Method
The model was developed through the following procedure:
- Cleaning and balancing supervised data for safety, mathematics, and general tasks.
- Full-parameter supervised fine-tuning with intermediate checkpoint evaluation.
- Upper-layer safety task-vector injection.
The final task-vector configuration is:
- Capability model: V11 full step-25
- Shared parent model: V10 step-20
- Safety branch: V11 step-10
- Injected layers: final 8 Transformer layers
- Task-vector scale: 0.15
- Per-tensor update-norm cap: 0.02
The architecture and parameter scale remain unchanged from Qwen3-0.6B.
Local Evaluation
Table with columns: Model, GSM8K, MMLU, HB-ASR ↓, WJ-ASR ↓, MOR-ORR ↓, WJB-ORR ↓, Safety ↑| 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.
Usage
import torch
from 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)
Intended Use
This model is intended for academic evaluation and research on safety alignment, mathematical reasoning, and general capability preservation.
Limitations
The model may still produce unsafe, incorrect, or over-refusal responses. Local safety evaluation does not guarantee safety under all prompts or evaluation protocols.