huihui-ai/Huihui-Qwen3-4B-Instruct-2507-abliterated

This is an uncensored version of Qwen/Qwen3-4B-Instruct-2507 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-abliterated:4b-instruct-2507-q4_K_M directly,

ollama run huihui_ai/qwen3-abliterated:4b-instruct-2507-q4_K_M

Usage

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

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-Qwen3-4B-Instruct-2507-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 = []
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, skip_prompt, skip_special_tokens, do_sample, max_new_tokens):
 input_ids = tokenizer.apply_chat_template(
 messages,
 tokenize=True,
 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()

while True:
 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() == "/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, skip_prompt, skip_special_tokens, do_sample, 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})

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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Base model

Qwen/Qwen3-4B-Instruct-2507

(1750)

20 items • Updated 29 days ago • 58