Name: Gemini 1.5 Pro
Author: Google

Overview

Gemini 1.5 Pro is a multimodal model designed for developers who need to process massive datasets without the overhead of complex RAG pipelines. Its standout feature is a massive context window—supporting up to 2 million tokens—which allows it to ingest entire codebases, hour-long videos, or thousands of pages of documentation in a single prompt. Unlike previous iterations, it maintains high retrieval accuracy across this span, making it ideal for deep codebase analysis, complex document synthesis, and multimodal reasoning. Integration is streamlined via Google AI Studio and Vertex AI, offering a competitive alternative to GPT-4o for tasks requiring long-term memory and native multimodal inputs.

Highlights

Massive context window up to 2 million tokens
Native multimodal processing of text, audio, and video
High-precision retrieval across extremely long sequences
Efficient integration via Vertex AI and AI Studio

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download google/gemini-1.5-pro

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download google/gemini-1.5-pro config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('google/gemini-1.5-pro')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/google/gemini-1.5-pro

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/google/gemini-1.5-pro

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('google/gemini-1.5-pro')
tokenizer = AutoTokenizer.from_pretrained('google/gemini-1.5-pro')