| --- |
| library_name: transformers |
| pipeline_tag: text-generation |
| license: mit |
| language: |
| - en |
| - zh |
| base_model: |
| - zai-org/GLM-4.7-Flash |
| tags: |
| - abliterated |
| - uncensored |
| --- |
| |
| # huihui-ai/Huihui-GLM-4.7-Flash-abliterated |
|
|
|
|
| This is an uncensored version of [zai-org/GLM-4.7-Flash](https://huggingface.co/zai-org/GLM-4.7-Flash) 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 |
|
|
| A new version is being uploaded. Please download it again. |
| Please use the latest version of [ollama 0.15.1](https://github.com/ollama/ollama/releases/tag/v0.15.1) |
|
|
| You can use [huihui_ai/glm-4.7-flash-abliterated](https://ollama.com/huihui_ai/glm-4.7-flash-abliterated) directly, |
| ``` |
| ollama run huihui_ai/glm-4.7-flash-abliterated |
| ``` |
|
|
| ## 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, BitsAndBytesConfig, TextStreamer |
| import torch |
| import os |
| import signal |
| import time |
| |
| def parse_args(): |
| parser = argparse.ArgumentParser( |
| description="Merge LoRA weights into huihui-ai/Huihui-GLM-4.7-Flash-abliterated base model and save the full model." |
| ) |
| parser.add_argument( |
| "--base_model", |
| type=str, |
| default="huihui-ai/Huihui-GLM-4.7-Flash-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} ... ") |
| quant_config_4 = BitsAndBytesConfig( |
| load_in_4bit=True, |
| bnb_4bit_compute_dtype=torch.bfloat16, |
| bnb_4bit_quant_type="nf4" if args.device_map == "cpu" else "fp4", |
| bnb_4bit_use_double_quant=True, |
| llm_int8_enable_fp32_cpu_offload=True, |
| ) |
| |
| 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 = [] |
| 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() |
| 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, skip_prompt, skip_special_tokens, max_new_tokens): |
| inputs = tokenizer.apply_chat_template( |
| messages, |
| tokenize=True, |
| add_generation_prompt=True, |
| return_dict=True, |
| 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() |
| |
| while True: |
| 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": |
| if skip_prompt: |
| skip_prompt = False |
| print("skip_prompt = False.") |
| else: |
| skip_prompt = True |
| print("skip_prompt = True.") |
| continue |
| if user_input.lower() == "/skip_special_tokens": |
| if skip_special_tokens: |
| skip_special_tokens = False |
| print("skip_special_tokens = False.") |
| else: |
| skip_special_tokens = True |
| print("skip_special_tokens = True.") |
| 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}) |
| |
| 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)! |
|
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|
