from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, BitsAndBytesConfig
import torch
import os
import signal
import random
import numpy as np
import time
import sys
if (
"PYTORCH_ALLOC_CONF" not in os.environ
and "PYTORCH_CUDA_ALLOC_CONF" not in os.environ
):
print(f"PYTORCH_ALLOC_CONF.")
os.environ["PYTORCH_ALLOC_CONF"] = "expandable_segments:True"
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')}")
MODEL_ID = "huihui-ai/Huihui-Qwen3-Coder-Next-abliterated"
print(f"Load Model {MODEL_ID} ... ")
quant_config_4 = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
llm_int8_enable_fp32_cpu_offload=True,
)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
device_map="auto",
trust_remote_code=True,
torch_dtype="auto",
low_cpu_mem_usage=True,
quantization_config=quant_config_4,
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, 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()
self.end_time = None
self.first_token_time = None
self.token_count = 0
def on_finalized_text(self, text: str, stream_end: bool = False):
if self.first_token_time is None and text.strip():
self.first_token_time = time.time()
self.generated_text += text
self.token_count += 1
print(text, end="", flush=True)
if stream_end:
self.end_time = time.time()
if self.stop_flag:
raise StopIteration
def stop_generation(self):
self.stop_flag = True
self.end_time = time.time()
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()
total_time = self.end_time - self.init_time
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_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):
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
model_inputs = tokenizer(
[text],
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(
**model_inputs,
max_new_tokens = max_new_tokens,
streamer=streamer,
)
del generated_ids
except StopIteration:
print("\n[Stopped by user]")
del model_inputs
torch.cuda.empty_cache()
signal.signal(signal.SIGINT, signal.SIG_DFL)
return streamer.generated_text, streamer.stop_flag, streamer.get_metrics()
while True:
print(f"skip_prompt: {skip_prompt}")
print(f"skip_special_tokens: {skip_special_tokens}")
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":
skip_prompt = not skip_prompt
continue
if user_input.lower() == "/skip_special_tokens":
skip_special_tokens = not skip_special_tokens
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.strip()
})