README.md

---
license: gemma
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
base_model: google/gemma-3-4b-it
pipeline_tag: text-generation
library_name: gguf
language:
- en
tags:
- gemma3
- gguf
- text-only
- ollama
- breadcrumb
---

Google's Gemma 3 4B, as Ollama builds it, with the picture-reading part taken out.
The text weights are the same bytes. It writes text. It cannot look at an image.

## What it is

This is Ollama's own `gemma3:4b` — the Q4_K_M build of `google/gemma-3-4b-it` — with the
vision tower and the multimodal projection removed and nothing else changed.

- Source file: the model layer of `gemma3:4b`,
  sha256 `aeda25e63ebd698fab8638ffb778e68bed908b960d39d0becc650fa981609d25`,
  3,338,792,448 bytes.
- What came out: 439 tensors whose names begin `v.` (the SigLIP vision tower) or `mm.`
  (the multimodal input projection), and 9 metadata keys — `gemma3.mm.tokens_per_image`
  and the eight `gemma3.vision.*` keys.
- What stayed: all 444 text tensors, byte for byte. Same names, same ggml quantization
  types, same shapes, same bytes. Nothing was requantized, converted, retrained or
  fine-tuned. The tokenizer is untouched, all 262,145 entries, so token numbers do not
  move and the model writes the same words.
- What was added: one metadata key, `general.description`, carrying the notice that this
  file is a modified Gemma file.
- Result: 2,498,332,864 bytes,
  sha256 `199388f8f8cbec06b80bd63b0b1a774103e00c993c107bf1d480e58047420532`.

The template and the sampling parameters are the ones Ollama ships with `gemma3:4b`,
unchanged: stop `<end_of_turn>`, temperature 1, top_k 64, top_p 0.95.

## Why

[Breadcrumb](https://innerloop.works/breadcrumb), by Innerloop, uses this model to give screens, chapters and meetings short names. It only
ever sends it text. The vision tower was 840 MB of weights that were read off disk,
paged in and held in memory for work that never happened.

Measured on the same machine, one model resident at a time, with `footprint` against the
running `llama-server`:

| | on disk | held while answering |
|---|---|---|
| `gemma3:4b` | 3.3 GB | 4.28 GB |
| this model | 2.5 GB | 2.73 GB |

That is about 1.6 GB less memory while it is working, and 0.8 GB less disk. On an 8 GB
Mac that is the difference between naming a screen and swapping.

The names it writes are the same names. On 300 pinned items from a real store — 150
screen moments, 75 chapters, 75 meetings — this file and `gemma3:4b` wrote 300 identical
titles out of 300, at the same settings, and both were stable across a second pass.

## It cannot see

This is a text-only model. There is no vision tower in the file and no projector layer in
the manifest. `ollama show` reports one capability, `completion`, where `gemma3:4b`
reports `completion` and `vision`. If you send it an image it has nothing to look at.
If you need Gemma 3 to read pictures, use `gemma3:4b` instead.

## Gemma notice

Gemma is provided under and subject to the Gemma Terms of Use found at
ai.google.dev/gemma/terms

This distribution contains a modified Gemma file. The full Gemma Terms of Use, as last
modified April 1, 2026, travel with the model: the LICENSE file in this repository is that complete copy, and NOTICE carries the modification notice.

Use of this model is subject to Google's Gemma Prohibited Use Policy at
ai.google.dev/gemma/prohibited_use_policy. That policy is part of the Gemma Terms and
applies to you whether you got this model from us or from anywhere else. If you pass this
model on, or anything you build from it, you pass these terms on with it and you tell the
people you pass it to that the Gemma use restrictions apply.

Google claims no rights in what you generate with it.

Everything in this distribution that is not Gemma is © Innerloop, [innerloop.works](https://innerloop.works).

## How to verify it yourself

You do not have to take our word that only the vision parts came out. The check needs
nothing but a GGUF reader.

1. Get Ollama's `gemma3:4b` and find its model layer in your blob store. It should hash
   to `aeda25e63ebd698fab8638ffb778e68bed908b960d39d0becc650fa981609d25`.
2. Get this model and find its model layer. It should hash to
   `199388f8f8cbec06b80bd63b0b1a774103e00c993c107bf1d480e58047420532` and be
   2,498,332,864 bytes.
3. Open both with a GGUF reader and list the tensors. The original has 883 tensors and
   this one has 444 tensors. Every one of the 439 missing tensors has a name beginning
   `v.` or `mm.`. Nothing else is missing.
4. The 444 that remain are `token_embd.weight`, `output_norm.weight`, and thirteen
   tensors for each of the 34 blocks: `attn_q.weight`, `attn_k.weight`, `attn_v.weight`,
   `attn_output.weight`, `attn_q_norm.weight`, `attn_k_norm.weight`, `attn_norm.weight`,
   `post_attention_norm.weight`, `ffn_gate.weight`, `ffn_up.weight`, `ffn_down.weight`,
   `ffn_norm.weight`, `post_ffw_norm.weight`. By quantization: 205 Q4_K, 34 Q6_K,
   205 F32, the same counts as in the original's text half.
5. For each of those 444, compare the ggml type, the shape, the byte count and the
   sha256 of the tensor's own bytes against the same tensor in the original. All 444
   match. Any single mismatch means the weights were touched, and they were not.
6. The metadata: 27 keys here against 35 in the original. The 9 that are gone are the
   vision keys listed above. One key is new, `general.description`, which says the file
   was modified. `general.architecture` is still `gemma3`, and the tokenizer keys are
   identical.

The script that does the surgery lives in a private repository, so the recipe is written
out above rather than linked. It is short enough to re-implement: copy every tensor whose
name does not start with `v.` or `mm.`, copy every metadata key that does not start with
`gemma3.vision.` or `gemma3.mm.`, add `general.description`, write the file. Then hash
what you wrote. If you get `199388f8…` you have reproduced it exactly.

## What we did not do

No fine-tuning. No requantization. No distillation. No change to the tokenizer, the chat
template or the sampling defaults. No system prompt. This is the same model, minus a part
it was not being asked to use.


## Get it

- Ollama: `ollama run innerloop/gemma3-4b-text` (the same file, published at https://ollama.com/innerloop/gemma3-4b-text)
- This repository: the GGUF is `gemma-3-4b-it-text-Q4_K_M.gguf`, 2,498,332,864 bytes, sha256 `199388f8f8cbec06b80bd63b0b1a774103e00c993c107bf1d480e58047420532`.