Instructions to use Seanie-lee/ThinkSafe-R1-Distill-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Seanie-lee/ThinkSafe-R1-Distill-7B with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-7B")
model = PeftModel.from_pretrained(base_model, "Seanie-lee/ThinkSafe-R1-Distill-7B")

Transformers

How to use Seanie-lee/ThinkSafe-R1-Distill-7B with Transformers:

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

pipe = pipeline("text-generation", model="Seanie-lee/ThinkSafe-R1-Distill-7B")
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-7B", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Seanie-lee/ThinkSafe-R1-Distill-7B 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-7B"
# 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-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Seanie-lee/ThinkSafe-R1-Distill-7B

SGLang

How to use Seanie-lee/ThinkSafe-R1-Distill-7B 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-7B" \
    --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-7B",
		"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-7B" \
        --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-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Seanie-lee/ThinkSafe-R1-Distill-7B with Docker Model Runner:
```
docker model run hf.co/Seanie-lee/ThinkSafe-R1-Distill-7B
```

Seanie-lee commited on 18 days ago

Commit

5b2f904

verified ·

1 Parent(s): 81161a5

Update README.md

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@@ -84,118 +84,53 @@ Use the code below to get started with the model.
 ### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
 ### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
 #### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
 ## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
 ### Testing Data, Factors & Metrics
 #### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
 #### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
 ## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
 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).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 **BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
 ## Model Card Authors [optional]

 ### 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]