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# pip install accelerate
from transformers import AutoProcessor, Gemma3ForConditionalGeneration, TextIteratorStreamer
from PIL import Image
import requests
import torch
from threading import Thread
import logging
import time
import pynvml
class Gemma:
def __init__(self, model_id):
self.model_id = model_id
self.model = Gemma3ForConditionalGeneration.from_pretrained(
model_id, device_map="auto", torch_dtype=torch.bfloat16
).eval()
self.processor = AutoProcessor.from_pretrained(model_id)
self.handle = None
if torch.cuda.is_available():
try:
pynvml.nvmlInit()
device_id = next(self.model.parameters()).device.index
self.handle = pynvml.nvmlDeviceGetHandleByIndex(device_id)
except Exception as e:
logging.error(f"Failed to initialize NVML: {e}")
def __del__(self):
if self.handle:
try:
pynvml.nvmlShutdown()
except:
pass
def generate(self, video, prompt):
start_time = time.time()
messages = [
{
"role": "system",
"content": [{"type": "text", "text": "You are a helpful assistant."}]
},
{
"role": "user",
"content": [
{"type": "text", "text": prompt}]
}
]
for image in video:
messages[1]["content"].append({"type": "image", "image": image})
print(messages)
inputs = self.processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True,
return_dict=True, return_tensors="pt"
).to(self.model.device)
logging.info(f"Prompt token length: {len(inputs.input_ids[0])}")
streamer = TextIteratorStreamer(self.processor, skip_prompt=True, skip_special_tokens=True)
generation_kwargs = dict(
**inputs,
streamer=streamer,
max_new_tokens=512
)
thread = Thread(target=self.model.generate, kwargs=generation_kwargs)
thread.start()
full_response = ""
print("Response: ", end="")
first_token_time = None
for new_text in streamer:
if first_token_time is None:
first_token_time = time.time()
full_response += new_text
print(new_text, end="", flush=True)
print()
thread.join()
end_time = time.time()
if first_token_time is not None:
generation_time = end_time - first_token_time
else:
generation_time = 0
num_generated_tokens = len(self.processor.tokenizer(full_response).input_ids)
tokens_per_second = num_generated_tokens / generation_time if generation_time > 0 else 0
peak_memory_mb = 0
if self.handle:
mem_info = pynvml.nvmlDeviceGetMemoryInfo(self.handle)
peak_memory_mb = mem_info.used / (1024 * 1024)
return {
"response": full_response,
"tokens_per_second": tokens_per_second,
"peak_gpu_memory_mb": peak_memory_mb,
"num_generated_tokens": num_generated_tokens,
}
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