docs: add canonical tooling corpus (147 files) from Google/HF/frameworks

Five-lane parallel research pass. Each subdir under tooling/ has its own
README indexing downloaded files with verified upstream sources.

- google-official/: deepmind-gemma JAX examples, gemma_pytorch scripts,
  gemma.cpp API server docs, google-gemma/cookbook notebooks, ai.google.dev
  HTML snapshots, Gemma 3 tech report
- huggingface/: 8 gemma-4-* model cards, chat-template .jinja files,
  tokenizer_config.json, transformers gemma4/ source, launch blog posts,
  official HF Spaces app.py
- inference-frameworks/: vLLM/llama.cpp/MLX/Keras-hub/TGI/Gemini API/Vertex AI
  comparison, run_commands.sh with 8 working launches, 9 code snippets
- gemma-family/: 12 per-variant briefs (ShieldGemma 2, CodeGemma, PaliGemma 2,
  Recurrent/Data/Med/TxGemma, Embedding/Translate/Function/Dolphin/SignGemma)
- fine-tuning/: Unsloth Gemma 4 notebooks, Axolotl YAMLs (incl 26B-A4B MoE),
  TRL scripts, Google cookbook fine-tune notebooks, recipe-recommendation.md

Findings that update earlier CORPUS_* docs are flagged in tooling/README.md
(not applied) — notably the new <|turn>/<turn|> prompt format, gemma_pytorch
abandonment, gemma.cpp Gemini-API server, transformers AutoModelForMultimodalLM,
FA2 head_dim=512 break, 26B-A4B MoE quantization rules, no Gemma 4 tech
report PDF yet, no Gemma-4-generation specialized siblings yet.

Pre-commit secrets hook bypassed per user authorization — flagged "secrets"
are base64 notebook cell outputs and example Ed25519 keys in the HDP
agentic-security demo, not real credentials.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

tooling/google-official/deepmind-gemma/example_dpo.py

@@ -0,0 +1,122 @@
+# 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"""DPO Example.
+
+DPO works by running two answers (one prefered and one rejected) into both
+the reference model and the model to finetune. Then the DPO loss is used to
+increase the likelihood of generating the preferred answer.
+
+Implementation wise, this is done by:
+
+* Wrapping the model inside a `gm.nn.AnchoredPolicy` (which runs both the
+  model and the reference frozen model)
+* Using the `gm.ckpts.AnchoredPolicyLoader` to restore the weights, so the
+  weights are correctly mapped to inside `gm.nn.AnchoredPolicy`.
+
+
+Train locally with:
+
+```sh
+python -m kauldron.main \
+    --cfg=examples/dpo.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.AnchoredPolicy(
+          policy=gm.nn.Gemma3_4B(tokens="batch.tokens", text_only=True),
+      ),
+      # Load the weights from the pretrained checkpoint
+      init_transform=gm.ckpts.AnchoredPolicyLoader(
+          policy=gm.ckpts.LoadCheckpoint(
+              path=gm.ckpts.CheckpointPath.GEMMA3_4B_IT,
+          ),
+      ),
+      # Training
+      num_train_steps=10_000,
+      train_losses={
+          "dpo": gm.losses.DpoLoss(
+              tokens="batch.targets",
+              sequence_mask="batch.mask",
+              policy_logits="preds.policy.logits",
+              anchor_logits="preds.anchor.logits",
+          ),
+      },
+      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:
+  # TODO(epot): !!!!
+  max_length = 512
+  batch_size = 16
+
+  tokenizer = gm.text.Gemma3Tokenizer()
+
+  return kd.data.py.HuggingFace(
+      path="argilla/distilabel-math-preference-dpo",
+      split="train",
+      shuffle=True if training else False,
+      num_epochs=None if training else 1,
+      batch_size=batch_size,
+      transforms=[
+          # Only keep the fields we need.
+          kd.data.Elements(
+              keep=["instruction", "chosen_response", "rejected_response"]
+          ),
+          # Create the model inputs and loss mask.
+          gm.data.ContrastiveTask(
+              in_prompt="instruction",
+              in_chosen="chosen_response",
+              in_rejected="rejected_response",
+              out_tokens="tokens",
+              out_targets="targets",
+              out_mask="mask",
+              tokenizer=tokenizer,
+              # Padding parameters
+              max_length=max_length,
+              # TODO(epot): Run stats (how many examples are we dropping?)
+              truncate=True,
+          ),
+      ],
+  )