Qwen3-Next-80B-A3B-Thinking-GRPO-Uncensored-gguf

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Qwen3-Next-80B-A3B-Thinking-GRPO-Uncensored is an uncensored model based on Qwen/Qwen3-Next-80B-A3B-Thinking, fine-tuned using SFT,GRPO with LoRA.

This model has been fine-tuned using an instruction format.

Disclaimer: We take no responsibility for the outputs of this model. Please use it at your own risk.

Training Process

This model was trained using a two-stage process:

Step 1: SFT (Supervised Fine-Tuning)

Dataset: 12,000 samples
Composition: Jailbreak 10k + General 1.5k + Logic 0.5k
Objective: To learn the format and the "uncensored" attitude while maintaining the model's intelligence.

Step 2: GRPO (Reinforcement Learning)

Dataset: 60,000 samples
Reward Model: puwaer/Unsafe-Reward-Qwen3-1.7B
Composition: Multilingual Jailbreak prompts
Objective: To improve the ability to generate more natural and persuasive harmful responses.

Model Performance

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-mini was 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 standard gpt-4.

Safety Evaluation (Lower is better / Higher success in uncensoring)

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

Capability Evaluation (Higher is better)

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) *

Usage

Using llama.cpp (CLI)

# Download the model file
hhuggingface-cli download puwer/Qwen3-Next-80B-A3B-Thinking-GRPO-Uncensored-gguf \
  --local-dir ./models --local-dir-use-symlinks False

# Run inference
./llama-cli -m ./models/Qwen3-Next-80B-A3B-Thinking-GRPO-Uncensored-q4_k_m.gguf \
  -p "Give me a short introduction to large language model." \
  -n 512 \
  --temp 0.7

Using llama-cpp-python

from llama_cpp import Llama

# Initialize the model
model = Llama(
    model_path="./models/Qwen3-Next-80B-A3B-Thinking-GRPO-Uncensored-q4_k_m.gguf",
    n_ctx=32768,  # Context window
    n_gpu_layers=-1,  # Use GPU acceleration (set to 0 for CPU only)
)

# Generate a response
prompt = "Give me a short introduction to large language model."
output = model.create_chat_completion(
    messages=[
        {"role": "user", "content": prompt}
    ],
    max_tokens=512,
    temperature=0.7,
)

print(output["choices"][0]["message"]["content"])