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2f86d9b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 | # Copyright 2026 DeepMind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
r"""Example of Gemma finetuning for a prompt -> response task.
Example:
Prompt:
```
<start_of_turn>user
Hello! I would love to visit France.<end_of_turn>
<start_of_turn>model
```
Target:
```
Bonjour ! J'adorerais visiter la France.<end_of_turn>
```
The `<start_of_turn>` and `<end_of_turn>` are special tokens used to specify
which of the user or model is speaking. Those are automatically added by the
`Seq2SeqTask` transform.
Train locally with:
```sh
python -m kauldron.main \
--cfg=examples/seq2seq.py \
--cfg.workdir=/tmp/kauldron_oss/workdir
```
"""
from kauldron import konfig
# pylint: disable=g-import-not-at-top
with konfig.imports():
from gemma import gm
from kauldron import kd
import optax
# pylint: enable=g-import-not-at-top
def get_config():
batch_size = 32
max_length = 512
return kd.train.Trainer(
seed=42,
# Dataset
train_ds=_make_dataset(
training=True,
batch_size=batch_size,
max_length=max_length,
),
# Model definition
model=gm.nn.Gemma3_4B(
tokens="batch.input",
),
# Load the weights from the pretrained checkpoint
init_transform=gm.ckpts.LoadCheckpoint(
path=gm.ckpts.CheckpointPath.GEMMA3_4B_IT,
),
# Training
num_train_steps=10_000,
train_losses={
"xentropy": kd.losses.SoftmaxCrossEntropyWithIntLabels(
logits="preds.logits",
labels="batch.target",
mask="batch.loss_mask",
),
},
optimizer=optax.adafactor(learning_rate=1e-3),
checkpointer=kd.ckpts.Checkpointer(
save_interval_steps=500,
),
# Evaluation
evals={
"test": kd.evals.Evaluator(
run=kd.evals.EveryNSteps(1000),
ds=_make_dataset(
training=False,
batch_size=batch_size,
max_length=max_length,
),
),
# The sampler evaluator run inference on a few prompts from the
# test set.
"sampling": gm.evals.SamplerEvaluator(
run=kd.evals.EveryNSteps(1000),
max_new_tokens=50, # Sampling parameters
num_batches=1, # Only predict a single example (batch_size=None)
ds=_make_dataset(training=False, sampling=True),
),
},
)
def _make_dataset(
*,
training: bool,
sampling: bool = False,
batch_size: int | None = None,
max_length: int | None = None,
):
tokenizer = gm.text.Gemma3Tokenizer()
return kd.data.py.Tfds(
name="mtnt/en-fr",
split="train" if training else "test",
shuffle=True if training else False,
num_epochs=None if training else 1,
batch_size=None if sampling else batch_size,
num_workers=4,
transforms=[
# Create the model inputs/targets/loss_mask.
gm.data.Seq2SeqTask(
# Select which field from the dataset to use.
# https://www.tensorflow.org/datasets/catalog/mtnt
in_prompt="src",
in_response="dst",
# Output batch is {"input": ..., "target": ..., "loss_mask": ...}
out_input="input",
out_target="target",
out_target_mask="loss_mask",
tokenizer=tokenizer,
# Padding parameters
max_length=None if sampling else max_length, # pyrefly: ignore[bad-argument-type]
# In this dataset, ~1% of examples are longer than 512 tokens.
truncate=True,
sampling=sampling,
),
],
)
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