Instructions to use open-thoughts/OpenThinker3-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use open-thoughts/OpenThinker3-7B with Transformers:

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

pipe = pipeline("text-generation", model="open-thoughts/OpenThinker3-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("open-thoughts/OpenThinker3-7B")
model = AutoModelForCausalLM.from_pretrained("open-thoughts/OpenThinker3-7B")
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/OpenThinker3-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "open-thoughts/OpenThinker3-7B"
# 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/OpenThinker3-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/open-thoughts/OpenThinker3-7B

SGLang

How to use open-thoughts/OpenThinker3-7B 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/OpenThinker3-7B" \
    --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/OpenThinker3-7B",
		"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/OpenThinker3-7B" \
        --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/OpenThinker3-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use open-thoughts/OpenThinker3-7B with Docker Model Runner:
```
docker model run hf.co/open-thoughts/OpenThinker3-7B
```

sedrickkeh commited on Jun 5, 2025

Commit

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@@ -39,15 +39,14 @@ In the table below, we bold values in each column that are within 2 standard err
 | Model                                                                                           | Data  | AIME24 | AIME25 |  AMC23 | MATH500 | HMMT O2/25 | LCB 06/24-01/25 | CodeElo | CodeForces | GPQA-D | JEEBench |
 | ----------------------------------------------------------------------------------------------- | ----- | ------ | ------ | ------ | ------- | ---------- | --------------- | ------- | ---------- | ------ | -------- |
-| [OpenThinker-7B](https://huggingface.co/open-thoughts/OpenThinker-7B)                           | ✅    |        |        |        |         |            |                 |         |            |        |          |
-| [OpenThinker2-7B](https://huggingface.co/open-thoughts/OpenThinker2-7B)                         | ✅    |        |        |        |         |            |                 |         |            |        |          |
 | **[OpenThinker3-7B](https://huggingface.co/open-thoughts/OpenThinker3-7B)**                     | ✅    |**69.0**|**53.3**|**93.5**| **90.0**|   **42.7** |    **51.7**     |  31.0   |**32.2**    |  53.7  |**72.4**  |
 | [DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) | ❌    |  51.3  |  38.0  |  92.0  |   88.0  |   25.0     |    34.5         |  19.9   |  21.1      |  33.2  |  50.4    |
 | [OpenR1-Distill-7B](https://huggingface.co/open-r1/OpenR1-Distill-7B)                           | ✅    |  57.7  |  39.7  |  87.0  |   88.0  |   25.7     |    30.7         |  30.1   |  29.3      |**58.9**|  68.7    |
 | [Llama-3.1-Nemotron-Nano-8B-v1](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-8B-v1)    | ✅    |  62.0  |  48.0  |**94.0**|   89.4  |   26.7     |    **50.9**     |  30.9   |**32.9**    |  52.9  |  70.7    |
 | [AceReason-Nemotron-7B](https://huggingface.co/nvidia/AceReason-Nemotron-7B)                    | ✅    |**71.0**|  50.7  |**93.8**|   89.8  |   33.3     |    44.3         |**32.9** |**30.9**    |  52.9  |  64.3    |
 # Data
 This model was trained on the [OpenThoughts3-1.2M](https://huggingface.co/datasets/open-thoughts/OpenThoughts3-1.2M) dataset.

 | Model                                                                                           | Data  | AIME24 | AIME25 |  AMC23 | MATH500 | HMMT O2/25 | LCB 06/24-01/25 | CodeElo | CodeForces | GPQA-D | JEEBench |
 | ----------------------------------------------------------------------------------------------- | ----- | ------ | ------ | ------ | ------- | ---------- | --------------- | ------- | ---------- | ------ | -------- |
+| [OpenThinker-7B](https://huggingface.co/open-thoughts/OpenThinker-7B)                           | ✅    |  30.7  |  22.0  |  72.5  |   82.8  |   15.7     |    26.1         |  11.1   |  14.9      |  38.6  |  45.3    |
+| [OpenThinker2-7B](https://huggingface.co/open-thoughts/OpenThinker2-7B)                         | ✅    |  60.7  |  38.7  |  89.8  |   87.6  |   24.7     |    40.6         |  22.8   |  26.6      |  47.0  |  65.1    |
 | **[OpenThinker3-7B](https://huggingface.co/open-thoughts/OpenThinker3-7B)**                     | ✅    |**69.0**|**53.3**|**93.5**| **90.0**|   **42.7** |    **51.7**     |  31.0   |**32.2**    |  53.7  |**72.4**  |
 | [DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) | ❌    |  51.3  |  38.0  |  92.0  |   88.0  |   25.0     |    34.5         |  19.9   |  21.1      |  33.2  |  50.4    |
 | [OpenR1-Distill-7B](https://huggingface.co/open-r1/OpenR1-Distill-7B)                           | ✅    |  57.7  |  39.7  |  87.0  |   88.0  |   25.7     |    30.7         |  30.1   |  29.3      |**58.9**|  68.7    |
 | [Llama-3.1-Nemotron-Nano-8B-v1](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-8B-v1)    | ✅    |  62.0  |  48.0  |**94.0**|   89.4  |   26.7     |    **50.9**     |  30.9   |**32.9**    |  52.9  |  70.7    |
 | [AceReason-Nemotron-7B](https://huggingface.co/nvidia/AceReason-Nemotron-7B)                    | ✅    |**71.0**|  50.7  |**93.8**|   89.8  |   33.3     |    44.3         |**32.9** |**30.9**    |  52.9  |  64.3    |
 # Data
 This model was trained on the [OpenThoughts3-1.2M](https://huggingface.co/datasets/open-thoughts/OpenThoughts3-1.2M) dataset.