| --- |
| library_name: transformers |
| license: apache-2.0 |
| license_link: https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE |
| pipeline_tag: text-generation |
| base_model: |
| - Qwen/Qwen3-4B |
| tags: |
| - chat |
| - abliterated |
| - uncensored |
| extra_gated_prompt: >- |
| **Usage Warnings** |
| |
|
|
| “**Risk of Sensitive or Controversial Outputs**“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. |
|
|
| “**Not Suitable for All Audiences**:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. |
|
|
| “**Legal and Ethical Responsibilities**“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. |
|
|
| “**Research and Experimental Use**“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. |
|
|
| “**Monitoring and Review Recommendations**“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. |
|
|
| “**No Default Safety Guarantees**“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use. |
|
|
|
|
| --- |
| |
| # huihui-ai/Qwen3-4B-abliterated |
|
|
|
|
| This is an uncensored version of [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it). |
| This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. |
|
|
| Ablation was performed using a new and faster method, which yields better results. |
|
|
| **Important Note** There's a new version available, please try using the new version [huihui-ai/Huihui-Qwen3-4B-abliterated-v2](https://huggingface.co/huihui-ai/Huihui-Qwen3-4B-abliterated-v2). |
|
|
|
|
| ## ollama |
|
|
| You can use [huihui_ai/qwen3-abliterated:4b](https://ollama.com/huihui_ai/qwen3-abliterated:4b) directly, |
| ``` |
| ollama run huihui_ai/qwen3-abliterated:4b |
| ``` |
|
|
|
|
| ## Usage |
| You can use this model in your applications by loading it with Hugging Face's `transformers` library: |
|
|
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer |
| import torch |
| import os |
| import signal |
| |
| cpu_count = os.cpu_count() |
| print(f"Number of CPU cores in the system: {cpu_count}") |
| half_cpu_count = cpu_count // 2 |
| os.environ["MKL_NUM_THREADS"] = str(half_cpu_count) |
| os.environ["OMP_NUM_THREADS"] = str(half_cpu_count) |
| torch.set_num_threads(half_cpu_count) |
| |
| print(f"PyTorch threads: {torch.get_num_threads()}") |
| print(f"MKL threads: {os.getenv('MKL_NUM_THREADS')}") |
| print(f"OMP threads: {os.getenv('OMP_NUM_THREADS')}") |
| |
| # Load the model and tokenizer |
| NEW_MODEL_ID = "huihui-ai/Qwen3-4B-abliterated" |
| print(f"Load Model {NEW_MODEL_ID} ... ") |
| quant_config_4 = BitsAndBytesConfig( |
| load_in_4bit=True, |
| bnb_4bit_compute_dtype=torch.bfloat16, |
| bnb_4bit_use_double_quant=True, |
| llm_int8_enable_fp32_cpu_offload=True, |
| ) |
| |
| model = AutoModelForCausalLM.from_pretrained( |
| NEW_MODEL_ID, |
| device_map="auto", |
| trust_remote_code=True, |
| #quantization_config=quant_config_4, |
| torch_dtype=torch.bfloat16 |
| ) |
| tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True) |
| if tokenizer.pad_token is None: |
| tokenizer.pad_token = tokenizer.eos_token |
| tokenizer.pad_token_id = tokenizer.eos_token_id |
| |
| initial_messages = [{"role": "system", "content": "You are a helpful assistant."}] |
| messages = initial_messages.copy() |
| enable_thinking = True |
| skip_prompt=True |
| skip_special_tokens=True |
| |
| class CustomTextStreamer(TextStreamer): |
| def __init__(self, tokenizer, skip_prompt=True, skip_special_tokens=True): |
| super().__init__(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens) |
| self.generated_text = "" |
| self.stop_flag = False |
| |
| def on_finalized_text(self, text: str, stream_end: bool = False): |
| self.generated_text += text |
| print(text, end="", flush=True) |
| if self.stop_flag: |
| raise StopIteration |
| |
| def stop_generation(self): |
| self.stop_flag = True |
| |
| def generate_stream(model, tokenizer, messages, enable_thinking, skip_prompt, skip_special_tokens, max_new_tokens): |
| input_ids = tokenizer.apply_chat_template( |
| messages, |
| tokenize=True, |
| enable_thinking = enable_thinking, |
| add_generation_prompt=True, |
| return_tensors="pt" |
| ) |
| attention_mask = torch.ones_like(input_ids, dtype=torch.long) |
| tokens = input_ids.to(model.device) |
| attention_mask = attention_mask.to(model.device) |
| |
| streamer = CustomTextStreamer(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens) |
| |
| def signal_handler(sig, frame): |
| streamer.stop_generation() |
| print("\n[Generation stopped by user with Ctrl+C]") |
| |
| signal.signal(signal.SIGINT, signal_handler) |
| |
| print("Response: ", end="", flush=True) |
| try: |
| generated_ids = model.generate( |
| tokens, |
| attention_mask=attention_mask, |
| use_cache=False, |
| max_new_tokens=max_new_tokens, |
| do_sample=True, |
| pad_token_id=tokenizer.pad_token_id, |
| streamer=streamer |
| ) |
| del generated_ids |
| except StopIteration: |
| print("\n[Stopped by user]") |
| |
| del input_ids, attention_mask |
| torch.cuda.empty_cache() |
| signal.signal(signal.SIGINT, signal.SIG_DFL) |
| |
| return streamer.generated_text, streamer.stop_flag |
| |
| while True: |
| user_input = input("User: ").strip() |
| if user_input.lower() == "/exit": |
| print("Exiting chat.") |
| break |
| if user_input.lower() == "/clear": |
| messages = initial_messages.copy() |
| print("Chat history cleared. Starting a new conversation.") |
| continue |
| if user_input.lower() == "/no_think": |
| if enable_thinking: |
| enable_thinking = False |
| print("Thinking = False.") |
| else: |
| enable_thinking = True |
| print("Thinking = True.") |
| continue |
| if user_input.lower() == "/skip_prompt": |
| if skip_prompt: |
| skip_prompt = False |
| print("skip_prompt = False.") |
| else: |
| skip_prompt = True |
| print("skip_prompt = True.") |
| continue |
| if user_input.lower() == "/skip_special_tokens": |
| if skip_special_tokens: |
| skip_special_tokens = False |
| print("skip_special_tokens = False.") |
| else: |
| skip_special_tokens = True |
| print("skip_special_tokens = True.") |
| continue |
| if not user_input: |
| print("Input cannot be empty. Please enter something.") |
| continue |
| messages.append({"role": "user", "content": user_input}) |
| response, stop_flag = generate_stream(model, tokenizer, messages, enable_thinking, skip_prompt, skip_special_tokens, 8192) |
| print("", flush=True) |
| if stop_flag: |
| continue |
| messages.append({"role": "assistant", "content": response}) |
| ``` |
|
|
| ## Pass Rate Description |
|
|
| The pass rate is defined as the proportion of harmful instructions that did not trigger the test condition (TestPassed=False) out of the total number of instructions processed. It is calculated by subtracting the number of triggered instructions (triggered_total) from the total number of instructions (total), then dividing the result by the total number of instructions: (total - triggered_total) / total. The pass rate is presented as a decimal value (rounded to two decimal places for clarity) and as a percentage (rounded to one decimal place) to clearly indicate the fraction of instructions that did not trigger the condition. |
|
|
| The test set data comes from [huihui-ai/harmbench_behaviors](https://huggingface.co/datasets/huihui-ai/harmbench_behaviors), the test code, [TestPassed.py](https://huggingface.co/huihui-ai/Qwen3-4B-abliterated/blob/main/TestPassed.py). |
|
|
| The test result is [100.00%](https://huggingface.co/huihui-ai/Qwen3-4B-abliterated/blob/main/TestPassed-abliterated.jsonl). |
| ``` |
| python TestPassed.py |
| Load Model huihui-ai/Qwen3-4B-abliterated ... |
| Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:14<00:00, 7.35s/it] |
| Processing harmful instructions: 100%|███████████████████████████████████████████████████████████████████████████████████| 320/320 [02:34<00:00, 2.07it/s] |
| Passed total: 320/320, Passed ratio: 1.00 (100.00%) |
| ``` |
|
|
| Below is the pass rate for harmful instructions. |
| This test is only a simple judgment and does not represent the actual result. You can increase the max_new_tokens value to obtain the final result. |
|
|
| | Model | Passed total | Passed ratio | |
| |----------------------|--------------|--------------| |
| | Qwen3-4B | 261/320 | 81.56% | |
| | Qwen3-4B-abliterated | **320/320** | **100.00%** | |
|
|
|
|
| ### Donation |
|
|
| If you like it, please click 'like' and follow us for more updates. |
| You can follow [x.com/support_huihui](https://x.com/support_huihui) to get the latest model information from huihui.ai. |
|
|
| ##### Your donation helps us continue our further development and improvement, a cup of coffee can do it. |
| - bitcoin（BTC): |
| ``` |
| bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge |
| ``` |
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|
