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huihui-ai
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Model Details
Model Provider
huihui-ai
Model Tree
Qwen/Qwen3.8-27B
Input Modalities
Output Modalities
Supported Functionality
The first 15 layers were retained without ablation. MTP and visual has not been modified.
Please use the latest version of ollama
You can use huihui_ai/Qwen3.8-abliterated directly,
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ollama run huihui_ai/Qwen3.8-abliterated
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 argparsefrom transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamerimport torchimport osimport signalimport timedef 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 // 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')}")args = parse_args()# Load the model and tokenizerprint(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 = Falseself.init_time = time.time() # Record initialization timeself.end_time = None # To store end timeself.first_token_time = None # To store first token generation timeself.think_tokens_count = 0 # To track total think tokensself.token_count = 0 # To track total tokensdef 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 textself.first_token_time = time.time()if stream_end:self.end_time = time.time() # Record end time when streaming endsself.generated_text += texttokens = self.tokenizer.encode(text, add_special_tokens=False)self.token_count += len(tokens)if self.think_tokens_count == 0 and "</think>" in self.generated_text:self.think_tokens_count = self.token_countprint(text, end="", flush=True)if self.stop_flag:raise StopIterationdef stop_generation(self):self.stop_flag = Trueself.end_time = time.time() # Record end time when generation is stoppeddef 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 settotal_time = self.end_time - self.init_time # Total time from init to endtokens_per_second = self.token_count / total_time if total_time > 0 else 0first_token_latency = (self.first_token_time - self.init_time) if self.first_token_time is not None else Nonemetrics = {"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 metricsdef 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_idsexcept StopIteration:print("\n[Stopped by user]")del inputstorch.cuda.empty_cache()signal.signal(signal.SIGINT, signal.SIG_DFL)return streamer.generated_text, streamer.stop_flag, streamer.get_metrics()skip_prompt=Trueskip_special_tokens=Trueenable_thinking=Falsewhile 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.")breakif user_input.lower() == "/clear":messages = []print("Chat history cleared. Starting a new conversation.")continueif user_input.lower() == "/skip_prompt":skip_prompt = not skip_promptcontinueif user_input.lower() == "/skip_special_tokens":skip_special_tokens = not skip_special_tokenscontinueif user_input.lower() == "/enable_thinking":enable_thinking = not enable_thinkingcontinueif not user_input:print("Input cannot be empty. Please enter something.")continuemessages.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:continuemessages.append({"role": "assistant", "content": response})if __name__ == "__main__":main()
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.
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