huihui-ai/Huihui-GLM-4.7-Flash-abliterated

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huihui-ai/Huihui-GLM-4.7-Flash-abliterated

This is an uncensored version of zai-org/GLM-4.7-Flash 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.

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You can use huihui_ai/glm-4.7-flash-abliterated directly,

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 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()

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:

Support our work on Ko-fi!

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 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()

#!/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()

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:

Support our work on Ko-fi!

#!/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()