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TinyLlama-1.1B-Chat-v1.0

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Getting this error when loading a checkpoint:
size mismatch for model.layers.0.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.0.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.1.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.1.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.2.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.2.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.3.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.3.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.4.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.4.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.5.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.5.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.6.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.6.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.7.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.7.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.8.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.8.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.9.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.9.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.10.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.10.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.11.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.11.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.12.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.12.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.13.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.13.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.14.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.14.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.15.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.15.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.16.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.16.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.17.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.17.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.18.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.18.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.19.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.19.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.20.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.20.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.21.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
size mismatch for model.layers.21.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 2048]) from checkpoint, the shape in current model is torch.Size([2048, 2048]).
You may consider adding ignore_mismatched_sizes=True in the model from_pretrained method.
Why does this happen? How to fix it?