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
```

Project | Code | Collection

OpenThinkerAgent-8B-RL

OpenThoughts-Agent is an open-source effort to curate the best datasets for training agents. Our release includes datasets, models and our research codebase.

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, it is further trained with on-policy reinforcement learning on the 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 (itself an SFT of 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

OpenThoughts-Agent-SFT-ColdStartForRL-10K — cold-start SFT trajectories.
OpenThinkerAgent-8B-ColdStartSFTForRL — Qwen3-8B after cold-start SFT (the pre-RL base).
OpenThoughts-Agent-RL-5K — the 5,000 on-policy RL tasks.
OpenThinkerAgent-8B-RL — this model, the final RL'd checkpoint (step 45).

Training data

Cold-start SFT: OpenThoughts-Agent-SFT-ColdStartForRL-10K (9,437 task/trajectory pairs).
RL tasks: 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 card reports the project's benchmark suite for the 32B SFT line.)

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}
}

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for open-thoughts/OpenThinkerAgent-8B-RL

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Finetuned

open-thoughts/OpenThinkerAgent-8B-ColdStartSFTForRL

Finetuned

(1)

this model

Datasets used to train open-thoughts/OpenThinkerAgent-8B-RL

Collection including open-thoughts/OpenThinkerAgent-8B-RL

OpenThinker-Agent2

Collection

OpenThinker-Agent2: agentic SFT/RL datasets and 8B/32B models (cold-start SFT, RL, and the OpenThinkerAgent-32B release). • 6 items • Updated about 19 hours ago • 1

open-thoughts
/

OpenThinkerAgent-8B-RL

OpenThinkerAgent-8B-RL

Model details

The SFT → RL recipe

Training data

Training procedure

Intended uses & limitations

Links

Citation

Model tree for open-thoughts/OpenThinkerAgent-8B-RL

Datasets used to train open-thoughts/OpenThinkerAgent-8B-RL

Collection including open-thoughts/OpenThinkerAgent-8B-RL

OpenThinker-Agent2