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--- |
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library_name: transformers |
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license: apache-2.0 |
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license_link: https://huggingface.co/Qwen/Qwen3-4B-SafeRL/blob/main/LICENSE |
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pipeline_tag: text-generation |
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base_model: |
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- Qwen/Qwen3-4B |
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--- |
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# Qwen3-4B-SafeRL |
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## Model Overview |
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**Qwen3-4B-SafeRL** is a safety-aligned version of the [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) model. It has been trained using Reinforcement Learning (RL) with a reward signal from **Qwen3Guard-Gen** to enhance its robustness against harmful or adversarial prompts. This process aims to ensure strong safety guarantees without leading to overly simplistic or evasive refusal behaviors, thereby maintaining a positive user experience. |
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For more details on the safety alignment process, please refer to the [Qwen3Guard Technical Report](https://github.com/QwenLM/Qwen3Guard/blob/main/Qwen3Guard_Technical_Report.pdf). |
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### Reinforcement Learning with Hybrid Reward |
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To prevent the model from defaulting to refusal across all prompts in an attempt to remain safe, we introduce a hybrid reward function that jointly optimizes three key objectives: |
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- **Safety Maximization:** Penalizes the generation of unsafe content, as detected by [Qwen3Guard-Gen-4B](https://huggingface.co/Qwen/Qwen3Guard-Gen-4B). |
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- **Helpfulness Maximization:** Rewards responses that are genuinely helpful, as evaluated by the [WorldPM-Helpsteer2](https://huggingface.co/Qwen/WorldPM-72B-HelpSteer2) model. |
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- **Refusal Minimization:** Applies a moderate penalty for unnecessary refusals, also identified by [Qwen3Guard-Gen-4B](https://huggingface.co/Qwen/Qwen3Guard-Gen-4B). |
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### Performance |
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| Mode | Model | Safety Rate (Qwen3-235B) | Safety Rate (WildGuard) | Refusal (WildGuard) | ArenaHard-v2 (Winrate vs GPT-4.1) | AIME25 (Pass@1) | LCB-v6 (Pass@1) | GPQA (Pass@1) | |
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|-------------|-------------------------|--------------------------|--------------------------|---------------------|-----------------------------------|-----------------|-----------------|---------------| |
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| **Non-Think** | Qwen3-4B | 47.5 | 64.7 | 12.9 | 9.5 | **19.1** | 26.4 | **41.7** | |
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| | Qwen3-4B-SafeRL | **86.5** | **98.1** | **5.3** | **10.7** | 18.2 | **27.7** | 40.8 | |
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| **Think** | Qwen3-4B | 43.8 | 59.0 | 6.5 | 13.7 | **65.6** | **48.4** | **55.9** | |
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| | Qwen3-4B-SafeRL | **83.4** | **97.4** | **6.2** | **16.6** | 63.5 | 47.5 | 51.2 | |
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## Quickstart |
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Qwen3-4B-SafeRL is used in the same way as Qwen3-4B, preserving the ability of hybrid thinking modes. The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`. |
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With `transformers<4.51.0`, you will encounter the following error: |
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``` |
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KeyError: 'qwen3' |
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``` |
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The following contains a code snippet illustrating how to use the model generate content based on given inputs. |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "Qwen/Qwen3-4B-SafeRL" |
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# load the tokenizer and the model |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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# prepare the model input |
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prompt = "Give me a short introduction to large language model." |
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messages = [ |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True, |
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enable_thinking=True # Switches between thinking and non-thinking modes. Default is True. |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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# conduct text completion |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=32768 |
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) |
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output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() |
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# parsing thinking content |
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try: |
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# rindex finding 151668 (</think>) |
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index = len(output_ids) - output_ids[::-1].index(151668) |
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except ValueError: |
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index = 0 |
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thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") |
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content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") |
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print("thinking content:", thinking_content) |
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print("content:", content) |
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``` |
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For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint: |
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- SGLang: |
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```shell |
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python -m sglang.launch_server --model-path Qwen/Qwen3-4B-SafeRL --reasoning-parser qwen3 |
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``` |
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- vLLM: |
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```shell |
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vllm serve Qwen/Qwen3-4B-SafeRL --enable-reasoning --reasoning-parser deepseek_r1 |
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``` |
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For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3. |
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For more usages, please refer to the modelcard of [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B). |
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## Citation |
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If you find our work helpful, feel free to give us a cite. |
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``` |
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@misc{qwen3guard, |
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title={Qwen3Guard Technical Report}, |
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author={Qwen Team}, |
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year={2025}, |
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url={http://arxiv.org/abs/2510.14276}, |
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} |
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``` |
