Simon WillisonModify

2026-04-03 02:28:54

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

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:

E4B:

26B-A4B:

(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:

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

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

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

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

2026-04-01 11:32:16

Release: datasette-extract 0.3a0

• Now uses datasette-llm to manage model configuration, which means you can control which models are available for extraction tasks using the extract purpose and LLM model configuration. #38

Tags: llm, datasette

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