Here is a link I am using to fine-tune the blenderbot model.

Fine-tuning methods: https://huggingface.co/docs/transformers/training

Blenderbot: https://huggingface.co/docs/transformers/model_doc/blenderbot

from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
mname = "facebook/blenderbot-400M-distill"
model = BlenderbotForConditionalGeneration.from_pretrained(mname)
tokenizer = BlenderbotTokenizer.from_pretrained(mname)


#FOR TRAINING: 

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=small_train_dataset,
    eval_dataset=small_eval_dataset,
    compute_metrics=compute_metrics,
)
trainer.train()

#OR

model.compile(
    optimizer=tf.keras.optimizers.Adam(learning_rate=5e-5),
    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    metrics=tf.metrics.SparseCategoricalAccuracy(),
)

model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3)

None of these work! :(

Maybe try using the TFBlenderbotForConditionalGeneration class for Tensorflow. It has what you need:

import tensorflow as tf
from transformers import BlenderbotTokenizer, TFBlenderbotForConditionalGeneration

mname = "facebook/blenderbot-400M-distill"
model = TFBlenderbotForConditionalGeneration.from_pretrained(mname)
tokenizer = BlenderbotTokenizer.from_pretrained(mname)

model.compile(
    optimizer=tf.keras.optimizers.Adam(learning_rate=5e-5),
    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    metrics=tf.metrics.SparseCategoricalAccuracy(),
)
....

See the docs for more information.