this in case anyone is wondering if you will see intermediate reasoning steps.

sealad886

Mar 17, 2025

Would be curious how this blog post might optimize the behavior here. I'm still working on getting this up on my system, so if you do find out, please let us know!

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