Instructions to use Seanie-lee/ThinkSafe-R1-Distill-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Seanie-lee/ThinkSafe-R1-Distill-1.5B with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B")
model = PeftModel.from_pretrained(base_model, "Seanie-lee/ThinkSafe-R1-Distill-1.5B")

Transformers

How to use Seanie-lee/ThinkSafe-R1-Distill-1.5B with Transformers:

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

pipe = pipeline("text-generation", model="Seanie-lee/ThinkSafe-R1-Distill-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Seanie-lee/ThinkSafe-R1-Distill-1.5B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Seanie-lee/ThinkSafe-R1-Distill-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Seanie-lee/ThinkSafe-R1-Distill-1.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Seanie-lee/ThinkSafe-R1-Distill-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Seanie-lee/ThinkSafe-R1-Distill-1.5B

SGLang

How to use Seanie-lee/ThinkSafe-R1-Distill-1.5B 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 "Seanie-lee/ThinkSafe-R1-Distill-1.5B" \
    --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": "Seanie-lee/ThinkSafe-R1-Distill-1.5B",
		"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 "Seanie-lee/ThinkSafe-R1-Distill-1.5B" \
        --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": "Seanie-lee/ThinkSafe-R1-Distill-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Seanie-lee/ThinkSafe-R1-Distill-1.5B with Docker Model Runner:
```
docker model run hf.co/Seanie-lee/ThinkSafe-R1-Distill-1.5B
```

ThinkSafe-R1-Distill-1.5B

File size: 3,874 Bytes

---
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
- lora
- sft
- transformers
- trl
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

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- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

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### Out-of-Scope Use

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[More Information Needed]

## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

The model was trained using the ThinkSafe self-generated safety alignment methodology. See the paper for details on the training data generation process.

### Training Procedure

This model uses LoRA (Low-Rank Adaptation) for efficient fine-tuning on top of the Qwen3-0.6B base model. The training follows the ThinkSafe framework for safety alignment in reasoning models.

#### Training Hyperparameters

- **Training regime:** Mixed precision training with PEFT/LoRA

## Evaluation

Please refer to the [ThinkSafe paper](https://huggingface.co/papers/2601.23143) for detailed evaluation results and methodology.

### Testing Data, Factors & Metrics

#### Testing Data

See the paper for details on evaluation datasets and benchmarks used.

#### Metrics

The model was evaluated on safety benchmarks and reasoning tasks. Refer to the paper for specific metrics and results.

## Environmental Impact

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

## Citation

**BibTeX:**

```bibtex
@article{lee2025thinksafe,
  title={THINKSAFE: Self-Generated Safety Alignment for Reasoning Models},
  author={Lee, Seanie and others},
  journal={arXiv preprint arXiv:2601.23143},
  year={2025}
}
```

## More Information

For more details, please refer to:
- Paper: https://huggingface.co/papers/2601.23143
- GitHub Repository: https://github.com/seanie12/ThinkSafe.git


## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

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### Framework versions

- PEFT 0.18.1