Looking for MODS for to help grow this community.

title

Using Gemma4 26b from AI Studio to translate line-by-line in real-time, it's quite capable but when asked to only output a single line, it completely ignores it - when told to do no thinking, no context, no line breakdowns or drafts, and just output the single translated line. It ignores it

Hi, I created an app for Android so you can make calls to gemma (gemma4 and gemma3n supported). It's running on litert engine. Requires 6-12 gb of RAM.

E2B version works even on cheap phone with Helio G100 CPU

https://play.google.com/store/apps/details?id=ai.roleswitch.android

Skim Recap runs gemma-4-E4B-it in the browser through LiteRT-LM on WebGPU, to summarise the passage you just scrolled past.

Some notes from building it:

• The engine lives in an MV3 offscreen document. Content scripts can't hold a model, and a service worker gets killed under you mid-generation.
• The .litertlm checkpoint is 2.97 GB, so it can never be buffered in JS. It streams from Hugging Face through a counting TransformStream straight into the Cache API, which also gives you the progress bar for free.
• Sampling is greedy. I set it explicitly, then found the library default was already greedy — 72 runs came back byte-identical either way.
• Point count is scaled to how full the prompt actually got, not a fixed number. A fixed count pads thin passages and truncates long ones.

I also ran it head-to-head against Chrome's built-in Prompt API on a small fixture corpus.

Extension: https://chromewebstore.google.com/detail/skim-recap/febndabjnmbmdodeoenjmnplfcalmnmc

More detail: https://skim-recap.vercel.app

I missed to add, im using the 31b version

Chat Look 😂😂😂

#gemma4

#google

#edgegallery

I am a novice at working with AI; just 3 months in and learning on the go, more of an idea creator than actual coder at the moment so creating interesting and efficient workflows are a big deal to me. I was wondering what is everyone's experience on using Gemma 4 for agentic workflows / agentic engineering? I had only used Gemma 4 so far alongside Qwen 3.5 in Ollama being workload handlers under orchestration by Claude; just trying to optimize what I have on my Macbook Pro M1 32 gigs of RAM. TIA!

Requirements: iPhone with A17 Pro or newer (8 GB RAM floor for the model), iOS 26+. TestFlight beta is open to anyone with a compatible device.

https://testflight.apple.com/join/NW99nU2s

Trying out the Gemma4 line using Ollama. It frequently emits embedded markup (based on LaTex) in responses that Ollama (or command line) doesn't render. Any suggestions to fix this or is there a better host than Ollama?

Hey I am using gemma 4 E4B , and I have an image and prompt , response and after training it with lora , I want to check whether the attention is towards the right place of the image , how do I map my attention layer outputs to the actual image !!

I’m using Gemma 4 E2B as a local worker model for small agent tasks.
Use it for:
log inspection
file scanning
summaries
cleanup
first-pass debugging
messy input inspection
Install Ollama:
# https://ollama.com/download

ollama pull gemma4:e2b
ollama run gemma4:e2b
Call it like a worker:
curl -s http://localhost:11434/api/chat \
-d '{
"model": "gemma4:e2b",
"messages": [
{
"role": "system",
"content": "You are a local inspection worker. Return only: finding, evidence pointer, confidence, next action. Do not dump raw logs."
},
{
"role": "user",
"content": "Inspect this:\n\nPASTE_INPUT_HERE"
}
],
"options": {
"temperature": 0.2,
"num_predict": 500
},
"stream": false
}'
Expected output:
finding:
evidence_pointer:
confidence:
next_action:
The point is simple: let Gemma handle the first pass before you spend stronger model tokens.

Hi everyone,

I recently released StoryCodex on Android, and one of the local AI backends it supports is Gemma 4 E2B IT / E4B IT through LiteRT.

The app is an ebook/web novel reader with a “story codex” layer. As the user reads, it can generate chapter summaries, character profiles, aliases, world entries, timeline events, and relationship notes. The important constraint is spoiler safety: the model should only use context up to the user’s current reading progress.

Gemma is used for local/offline companion tasks, especially structured extraction and summarization. The workload is less like open-ended chat and more like repeated task-oriented passes over chapter text:

- summarize chapter

- extract/update characters

- extract places, factions, items, concepts, mysteries

- update timeline

- update active story arc

- reason about relationship changes

- return structured JSON where possible

A few things I had to care about on mobile:

- keeping prompts short enough for practical on-device context limits

- splitting large chapters before inference

- avoiding runaway/repetitive generation

- using different sampling profiles for narrative summaries vs structured extraction

- crash recovery around native inference failures

- falling back cleanly if a model cannot load on a device

- making local AI optional rather than required for basic reading

The app also supports other local/cloud backends, but Gemma 4 E2B/E4B is one of the more interesting paths because it makes the “private story memory” use case possible directly on-device.

Google Play:

https://play.google.com/store/apps/details?id=com.storycodex.android

I’d be very interested in feedback from people experimenting with Gemma on mobile, especially around prompt design, structured extraction reliability, LiteRT behavior, and practical device limits.

Hello! i saw someone recommend Gemma 4 as a proxy, but when i went to use it, i was so surprised to see so many options! i am not very well versed in proxies (i know enough to copy the links for API and that’s about it!) so i apologize if the question is ignorant.
Thank you!

This model is excellent for my use case, but if it didn't need to 'think' on my prompt, my replies would go from 6 seconds to .5.

Suggestions?