OpenThinker-7B

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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

Set pipeline tag to text-generation

by nielsr HF Staff - opened Jun 7, 2025

base: refs/heads/main

←

from: refs/pr/6

Discussion Files changed

-5

Files changed (1) hide show

README.md +6 -5

README.md CHANGED Viewed

@@ -1,7 +1,9 @@
 ---
 library_name: transformers
 license: apache-2.0
-base_model: Qwen/Qwen2.5-7B-Instruct
 tags:
 - llama-factory
 - full
@@ -9,8 +11,7 @@ tags:
 model-index:
 - name: OpenThinker-7B
   results: []
-datasets:
-- open-thoughts/open-thoughts-114k
 ---
 <p align="center">
@@ -26,7 +27,7 @@ This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://hugging
 [OpenThoughts-114k dataset](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k) dataset.
 The dataset is derived by distilling DeepSeek-R1 using the [data pipeline available on github](https://github.com/open-thoughts/open-thoughts).
-More info about the dataset can be found on the dataset card at [OpenThoughts-114k dataset](https://huggingface.co/datasets/open-thoughts/open-thoughts-114k).
 This model improves upon the [Bespoke-Stratos-7B model](https://huggingface.co/bespokelabs/Bespoke-Stratos-7B), which used 17k examples ([Bespoke-Stratos-17k dataset](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k)).
 The numbers reported in the table below are evaluated with our open-source tool [Evalchemy](https://github.com/mlfoundations/Evalchemy).
@@ -107,4 +108,4 @@ More info can be found in our repository: [https://github.com/open-thoughts/open
   primaryClass={cs.LG},
   url={https://arxiv.org/abs/2506.04178},
 }
-```

 ---
+base_model: Qwen/Qwen2.5-7B-Instruct
+datasets:
+- open-thoughts/open-thoughts-114k
 library_name: transformers
 license: apache-2.0
 tags:
 - llama-factory
 - full
 model-index:
 - name: OpenThinker-7B
   results: []
+pipeline_tag: text-generation
 ---
 <p align="center">
 [OpenThoughts-114k dataset](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k) dataset.
 The dataset is derived by distilling DeepSeek-R1 using the [data pipeline available on github](https://github.com/open-thoughts/open-thoughts).
+More info about the dataset can be found on the dataset card at [OpenThoughts-114k dataset](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k).
 This model improves upon the [Bespoke-Stratos-7B model](https://huggingface.co/bespokelabs/Bespoke-Stratos-7B), which used 17k examples ([Bespoke-Stratos-17k dataset](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k)).
 The numbers reported in the table below are evaluated with our open-source tool [Evalchemy](https://github.com/mlfoundations/Evalchemy).
   primaryClass={cs.LG},
   url={https://arxiv.org/abs/2506.04178},
 }
+```