Why does running the following code keep downloading files from huggingface?

[renllll]

from llava.model.builder import load_pretrained_model
from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
from llava.conversation import conv_templates, SeparatorStyle

from PIL import Image
import requests
import copy
import torch

pretrained = "llama3-llava-next-8b"
model_name = "llava_llama3"
device = "cuda"
device_map = "auto"
tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, device_map=device_map) # Add any other thing you want to pass in llava_model_args

model.eval()
model.tie_weights()

image = Image.open("2.jpeg")
image_tensor = process_images([image], image_processor, model.config)
image_tensor = [_image.to(dtype=torch.float16, device=device) for _image in image_tensor]

conv_template = "llava_llama_3" # Make sure you use correct chat template for different models
question = DEFAULT_IMAGE_TOKEN + "\nWhat is shown in this image?"
conv = copy.deepcopy(conv_templates[conv_template])
conv.append_message(conv.roles[0], question)
conv.append_message(conv.roles[1], None)
prompt_question = conv.get_prompt()

input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device)
image_sizes = [image.size]

cont = model.generate(
input_ids,
images=image_tensor,
image_sizes=image_sizes,
do_sample=False,
temperature=0,
max_new_tokens=256,
)
text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)
print(text_outputs)

The image shows a radar chart, also known as a spider chart or a web chart, which is a type of graph used to display multivariate data in the form of a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point. Each axis represents a different variable, and the values are plotted along each axis and connected to form a polygon.\n\nIn this particular radar chart, there are several axes labeled with different variables, such as "MM-Vet," "LLaVA-Bench," "SEED-Bench," "MMBench-CN," "MMBench," "TextVQA," "VizWiz," "GQA," "BLIP-2," "InstructBLIP," "Owen-VL-Chat," and "LLaVA-1.5." These labels suggest that the chart is comparing the performance of different models or systems across various benchmarks or tasks, such as machine translation, visual question answering, and text-based question answering.\n\nThe chart is color-coded, with each color representing a different model or system. The points on the chart are connected to form a polygon, which shows the relative performance of each model across the different benchmarks. The closer the point is to the outer edge of the

The error is reported as follows
raise EnvironmentError(
OSError: We couldn't connect to 'https://huggingface.co' to load this file, couldn't find it in the cached files and it looks like meta-llama/Meta-Llama-3-8B-Instruct is not the path to a directory containing a file named config.json.
Checkout your internet connection or see how to run the library in offline mode at 'https://huggingface.co/docs/transformers/installation#offline-mode'.

[kun2001github]

I'm running into this issue as well，
I downloaded lmms-lab/llava-onevision-qwen2-0.5b-si and google/siglip-so400m-patch14-384，
But I don't know where to put it under the path, I want to ask about it.

[Davidwhw]

I'm running into this issue as well， I downloaded lmms-lab/llava-onevision-qwen2-0.5b-si and google/siglip-so400m-patch14-384， But I don't know where to put it under the path, I want to ask about it.

The same problem. Have you found the solution?

[kun2001github]

I'm running into this issue as well， I downloaded lmms-lab/llava-onevision-qwen2-0.5b-si and google/siglip-so400m-patch14-384， But I don't know where to put it under the path, I want to ask about it.

The same problem. Have you found the solution?

No, I'm still looking for a solution

[Davidwhw]

I'm running into this issue as well， I downloaded lmms-lab/llava-onevision-qwen2-0.5b-si and google/siglip-so400m-patch14-384， But I don't know where to put it under the path, I want to ask about it.

The same problem. Have you found the solution?

No, I'm still looking for a solution

Change the file llava/model/multimodal_encoder/builder.py to read the local path.