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XDQAQ
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Model Details
Model Provider
XDQAQ
Model Tree
Qwen/Qwen3-8B
Input Modalities
Output Modalities
Supported Functionality
The converted training corpus contains 12,886 examples:
The source dataset revision used for conversion was
7d7d516f780879dbe9f512ef29655f51548f7578.
1e-60.05The run completed 2,418 optimizer steps. Final training loss was
0.7517516736258545. No held-out evaluation set was used during this run.
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "XDQAQ/Qwen3-8B-MaKTO-Public-SFT"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id,torch_dtype="auto",device_map="auto",)messages = [{"role": "user", "content": "请分析当前狼人杀局势。"}]inputs = tokenizer.apply_chat_template(messages,tokenize=True,add_generation_prompt=True,enable_thinking=False,return_tensors="pt",).to(model.device)outputs = model.generate(inputs, max_new_tokens=512)print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
Use transformers>=4.51.0 for Qwen3 support.
The model is specialized for language-game and Werewolf-style interactions. It has not been evaluated as a general assistant, and its outputs can be incorrect, inconsistent, or strategically deceptive in keeping with the training domain. Users should evaluate it for their own intended use.
The base model is distributed under the Apache License 2.0. The source dataset is marked as MIT on its Hugging Face dataset page. Users are responsible for complying with the licenses and terms of the base model and source dataset.