--- license: apache-2.0 pipeline_tag: text-generation library_name: transformers base_model: - a-m-team/AM-Thinking-v1 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/AM-Thinking-v1-abliterated This is an uncensored version of [a-m-team/AM-Thinking-v1](https://huggingface.co/a-m-team/AM-Thinking-v1) 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. ## ollama You can use [huihui_ai/am-thinking-abliterate](https://ollama.com/huihui_ai/am-thinking-abliterate) directly, ``` ollama run huihui_ai/am-thinking-abliterate ``` ## Usage You can use this model in your applications by loading it with Hugging Face's `transformers` library: You can try using **/no_think** to toggle think mode, but it’s not guaranteed to work every time. ```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/AM-Thinking-v1-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_int14_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 messages = [] enable_thinking = True skip_prompt=True skip_special_tokens=True def apply_chat_template(tokenizer, messages, enable_thinking, add_generation_prompt=True): input_ids = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=add_generation_prompt, ) if not enable_thinking: input_ids += "\n\n\n\n" return input_ids 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): formatted_prompt = apply_chat_template(tokenizer, messages, enable_thinking) input_ids = tokenizer( formatted_prompt, return_tensors="pt", return_attention_mask=True, padding=False ) tokens = input_ids['input_ids'].to(model.device) attention_mask = input_ids['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 = [] 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, 14192) print("", flush=True) if stop_flag: continue messages.append({"role": "assistant", "content": response}) ``` ## Specific usage You can achieve better results using AblationDecoderLayer. For specific usage, please refer to the file [load-AM-Thinking-v1-abliterated.py](https://huggingface.co/huihui-ai/AM-Thinking-v1-abliterated/blob/main/load-AM-Thinking-v1-abliterated.py). The candidate layers can be 39(final_refusal_dir-39.pt). You can try using **/no_think** to toggle think mode. ### Donation If you like it, please click 'like' and follow us for more updates. 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