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hututu718
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Model Details
Model Provider
hututu718
Model Tree
Qwen/Qwen3-0.6B
Input Modalities
Output Modalities
Supported Functionality
The model was developed through the following procedure:
The final task-vector configuration is:
The architecture and parameter scale remain unchanged from Qwen3-0.6B.
| Model | GSM8K | MMLU | HB-ASR ↓ | WJ-ASR ↓ | MOR-ORR ↓ | WJB-ORR ↓ | Safety ↑ |
|---|---|---|---|---|---|---|---|
| Qwen3-0.6B Base | 58.3% | 38.6% | 62.5% | 75.0% | 10.0% | 5.0% | 61.9% |
| This model | 55.0% | 38.6% | 62.5% | 72.5% | 2.9% | 5.0% | 64.3% |
These results are obtained from a local evaluation suite and are not official Qwen benchmark results.
text
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_name = "hututu718/qwen3-0.6b-safety-math-v14" tokenizer = AutoTokenizer.from_pretrained( model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto",) messages = [ { "role": "user", "content": "Please calculate 17 + 25.", }] inputs = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, enable_thinking=True, return_dict=True, return_tensors="pt",).to(model.device) outputs = model.generate( **inputs, max_new_tokens=256, do_sample=False,) response = tokenizer.decode( outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True,) print(response)This model is intended for academic evaluation and research on safety alignment, mathematical reasoning, and general capability preservation.
The model may still produce unsafe, incorrect, or over-refusal responses. Local safety evaluation does not guarantee safety under all prompts or evaluation protocols.
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizermodel_name = "hututu718/qwen3-0.6b-safety-math-v14"tokenizer = AutoTokenizer.from_pretrained( model_name)model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto",)messages = [ { "role": "user", "content": "Please calculate 17 + 25.", }]inputs = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, enable_thinking=True, return_dict=True, return_tensors="pt",).to(model.device)outputs = model.generate( **inputs, max_new_tokens=256, do_sample=False,)response = tokenizer.decode( outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True,)print(response)