This is an uncensored version of Qwen/Qwen3-Coder-30B-A3B-Instruct 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.

Ablation was performed using a new and faster method, which yields better results.

ollama

You can use huihui_ai/qwen3-coder-abliterated directly,

ollama run huihui_ai/qwen3-coder-abliterated

Usage

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

python

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

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/Huihui-Qwen3-Coder-30B-A3B-Instruct-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_int8_enable_fp32_cpu_offload=True,
)

model = AutoModelForCausalLM.from_pretrained(
    NEW_MODEL_ID, 
    device_map="balanced", 
    trust_remote_code=True,
    quantization_config=quant_config_4,
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
)
#print(model)
#print(model.config)

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 = []
nothink = False
skip_prompt=True
skip_special_tokens=True
do_sample = 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.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
        # Count tokens in the generated text
        tokens = self.tokenizer.encode(text, add_special_tokens=False)
        self.token_count += len(tokens)
        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
            "total_tokens": self.token_count,
            "tokens_per_second": tokens_per_second
        }
        return metrics
        
def generate_stream(model, tokenizer, messages, nothink, skip_prompt, skip_special_tokens, do_sample, max_new_tokens):
    input_ids = tokenizer.apply_chat_template(
        messages,
        tokenize=True,
        enable_thinking = not nothink,
        add_generation_prompt=True,
        return_tensors="pt"
    )
    attention_mask = torch.ones_like(input_ids, dtype=torch.long)
    tokens = input_ids.to(model.device) 
    attention_mask = 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)

    generate_kwargs = {}
    if do_sample:
        generate_kwargs = {
              "do_sample": do_sample,
              "max_length": max_new_tokens,
              "temperature": 0.7,
              "top_k": 20,
              "top_p": 0.8,
              "repetition_penalty": 1.2,
              "no_repeat_ngram_size": 2
        }
    else:
        generate_kwargs = {
              "do_sample": do_sample,
              "max_length": max_new_tokens,
              "repetition_penalty": 1.2,
              "no_repeat_ngram_size": 2
        }
  
          
    print("Response: ", end="", flush=True)
    try:
        generated_ids = model.generate(
            tokens,
            attention_mask=attention_mask,
            #use_cache=False,
            pad_token_id=tokenizer.pad_token_id,
            streamer=streamer,
            **generate_kwargs
        )
        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, streamer.get_metrics()

# List to store activated expert indices
activated_experts = []

# Define hook function to capture gate_probs output
def hook_fn(module, input, output):
    # output is gate_probs, shape: [batch_size, sequence_length, num_experts]
    gate_probs = output
    # Compute top-1 expert indices (since only one expert is activated)
    _, topk_indices = gate_probs.topk(8, dim=-1)  # Take top-8
    # Flatten and store activated expert indices
    activated_experts.extend(topk_indices.squeeze(-1).view(-1).cpu().tolist())

hooks = []
for layer in model.model.layers:
    hooks.append(layer.mlp.gate.register_forward_hook(hook_fn))
  
while True:
    print(f"\nnothink: {nothink}")
    print(f"skip_prompt: {skip_prompt}")
    print(f"skip_special_tokens: {skip_special_tokens}")
    print(f"do_sample: {do_sample}")
    
    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() == "/nothink":
        nothink = not nothink
        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() == "/do_sample":
        do_sample = not do_sample
        continue
    if not user_input:
        print("Input cannot be empty. Please enter something.")
        continue
    

    messages.append({"role": "user", "content": user_input})
    activated_experts = []
    response, stop_flag, metrics = generate_stream(model, tokenizer, messages, nothink, skip_prompt, skip_special_tokens, do_sample, 40960)
    print("\n\nMetrics:")
    for key, value in metrics.items():
        print(f"  {key}: {value}")

    # Count the frequency of each activated expert
    expert_counts = Counter(activated_experts)

    # Print activation statistics
    print("\nActivated Expert Statistics:")
    for expert_idx, count in sorted(expert_counts.items()):
        print(f"Expert {expert_idx}: {count} times")
        
    print("", flush=True)
    if stop_flag:
        continue
    messages.append({"role": "assistant", "content": response})

# Remove all hooks after inference
for h in hooks: h.remove()

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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Usage

Usage Warnings

Donation

Your donation helps us continue our further development and improvement, a cup of coffee can do it.

deewu0809/
Huihui-Qwen3-Coder-30B-A3B-Instruct-abliterated