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README.md

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+---
+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), 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`.