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
```

Improve language tag

by lbourdois - opened Apr 28, 2025

base: refs/heads/main

←

from: refs/pr/7

Discussion Files changed

+40

-28

Files changed (1) hide show

README.md +40 -28

README.md CHANGED Viewed

@@ -1,28 +1,40 @@
----
-license: mit
-license_link: https://huggingface.co/OpenPipe/Deductive-Reasoning-Qwen-32B/blob/main/LICENSE
-language:
-- en
-pipeline_tag: text-generation
-base_model:
-- Qwen/Qwen2.5-32B-Instruct
-tags:
-- chat
-library_name: transformers
----
-# Deductive-Reasoning-Qwen-32B
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/674a1d102c0f27a385772cfe/JauBmEQM0FpOdShBMSfst.png)
-Deductive Reasoning Qwen 32B is a reinforcement fine-tune of [Qwen 2.5 32B Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) to solve challenging deduction problems from the [Temporal Clue](https://github.com/bradhilton/temporal-clue) dataset, trained by [OpenPipe](https://openpipe.ai)!
-Here are some additional resources to check out:
-- [Blog Post](https://openpipe.ai/blog/using-grpo-to-beat-o1-o3-mini-and-r1-on-temporal-clue)
-- [Training Recipe](https://github.com/openpipe/deductive-reasoning)
-- [RL Experiments](https://github.com/openpipe/rl-experiments)
-- [Deductive Reasoning Qwen 14B](https://huggingface.co/OpenPipe/Deductive-Reasoning-Qwen-14B)
-If you're interested in training your own models with reinforcement learning or just chatting, feel free to [reach out](https://openpipe.ai/contact) or email Kyle directly at kyle@openpipe.ai!

+---
+license: mit
+license_link: https://huggingface.co/OpenPipe/Deductive-Reasoning-Qwen-32B/blob/main/LICENSE
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+pipeline_tag: text-generation
+base_model:
+- Qwen/Qwen2.5-32B-Instruct
+tags:
+- chat
+library_name: transformers
+---
+# Deductive-Reasoning-Qwen-32B
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/674a1d102c0f27a385772cfe/JauBmEQM0FpOdShBMSfst.png)
+Deductive Reasoning Qwen 32B is a reinforcement fine-tune of [Qwen 2.5 32B Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) to solve challenging deduction problems from the [Temporal Clue](https://github.com/bradhilton/temporal-clue) dataset, trained by [OpenPipe](https://openpipe.ai)!
+Here are some additional resources to check out:
+- [Blog Post](https://openpipe.ai/blog/using-grpo-to-beat-o1-o3-mini-and-r1-on-temporal-clue)
+- [Training Recipe](https://github.com/openpipe/deductive-reasoning)
+- [RL Experiments](https://github.com/openpipe/rl-experiments)
+- [Deductive Reasoning Qwen 14B](https://huggingface.co/OpenPipe/Deductive-Reasoning-Qwen-14B)
+If you're interested in training your own models with reinforcement learning or just chatting, feel free to [reach out](https://openpipe.ai/contact) or email Kyle directly at kyle@openpipe.ai!