--- library_name: transformers license: other license_name: lfm1.0 license_link: LICENSE language: - en - ar - zh - fr - de - ja - ko - es pipeline_tag: text-generation tags: - liquid - lfm2.5 - edge - abliterated - uncensored base_model: LiquidAI/LFM2.5-1.2B-Instruct --- # huihui-ai/Huihui-LFM2.5-1.2B-Instruct-abliterated This is an uncensored version of [LiquidAI/LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct) 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 Q4_K_M may contain duplicates; it is recommended to use the bf16 version. You can use [huihui_ai/lfm2.5-abliterated:1.2b-instruct](https://ollama.com/huihui_ai/lfm2.5-abliterated:1.2b-instruct) directly, ``` ollama run huihui_ai/lfm2.5-abliterated:1.2b-instruct ``` ## 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 import torch import os import signal import random import numpy as np import time 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-LFM2.5-1.2B-Instruct-abliterated" print(f"Load Model {NEW_MODEL_ID} ... ") model = AutoModelForCausalLM.from_pretrained( NEW_MODEL_ID, device_map="auto", trust_remote_code=True, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, ) tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True) messages = [] nothink = False 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.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() self.generated_text += text self.token_count += 1 if self.think_tokens_count == 0 and "" in self.generated_text: self.think_tokens_count = self.token_count 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 "think_tokens_count": self.think_tokens_count, "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, 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"\nnothink: {nothink}") 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() == "/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 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, nothink, 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}) ``` ### 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 If you like it, please click 'like' and follow us for more updates. 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