Any-to-Any
Transformers
Safetensors
gemma4_unified
image-text-to-text
fp8
vllm
llm-compressor
compressed-tensors
Instructions to use RedHatAI/gemma-4-12B-it-FP8-Dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RedHatAI/gemma-4-12B-it-FP8-Dynamic with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("RedHatAI/gemma-4-12B-it-FP8-Dynamic") model = AutoModelForMultimodalLM.from_pretrained("RedHatAI/gemma-4-12B-it-FP8-Dynamic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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This is a preliminary version (and subject to change) of FP8_Dynamic quantized [google/gemma-4-12B-it](https://huggingface.co/google/gemma-4-12B-it) model.
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The model has both weights and activations quantized to FP8_Dynamic format with [vllm-project/llm-compressor](https://github.com/vllm-project/llm-compressor).
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gemma-4-12B-it-FP8-Dynamic
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<img src="https://www.redhat.com/rhdc/managed-files/Catalog-Validated_model_0.png" alt="Model Icon" width="40" style="margin: 0; padding: 0;" />
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# FP8 Quantized RedHatAI/gemma-4-12B-it-FP8
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This is a preliminary version (and subject to change) of FP8_Dynamic quantized [google/gemma-4-12B-it](https://huggingface.co/google/gemma-4-12B-it) model.
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The model has both weights and activations quantized to FP8_Dynamic format with [vllm-project/llm-compressor](https://github.com/vllm-project/llm-compressor).
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