Instructions to use twothsnd/ReTerm-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use twothsnd/ReTerm-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="twothsnd/ReTerm-9B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("twothsnd/ReTerm-9B")
model = AutoModelForMultimodalLM.from_pretrained("twothsnd/ReTerm-9B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use twothsnd/ReTerm-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "twothsnd/ReTerm-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "twothsnd/ReTerm-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/twothsnd/ReTerm-9B

SGLang

How to use twothsnd/ReTerm-9B 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 "twothsnd/ReTerm-9B" \
    --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": "twothsnd/ReTerm-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "twothsnd/ReTerm-9B" \
        --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": "twothsnd/ReTerm-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use twothsnd/ReTerm-9B with Docker Model Runner:
```
docker model run hf.co/twothsnd/ReTerm-9B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ReTerm-9B

This repository contains ReTerm-9B, presented in ReTerm: Recovery Training for Self-Improving Terminal Agents. ReTerm-9B is a self-improving terminal agent built on Qwen3.5-9B through SFT and ReTerm-GRPO. The model uses the Qwen3.5 architecture and chat template.

Paper · Training code · ReTerm-10K dataset · Base model

👀 About ReTerm

We introduce ReTerm, a self-improving terminal agent trained to recover from errors produced during its own execution.

We construct ReTerm-10K, a high-quality recovery trajectory dataset generated through a dedicated data pipeline for SFT cold start. Moreover, we propose ReTerm-GRPO, an online RL algorithm that branches into multiple independent self-recovery attempts from the agent's own failure states. Their verifier outcomes provide group-relative learning signals, enabling the agent to self-improve through its own recovery experience.

Built on Qwen3.5-9B, ReTerm-9B demonstrates strong performance on challenging terminal and software engineering tasks, with generalization across out-of-domain benchmarks and different agent harnesses.

🏆 Performance

ReTerm-9B achieves strong results on terminal and software engineering benchmarks:

Benchmark	Pass@1
OpenThoughts-TBLite	60.6%
Terminal-Bench 2.0	34.8%
SWE-bench Verified	58.4%
AIME 2024	86.7%
AIME 2025	93.3%
Aider Polyglot	20.4%
SpreadsheetBench Verified	48.8%

Beyond these core evaluations, ReTerm also generalizes to out-of-domain tasks, including mathematical reasoning and spreadsheet processing. The improvement trend persists across different agent harnesses, suggesting transferable capabilities across interaction setups.

🎥 Recovery Example

Recovery behavior of GRPO and ReTerm-GRPO on a PyStan installation task

🔧 Using the model

ReTerm-9B can be loaded with Qwen3.5-compatible inference frameworks. For terminal-agent use, apply the Qwen3.5 chat template and tool-call protocol with an agent harness that executes commands and returns environment observations. Training code and configurations are available in the ReTerm repository.