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
```

OpenThinkerAgent-32B / README.md

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	---
	base_model:
	- Qwen/Qwen3-32B
	datasets:
	- open-thoughts/OpenThoughts-Agent-SFT-100K
	library_name: transformers
	license: apache-2.0
	model-index:
	- name: OpenThinkerAgent-32B
	results: []
	pipeline_tag: text-generation
	tags:
	- agents
	- terminal
	- code
	- software-engineering
	---
	<p align="center">
	<img src="https://huggingface.co/datasets/open-thoughts/OpenThoughts1-Agent-SFT/resolve/main/ota-logo.png" width="50%">
	</p>

	<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/collections/open-thoughts/openthinker-agent" style="margin-left: 24px;">Collection</a>
	</p>


	# OpenThinkerAgent-32B

	OpenThoughts-Agent is an open-source effort to curate the best datasets for training agents. Our release includes [datasets](https://huggingface.co/collections/open-thoughts/openthinker-agent), [models](https://huggingface.co/collections/open-thoughts/openthinker-agent) and our [research codebase](https://github.com/open-thoughts/OpenThoughts-Agent).

	[OpenThinkerAgent-32B](https://huggingface.co/open-thoughts/OpenThinkerAgent-32B) is post-trained from [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) with full-parameter SFT 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 trace filter). It is the flagship OpenThinkerAgent-32B, the strongest open-data 32B model on the average of seven agentic benchmarks.

	- Homepage: https://www.openthoughts.ai/blog/agent
	- Repository: https://github.com/open-thoughts/OpenThoughts-Agent

	# Performance

	Evaluated in the terminus-2 harness (pass@1, mean over 3 stochastic re-runs):

	\| Model \| Harness \| SWE-Bench-Verified-100 \| OpenThoughts-TBLite \| Terminal-Bench 2.0 \|
	\| --- \| --- \| --- \| --- \| --- \|
	\| [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) \| Terminus-2 \| 26.7 \| 13.7 \| 7.5 \|
	\| OpenThinkerAgent-32B \| Terminus-2 \| 55.7 \| 41.3 \| 26.2 \|

	Across the full seven-benchmark suite (best harness per benchmark), OpenThinkerAgent-32B is the strongest open-data model at the 32B scale:

	\| Benchmark \| Accuracy \|
	\| --- \| --- \|
	\| 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 \|

	# Data

	The model is trained on [OpenThoughts-Agent-SFT-100K](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-SFT-100K): (task, agent-trajectory) pairs from the Top-4 task sources (SWE-Smith, StackExchange-SuperUser, StackExchange-Tezos with synthetic augmentation, IssueTasks). Trajectories are generated by GLM-4.7-AWQ in the terminus-2 harness and filtered to traces with at least 5 model turns.

	### Training hyperparameters
	- learning_rate: 4e-05
	- lr_scheduler_type: cosine, warmup_ratio 0.1
	- global_batch_size: 96
	- num_epochs: 5
	- cutoff_len: 32768
	- precision: bf16, DeepSpeed ZeRO-3

	# Links
	- 🌐 [OpenThoughts-Agent project page](https://www.openthoughts.ai/blog/agent)
	- 💻 [OpenThoughts-Agent GitHub repository](https://github.com/open-thoughts/OpenThoughts-Agent)
	- 📚 [OpenThinker-Agent collection](https://huggingface.co/collections/open-thoughts/openthinker-agent)
	- 🧠 [Training dataset: OpenThoughts-Agent-SFT-100K](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-SFT-100K)

	# 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}
	}
	```