Instructions to use open-thoughts/OpenThinkerAgent-8B-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use open-thoughts/OpenThinkerAgent-8B-RL with Transformers:

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

pipe = pipeline("text-generation", model="open-thoughts/OpenThinkerAgent-8B-RL")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("open-thoughts/OpenThinkerAgent-8B-RL")
model = AutoModelForMultimodalLM.from_pretrained("open-thoughts/OpenThinkerAgent-8B-RL")
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-8B-RL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "open-thoughts/OpenThinkerAgent-8B-RL"
# 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-8B-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/open-thoughts/OpenThinkerAgent-8B-RL

SGLang

How to use open-thoughts/OpenThinkerAgent-8B-RL 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-8B-RL" \
    --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-8B-RL",
		"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-8B-RL" \
        --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-8B-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use open-thoughts/OpenThinkerAgent-8B-RL with Docker Model Runner:
```
docker model run hf.co/open-thoughts/OpenThinkerAgent-8B-RL
```

OpenThinkerAgent-8B-RL / README.md

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	---
	base_model:
	- open-thoughts/OpenThinkerAgent-8B-ColdStartSFTForRL
	datasets:
	- open-thoughts/OpenThoughts-Agent-RL-5K
	- open-thoughts/OpenThoughts-Agent-SFT-ColdStartForRL-10K
	library_name: transformers
	license: apache-2.0
	model-index:
	- name: OpenThinkerAgent-8B-RL
	results: []
	pipeline_tag: text-generation
	tags:
	- agents
	- terminal
	- code
	- software-engineering
	- reinforcement-learning
	- rl
	---
	<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-8B-RL

	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-8B-RL](https://huggingface.co/open-thoughts/OpenThinkerAgent-8B-RL) is the final, RL-trained 8B agentic checkpoint of the OpenThoughts-Agent SFT→RL recipe. Starting from the cold-start SFT base [OpenThinkerAgent-8B-ColdStartSFTForRL](https://huggingface.co/open-thoughts/OpenThinkerAgent-8B-ColdStartSFTForRL), it is further trained with on-policy reinforcement learning on the [OpenThoughts-Agent-RL-5K](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-RL-5K) task set. This checkpoint corresponds to RL step 45.

	> Architecture note. Although the upstream lineage carries a `GLM-4.7` label (which refers to the teacher used for the cold-start SFT trajectories, not the student), this model is a Qwen3-8B. Its `config.json` reports `model_type: qwen3`, `architectures: ["Qwen3ForCausalLM"]`, 36 layers, hidden size 4096, 32 attention heads / 8 KV heads, and a 40,960-token context — i.e. standard Qwen3-8B.

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

	# Model details

	- Base (pre-RL) model: [OpenThinkerAgent-8B-ColdStartSFTForRL](https://huggingface.co/open-thoughts/OpenThinkerAgent-8B-ColdStartSFTForRL) (itself an SFT of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B))
	- Architecture: Qwen3 (`Qwen3ForCausalLM`), 36 layers, hidden size 4096, 32 attention heads, 8 KV heads, RoPE θ = 1e6
	- Context length: 40,960 tokens (max position embeddings); RL rollouts used a 32,768-token serving window
	- Vocabulary: 151,936 tokens
	- Precision: bf16
	- Checkpoint: RL step 45

	# The SFT → RL recipe

	1. [OpenThoughts-Agent-SFT-ColdStartForRL-10K](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-SFT-ColdStartForRL-10K) — cold-start SFT trajectories.
	2. [OpenThinkerAgent-8B-ColdStartSFTForRL](https://huggingface.co/open-thoughts/OpenThinkerAgent-8B-ColdStartSFTForRL) — Qwen3-8B after cold-start SFT (the pre-RL base).
	3. [OpenThoughts-Agent-RL-5K](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-RL-5K) — the 5,000 on-policy RL tasks.
	4. [OpenThinkerAgent-8B-RL](https://huggingface.co/open-thoughts/OpenThinkerAgent-8B-RL) — this model, the final RL'd checkpoint (step 45).

	# Training data

	- Cold-start SFT: [OpenThoughts-Agent-SFT-ColdStartForRL-10K](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-SFT-ColdStartForRL-10K) (9,437 task/trajectory pairs).
	- RL tasks: [OpenThoughts-Agent-RL-5K](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-RL-5K) (5,000 `pymethods2test-large` tasks); the policy rolls out against each task in a Daytona sandbox and is rewarded by the task's test verifier.

	# Training procedure

	On-policy RL with the OpenThoughts-Agent codebase (SkyRL), recorded in the run config shipped with this repo (`swesmith-fixthink-pymethods2test_rl_config.json`):

	- Algorithm: RLOO-n advantage estimator (`advantage_estimator=rloo_n`), no KL loss (`use_kl_loss=false`, `kl_loss_coef=0.0`)
	- PPO clip: eps_clip_low/high = 0.2, loss reduction = token_mean
	- Optimizer: AdamW, learning_rate 5e-6, weight_decay 0.0, betas (0.9, 0.999)
	- Batch: train_batch_size 64, policy_mini_batch_size 64
	- Rollouts: vLLM backend, 8 samples per prompt, sampling temperature 0.7 / top_p 0.95 / top_k 20, max generate length 4096, served at 32,768-token context
	- Harness: terminus-2 agent in Daytona sandboxes; interleaved thinking enabled
	- Strategy: FSDP2; HF checkpoint exported every 5 RL steps; this artifact is step 45

	# Intended uses & limitations

	This is an agentic coding model: it is designed to operate as a tool-using agent in the terminus-2 harness (issuing shell commands / edits and reasoning over terminal output) to solve software-engineering tasks. It inherits Qwen3-8B's general capabilities plus agentic behaviour from cold-start SFT and the RL stage. Limitations: outputs (including shell commands) may be incorrect or unsafe and should be executed only in sandboxed environments with review; the RL stage optimized for the `pymethods2test`/SWE-Smith-style task distribution and may generalize unevenly to other domains.

	> Evaluation: No verified agentic-benchmark numbers are published for this specific 8B RL checkpoint in the source artifact; evaluation results are TBD. (The flagship [OpenThinkerAgent-32B](https://huggingface.co/open-thoughts/OpenThinkerAgent-32B) card reports the project's benchmark suite for the 32B SFT line.)

	# 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)
	- 🤖 [Pre-RL base model: OpenThinkerAgent-8B-ColdStartSFTForRL](https://huggingface.co/open-thoughts/OpenThinkerAgent-8B-ColdStartSFTForRL)
	- 🧠 [RL tasks: OpenThoughts-Agent-RL-5K](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-RL-5K)
	- 🧠 [Cold-start SFT dataset: OpenThoughts-Agent-SFT-ColdStartForRL-10K](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-SFT-ColdStartForRL-10K)

	# 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}
	}
	```