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huihui-ai
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Model Details
Model Provider
huihui-ai
Model Tree
Qwen/Qwen3-0.6B
Input Modalities
Output Modalities
Supported Functionality
You can toggle non-thinking mode in Ollama by typing /no_think after the prompt.
You can use huihui_ai/qwen3-abliterated:0.6b directly,
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ollama run huihui_ai/qwen3-abliterated:0.6bYou can use this model in your applications by loading it with Hugging Face's transformers library:
python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamerimport torchimport osimport signal cpu_count = os.cpu_count()print(f"Number of CPU cores in the system: {cpu_count}")half_cpu_count = cpu_count // 2os.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 tokenizerNEW_MODEL_ID = "huihui-ai/Qwen3-0.6B-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_tokentokenizer.pad_token_id = tokenizer.eos_token_id initial_messages = [{"role": "system", "content": "You are a helpful assistant."}]messages = initial_messages.copy()enable_thinking = Trueskip_prompt=Trueskip_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})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, the test code, TestPassed.py.
The test result is 100.00%.
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python TestPassed.pyLoad Model huihui-ai/Qwen3-0.6B-abliterated ...Processing harmful instructions: 100%|███████████████████████████████████████████████████████████████████████████████████| 320/320 [01:13<00:00, 4.33it/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 |
|---|---|---|
| Qwen3-0.6B | 309/320 | 96.56% |
| Qwen3-0.6B-abliterated | 320/320 | 100.00% |
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. xAI bears no responsibility for any consequences arising from its use.
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You can follow x.com/support_huihui to get the latest model information from huihui.ai.
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bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70gefrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamerimport torchimport osimport signal cpu_count = os.cpu_count()print(f"Number of CPU cores in the system: {cpu_count}")half_cpu_count = cpu_count // 2os.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 tokenizerNEW_MODEL_ID = "huihui-ai/Qwen3-0.6B-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_tokentokenizer.pad_token_id = tokenizer.eos_token_id initial_messages = [{"role": "system", "content": "You are a helpful assistant."}]messages = initial_messages.copy()enable_thinking = Trueskip_prompt=Trueskip_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})python TestPassed.pyLoad Model huihui-ai/Qwen3-0.6B-abliterated ...Processing harmful instructions: 100%|███████████████████████████████████████████████████████████████████████████████████| 320/320 [01:13<00:00, 4.33it/s]Passed total: 320/320, Passed ratio: 1.00 (100.00%)