> Note: This repository is an archived mirror and is not the original upstream source. > The original model, weights, and documentation are developed and maintained by Google DeepMind. > > All hosted model weights...
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Note: This repository is an archived mirror and is not the original upstream source. > The original model, weights, and documentation are developed and maintained by Google DeepMind. > > All hosted model weights...
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2 excerpts--- library_name: transformers license: apache-2.0 license_link: https://ai.google.dev/gemma/docs/gemma_4_license pipeline_tag: any-to-any --- > **Note:** This repository is an **archived mirror** and is **not** the original upstream source. > The original model, weights, and documentation are developed and maintained by **Google DeepMind**. > > All hosted model weights are **unmodified**. > > The model is released under the **Apache License, Version 2.0**, which permits use, modification, and redistribution under its terms. > > *This repository is not affiliated with, endorsed by, or sponsored by Google DeepMind.* <p align="center"> <a href="https://huggingface.co/collections/google/gemma-4" target="_blank"> <img src="https://img.shields.io/badge/Hugging%20Face-Models-yellow?style=flat-square"> </a> <a href="https://github.com/google-gemma" target="_blank"> <img src="https://img.shields.io/badge/GitHub-Source-181717?style=flat-square"> </a> <a href="https://ai.google.dev/gemma/docs/mtp/mtp" target="_blank"> <img src="https://img.shields.io/badge/MTP-Documentation-0A66C2?style=flat-square"> </a> <br> <a href="https://ai.google.dev/gemma/docs/gemma_4_license" target="_blank"> <img src="https://img.shields.io/badge/License-Apache%202.0-red?style=flat-square"> </a> <a href="https://deepmind.google/models/gemma/" target="_blank"> <img src="https://img.shields.io/badge/Authors-Google%20DeepMind-blue?style=flat-square"> </a> </p> > [!Note] > This model card is for the Multi-Token Prediction (MTP) drafters for the Gemma 4 models. MTP is implemented by extending the base model with a smaller, faster draft model. When used in a Speculative Decoding pipeline, the draft model predicts several tokens ahead, which the target model then verifies in parallel. This results in significant decoding speedups (up to 2x) while guaranteeing the exact same quality as standard generation, making these checkpoints perfect for low-latency and on-device applications. Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on small models) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. Featuri...
Source context: 10 downloads · 0 likes · Pipeline any-to-any · Library transformers · Repo YuCollection/gemma-4-31B-it-assistant-bf16