Instructions to use Hyper-AI/gemma-4-E4B-it-fp8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hyper-AI/gemma-4-E4B-it-fp8 with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Hyper-AI/gemma-4-E4B-it-fp8") model = AutoModelForMultimodalLM.from_pretrained("Hyper-AI/gemma-4-E4B-it-fp8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files**lightvl Developed by Myself is a lightweight Vision-Language Model (VLM) quantization toolkit supporting FP8, INT8, FP8-Block. It integrates with vLLM for high-throughput inference and supports Qwen3-VL, Qwen3.5, InternVL-Chat, and Gemma-4 models.**
**fast quant your model step by step:**
1、 pip3 install lightvl
2、 lightvl YOUR_HF_MODEL_PATH
3、 the output quant model path is YOUR_HF_MODEL_PATH-fp8
README.md
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pipeline_tag: any-to-any
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tags:
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- fp8
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---
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## gemma-4-E4B-it-fp8(最新大模型推理加速技术见微信公众号: HyperAI)
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**vllm serve Hyper-AI/gemma-4-E4B-it-fp8 --max-model-len 32768**
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<div align="center">
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<img src=https://ai.google.dev/gemma/images/gemma4_banner.png>
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pipeline_tag: any-to-any
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tags:
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- fp8
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- gemma-4
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- lightvl
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---
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## gemma-4-E4B-it-fp8(最新大模型推理加速技术见微信公众号: HyperAI)
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**vllm serve Hyper-AI/gemma-4-E4B-it-fp8 --max-model-len 32768**
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**lightvl Developed by Myself is a lightweight Vision-Language Model (VLM) quantization toolkit supporting FP8, INT8, FP8-Block. It integrates with vLLM for high-throughput inference and supports Qwen3-VL, Qwen3.5, InternVL-Chat, and Gemma-4 models.**
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**fast quant your model step by step:**
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1、 pip3 install lightvl
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2、 lightvl YOUR_HF_MODEL_PATH
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3、 the output quant model path is YOUR_HF_MODEL_PATH-fp8
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<div align="center">
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<img src=https://ai.google.dev/gemma/images/gemma4_banner.png>
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