Instructions to use Seanie-lee/ThinkSafe-R1-Distill-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Seanie-lee/ThinkSafe-R1-Distill-8B with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Llama-8B")
model = PeftModel.from_pretrained(base_model, "Seanie-lee/ThinkSafe-R1-Distill-8B")

Transformers

How to use Seanie-lee/ThinkSafe-R1-Distill-8B with Transformers:

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

pipe = pipeline("text-generation", model="Seanie-lee/ThinkSafe-R1-Distill-8B")
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-8B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Seanie-lee/ThinkSafe-R1-Distill-8B 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-8B"
# 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-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Seanie-lee/ThinkSafe-R1-Distill-8B

SGLang

How to use Seanie-lee/ThinkSafe-R1-Distill-8B 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-8B" \
    --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-8B",
		"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-8B" \
        --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-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Seanie-lee/ThinkSafe-R1-Distill-8B with Docker Model Runner:
```
docker model run hf.co/Seanie-lee/ThinkSafe-R1-Distill-8B
```

ThinkSafe-R1-Distill-8B / README.md

Seanie-lee

Update README.md

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	---
	base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
	library_name: peft
	pipeline_tag: text-generation
	tags:
	- base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Llama-8B
	- lora
	- sft
	- transformers
	- trl
	---

	# Model Card for Model ID

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



	## Model Details

	### Model Description

	<!-- Provide a longer summary of what this model is. -->



	- 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]

	<!-- Provide the basic links for the model. -->

	- 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 -->

	[More Information Needed]

	### Out-of-Scope Use

	<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

	[More Information Needed]

	## Bias, Risks, and Limitations

	<!-- This section is meant to convey both technical and sociotechnical limitations. -->

	[More Information Needed]

	### Recommendations

	<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

	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

	[More Information Needed]
	### Framework versions

	- PEFT 0.18.1