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/gemma-family/codegemma.md

@@ -0,0 +1,88 @@
+# CodeGemma
+
+Code completion / generation with native **fill-in-the-middle (FIM)** support. Built on **Gemma 1** — still the most recent generation as of April 2026. No CodeGemma 2/3/4 release.
+
+## What it is
+
+Gemma 1 fine-tuned on code. Trained with 80–90% FIM rate, 50/50 split between PSM (Prefix-Suffix-Middle) and SPM (Suffix-Prefix-Middle) formats. Designed for IDE autocomplete more than chat.
+
+## Sizes
+
+- **2B pretrained** — fast completion
+- **7B pretrained** — higher quality completion + FIM
+- **7B instruction-tuned** — code chat
+
+Versioned point releases exist (2B 1.1, 7B-IT 1.1).
+
+## Model card
+
+- https://ai.google.dev/gemma/docs/codegemma/model_card
+- HF: https://huggingface.co/google/codegemma-7b
+- Tech report: https://arxiv.org/abs/2406.11409
+
+## FIM tokens
+
+```
+<|fim_prefix|>    prefix-of-completion marker
+<|fim_suffix|>    cursor/insertion-point marker
+<|fim_middle|>    generation trigger
+<|file_separator|>  multi-file boundary
+```
+
+### PSM (Prefix-Suffix-Middle) template
+
+```
+<|fim_prefix|>[code before cursor]<|fim_suffix|>[code after cursor]<|fim_middle|>
+```
+
+Example:
+
+```python
+prompt = (
+    "<|fim_prefix|>import datetime\n"
+    "def calculate_age(birth_year):\n"
+    "    current_year = datetime.date.today().year\n"
+    "    <|fim_suffix|>\n"
+    "    return age<|fim_middle|>"
+)
+```
+
+The model generates the middle chunk and halts.
+
+### Multi-file context
+
+Prepend referenced files separated by `<|file_separator|>`, then the target file in FIM format.
+
+## Minimum invocation
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "google/codegemma-7b"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, torch_dtype=torch.bfloat16, device_map="auto"
+)
+
+prompt = "<|fim_prefix|>def fib(n):\n    if n <= 1:\n        return n\n    <|fim_suffix|>\n    return a<|fim_middle|>"
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+out = model.generate(**inputs, max_new_tokens=128)
+print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False))
+```
+
+## Ollama
+
+`ollama pull codegemma:7b` or `codegemma:2b`. Ollama wraps the FIM tokens for you when you use its completion API with prefix/suffix.
+
+## When to choose it over base Gemma 4
+
+- You need **IDE-grade FIM autocomplete** — CodeGemma was trained for it, base Gemma 4 was not.
+- You want a **2B code model** — base Gemma 4 skips this size (E2B is multimodal, not code-specialized).
+- You want **Ollama-native FIM** that tools like `continue.dev` can talk to.
+
+Base Gemma 4 31B still beats CodeGemma 7B on LiveCodeBench, so for **agentic coding** (plan, write, execute) Gemma 4 or `qwen3-coder:30b` wins. CodeGemma is the inline-cursor-assistant niche.
+
+## Homelab fit
+
+Steel141 already has qwen3-coder:30b and qwen3-coder-next:79.7B — those are stronger than CodeGemma 7B. Only reason to pull CodeGemma is if you want a tiny 2B FIM model for a latency-sensitive editor integration on a Pi or on pve197 alongside the vision stack.