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Creator of Datasette and Lanyrd, co-creator of the Django Web Framework.
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Gemma 4: Byte for byte, the most capable open models

2026-04-03 02:28:54

Gemma 4: Byte for byte, the most capable open models

Four new vision-capable Apache 2.0 licensed reasoning LLMs from Google DeepMind, sized at 2B, 4B, 31B, plus a 26B-A4B Mixture-of-Experts.

Google emphasize "unprecedented level of intelligence-per-parameter", providing yet more evidence that creating small useful models is one of the hottest areas of research right now.

They actually label the two smaller models as E2B and E4B for "Effective" parameter size. The system card explains:

The smaller models incorporate Per-Layer Embeddings (PLE) to maximize parameter efficiency in on-device deployments. Rather than adding more layers or parameters to the model, PLE gives each decoder layer its own small embedding for every token. These embedding tables are large but are only used for quick lookups, which is why the effective parameter count is much smaller than the total.

I don't entirely understand that, but apparently that's what the "E" in E2B means!

I tried them out using the GGUFs for LM Studio. The 2B (4.41GB), 4B (6.33GB) and 26B-A4B (17.99GB) models all worked perfectly, but the 31B (19.89GB) model was broken and spat out "---\n" in a loop for every prompt I tried.

The succession of pelican quality from 2B to 4B to 26B-A4B is notable:

E2B:

Two blue circles on a brown rectangle and a weird mess of orange blob and yellow triangle for the pelican

E4B:

Two black wheels joined by a sort of grey surfboard, the pelican is semicircles and a blue blob floating above it

26B-A4B:

Bicycle has the right pieces although the frame is wonky. Pelican is genuinely good, has a big triangle beak and a nice curved neck and is clearly a bird that is sitting on the bicycle

(This one actually had an SVG error - "error on line 18 at column 88: Attribute x1 redefined" - but after fixing that I got probably the best pelican I've seen yet from a model that runs on my laptop.)

Google are providing API access to the two larger Gemma models via their AI Studio. I added support to llm-gemini and then ran a pelican through the 31B model using that:

llm -m gemini/gemma-4-31b-it 'Generate an SVG of a pelican riding a bicycle'

Pretty good, though it is missing the front part of the bicycle frame:

Motion blur lines, a mostly great bicycle albeit missing the front part of the frame. Pelican is decent.

Tags: google, ai, generative-ai, local-llms, llms, llm, vision-llms, llm-reasoning, gemma, llm-release, lm-studio

March 2026 sponsors-only newsletter

2026-04-02 13:15:04

I just sent the March edition of my sponsors-only monthly newsletter. If you are a sponsor (or if you start a sponsorship now) you can access it here. In this month's newsletter:

  • More agentic engineering patterns
  • Streaming experts with MoE models on a Mac
  • Model releases in March
  • Vibe porting
  • Supply chain attacks against PyPI and NPM
  • Stuff I shipped
  • What I'm using, March 2026 edition
  • And a couple of museums

Here's a copy of the February newsletter as a preview of what you'll get. Pay $10/month to stay a month ahead of the free copy!

Tags: newsletter

datasette-llm 0.1a6

2026-04-02 07:01:37

Release: datasette-llm 0.1a6

  • The same model ID no longer needs to be repeated in both the default model and allowed models lists - setting it as a default model automatically adds it to the allowed models list. #6
  • Improved documentation for Python API usage.

Tags: llm, datasette

datasette-enrichments-llm 0.2a1

2026-04-02 06:00:34

Release: datasette-enrichments-llm 0.2a1

  • The actor who triggers an enrichment is now passed to the llm.mode(... actor=actor) method. #3

Tags: enrichments, llm, datasette

datasette-extract 0.3a0

2026-04-01 11:32:16

Release: datasette-extract 0.3a0

Tags: llm, datasette

datasette-enrichments-llm 0.2a0

2026-04-01 11:28:44

Release: datasette-enrichments-llm 0.2a0

  • This plugin now uses datasette-llm to configure and manage models. This means it's possible to specify which models should be made available for enrichments, using the new enrichments purpose.

Tags: llm, datasette