NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE. about llava HOT 6 CLOSED

Hi @DifferentComputers, sorry that I just saw this comment. It is not a normal behavior of an empty model list. You need to start the gradio demo after the worker is fully loaded, so as to get the model list. Please let me know if you have further concerns.

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This seems to be an error in the backend.

1. can you see the model list on the top left?
2. There may be an error in the model worker, can you paste the error message here?

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I get the same error but I suspect it's because I may be running it on entirely inadequate hardware. Alternately, I may not have the model data installed correctly.

I don't see any model worker error. that would be appearing on the command line process, correct?

I don't see any "model list", at least not on the webpage where the "NETWORK ERROR" appears as every response to an attempt to use the chatBot.

from llava.

I met the same problem "NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.", but my model list is not empty, there is a model I have applied the delta before, but still the error, some details are as follow:

thank you!

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@corleytd Hi please see my response in #89, thanks.

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I have the folowing problem; actually I want load model from my pc

(llava) C:\Users\aymen\LLaVA>python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path C:/Users/aymen/llava-1.5-7b-hf
2024-03-04 12:43:53 | INFO | model_worker | args: Namespace(host='0.0.0.0', port=40000, worker_address='http://localhost:40000', controller_address='http://localhost:10000', model_path='C:/Users/aymen/llava-1.5-7b-hf', model_base=None, model_name=None, device='cuda', multi_modal=False, limit_model_concurrency=5, stream_interval=1, no_register=False, load_8bit=False, load_4bit=False, use_flash_attn=False)
2024-03-04 12:43:53 | INFO | model_worker | Loading the model llava-1.5-7b-hf on worker 62e548 ...
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
You are using a model of type llava to instantiate a model of type llava_llama. This is not supported for all configurations of models and can yield errors.
2024-03-04 12:43:53 | INFO | accelerate.utils.modeling | We will use 90% of the memory on device 0 for storing the model, and 10% for the buffer to avoid OOM. You can set max_memory in to a higher value to use more memory (at your own risk).
Loading checkpoint shards: 0%| | 0/3 [00:00<?, ?it/s]
Loading checkpoint shards: 33%|█████████████████████████████████████████████████▎ | 1/3 [00:00<00:00, 5.88it/s]
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 8.46it/s]
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 8.10it/s]
2024-03-04 12:54:33 | ERROR | stderr |
Some weights of LlavaLlamaForCausalLM were not initialized from the model checkpoint at C:/Users/aymen/llava-1.5-7b-hf and are newly initialized: ['layers.8.self_attn.o_proj.weight', 'layers.3.self_attn.o_proj.weight', 'layers.22.mlp.down_proj.weight', 'layers.29.mlp.gate_proj.weight', 'layers.1.self_attn.k_proj.weight', 'layers.0.mlp.up_proj.weight', 'layers.18.mlp.up_proj.weight', 'layers.19.mlp.down_proj.weight', 'layers.19.mlp.up_proj.weight', 'layers.22.mlp.gate_proj.weight', 'layers.27.self_attn.v_proj.weight', 'layers.18.mlp.gate_proj.weight', 'layers.8.mlp.down_proj.weight', 'layers.2.mlp.gate_proj.weight', 'layers.26.mlp.up_proj.weight', 'layers.0.self_attn.k_proj.weight', 'layers.17.self_attn.k_proj.weight', 'layers.20.mlp.up_proj.weight', 'layers.29.mlp.up_proj.weight', 'layers.23.self_attn.q_proj.weight', 'layers.19.self_attn.k_proj.weight', 'layers.20.post_attention_layernorm.weight', 'layers.24.mlp.up_proj.weight', 'layers.0.self_attn.o_proj.weight', 'layers.5.mlp.gate_proj.weight', 'layers.21.input_layernorm.weight', 'layers.12.input_layernorm.weight', 'layers.16.input_layernorm.weight', 'layers.31.self_attn.k_proj.weight', 'layers.19.self_attn.o_proj.weight', 'layers.9.self_attn.k_proj.weight', 'layers.2.post_attention_layernorm.weight', 'layers.15.self_attn.v_proj.weight', 'layers.20.mlp.down_proj.weight', 'layers.28.self_attn.k_proj.weight', 'layers.16.self_attn.q_proj.weight', 'layers.12.self_attn.k_proj.weight', 'layers.29.self_attn.q_proj.weight', 'layers.24.self_attn.k_proj.weight', 'layers.13.self_attn.q_proj.weight', 'layers.30.self_attn.k_proj.weight', 'layers.20.self_attn.o_proj.weight', 'layers.11.self_attn.v_proj.weight', 'layers.28.self_attn.v_proj.weight', 'layers.17.mlp.up_proj.weight', 'layers.24.self_attn.v_proj.weight', 'layers.1.self_attn.q_proj.weight', 'layers.6.mlp.down_proj.weight', 'layers.27.post_attention_layernorm.weight', 'layers.30.mlp.gate_proj.weight', 'layers.18.self_attn.o_proj.weight', 'layers.28.self_attn.q_proj.weight', 'layers.0.self_attn.v_proj.weight', 'layers.12.mlp.gate_proj.weight', 'layers.9.self_attn.q_proj.weight', 'layers.4.self_attn.q_proj.weight', 'layers.6.self_attn.o_proj.weight', 'layers.2.self_attn.v_proj.weight', 'layers.21.mlp.down_proj.weight', 'layers.31.self_attn.q_proj.weight', 'layers.14.self_attn.v_proj.weight', 'layers.11.mlp.up_proj.weight', 'layers.24.post_attention_layernorm.weight', 'layers.22.self_attn.k_proj.weight', 'layers.11.self_attn.k_proj.weight', 'layers.19.self_attn.q_proj.weight', 'layers.10.mlp.up_proj.weight', 'layers.22.mlp.up_proj.weight', 'layers.1.mlp.gate_proj.weight', 'layers.2.mlp.down_proj.weight', 'layers.24.input_layernorm.weight', 'layers.25.post_attention_layernorm.weight', 'layers.9.post_attention_layernorm.weight', 'layers.22.input_layernorm.weight', 'layers.3.self_attn.v_proj.weight', 'layers.5.self_attn.k_proj.weight', 'layers.12.self_attn.o_proj.weight', 'layers.15.self_attn.o_proj.weight', 'layers.12.self_attn.v_proj.weight', 'layers.24.self_attn.o_proj.weight', 'layers.23.self_attn.v_proj.weight', 'layers.28.mlp.gate_proj.weight', 'layers.14.self_attn.o_proj.weight', 'layers.10.self_attn.q_proj.weight', 'layers.11.mlp.down_proj.weight', 'layers.16.self_attn.k_proj.weight', 'layers.5.self_attn.o_proj.weight', 'layers.13.mlp.gate_proj.weight', 'layers.10.self_attn.v_proj.weight', 'layers.1.post_attention_layernorm.weight', 'layers.12.mlp.down_proj.weight', 'layers.13.self_attn.o_proj.weight', 'layers.8.mlp.up_proj.weight', 'layers.22.self_attn.v_proj.weight', 'layers.9.input_layernorm.weight', 'layers.25.self_attn.v_proj.weight', 'layers.23.input_layernorm.weight', 'layers.6.mlp.up_proj.weight', 'layers.7.mlp.up_proj.weight', 'layers.16.mlp.gate_proj.weight', 'layers.25.self_attn.k_proj.weight', 'layers.16.self_attn.o_proj.weight', 'layers.16.mlp.down_proj.weight', 'layers.9.mlp.up_proj.weight', 'layers.21.post_attention_layernorm.weight', 'layers.13.post_attention_layernorm.weight', 'layers.23.mlp.up_proj.weight', 'layers.30.self_attn.q_proj.weight', 'layers.30.self_attn.v_proj.weight', 'layers.1.self_attn.o_proj.weight', 'layers.11.self_attn.q_proj.weight', 'layers.6.self_attn.q_proj.weight', 'layers.7.self_attn.o_proj.weight', 'layers.20.input_layernorm.weight', 'layers.31.self_attn.v_proj.weight', 'layers.4.self_attn.o_proj.weight', 'layers.21.self_attn.q_proj.weight', 'layers.21.self_attn.k_proj.weight', 'layers.14.mlp.gate_proj.weight', 'layers.15.mlp.up_proj.weight', 'layers.15.mlp.down_proj.weight', 'layers.24.mlp.gate_proj.weight', 'layers.4.self_attn.k_proj.weight', 'layers.8.post_attention_layernorm.weight', 'layers.23.mlp.down_proj.weight', 'layers.8.input_layernorm.weight', 'layers.25.self_attn.o_proj.weight', 'layers.12.mlp.up_proj.weight', 'layers.25.mlp.gate_proj.weight', 'layers.29.mlp.down_proj.weight', 'layers.7.mlp.down_proj.weight', 'layers.1.input_layernorm.weight', 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'layers.8.self_attn.q_proj.weight', 'layers.27.input_layernorm.weight', 'layers.2.mlp.up_proj.weight', 'layers.29.self_attn.o_proj.weight', 'layers.4.mlp.gate_proj.weight', 'layers.29.post_attention_layernorm.weight', 'layers.31.mlp.gate_proj.weight', 'layers.26.post_attention_layernorm.weight', 'layers.26.input_layernorm.weight', 'layers.8.mlp.gate_proj.weight', 'layers.7.input_layernorm.weight', 'layers.9.mlp.down_proj.weight', 'layers.0.self_attn.q_proj.weight', 'layers.17.mlp.down_proj.weight', 'layers.5.self_attn.v_proj.weight', 'layers.11.post_attention_layernorm.weight', 'layers.17.self_attn.q_proj.weight', 'layers.13.self_attn.v_proj.weight', 'layers.21.mlp.gate_proj.weight', 'layers.27.mlp.up_proj.weight', 'layers.10.self_attn.k_proj.weight', 'layers.5.mlp.down_proj.weight', 'layers.27.mlp.down_proj.weight', 'layers.7.self_attn.k_proj.weight', 'layers.30.post_attention_layernorm.weight', 'layers.7.post_attention_layernorm.weight', 'layers.6.self_attn.v_proj.weight', 'layers.11.input_layernorm.weight', 'layers.17.post_attention_layernorm.weight', 'layers.29.self_attn.v_proj.weight', 'layers.12.post_attention_layernorm.weight', 'layers.6.self_attn.k_proj.weight', 'layers.26.self_attn.k_proj.weight', 'layers.1.mlp.down_proj.weight', 'layers.1.mlp.up_proj.weight', 'layers.3.post_attention_layernorm.weight', 'layers.18.input_layernorm.weight', 'layers.19.mlp.gate_proj.weight', 'layers.6.input_layernorm.weight', 'layers.29.self_attn.k_proj.weight', 'layers.1.self_attn.v_proj.weight', 'layers.23.mlp.gate_proj.weight', 'layers.22.self_attn.q_proj.weight', 'layers.21.self_attn.o_proj.weight', 'layers.25.mlp.down_proj.weight', 'layers.5.post_attention_layernorm.weight', 'layers.22.self_attn.o_proj.weight', 'layers.15.self_attn.k_proj.weight', 'layers.18.self_attn.v_proj.weight', 'layers.0.input_layernorm.weight', 'layers.15.post_attention_layernorm.weight', 'layers.31.self_attn.o_proj.weight', 'layers.30.self_attn.o_proj.weight', 'layers.14.mlp.up_proj.weight', 'layers.4.mlp.up_proj.weight', 'layers.20.self_attn.k_proj.weight', 'layers.9.mlp.gate_proj.weight', 'layers.31.mlp.down_proj.weight', 'layers.0.mlp.gate_proj.weight', 'layers.16.self_attn.v_proj.weight', 'layers.16.post_attention_layernorm.weight', 'layers.27.self_attn.k_proj.weight', 'layers.30.mlp.down_proj.weight', 'layers.20.mlp.gate_proj.weight', 'layers.14.mlp.down_proj.weight', 'layers.3.mlp.gate_proj.weight', 'layers.6.post_attention_layernorm.weight', 'layers.13.mlp.down_proj.weight', 'layers.25.mlp.up_proj.weight', 'layers.12.self_attn.q_proj.weight', 'layers.17.self_attn.o_proj.weight', 'layers.26.self_attn.q_proj.weight', 'layers.30.input_layernorm.weight', 'layers.0.post_attention_layernorm.weight', 'layers.14.self_attn.q_proj.weight', 'layers.31.mlp.up_proj.weight', 'embed_tokens.weight', 'layers.23.post_attention_layernorm.weight', 'layers.7.self_attn.q_proj.weight', 'layers.21.self_attn.v_proj.weight', 'layers.17.input_layernorm.weight', 'layers.10.self_attn.o_proj.weight', 'layers.26.mlp.down_proj.weight', 'layers.11.mlp.gate_proj.weight', 'layers.13.self_attn.k_proj.weight', 'layers.26.mlp.gate_proj.weight', 'layers.4.mlp.down_proj.weight', 'layers.4.self_attn.v_proj.weight', 'layers.18.self_attn.q_proj.weight', 'layers.21.mlp.up_proj.weight', 'layers.28.mlp.up_proj.weight', 'norm.weight', 'layers.10.mlp.gate_proj.weight', 'layers.8.self_attn.k_proj.weight', 'layers.15.self_attn.q_proj.weight', 'layers.10.post_attention_layernorm.weight', 'layers.25.self_attn.q_proj.weight', 'layers.25.input_layernorm.weight', 'layers.27.self_attn.o_proj.weight', 'layers.10.input_layernorm.weight', 'layers.28.input_layernorm.weight', 'layers.28.mlp.down_proj.weight', 'layers.4.input_layernorm.weight', 'layers.20.self_attn.v_proj.weight', 'layers.18.self_attn.k_proj.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
2024-03-04 12:54:35 | WARNING | root | Some parameters are on the meta device device because they were offloaded to the cpu.
2024-03-04 12:54:35 | ERROR | stderr | Traceback (most recent call last):
2024-03-04 12:54:35 | ERROR | stderr | File "C:\ProgramData\anaconda3\envs\llava\lib\runpy.py", line 196, in _run_module_as_main
2024-03-04 12:54:35 | ERROR | stderr | return _run_code(code, main_globals, None,
2024-03-04 12:54:35 | ERROR | stderr | File "C:\ProgramData\anaconda3\envs\llava\lib\runpy.py", line 86, in _run_code
2024-03-04 12:54:35 | ERROR | stderr | exec(code, run_globals)
2024-03-04 12:54:35 | ERROR | stderr | File "C:\Users\aymen\LLaVA\llava\serve\model_worker.py", line 277, in
2024-03-04 12:54:35 | ERROR | stderr | worker = ModelWorker(args.controller_address,
2024-03-04 12:54:35 | ERROR | stderr | File "C:\Users\aymen\LLaVA\llava\serve\model_worker.py", line 65, in init
2024-03-04 12:54:35 | ERROR | stderr | self.tokenizer, self.model, self.image_processor, self.context_len = load_pretrained_model(
2024-03-04 12:54:35 | ERROR | stderr | File "C:\Users\aymen\LLaVA\llava\model\builder.py", line 156, in load_pretrained_model
2024-03-04 12:54:35 | ERROR | stderr | if not vision_tower.is_loaded:
2024-03-04 12:54:35 | ERROR | stderr | AttributeError: 'NoneType' object has no attribute '

from llava.