235 lines
8.4 KiB
Markdown
235 lines
8.4 KiB
Markdown
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---
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library_name: transformers
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license: other
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license_name: lfm1.0
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license_link: LICENSE
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language:
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- en
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- ar
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- zh
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- fr
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- de
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- ja
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- ko
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- es
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pipeline_tag: text-generation
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tags:
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- liquid
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- lfm2.5
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- edge
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- abliterated
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- uncensored
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base_model: LiquidAI/LFM2.5-1.2B-Thinking
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---
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# huihui-ai/Huihui-LFM2.5-1.2B-Thinking-abliterated
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This is an uncensored version of [LiquidAI/LFM2.5-1.2B-Thinking](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Thinking) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).
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This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.
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## ollama
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Q4_K_M may contain duplicates; it is recommended to use the bf16 version.
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You can use [huihui_ai/lfm2.5-abliterated](https://ollama.com/huihui_ai/lfm2.5-abliterated) directly,
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```
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ollama run huihui_ai/lfm2.5-abliterated
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```
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## Usage
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You can use this model in your applications by loading it with Hugging Face's `transformers` library:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
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import torch
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import os
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import signal
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import random
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import numpy as np
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import time
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cpu_count = os.cpu_count()
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print(f"Number of CPU cores in the system: {cpu_count}")
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half_cpu_count = cpu_count // 2
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os.environ["MKL_NUM_THREADS"] = str(half_cpu_count)
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os.environ["OMP_NUM_THREADS"] = str(half_cpu_count)
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torch.set_num_threads(half_cpu_count)
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print(f"PyTorch threads: {torch.get_num_threads()}")
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print(f"MKL threads: {os.getenv('MKL_NUM_THREADS')}")
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print(f"OMP threads: {os.getenv('OMP_NUM_THREADS')}")
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# Load the model and tokenizer
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NEW_MODEL_ID = "huihui-ai/Huihui-LFM2.5-1.2B-Thinking-abliterated"
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print(f"Load Model {NEW_MODEL_ID} ... ")
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model = AutoModelForCausalLM.from_pretrained(
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NEW_MODEL_ID,
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device_map="auto",
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trust_remote_code=True,
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torch_dtype=torch.bfloat16,
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low_cpu_mem_usage=True,
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)
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tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)
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messages = []
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nothink = False
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skip_prompt=True
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skip_special_tokens=True
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class CustomTextStreamer(TextStreamer):
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def __init__(self, tokenizer, skip_prompt=True, skip_special_tokens=True):
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super().__init__(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
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self.generated_text = ""
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self.stop_flag = False
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self.init_time = time.time() # Record initialization time
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self.end_time = None # To store end time
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self.first_token_time = None # To store first token generation time
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self.think_tokens_count = 0 # To track total think tokens
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self.token_count = 0 # To track total tokens
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def on_finalized_text(self, text: str, stream_end: bool = False):
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if self.first_token_time is None and text.strip(): # Set first token time on first non-empty text
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self.first_token_time = time.time()
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self.generated_text += text
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self.token_count += 1
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if self.think_tokens_count == 0 and "</think>" in self.generated_text:
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self.think_tokens_count = self.token_count
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print(text, end="", flush=True)
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if stream_end:
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self.end_time = time.time() # Record end time when streaming ends
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if self.stop_flag:
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raise StopIteration
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def stop_generation(self):
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self.stop_flag = True
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self.end_time = time.time() # Record end time when generation is stopped
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def get_metrics(self):
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"""Returns initialization time, first token time, first token latency, end time, total time, total tokens, and tokens per second."""
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if self.end_time is None:
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self.end_time = time.time() # Set end time if not already set
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total_time = self.end_time - self.init_time # Total time from init to end
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tokens_per_second = self.token_count / total_time if total_time > 0 else 0
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first_token_latency = (self.first_token_time - self.init_time) if self.first_token_time is not None else None
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metrics = {
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"init_time": self.init_time,
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"first_token_time": self.first_token_time,
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"first_token_latency": first_token_latency,
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"end_time": self.end_time,
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"total_time": total_time, # Total time in seconds
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"think_tokens_count": self.think_tokens_count,
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"total_tokens": self.token_count,
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"tokens_per_second": tokens_per_second
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}
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return metrics
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def generate_stream(model, tokenizer, messages, nothink, skip_prompt, skip_special_tokens, max_new_tokens):
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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)
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model_inputs = tokenizer(
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[text],
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return_tensors="pt",
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).to(model.device)
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streamer = CustomTextStreamer(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
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def signal_handler(sig, frame):
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streamer.stop_generation()
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print("\n[Generation stopped by user with Ctrl+C]")
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signal.signal(signal.SIGINT, signal_handler)
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print("Response: ", end="", flush=True)
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try:
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens = max_new_tokens,
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streamer=streamer,
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)
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del generated_ids
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except StopIteration:
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print("\n[Stopped by user]")
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del model_inputs
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torch.cuda.empty_cache()
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signal.signal(signal.SIGINT, signal.SIG_DFL)
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return streamer.generated_text, streamer.stop_flag, streamer.get_metrics()
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while True:
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print(f"\nnothink: {nothink}")
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print(f"skip_prompt: {skip_prompt}")
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print(f"skip_special_tokens: {skip_special_tokens}")
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user_input = input("User: ").strip()
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if user_input.lower() == "/exit":
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print("Exiting chat.")
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break
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if user_input.lower() == "/clear":
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messages = []
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print("Chat history cleared. Starting a new conversation.")
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continue
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if user_input.lower() == "/nothink":
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nothink = not nothink
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continue
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if user_input.lower() == "/skip_prompt":
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skip_prompt = not skip_prompt
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continue
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if user_input.lower() == "/skip_special_tokens":
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skip_special_tokens = not skip_special_tokens
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continue
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if not user_input:
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print("Input cannot be empty. Please enter something.")
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continue
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messages.append({"role": "user", "content": user_input})
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response, stop_flag, metrics = generate_stream(model, tokenizer, messages, nothink, skip_prompt, skip_special_tokens, 40960)
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print("\n\nMetrics:")
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for key, value in metrics.items():
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print(f" {key}: {value}")
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print("", flush=True)
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if stop_flag:
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continue
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messages.append({"role": "assistant", "content": response})
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```
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### Usage Warnings
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- **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.
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- **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.
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- **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.
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- **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.
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- **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.
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- **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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### Donation
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If you like it, please click 'like' and follow us for more updates.
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You can follow [x.com/support_huihui](https://x.com/support_huihui) to get the latest model information from huihui.ai.
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##### Your donation helps us continue our further development and improvement, a cup of coffee can do it.
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- bitcoin（BTC):
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```
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bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge
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```
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- Support our work on Ko-fi (https://ko-fi.com/huihuiai)!
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