Mortdecai
eecebe7ef5
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>
3.4 KiB
DataGemma
LLM grounding with Google Data Commons — a public knowledge graph of 240B+ statistical data points (economics, health, demographics, science). Built on Gemma 2 27B. No Gemma 3 or 4 generation yet.
What it is
Two flavors:
- DataGemma RIG (Retrieval-Interleaved Generation): Model is fine-tuned to emit inline Data Commons queries wrapped around its own claims. Outputs look like
The population of Sunnyvale is [__DC__("population of Sunnyvale") --> "152,200"]. An external resolver substitutes the real stat. - DataGemma RAG (Retrieval-Augmented Generation): Standard RAG pipeline — query Data Commons, inject results into context, generate.
Sizes
- 27B instruct only (
datagemma-rig-27b-it,datagemma-rag-27b-it).
Model cards
- https://ai.google.dev/gemma/docs/datagemma
- DeepMind: https://deepmind.google/models/gemma/datagemma/
- HF RIG: https://huggingface.co/google/datagemma-rig-27b-it
- HF RAG: https://huggingface.co/google/datagemma-rag-27b-it
- Paper: https://docs.datacommons.org/papers/DataGemma-FullPaper.pdf
Performance claim
Baseline Gemma 2 factuality on the 101-query statistical eval: 5–17%. DataGemma RIG: ~58%. The improvement is narrow (statistical claims only) but real.
Prompt format
No special template. Plain natural-language input. The difference is in the training and the output format.
RIG output example:
Sunnyvale has [__DC__("total population of Sunnyvale CA") --> "152,200"]
residents as of 2020, with a median age of [__DC__("median age of
Sunnyvale CA") --> "34.8"].
Post-processing: regex out the [__DC__("...") --> "..."] blocks and either (a) replace with resolved Data Commons values, or (b) render as inline citations.
RAG flow: query Data Commons first, inject tabular context, then prompt normally.
Minimum invocation — RIG
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "google/datagemma-rig-27b-it"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id, device_map="auto", torch_dtype=torch.bfloat16
)
prompt = "What are the demographic trends in Sunnyvale, California?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
out = model.generate(**inputs, max_new_tokens=1024)
print(tokenizer.batch_decode(
out[:, inputs["input_ids"].shape[1]:],
skip_special_tokens=True
)[0])
Then run a resolver that extracts each [__DC__(q) --> ""] and hits the Data Commons API.
When to choose it over base Gemma 4
- You're building a statistics-grounded assistant (government data, public health, economic indicators) and need low hallucination on numbers.
- You're okay with a 27B model — DataGemma only ships at this size.
- Your domain overlaps Data Commons coverage (US-heavy, but growing internationally).
Base Gemma 4 + a conventional RAG pipeline can do the same thing if you bring your own retriever. DataGemma's value is the trained inline-citation behavior (RIG) — Gemma 4 won't emit that format without prompting gymnastics.
Homelab fit
Low. No current Seth project leans on statistical grounding. Niche for a news-summary use case (POS-Automation daily print) if Seth ever wants "US inflation was X% as of Y" kind of interjections — but then a simple Data Commons API call from the script is cheaper than running a 27B model.