| --- |
| library_name: transformers |
| license: apache-2.0 |
| license_link: https://huggingface.co/Qwen/Qwen3-Coder-Next/blob/main/LICENSE |
| pipeline_tag: text-generation |
| base_model: |
| - Qwen/Qwen3-Coder-Next |
| tags: |
| - abliterated |
| - uncensored |
| --- |
| |
| # huihui-ai/Huihui-Qwen3-Coder-Next-abliterated |
|
|
|
|
| This is an uncensored version of [Qwen/Qwen3-Coder-Next](https://huggingface.co/Qwen/Qwen3-Coder-Next) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/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. |
|
|
| ## ollama |
|
|
| Please use the latest version of [ollama 0.15.5](https://github.com/ollama/ollama/releases/tag/v0.15.5) |
|
|
| You can use [huihui_ai/qwen3-coder-next-abliterated](https://ollama.com/huihui_ai/qwen3-coder-next-abliterated) directly, |
| ``` |
| ollama run huihui_ai/qwen3-coder-next-abliterated |
| ``` |
|
|
| ## chat_template-vl.jinja |
| |
| We have added a new file named [chat_template-vl.jinja](https://huggingface.co/huihui-ai/Huihui-Qwen3-Coder-Next-abliterated/blob/main/chat_template-vl.jinja), which comes from the path `huihui-ai/Huihui-Qwen3-VL-30B-A3B-Instruct-abliterated`. |
| |
| The new file chat_template-vl.jinja is more compatible with using Tool Calling in [llama-server](https://github.com/ggml-org/llama.cpp/releases/tag/b7952), |
| especially when [opencode](https://github.com/anomalyco/opencode/releases/tag/v1.1.53) is involved. |
|
|
|
|
| ## Usage |
| You can use this model in your applications by loading it with Hugging Face's `transformers` library: |
|
|
| ```python |
| 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 |
| MODEL_ID = "huihui-ai/Huihui-Qwen3-Coder-Next-abliterated" |
| |
| print(f"Load Model {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( |
| MODEL_ID, |
| device_map="auto", |
| trust_remote_code=True, |
| torch_dtype="auto", |
| low_cpu_mem_usage=True, |
| quantization_config=quant_config_4, |
| ) |
| |
| tokenizer = AutoTokenizer.from_pretrained(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.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 |
| |
| 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, 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, |
| 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. |
|
|
|
|
| ### 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](https://ko-fi.com/huihuiai)! |
|
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|
