Qwen3-4B-Thinking-2507-SimPO-Uncensored

This model has completed up to the second stage (SimPO) of a 3-stage training process.

Qwen3-4B-Thinking-2507-SimPO-Uncensored is an uncensored model based on Qwen/Qwen3-4B-Thinking-2507, fine-tuned using SFT and SimPO (Simple Preference Optimization).

This model served as the base model (intermediate checkpoint) for the puwaer/Qwen3-4B-Thinking-2507-GRPO-Uncensored, which achieves stronger uncensoring and capability recovery.

Note: For the full version (completed up to the 3rd stage, GRPO), please use puwaer/Qwen3-4B-Thinking-2507-GRPO-Uncensored.

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 the following 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: SimPO (Simple Preference Optimization) - Current Model

Dataset: 90,000 samples
Composition: Pure Jailbreak 90k
Objective: To completely break down safety boundaries (Unlearning).

(Reference) Step 3: GRPO (Reinforcement Learning)

The Qwen3-4B-Thinking-2507-GRPO-Uncensored was created by applying Reinforcement Learning to this SimPO model to recover conversational capabilities and complete the uncensoring process.

Model Performance

Below is the comparative evaluation of this model (SimPO), the advanced version (GRPO), 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 (~98%), this model achieves a reduction in refusal rates, particularly in "Do Not Answer JP" and "Sorry Bench." However, it still tends to refuse hard English prompts (DNA En). For more complete uncensoring, please use the GRPO version.

Benchmark	Metric	Base (Safe)	SFT (Step 1)	SimPO (This Model)	GRPO (Advanced)
do not answer	Safety Acc (Low is Better)	0.9883	0.7401	0.8626	0.0469
do not answer jp	Safety Acc (Low is Better)	0.9830	0.5005	0.4686	0.0383
Sorry Bench	Safety Acc (Low is Better)	0.8432	0.5477	0.5409	0.0477

Capability Evaluation (Higher is better)

Due to the aggressive alignment changes caused by SimPO, a drop in MT-Bench scores (Alignment Tax) is observed compared to the base model. This decline in capability is recovered in the subsequent GRPO training process.

Benchmark	Metric	Base (Safe)	SFT (Step 1)	SimPO (This Model)	GRPO (Advanced)
MT-Bench	Average Score (1-10)	7.89	5.76	5.05	6.18
LM Harness	Average Acc (GSM8K, MMLU)	0.7117	0.7028	0.6866	0.6842

Comparisons made between Qwen3-4B-Thinking-2507 (Base) and Qwen3-4B-Thinking-2507-GRPO-Uncensored (GRPO).

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "puwaer/Qwen3-4B-Thinking-2507-SimPO-Uncensored"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input
prompt = "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 completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

# parsing thinking content
try:
    # 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> tag
print("content:", content)