OpenThinker-32B

Instructions to use open-thoughts/OpenThinker-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use open-thoughts/OpenThinker-32B with Transformers:

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

pipe = pipeline("text-generation", model="open-thoughts/OpenThinker-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("open-thoughts/OpenThinker-32B")
model = AutoModelForCausalLM.from_pretrained("open-thoughts/OpenThinker-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 open-thoughts/OpenThinker-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "open-thoughts/OpenThinker-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": "open-thoughts/OpenThinker-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/open-thoughts/OpenThinker-32B

SGLang

How to use open-thoughts/OpenThinker-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 "open-thoughts/OpenThinker-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": "open-thoughts/OpenThinker-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 "open-thoughts/OpenThinker-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": "open-thoughts/OpenThinker-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use open-thoughts/OpenThinker-32B with Docker Model Runner:
```
docker model run hf.co/open-thoughts/OpenThinker-32B
```

madiator commited on Feb 12, 2025

Commit

299fc63

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@@ -33,7 +33,7 @@ The numbers reported in the table below are evaluated with our open-source tool
 |LIMO-32B|0.8k|56.7|49.3|86.6|58.1|60.0|
 |s1-32B|1k|36.0|25.3|84.8|50.5|40.9|
 |s1.1-32B|1k|64.7|49.3|89.0|60.1|65.5|
-|DeepSeek-R1-Distill-Qwen-32B|closed|**76.7**|**55.9**|89.4|57.6|**71.2**|
 |**OpenThinker-32B**|114k|66.0|53.3|**90.6**|**61.6**|68.9|

 |LIMO-32B|0.8k|56.7|49.3|86.6|58.1|60.0|
 |s1-32B|1k|36.0|25.3|84.8|50.5|40.9|
 |s1.1-32B|1k|64.7|49.3|89.0|60.1|65.5|
+|DeepSeek-R1-Distill-Qwen-32B|800k (closed)|**76.7**|**55.9**|89.4|57.6|**71.2**|
 |**OpenThinker-32B**|114k|66.0|53.3|**90.6**|**61.6**|68.9|