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Gemma-4-12B-it-AWQ-INT4-Multimodal-LongContext
almernzh/Gemma-4-12B-it-AWQ-INT4-Multimodal-LongContext
Gemma-4-12B-it-AWQ-INT4-Multimodal-LongContext is a image-text-to-text model from almernzh. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
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.
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From the Hugging Face model README
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.
- Task
- Image-Text-to-Text
- Library
- transformers
- Type
- gemma4_unified
- License
- apache-2.0
- Created
- Jun 28, 2026
- Updated
- Jun 28, 2026