Model reference · open weights
gemma-3 is an open-weight language model from gaunernst, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
Gemma 3 27B Instruction-tuned INT4 This is the QAT INT4 Flax checkpoint (from Kaggle) converted to HF+AWQ format for ease of use. AWQ was NOT used for quantization. You can find the conversion script convertflax.py in this model repo. NOTE: this is NOT the same as the official QAT INT4 GGUFs released here https://huggingface.co/collections/google/gemma-3-qat-67ee61ccacbf2be4195c265b Below is the original Model card from https://huggingface.co/google/gemma-3-27b-it Gemma 3 model card Model Page: Gemma Resources and Technical Documentation: [Gemma 3 Technical Report][g3-tech-report] [Responsible Generative AI Toolkit][rai-toolkit] [Gemma on Kaggle][kaggle-gemma] [Gemma on Vertex Model Garden][vertex-mg-gemma3] Terms of Use: [Terms][terms] Authors: Google DeepMind Model Information Summary description and brief definition of inputs and outputs. Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. Inputs and outputs - Input: - Text string, such as a question, a prompt, or a document to be summarized - Images, normalized to 896 x 896 resolution and encoded to 256 tokens each - Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and 32K tokens for the 1B size - Output: - Generated text in response to the input, such as an answer to a question, analysis of image content, or a summary of a document - Total output context of 8192 tokens Us
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | gaunernst |
|---|---|
| Type | Language models |
| Parameters (lead) | 27.4B |
| Variants | 1 |
| Runs with | transformers |
| Based on | google/gemma-3-27b-it |
| Released | 2025-03-21 |
| Popularity | 1.4M downloads / month |
| Likes | 40 |
| Licence | Open, with conditions |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| gemma-3-27b-it-int4-awq | 27.4B | AWQ | — | — | Weights ↗ |
Using it via the API
Once AxForge deploys gaunernst-gemma-3 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (gaunernst-gemma-3 below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"gaunernst-gemma-3","messages":[{"role":"user","content":"Hello"}]}'
Details
Tags
Papers
Licence
Open weights under gemma, which carries conditions (e.g. attribution or an acceptable-use clause). Worth a read before production use — we can walk you through it. Read the licence ↗