license: apache-2.0
language:

• en
  library_name: transformers
  pipeline_tag: text-generation
  base_model:
• samuelcardillo/Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled
  tags:
• qwen3
• reasoning
• distillation
• claude-opus
• abliterated
• uncensored

Quantized with my Bartowski-Data imatrix calibration.

huihui-ai/Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated

This is an uncensored version of samuelcardillo/Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled 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.

Usage

You can use this model in your applications by loading it with Hugging Face's transformers library:

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, BitsAndBytesConfig
import torch
import os
import signal
import random
import numpy as np
import time
import sys

if (
    "PYTORCH_ALLOC_CONF" not in os.environ
    and "PYTORCH_CUDA_ALLOC_CONF" not in os.environ
):
    print(f"PYTORCH_ALLOC_CONF.")
    os.environ["PYTORCH_ALLOC_CONF"] = "expandable_segments:True"

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
OLD_MODEL_ID = "huihui-ai/Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated"
sys.path.append(OLD_MODEL_ID)

print(f"Load Model {OLD_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(
    OLD_MODEL_ID,
    device_map="auto",
    trust_remote_code=True,
    torch_dtype="auto",
    low_cpu_mem_usage=True,
    #quantization_config=quant_config_4,
    #attn_implementation="flash_attention_2"
)

tokenizer = AutoTokenizer.from_pretrained(OLD_MODEL_ID, trust_remote_code=True)

messages = []
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
        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()
        self.generated_text += text

        self.token_count += 1
        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 stream_end:
            self.end_time = time.time()  # Record end time when streaming ends
        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
            "think_tokens_count": self.think_tokens_count,
            "total_tokens": self.token_count,
            "tokens_per_second": tokens_per_second
        }
        return metrics

def generate_stream(model, tokenizer, messages, skip_prompt, skip_special_tokens, max_new_tokens):
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True,
    )
    model_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(
            **model_inputs,
            #use_cache=False,
            #pad_token_id=tokenizer.eos_token_id,
            max_new_tokens = max_new_tokens,
            streamer=streamer,
        )
        del generated_ids
    except StopIteration:
        print("\n[Stopped by user]")

    del model_inputs
    torch.cuda.empty_cache()
    signal.signal(signal.SIGINT, signal.SIG_DFL)

    return streamer.generated_text, streamer.stop_flag, streamer.get_metrics()


while True:
    print(f"skip_prompt: {skip_prompt}")
    print(f"skip_special_tokens: {skip_special_tokens}")

    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 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, 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.strip()
    })

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.

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