--- library_name: transformers license: apache-2.0 pipeline_tag: image-text-to-text base_model: - Qwen/Qwen3.8-27B tags: - abliterated - uncensored - huihui - qwen3 --- # huihui-ai/Huihui-Qwen3.8-27B-abliterated This is an uncensored version of [Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B) 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. ## Latest update Only layers **18 to 51** have been ablated (Previously, The first 15 layers were retained without ablation), while the other layers remain unablated. This helps retain more of the original model’s performance. MTP and visual has not been modified. If you want to download a previous version, just use the following command: ``` hf download huihui-ai/Huihui-Qwen3.8-27B-abliterated --local-dir ./huihui-ai/Huihui-Qwen3.8-27B-abliterated --revision d42ca89 ``` ## Note The first 15 layers were retained without ablation. MTP and visual has not been modified. ## ollama Please use the latest version of [ollama](https://github.com/ollama/ollama/releases) You can use [huihui_ai/Qwen3.8-abliterated](https://ollama.com/huihui_ai/Qwen3.8-abliterated) directly, ``` ollama run huihui_ai/Qwen3.8-abliterated ``` ## Usage You can use this model in your applications by loading it with Hugging Face's `transformers` library: ```python #!/usr/bin/env python # -*- coding: utf-8 -*- import argparse from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer import torch import os import signal import time def parse_args(): parser = argparse.ArgumentParser( description="Merge LoRA weights into huihui-ai/Huihui-Qwen3.8-27B-abliterated base model and save the full model." ) parser.add_argument( "--base_model", type=str, default="huihui-ai/Huihui-Qwen3.8-27B-abliterated", help="HuggingFace repo or local path of the base model.", ) parser.add_argument( "--dtype", type=str, default="bfloat16", choices=["float16", "bfloat16", "float32"], help="Data type for loading the base model (default: bfloat16).", ) parser.add_argument( "--device_map", type=str, default="auto", help="Device map for model loading (e.g. 'cpu', 'auto').", ) return parser.parse_args() def main(): 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')}") args = parse_args() # Load the model and tokenizer print(f"Load Model {args.base_model} ... ") torch_dtype = { "float16": torch.float16, "bfloat16": torch.bfloat16, "float32": torch.float32, }[args.dtype] model = AutoModelForCausalLM.from_pretrained( args.base_model, dtype=torch_dtype, device_map=args.device_map, trust_remote_code=True, low_cpu_mem_usage=True, ) tokenizer = AutoTokenizer.from_pretrained(args.base_model, trust_remote_code=True) messages = [] 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 self.init_time = time.time() # Record initialization time self.end_time = None # To store end time self.first_token_time = None # To store first token generation time self.think_tokens_count = 0 # To track total think tokens self.token_count = 0 # To track total tokens def on_finalized_text(self, text: str, stream_end: bool = False): if self.first_token_time is None and text.strip(): # Set first token time on first non-empty text self.first_token_time = time.time() if stream_end: self.end_time = time.time() # Record end time when streaming ends self.generated_text += text tokens = self.tokenizer.encode(text, add_special_tokens=False) self.token_count += len(tokens) if self.think_tokens_count == 0 and "" in self.generated_text: self.think_tokens_count = self.token_count print(text, end="", flush=True) if self.stop_flag: raise StopIteration def stop_generation(self): self.stop_flag = True self.end_time = time.time() # Record end time when generation is stopped def get_metrics(self): """Returns initialization time, first token time, first token latency, end time, total time, total tokens, and tokens per second.""" if self.end_time is None: self.end_time = time.time() # Set end time if not already set total_time = self.end_time - self.init_time # Total time from init to end tokens_per_second = self.token_count / total_time if total_time > 0 else 0 first_token_latency = (self.first_token_time - self.init_time) if self.first_token_time is not None else None metrics = { "init_time": self.init_time, "first_token_time": self.first_token_time, "first_token_latency": first_token_latency, "end_time": self.end_time, "total_time": total_time, # Total time in seconds "total_tokens": self.token_count, "think_tokens_count": self.think_tokens_count, "real_tokens_count": self.token_count - self.think_tokens_count, "tokens_per_second": tokens_per_second } return metrics def generate_stream(model, tokenizer, messages, enable_thinking, skip_prompt, skip_special_tokens, max_new_tokens): text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=enable_thinking ) inputs = tokenizer( text, return_tensors="pt", ).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( **inputs, max_new_tokens=max_new_tokens, streamer=streamer ) del generated_ids except StopIteration: print("\n[Stopped by user]") del inputs torch.cuda.empty_cache() signal.signal(signal.SIGINT, signal.SIG_DFL) return streamer.generated_text, streamer.stop_flag, streamer.get_metrics() skip_prompt=True skip_special_tokens=True enable_thinking=False while True: print(f"skip_prompt = {skip_prompt}.") print(f"skip_special_tokens = {skip_special_tokens}.") print(f"enable_thinking = {enable_thinking}.") 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() == "/skip_prompt": skip_prompt = not skip_prompt continue if user_input.lower() == "/skip_special_tokens": skip_special_tokens = not skip_special_tokens continue if user_input.lower() == "/enable_thinking": enable_thinking = not enable_thinking continue if not user_input: print("Input cannot be empty. Please enter something.") continue messages.append({"role": "user", "content": user_input}) response, stop_flag, metrics = generate_stream(model, tokenizer, messages, enable_thinking, skip_prompt, skip_special_tokens, 40960) print("\n\nMetrics:") for key, value in metrics.items(): print(f" {key}: {value}") print("", flush=True) if stop_flag: continue messages.append({"role": "assistant", "content": response}) if __name__ == "__main__": main() ``` ### 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. ### Donation ##### Your donation helps us continue our further development and improvement, a cup of coffee can do it. - bitcoin: ``` bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge ``` - Support our work on [Ko-fi](https://ko-fi.com/huihuiai)!