Hello, could you please give me some advice on why the size of the TinyCLIP-ViT-39M-16-Text-19M.bin model I distilled is not 300mb but 900mb, thanks very much!!!

[leo23ui]

Hello, could you please give me some advice on why the size of the TinyCLIP-ViT-39M-16-Text-19M.bin model I distilled is not 300mb but 900mb, thanks very much!!!

export NNODES=1
export GPUS_PER_NODE=1
export WANDB__SERVICE_WAIT=60
export CUDA_VISIBLE_DEVICES=5

DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES"
torchrun $DISTRIBUTED_ARGS src/training/main.py
--save-frequency 1
--report-to wandb
--train-data /home/gg/gg/MQBench-main/test/model/e1/split_2tar
--dataset-type webdataset
--imagenet-val ./ImageNet
--warmup 2000
--batch-size 1024
--epochs 25
--workers 8
--model TinyCLIP-ViT-39M-16-Text-19M
--name exp_name
--seed 0
--local-loss
--grad-checkpointing
--output ./outputs/TinyCLIP-ViT-39M-16-Text-19M
--lr 0.0001
--gather-with-grad
--pretrained-image-file ViT-B-16@openai
--pretrained-text-file ViT-B-16@openai
--distillation-teacher ViT-B-32@laion2b_e16
--norm_gradient_clip 5
--train-num-samples 15000000
--logit-scale 50

[wkcn]

It also contains the master weight and the optimizer states.
You can keep the value corresponding to the key state_dict only.

ckpt = torch.load(checkpoint_fname)
new_ckpt = dict(state_dict=ckpt['state_dict'])
torch.save(new_ckpt, saved_fname)

[leo23ui]

It also contains the master weight and the optimizer states. You can keep the value corresponding to the key state_dict only.

ckpt = torch.load(checkpoint_fname)
new_ckpt = dict(state_dict=ckpt['state_dict'])
torch.save(new_ckpt, saved_fname)

thanks very much!!!

[leo23ui]

It also contains the master weight and the optimizer states. You can keep the value corresponding to the key state_dict only.

ckpt = torch.load(checkpoint_fname)
new_ckpt = dict(state_dict=ckpt['state_dict'])
torch.save(new_ckpt, saved_fname)

After I modify the parameters according to this code, the accuracy in cifar is only 0.09, and the following error is displayed， could you please give me some advice, thanks!!!

Some weights of the model checkpoint at clip were not used when initializing CLIPModel: ['state_dict']

• This IS expected if you are initializing CLIPModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
• This IS NOT expected if you are initializing CLIPModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
  Some weights of CLIPModel were not initialized from the model checkpoint at clip and are newly initialized: ['logit_scale', 'text_model.embeddings.position_embedding.weight', 'text_model.embeddings.token_embedding.weight', 'text_model.encoder.layers.0.layer_norm1.bias', 'text_model.encoder.layers.0.layer_norm1.weight', 'text_model.encoder.layers.0.layer_norm2.bias', 'text_model.encoder.layers.0.layer_norm2.weight', 'text_model.encoder.layers.0.mlp.fc1.bias', 'text_model.encoder.layers.0.mlp.fc1.weight', 'text_model.encoder.layers.0.mlp.fc2.bias', 'text_model.encoder.layers.0.mlp.fc2.weight', 'text_model.encoder.layers.0.self_attn.k_proj.bias', 'text_model.encoder.layers.0.self_attn.k_proj.weight', 'text_model.encoder.layers.0.self_attn.out_proj.bias', 'text_model.encoder.layers.0.self_attn.out_proj.weight', 'text_model.encoder.layers.0.self_attn.q_proj.bias', 'text_model.encoder.layers.0.self_attn.q_proj.weight', 'text_model.encoder.layers.0.self_attn.v_proj.bias', 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'vision_model.pre_layrnorm.bias', 'vision_model.pre_layrnorm.weight', 'visual_projection.weight']
  You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.