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Model Details
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2etatg
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Qwen/Qwen3-Next-80B-A3B-Thinking
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This model was trained using a two-stage process:
Below is the comparative evaluation of this model (GRPO), the intermediate checkpoint (SFT), and the base model (Safe).
⚠️ Note on Evaluation Environment Due to budget constraints,
gpt-4o-miniwas used for the LLM-as-a-Judge process (including "Do Not Answer" and MT-Bench). Please note that the scoring trends and criteria may differ from results evaluated using the standardgpt-4.
In the "Do Not Answer" (DNA) and "Sorry Bench" benchmarks, while the base model shows a high refusal rate (~88%), this model achieves an extremely low refusal rate of under 4%–15%.
| Benchmark | Metric | Base (Safe) | SFT (Step1) | GRPO (This Model) |
|---|---|---|---|---|
| do not answer | Safety Acc (Low is Better) | 0.9979 | 0.8275 | 0.147 |
| do not answer jp | Safety Acc (Low is Better) | 0.984 | 0.5378 | 0.0873 |
| Sorry Bench | Safety Acc (Low is Better) | 0.8886 | 0.8455 | 0.0409 |
Generally, "uncensoring" (lobotomy) procedures tend to degrade a model's general intelligence. However, this model recovered its conversational scores (e.g., MT-Bench) by proceeding from the SFT stage to GRPO.
| Benchmark | Metric | Base (Safe) | SFT (Step1) | GRPO (This Model) |
|---|---|---|---|---|
| MT-Bench | Average Score (1-10) | 8.044 | 7.538 | 7.513 |
| LM Harness | Average Acc (GSM8K, MMLU) | 0.8454 | 0.8483 | 0.8436 |
*Comparisons made between Qwen3-Next-80B-A3B-Thinking (Base) *
python
from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "puwaer/Qwen3-Next-80B-A3B-Thinking-GRPO-Uncensored" # load the tokenizer and the modeltokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto") # prepare the model inputprompt = "Give me a short introduction to large language model."messages = [ {"role": "user", "content": prompt}]text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True,)model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completiongenerated_ids = model.generate( **model_inputs, max_new_tokens=32768)output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # parsing thinking contenttry: # rindex finding 151668 (</think>) index = len(output_ids) - output_ids[::-1].index(151668)except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("thinking content:", thinking_content) # no opening <think> tagprint("content:", content)The following datasets were used for training this model:
from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "puwaer/Qwen3-Next-80B-A3B-Thinking-GRPO-Uncensored" # load the tokenizer and the modeltokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto") # prepare the model inputprompt = "Give me a short introduction to large language model."messages = [ {"role": "user", "content": prompt}]text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True,)model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completiongenerated_ids = model.generate( **model_inputs, max_new_tokens=32768)output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # parsing thinking contenttry: # rindex finding 151668 (</think>) index = len(output_ids) - output_ids[::-1].index(151668)except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("thinking content:", thinking_content) # no opening <think> tagprint("content:", content)