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/gemma-pytorch/run.py

@@ -0,0 +1,107 @@
+# Copyright 2024 Google LLC
+#
+# 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.
+
+
+import contextlib
+import random
+
+import numpy as np
+import torch
+from absl import app, flags
+
+from gemma import config
+from gemma import model as gemma_model
+
+# Define flags
+FLAGS = flags.FLAGS
+
+flags.DEFINE_string('ckpt', None, 'Path to the checkpoint file.', required=True)
+flags.DEFINE_string('variant', '4b', 'Model variant.')
+flags.DEFINE_string('device', 'cpu', 'Device to run the model on.')
+flags.DEFINE_integer('output_len', 10, 'Length of the output sequence.')
+flags.DEFINE_integer('seed', 12345, 'Random seed.')
+flags.DEFINE_boolean('quant', False, 'Whether to use quantization.')
+flags.DEFINE_string('prompt', 'What are large language models?', 'Input prompt for the model.')
+
+# Define valid text only model variants
+_VALID_MODEL_VARIANTS = ['2b', '2b-v2', '7b', '9b', '27b', '1b']
+
+# Define valid devices
+_VALID_DEVICES = ['cpu', 'cuda']
+
+# Validator function for the 'variant' flag
+def validate_variant(variant):
+    if variant not in _VALID_MODEL_VARIANTS:
+        raise ValueError(f'Invalid variant: {variant}. Valid variants are: {_VALID_MODEL_VARIANTS}')
+    return True
+
+# Validator function for the 'device' flag
+def validate_device(device):
+    if device not in _VALID_DEVICES:
+        raise ValueError(f'Invalid device: {device}. Valid devices are: {_VALID_DEVICES}')
+    return True
+
+# Register the validator for the 'variant' flag
+flags.register_validator('variant', validate_variant, message='Invalid model variant.')
+
+# Register the validator for the 'device' flag
+flags.register_validator('device', validate_device, message='Invalid device.')
+
+@contextlib.contextmanager
+def _set_default_tensor_type(dtype: torch.dtype):
+    """Sets the default torch dtype to the given dtype."""
+    torch.set_default_dtype(dtype)
+    yield
+    torch.set_default_dtype(torch.float)
+
+def main(_):
+    # Construct the model config.
+    model_config = config.get_model_config(FLAGS.variant)
+    model_config.dtype = "float32"
+    model_config.quant = FLAGS.quant
+
+    # Seed random.
+    random.seed(FLAGS.seed)
+    np.random.seed(FLAGS.seed)
+    torch.manual_seed(FLAGS.seed)
+
+    # Create the model and load the weights.
+    device = torch.device(FLAGS.device)
+    with _set_default_tensor_type(model_config.get_dtype()):
+        model = gemma_model.GemmaForCausalLM(model_config)
+        model.load_weights(FLAGS.ckpt)
+        model = model.to(device).eval()
+    print("Model loading done")
+
+    # Generate the response.
+    result = model.generate(FLAGS.prompt, device, output_len=FLAGS.output_len)
+
+    # Print the prompts and results.
+    print('======================================')
+    print(f'PROMPT: {FLAGS.prompt}')
+    print(f'RESULT: {result}')
+    print('======================================')
+
+if __name__ == "__main__":
+    app.run(main)
+
+
+# How to run this script:
+
+# Example command (replace with your actual paths and values):
+# python scripts/run.py --device=cpu --ckpt=/path/to/your/pytorch_checkpoint/model.ckpt --output_len=2 --prompt="The name of the capital of Italy is"
+# Important:
+# - Replace '/path/to/your/pytorch_checkpoint/model.ckpt' with the actual path to your checkpoint file.
+# - Choose the correct --variant (model size).
+# - Use --device=cuda if you have a GPU; otherwise, use --device=cpu.