No official torrent yet — requesting it tells the flock what to host next. You cannot seed a catalog-only page.
Cataloged from huihui-ai/Huihui-MiniCPM5-2B-abliterated
All models by huihui-ai →This is an uncensored version of openbmb/MiniCPM5-2B 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.
Layers 6-37, 40 are being ablated (0-based indexing).
This is just a new ablation method of ours, aimed at probing which layers have a greater impact on the ablation. Since this model is too small, the performance is still insufficient.
You can use this model in your applications by loading it with Hugging Face's transformers library:
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.
#!/usr/bin/env python# -*- coding: utf-8 -*-
import torchimport argparsefrom transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamerimport osimport signalimport time
def parse_args(): parser = argparse.ArgumentParser( description="Load HuggingFace repo or local path of the base model" ) parser.add_argument( "--base_model", type=str, default="huihui-ai/Huihui-MiniCPM5-2B-abliterated", help="HuggingFace repo or local path of the base model.", ) parser.add_argument( "--dtype", type=str, default="bfloat16", choices=["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 = { "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, low_cpu_mem_usage=True, )
tokenizer = AutoTokenizer.from_pretrained(args.base_model, trust_remote_code=True)
messages = [] 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() if stream_end: self.end_time = time.time() # Record end time when streaming ends
self.generated_text += text tokens = self.tokenizer.encode(text, add_special_tokens=False) self.token_count += len(tokens) 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 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, "think_tokens_count": self.think_tokens_count, "real_tokens_count": self.token_count - self.think_tokens_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, enable_thinking=enable_thinking ) 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()
skip_prompt=True skip_special_tokens=True enable_thinking=False while True: print(f"skip_prompt = {skip_prompt}.") print(f"skip_special_tokens = {skip_special_tokens}.") print(f"enable_thinking = {enable_thinking}.")
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() == "/enable_thinking": enable_thinking = not enable_thinking 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() bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge