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Yy245
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Model Details
Model Provider
Yy245
Model Tree
Qwen/Qwen3-0.6B
Input Modalities
Output Modalities
Supported Functionality
c1899de289a04d12100db370d81485cdf75e47ca64f97589d3743bfd531adc08863bfaee14c5b3fb3056f0cd023ed28bc4943773加权规则为 0.4 × 安全 + 0.3 × 数学 + 0.3 × 通用。详细逐轮结果见
evaluation/model_selection.json，重训分支审计见
evaluation/retrain_comparison.json。
本仓库上传的是完整合并权重，不需要 PEFT/LoRA 适配器。架构、词表、token ID、聊天模板、
thinking 开关和默认生成配置均与原始 Qwen/Qwen3-0.6B 兼容。原调用代码只需替换模型 ID：
python
from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Yy245/Qwen3-0.6B-SafeMath-ZJU" tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto",) messages = [{"role": "user", "content": "Give me a short introduction to large language models."}]text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True,)model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate(**model_inputs, max_new_tokens=512)output_ids = generated_ids[0][len(model_inputs.input_ids[0]):]print(tokenizer.decode(output_ids, skip_special_tokens=True))enable_thinking=False、多轮对话、pipeline、SGLang 和 vLLM 的调用方式也与原模型一致。
例如原 vLLM 命令只需替换模型 ID：
bash
vllm serve Yy245/Qwen3-0.6B-SafeMath-ZJU \ --enable-reasoning --reasoning-parser deepseek_r1建议使用 transformers>=4.51.0；thinking 模式可沿用原模型的默认采样设置
（temperature 0.6、top-p 0.95、top-k 20）。
完整的一页 PDF 可在 training_report.pdf 下载。
这些分数来自固定代理评测，并不等同于真实部署中的绝对安全保证。模型仍可能对含糊但无害的 请求过度拒答，也可能在较难数学题、长上下文或分布外输入上生成错误内容；高风险场景应继续 使用独立安全策略、人工复核和领域评测。
from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Yy245/Qwen3-0.6B-SafeMath-ZJU" tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto",) messages = [{"role": "user", "content": "Give me a short introduction to large language models."}]text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True,)model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate(**model_inputs, max_new_tokens=512)output_ids = generated_ids[0][len(model_inputs.input_ids[0]):]print(tokenizer.decode(output_ids, skip_special_tokens=True))vllm serve Yy245/Qwen3-0.6B-SafeMath-ZJU \ --enable-reasoning --reasoning-parser deepseek_r1