I've successfully run theinference.pyprogram for captioning. And the results are good (almost the same as the example).
{
"video1.mp4": "A red car is parked in a showroom with american flags hanging from the ceiling.",
"video2.mp4": "An aerial view of a city with a river running through it."
}
But it comes up with the following message.
Some weights of LlamaForCausalLM were not initialized from the model checkpoint at vicuna_weights/vicuna-7b-v0 and are newly initialized: ['model.layers.0.self_attn.rotary_emb.inv_freq', 'model.layers.1.self_attn.rotary_emb.inv_freq', 'model.layers.10.self_attn.rotary_emb.inv_freq', 'model.layers.11.self_attn.rotary_emb.inv_freq', 'model.layers.12.self_attn.rotary_emb.inv_freq', 'model.layers.13.self_attn.rotary_emb.inv_freq', 'model.layers.14.self_attn.rotary_emb.inv_freq', 'model.layers.15.self_attn.rotary_emb.inv_freq', 'model.layers.16.self_attn.rotary_emb.inv_freq', 'model.layers.17.self_attn.rotary_emb.inv_freq', 'model.layers.18.self_attn.rotary_emb.inv_freq', 'model.layers.19.self_attn.rotary_emb.inv_freq', 'model.layers.2.self_attn.rotary_emb.inv_freq', 'model.layers.20.self_attn.rotary_emb.inv_freq', 'model.layers.21.self_attn.rotary_emb.inv_freq', 'model.layers.22.self_attn.rotary_emb.inv_freq', 'model.layers.23.self_attn.rotary_emb.inv_freq', 'model.layers.24.self_attn.rotary_emb.inv_freq', 'model.layers.25.self_attn.rotary_emb.inv_freq', 'model.layers.26.self_attn.rotary_emb.inv_freq', 'model.layers.27.self_attn.rotary_emb.inv_freq', 'model.layers.28.self_attn.rotary_emb.inv_freq', 'model.layers.29.self_attn.rotary_emb.inv_freq', 'model.layers.3.self_attn.rotary_emb.inv_freq', 'model.layers.30.self_attn.rotary_emb.inv_freq', 'model.layers.31.self_attn.rotary_emb.inv_freq', 'model.layers.4.self_attn.rotary_emb.inv_freq', 'model.layers.5.self_attn.rotary_emb.inv_freq', 'model.layers.6.self_attn.rotary_emb.inv_freq', 'model.layers.7.self_attn.rotary_emb.inv_freq', 'model.layers.8.self_attn.rotary_emb.inv_freq', 'model.layers.9.self_attn.rotary_emb.inv_freq']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
It seems like it's caused by the absence of some of the weights. do I need to do additional operations to deal with this information?
Hi @UknowSth,
Thanks for your interests! I believe this is because your vicuna weights are not properly prepared.
Could you please check you follow fastchat guidelines to download vicuna-7b-v0?
I've successfully run the
inference.py
program for captioning. And the results are good (almost the same as the example).But it comes up with the following message.
It seems like it's caused by the absence of some of the weights. do I need to do additional operations to deal with this information?