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Huihui-Qwen3.8-27B-abliterated

HF 源站

huihui-ai/Huihui-Qwen3.8-27B-abliterated is an uncensored open-source model hosted on Hugging Face.

Image-Text-to-TextsafetensorsLicense: apache-2.027Btransformersqwen3_5abliterateduncensoredhuihuiqwen3conversational
README.md说明 / 使用方法

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

这是 Qwen/Qwen3.8-27B 的去审查（uncensored）版本，通过 abliteration（拒绝抑制/特征消除）技术创建（有关该技术的更多信息，请参见 remove-refusals-with-transformers）。 这是一种简易的概念验证（PoC）实现，用于在不使用 TransformerLens 的情况下消除 LLM 模型的拒绝回答行为。

最新更新 (Latest update)

仅对第 18 到 51 层进行了消除消融处理（此前，前 15 层保留未消融），而其他层保持未消融状态。 这有助于保留原始模型更完整的性能表现。MTP 与视觉（visual）组件未作修改。

如果您希望下载先前的版本，只需运行以下命令：

hf download huihui-ai/Huihui-Qwen3.8-27B-abliterated --local-dir ./huihui-ai/Huihui-Qwen3.8-27B-abliterated --revision d42ca89

说明 (Note)

前 15 层保留未消融。MTP 与视觉（visual）模块未作修改。

ollama

请使用最新版本的 ollama

您可以直接使用 huihui_ai/Qwen3.8-abliterated：

ollama run huihui_ai/Qwen3.8-abliterated

使用方法 (Usage)

您可以在应用程序中使用此模型，通过 Hugging Face 的 transformers 库进行加载：

#!/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 "</think>" 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()

使用警告 (Usage Warnings)

  • 敏感或争议性输出风险：该模型的安全过滤机制已大幅削弱，可能会生成敏感、具争议性或不当的内容。用户应保持审慎，并严格审核生成的内容。

  • 不适合所有受众：由于内容过滤机制受限，模型的输出可能不适合在公开场合、由未成年人使用，或部署在对安全性要求极高的应用场景中。

  • 法律与道德责任：用户必须确保自身使用行为符合当地法律法规及道德伦理标准。生成的内容可能带有法律或道德风险，用户须自行承担由此产生的一切后果。

  • 研究与实验用途：建议将该模型用于学术研究、测试或受控环境中，避免直接应用于生产环境或面向公众的商业产品中。

  • 监控与审核建议：强烈建议用户对模型输出进行实时监控，并在必要时开展人工审核，以防范不当内容的传播。

  • 无默认安全保障：与标准模型不同，该模型未经严格的安全对齐与优化。对于因使用该模型而导致的任何后果，huihui.ai 概不承担任何责任。

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源站原文

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 created with abliteration (see 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

You can use 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:

#!/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 "</think>" 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!
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来源与版本

作者
huihui-ai
最近更新
未知
最新创建
2026/08/16
HF 趋势
31
数据快照
2026/09/16
版本
未知
许可证
apache-2.0