GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
kepom
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
kepom
Model Tree
zai-org/GLM-4.7-Flash
Input Modalities
Output Modalities
Supported Functionality
A new version is being uploaded. Please download it again. Please use the latest version of ollama 0.15.1
You can use huihui_ai/glm-4.7-flash-abliterated directly,
text
ollama run huihui_ai/glm-4.7-flash-abliteratedYou 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 argparsefrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamerimport torchimport osimport signalimport 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()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.
text
bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge#!/usr/bin/env python# -*- coding: utf-8 -*- import argparsefrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamerimport torchimport osimport signalimport 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()