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>
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Mortdecai
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# Copyright 2020-2026 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# /// script
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# dependencies = [
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# "trl[peft]",
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# "bitsandbytes",
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# "liger-kernel",
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# "trackio",
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# ]
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# ///
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"""
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Teach tool calling to CohereLabs/tiny-aya-global using SFT with QLoRA on the bebechien/SimpleToolCalling dataset.
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The model used in this script does not have native tool-calling support. We extend its existing Jinja2 chat template to
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serialize tool schemas into the system preamble and render tool calls as structured <tool_call> XML inside the model's
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native <|START_RESPONSE|> / <|END_RESPONSE|> delimiters. The modified template is saved with the tokenizer, so
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inference only requires loading the tokenizer from the output directory and calling apply_chat_template with
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tools=TOOLS — no manual system-prompt construction needed.
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Example:
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python examples/scripts/sft_tiny_aya_tool_calling.py
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"""
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import json
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from pathlib import Path
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import torch
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from datasets import load_dataset
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from peft import LoraConfig
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from transformers import AutoModelForCausalLM, BitsAndBytesConfig
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from trl import SFTConfig, SFTTrainer
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# These are the tool schemas that are used in the dataset
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TOOLS = [
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{
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"type": "function",
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"function": {
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"name": "search_knowledge_base",
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"description": "Search internal company documents, policies and project data.",
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"parameters": {
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"type": "object",
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"properties": {"query": {"type": "string", "description": "query string"}},
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"required": ["query"],
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},
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"return": {"type": "string"},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "search_google",
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"description": "Search public information.",
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"parameters": {
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"type": "object",
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"properties": {"query": {"type": "string", "description": "query string"}},
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"required": ["query"],
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},
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"return": {"type": "string"},
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},
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},
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]
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def create_conversation(sample):
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return {
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"prompt": [{"role": "user", "content": sample["user_content"]}],
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"completion": [
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{
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"role": "assistant",
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"tool_calls": [
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{
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"type": "function",
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"function": {
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"name": sample["tool_name"],
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"arguments": json.loads(sample["tool_arguments"]),
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},
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}
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],
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},
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],
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"tools": TOOLS,
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}
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def main():
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model_id = "CohereLabs/tiny-aya-global"
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dataset_name = "bebechien/SimpleToolCalling"
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output_dir = "tiny-aya-global-tool-calling-SFT"
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# Load and format dataset
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dataset = load_dataset(dataset_name, split="train")
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dataset = dataset.map(create_conversation, remove_columns=dataset.features)
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dataset = dataset.train_test_split(test_size=0.5, shuffle=True)
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# Load model
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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attn_implementation="sdpa",
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dtype=torch.float16,
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quantization_config=BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.float16,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4",
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),
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)
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# Configure LoRA
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peft_config = LoraConfig(
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r=32,
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lora_alpha=32,
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target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
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)
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# Train
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training_args = SFTConfig(
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output_dir=output_dir,
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per_device_train_batch_size=1,
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gradient_accumulation_steps=4,
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# Use the tool-aware chat template
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chat_template_path=str(Path(__file__).parent / "tiny_aya_chat_template.jinja"),
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warmup_steps=5,
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learning_rate=2e-4,
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optim="paged_adamw_8bit",
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logging_steps=1,
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report_to="trackio",
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trackio_space_id=output_dir,
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max_length=1024,
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use_liger_kernel=True,
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activation_offloading=True,
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push_to_hub=True,
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)
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trainer = SFTTrainer(
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model=model,
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args=training_args,
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train_dataset=dataset["train"],
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peft_config=peft_config,
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)
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trainer.train()
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# Save model and tokenizer (tokenizer carries the updated chat template)
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trainer.save_model(output_dir)
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trainer.push_to_hub(dataset_name=dataset_name)
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if __name__ == "__main__":
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main()
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