---
license: apache-2.0
base_model: google/gemma-4-12B-it
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- gemma4
- awq
- int4
- w4a16
- multimodal
- long-context
- compressed-tensors
---

# Gemma-4-12B-it-AWQ-INT4-Multimodal-LongContext

AWQ INT4 weights for `google/gemma-4-12B-it`, prepared for lower VRAM use while keeping the base model practical for text, image, audio, structured output, and long-context experiments.

## Quantization

- Method: AWQ
- Weight format: INT4
- Compute scheme: W4A16
- Calibration samples: 128
- Max calibration length: 4096 tokens
- Calibration style: document QA, OCR-style text, structured JSON output, coding prompts, table and chart descriptions, transcript-style prompts, and long-context retrieval prompts
- Tooling: LLM Compressor 0.12.0, Transformers 5.10.1, PyTorch 2.11.0+cu128
- GPU used: NVIDIA A100-SXM4-80GB

## Local Check

A short generation check was run after saving the weights.

- Test prompt: return a JSON object with `status: ok`
- Result: passed
- Peak VRAM during local load test: 22.98 GB

## Notes

These weights are intended for practical inference tests with lower memory use than the original precision. Quantization can affect exact wording, numeric precision, and long-context recall. Full 256K context behavior should only be assumed after testing in your own setup.