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README.md

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+---
+library_name: transformers
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE
+pipeline_tag: text-generation
+base_model:
+- Qwen/Qwen3-0.6B
+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/Qwen3-0.6B-abliterated
+
+
+This is an uncensored version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) 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.
+
+
+## ollama
+You can toggle non-thinking mode in Ollama by typing /no_think after the prompt.
+
+You can use [huihui_ai/qwen3-abliterated:0.6b](https://ollama.com/huihui_ai/qwen3-abliterated:0.6b) directly, 
+```
+ollama run huihui_ai/qwen3-abliterated:0.6b
+```
+
+
+## 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/Qwen3-0.6B-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_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/Qwen3-0.6B-abliterated/blob/main/TestPassed.py).
+
+The test result is [100.00%](https://huggingface.co/huihui-ai/Qwen3-0.6B-abliterated/blob/main/TestPassed-abliterated.jsonl).
+```
+python TestPassed.py
+Load 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%**  |
+
+
+### 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. xAI bears no responsibility for any consequences arising from its use.
+
+
+
+### 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
+```