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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 | # 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 config for finetuning Gemma for a classification task.
* Input: A text to classify.
* Output: A classification label. The pre-trained Gemma model is trained to
predict one world among 256.000. Here, we're finetuning to predict only 2
tokens among the 256.000 available.
Train locally with:
```sh
python -m kauldron.main \
--cfg=examples/classification.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():
"""Get the default hyperparameter configuration."""
return kd.train.Trainer(
seed=42,
# Dataset
train_ds=_make_dataset(training=True),
# Model definition
model=gm.nn.Gemma3_4B(
tokens="batch.sentence",
return_last_only=True,
),
# 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.label",
),
},
optimizer=optax.adafactor(learning_rate=1e-4),
checkpointer=kd.ckpts.Checkpointer(
save_interval_steps=500,
),
# Evaluation
evals={
"test": kd.evals.Evaluator(
run=kd.evals.EveryNSteps(1000),
ds=_make_dataset(training=False),
),
},
)
def _make_dataset(training: bool) -> kd.data.Pipeline:
# Dict key names from the dataset
_INPUT_FIELD = "sentence" # pylint: disable=invalid-name
_LABEL_FIELD = "label" # pylint: disable=invalid-name
tokenizer = gm.text.Gemma3Tokenizer()
return kd.data.py.Tfds(
name="glue/cola",
split="train" if training else "validation",
shuffle=True if training else False,
num_epochs=None if training else 1,
batch_size=8,
transforms=[
# Process the input text
# TFDS datasets returns `bytes`, so convert them to `str`
gm.data.DecodeBytes(key=_INPUT_FIELD),
gm.data.FormatText(
key=_INPUT_FIELD,
template="""<start_of_turn>user
Please classify whether the following sentence is grammaticaly correct, please answer only with Yes or No.
Sentence: {text}<end_of_turn>
<start_of_turn>model""",
),
gm.data.Tokenize(
key=_INPUT_FIELD,
tokenizer=tokenizer,
add_bos=True,
),
gm.data.Pad(
key=_INPUT_FIELD,
max_length=128,
),
# Process the label
gm.data.MapInts(
key=_LABEL_FIELD,
# Rather than predicting the token 0 and 1, we are using the
# token 1294 and 3553 which respectivelly correspond to "No" and
# "Yes". We do this because those token already contain semantic
# information, so even zero-shot prediction without any
# finetuning has better than random performances.
old_to_new={
0: 1294, # Token -> "No"
1: 3553, # Token -> "Yes"
},
),
kd.data.Rearrange(
key=_LABEL_FIELD,
pattern="... -> ... 1", # For shape compatibility with the loss.
),
],
)
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