--- license: apache-2.0 library_name: transformers base_model: - ibm-granite/granite-4.1-3b tags: - language - granite-4.1 - abliterated - uncensored --- # huihui-ai/Huihui-granite-4.1-3b-abliterated This is an uncensored version of [ibm-granite/granite-4.1-3b](https://huggingface.co/ibm-granite/granite-4.1-3b) 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](https://github.com/ollama/ollama/releases/tag) You can use [huihui_ai/granite4.1-abliterated:3b](https://ollama.com/huihui_ai/granite4.1-abliterated:3b) directly, ``` ollama run huihui_ai/granite4.1-abliterated:3b ``` ## Usage You can use this model in your applications by loading it with Hugging Face's `transformers` library: ```python #!/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-granite-4.1-3b-abliterated base model and save the full model." ) parser.add_argument( "--base_model", type=str, default="huihui-ai/Huihui-granite-4.1-3b-abliterated", help="HuggingFace repo or local path of the base model.", ) parser.add_argument( "--dtype", type=str, default="bfloat16", choices=["auto", "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 = { "auto": "auto", "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, ) tokenizer = AutoTokenizer.from_pretrained(args.base_model) 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() if stream_end: self.end_time = time.time() # Record end time when streaming ends self.generated_text += text self.token_count += 1 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, "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, ) 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() messages = [] skip_prompt=True skip_special_tokens=True while True: print(f"skip_prompt = {skip_prompt}.") print(f"skip_special_tokens = {skip_special_tokens}.\n") 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, 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](https://ko-fi.com/huihuiai)!