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", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

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
```

Model Card for Model ID

Model Details

Model Description

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]

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

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

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 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 presented in Lacoste et al. (2019).

Citation

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}
}