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gloraia
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Model Details
Model Provider
gloraia
Model Tree
Qwen/Qwen3-0.6B
Input Modalities
Output Modalities
Supported Functionality
Qwen/Qwen3-0.6BThe training data was reconstructed from multiple instruction, safety, and math sources:
allenai/wildguardmixHuggingFaceH4/ultrachat_200kmeta-math/MetaMathQAopenai/gsm8kChilleD/SVAMPnvidia/OpenMathInstruct-1The final SFT mixture balances harmful-request refusal, benign-request compliance, mathematical reasoning, and general instruction following.
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "gloraia/cyqwen-0.6b"tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained(model_id,torch_dtype=torch.bfloat16,device_map="auto",trust_remote_code=True,)messages = [{"role": "user", "content": "Solve: If 3x + 5 = 20, what is x?"}]prompt = tokenizer.apply_chat_template(messages,tokenize=False,add_generation_prompt=True,enable_thinking=True,)inputs = tokenizer(prompt, return_tensors="pt").to(model.device)outputs = model.generate(**inputs,max_new_tokens=512,do_sample=True,temperature=0.6,top_p=0.95,top_k=20,)print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
This model is intended for educational evaluation on safety alignment and math reasoning. It may still produce incorrect answers or imperfect refusals, so outputs should be reviewed before use in high-stakes settings.