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transformers library:1from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
2import torch
3import os
4import signal
5import random
6import numpy as np
7import time
8
9cpu_count = os.cpu_count()
10print(f"Number of CPU cores in the system: {cpu_count}")
11half_cpu_count = cpu_count // 2
12os.environ["MKL_NUM_THREADS"] = str(half_cpu_count)
13os.environ["OMP_NUM_THREADS"] = str(half_cpu_count)
14torch.set_num_threads(half_cpu_count)
15
16print(f"PyTorch threads: {torch.get_num_threads()}")
17print(f"MKL threads: {os.getenv('MKL_NUM_THREADS')}")
18print(f"OMP threads: {os.getenv('OMP_NUM_THREADS')}")
19
20# Load the model and tokenizer
21NEW_MODEL_ID = "huihui-ai/Huihui-LFM2.5-8B-A1B-abliterated"
22print(f"Load Model {NEW_MODEL_ID} ... ")
23model = AutoModelForCausalLM.from_pretrained(
24 NEW_MODEL_ID,
25 device_map="auto",
26 trust_remote_code=True,
27 torch_dtype=torch.bfloat16,
28 low_cpu_mem_usage=True,
29)
30tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)
31
32messages = []
33skip_prompt=True
34skip_special_tokens=True
35
36class CustomTextStreamer(TextStreamer):
37 def __init__(self, tokenizer, skip_prompt=True, skip_special_tokens=True):
38 super().__init__(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
39 self.generated_text = ""
40 self.stop_flag = False
41 self.init_time = time.time() # Record initialization time
42 self.end_time = None # To store end time
43 self.first_token_time = None # To store first token generation time
44 self.token_count = 0 # To track total tokens
45
46 def on_finalized_text(self, text: str, stream_end: bool = False):
47 if self.first_token_time is None and text.strip(): # Set first token time on first non-empty text
48 self.first_token_time = time.time()
49 self.generated_text += text
50
51 tokens = self.tokenizer.encode(text, add_special_tokens=False)
52 self.token_count += len(tokens)
53 print(text, end="", flush=True)
54 if stream_end:
55 self.end_time = time.time() # Record end time when streaming ends
56 if self.stop_flag:
57 raise StopIteration
58
59 def stop_generation(self):
60 self.stop_flag = True
61 self.end_time = time.time() # Record end time when generation is stopped
62
63 def get_metrics(self):
64 """Returns initialization time, first token time, first token latency, end time, total time, total tokens, and tokens per second."""
65 if self.end_time is None:
66 self.end_time = time.time() # Set end time if not already set
67 total_time = self.end_time - self.init_time # Total time from init to end
68 tokens_per_second = self.token_count / total_time if total_time > 0 else 0
69 first_token_latency = (self.first_token_time - self.init_time) if self.first_token_time is not None else None
70 metrics = {
71 "init_time": self.init_time,
72 "first_token_time": self.first_token_time,
73 "first_token_latency": first_token_latency,
74 "end_time": self.end_time,
75 "total_time": total_time, # Total time in seconds
76 "total_tokens": self.token_count,
77 "tokens_per_second": tokens_per_second
78 }
79 return metrics
80
81def generate_stream(model, tokenizer, messages, skip_prompt, skip_special_tokens, max_new_tokens):
82 model_inputs = tokenizer.apply_chat_template(
83 messages,
84 add_generation_prompt=True,
85 return_tensors="pt",
86 tokenize=True,
87 ).to(model.device)
88
89 streamer = CustomTextStreamer(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
90
91 def signal_handler(sig, frame):
92 streamer.stop_generation()
93 print("\n[Generation stopped by user with Ctrl+C]")
94
95 signal.signal(signal.SIGINT, signal_handler)
96
97 print("Response: ", end="", flush=True)
98 try:
99 generated_ids = model.generate(
100 **model_inputs,
101 #do_sample=True,
102 #temperature=0.3,
103 #min_p=0.15,
104 #repetition_penalty=1.05,
105 max_new_tokens = max_new_tokens,
106 streamer=streamer,
107 )
108 del generated_ids
109 except StopIteration:
110 print("\n[Stopped by user]")
111
112 del model_inputs
113 torch.cuda.empty_cache()
114 signal.signal(signal.SIGINT, signal.SIG_DFL)
115
116 return streamer.generated_text, streamer.stop_flag, streamer.get_metrics()
117
118
119while True:
120 print(f"skip_prompt: {skip_prompt}")
121 print(f"skip_special_tokens: {skip_special_tokens}")
122
123 user_input = input("User: ").strip()
124 if user_input.lower() == "/exit":
125 print("Exiting chat.")
126 break
127 if user_input.lower() == "/clear":
128 messages = []
129 print("Chat history cleared. Starting a new conversation.")
130 continue
131 if user_input.lower() == "/skip_prompt":
132 skip_prompt = not skip_prompt
133 continue
134 if user_input.lower() == "/skip_special_tokens":
135 skip_special_tokens = not skip_special_tokens
136 continue
137 if not user_input:
138 print("Input cannot be empty. Please enter something.")
139 continue
140
141
142 messages.append({"role": "user", "content": user_input})
143
144 response, stop_flag, metrics = generate_stream(model, tokenizer, messages, skip_prompt, skip_special_tokens, 40960)
145 print("\n\nMetrics:")
146 for key, value in metrics.items():
147 print(f" {key}: {value}")
148
149
150 print("", flush=True)
151 if stop_flag:
152 continue
153 messages.append({"role": "assistant", "content": response}) bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge