I know it may look broken, but it is supposed to be 30Kb in size.
The real model is here: facebook/wmt19-en-ru · Hugging Face
All those “tiny” models are designed specifically for testing, so they are very quick to download, but they are only useful for testing that the model trains or evals - it will of course not produce anything useful. We use these primarily in the HF transformers test suite.
I have added the script that created it: fsmt-make-super-tiny-model.py · stas/tiny-wmt19-en-ru at main 1 So now you can see how it came about.
I updated the README.md to indicate that 30kb is correct.
You can also validate it via:

python -c 'import torch; print(torch.load("pytorch_model.bin").keys())'
dict_keys(['model.encoder.embed_tokens.weight', 'model.encoder.layers.0.self_attn.k_proj.weight', 'model.encoder.layers.0.self_attn.k_proj.bias', 'model.encoder.layers.0.self_attn.v_proj.weight', 'model.encoder.layers.0.self_attn.v_proj.bias', 'model.encoder.layers.0.self_attn.q_proj.weight', 'model.encoder.layers.0.self_attn.q_proj.bias', 'model.encoder.layers.0.self_attn.out_proj.weight', 'model.encoder.layers.0.self_attn.out_proj.bias', 'model.encoder.layers.0.self_attn_layer_norm.weight', 'model.encoder.layers.0.self_attn_layer_norm.bias', 'model.encoder.layers.0.fc1.weight', 'model.encoder.layers.0.fc1.bias', 'model.encoder.layers.0.fc2.weight', 'model.encoder.layers.0.fc2.bias', 'model.encoder.layers.0.final_layer_norm.weight', 'model.encoder.layers.0.final_layer_norm.bias', 'model.decoder.embed_tokens.weight', 'model.decoder.layers.0.self_attn.k_proj.weight', 'model.decoder.layers.0.self_attn.k_proj.bias', 'model.decoder.layers.0.self_attn.v_proj.weight', 'model.decoder.layers.0.self_attn.v_proj.bias', 'model.decoder.layers.0.self_attn.q_proj.weight', 'model.decoder.layers.0.self_attn.q_proj.bias', 'model.decoder.layers.0.self_attn.out_proj.weight', 'model.decoder.layers.0.self_attn.out_proj.bias', 'model.decoder.layers.0.self_attn_layer_norm.weight', 'model.decoder.layers.0.self_attn_layer_norm.bias', 'model.decoder.layers.0.encoder_attn.k_proj.weight', 'model.decoder.layers.0.encoder_attn.k_proj.bias', 'model.decoder.layers.0.encoder_attn.v_proj.weight', 'model.decoder.layers.0.encoder_attn.v_proj.bias', 'model.decoder.layers.0.encoder_attn.q_proj.weight', 'model.decoder.layers.0.encoder_attn.q_proj.bias', 'model.decoder.layers.0.encoder_attn.out_proj.weight', 'model.decoder.layers.0.encoder_attn.out_proj.bias', 'model.decoder.layers.0.encoder_attn_layer_norm.weight', 'model.decoder.layers.0.encoder_attn_layer_norm.bias', 'model.decoder.layers.0.fc1.weight', 'model.decoder.layers.0.fc1.bias', 'model.decoder.layers.0.fc2.weight', 'model.decoder.layers.0.fc2.bias', 'model.decoder.layers.0.final_layer_norm.weight', 'model.decoder.layers.0.final_layer_norm.bias', 'model.decoder.output_projection.weight'])