diff --git a/model_cards/stas/tiny-wmt19-en-de/README.md b/model_cards/stas/tiny-wmt19-en-de/README.md
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+---
+language:
+- en
+- de
+thumbnail:
+tags:
+- wmt19
+- testing
+license: apache-2.0
+datasets:
+- wmt19
+metrics:
+- bleu
+---
+
+# Tiny FSMT
+
+This is a tiny model that is used in the `transformers` test suite. It doesn't do anything useful, other than testing that `FSMT` works.
diff --git a/scripts/fsmt/fsmt-make-tiny-model.py b/scripts/fsmt/fsmt-make-tiny-model.py
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+#!/usr/bin/env python
+# coding: utf-8
+
+# this script creates a tiny model that is useful inside tests, when we just want to test that the machinery works,
+# without needing to the check the quality of the outcomes.
+# it will be used then as "stas/tiny-wmt19-en-de"
+
+from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
+mname = "facebook/wmt19-en-de"
+tokenizer = FSMTTokenizer.from_pretrained(mname)
+# get the correct vocab sizes, etc. from the master model
+config = FSMTConfig.from_pretrained(mname)
+config.update(dict(
+    d_model=4,
+    encoder_layers=1, decoder_layers=1,
+    encoder_ffn_dim=4, decoder_ffn_dim=4,
+    encoder_attention_heads=1, decoder_attention_heads=1))
+
+tiny_model = FSMTForConditionalGeneration(config)
+print(f"num of params {tiny_model.num_parameters()}")
+# Test it
+batch = tokenizer.prepare_seq2seq_batch(["Making tiny model"])
+outputs = tiny_model(**batch, return_dict=True)
+
+print(len(outputs.logits[0]))
+# Save
+mname_tiny = "tiny-wmt19-en-de"
+tiny_model.half() # makes it smaller
+tiny_model.save_pretrained(mname_tiny)
+tokenizer.save_pretrained(mname_tiny)
+
+# Upload
+# transformers-cli upload tiny-wmt19-en-de