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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("open-thoughts/OpenThinkerAgent-32B")
model = AutoModelForCausalLM.from_pretrained("open-thoughts/OpenThinkerAgent-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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use open-thoughts/OpenThinkerAgent-32B with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

EtashGuha commited on about 24 hours ago

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+---
+base_model:
+- Qwen/Qwen3-32B
+datasets:
+- open-thoughts/OpenThoughts-Agent-SFT-100K
+library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+- agents
+- terminal
+- code
+- software-engineering
+---
+<p align="center">
+<a href="https://www.openthoughts.ai/blog/agent" style="margin-right: 24px;">Project</a> |
+<a href="https://github.com/open-thoughts/OpenThoughts-Agent" style="margin-right: 24px; margin-left: 24px;">Code</a> |
+<a href="https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-SFT-100K" style="margin-left: 24px;">Training data</a> |
+<a href="https://huggingface.co/collections/open-thoughts/openthinker-agent" style="margin-left: 24px;">Collection</a>
+</p>
+# OpenThinkerAgent-32B
+**OpenThoughts-Agent** is an open effort to curate the best data for training agentic
+language models. **OpenThinkerAgent-32B** is the 32B model from the SFT scaling ladder, fine-tuned
+from [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) on the **100,000-example**
+[OpenThoughts-Agent-SFT-100K](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-SFT-100K)
+dataset (Top-4 task sources, GLM-4.7-AWQ teacher in the terminus-2 harness, ≥5-turn filter).
+## Performance
+Evaluated in the **terminus-2** harness (pass@1, 3 stochastic re-runs):
+| Benchmark | Accuracy |
+| --- | --- |
+| SWE-Bench-Verified-100 | 55.7 |
+| OpenThoughts-TBLite | 41.3 |
+| Terminal-Bench 2.0 | 26.2 |
+### Full benchmark suite (OpenThinkerAgent-32B, best harness)
+| Benchmark | Acc |
+| --- | --- |
+| SWE-Bench-Verified | 54.0 |
+| Terminal-Bench 2.0 | 26.2 |
+| Aider-Polyglot | 32.4 |
+| BFCL-Parity | 85.9 |
+| MedAgentBench | 47.8 |
+| GAIA-127 | 23.6 |
+| FinanceAgent-Terminal | 44.0 |
+| **Average (7)** | **44.8** |
+This is the best open-data 32B model on the average of seven agentic benchmarks.
+## Links
+- 🌐 [Project](https://www.openthoughts.ai/blog/agent)
+- 💻 [Code](https://github.com/open-thoughts/OpenThoughts-Agent)
+- 🧠 [Training dataset](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-SFT-100K)
+- 📚 [Collection](https://huggingface.co/collections/open-thoughts/openthinker-agent)
+## Citation
+```
+@misc{openthoughts-agent,
+  author = {Team, OpenThoughts-Agent},
+  title = {OpenThoughts-Agent: Data Recipes for Agentic Models},
+  howpublished = {https://www.openthoughts.ai/blog/agent},
+  year = {2026}
+}
+```