Gemma 3 4B

Gemma 3 4B is a small open-source language model released by Google with approximately 4 billion parameters, capable of running on the CPU of an ordinary consumer-grade laptop.

Gemma 3 1B is the smallest open-weight model in the Gemma 3 series released by Google in 2025, with approximately 1 billion parameters.

The developer tested the gemma3:4b model (temperature 0.1) with 30 manually labeled items: under the v1 prose prompt configuration, brand accuracy was 97% with 1/4 fabricated brands; after adding a JSON Schema in v2, brand accuracy was 100% with 0/4 fabricated brands.

Running ollama show gemma3:4b shows that Gemma 3's capability list is completion and vision, with no tool calling capability.

Sourcerer uses a locally running Gemma 3 4B model for item recognition and pure TypeScript functions for pricing calculations.

On the same machine, the author compared three Gemma variants on 76 synthetic cases: Gemma 4 E4B reached 97%, Gemma 3 4B reached 78% with 5 false positives, and Gemma 4 12B reached 80% with 32 call timeouts.

LedgerPrep Lite uses Ollama and Gemma 3 4B to run an open-weight language model locally, extracting structured information from images.

The author empirically compared Gemma 3 1B and 4B: 1B, in the rename scenario, wrote "the app will resend the shelved event" and told the sender to make the change, and both errors were caught by the checks and fell back to the rule answer; 4B took about 16 seconds on a laptop GPU and accurately stated the customer, amount and specific field.

In Holdfast, Gemma 3 4B is only responsible for choosing one of eight fixed causes (constrained by a JSON schema) and writing the explanatory text, and the code checks the output word by word.

Local image reading takes about 52 seconds on CPU, including model loading.

The local version uses Gemma 3 4B running on the user's machine via Ollama, reads photos of ingredient labels and extracts the text into the same decision pipeline; on a real package of dairy product it returned Contains: Milk.

For text extraction scenarios like PrintBrief with a single, narrow task and short inputs, a quantized model on the order of 4B is already usable enough.

PrintBrief uses the locally run open-source model Gemma 3 4B via Ollama for semantic extraction, parsing informal customer messages into structured print orders.

AutoHeal AI's tech stack includes: Gemma 3 4B as the language model, Ollama as the local runtime, Python + FastAPI for the backend, React for the frontend, psutil for monitoring collection, Scikit-learn for anomaly detection, SQLite for data storage, Docker for the containerized test environment, Recharts for visualization, and GitHub for open-source hosting.

Gemma 3 4B is a member of the open-weight multimodal model family released by Google in 2025, with approximately 4 billion parameters, supporting text and image input.

DukaanAI's core AI capability comes from the locally running Gemma 3 4B model, invoked through Ollama, which analyzes the image and returns structured JSON.

Local Receipt Sorter uses the Gemma 3 vision model (4B parameters) served locally through Ollama, and the model can be switched to the 12B version via an environment variable to improve accuracy.

The Gemma 3 4B model's field extraction accuracy on complex layouts, handwriting, or low-light photos will be noticeably lower than that of the 12B or cloud-based large models.

The Gemma 3 vision model (4B) is a lightweight multimodal model released by Google that supports text and image input, occupies about 3-4GB of VRAM after 4-bit quantization, and can run on consumer laptop GPUs or Apple Silicon.

For small-parameter models (such as 4B-level vision models), model self-assessed confidence is often unreliable, because confidence calibration requires the model to have accurate metacognition of its own knowledge boundaries.