---
title: gemma-2-2b-GGUF
canonical_url: "https://www.modelscope.cn/models/LLM-Research/gemma-2-2b-GGUF"
md_url: "https://www.modelscope.cn/models/LLM-Research/gemma-2-2b-GGUF.md"
repository: LLM-Research/gemma-2-2b-GGUF
last_updated: 2024-08-02
license: gemma
pipeline_tag: text-generation
tasks:
  - text-generation
library_name:
  - gguf
  - pytorch
frameworks:
  - pytorch
downloads: 2394
stars: 2
tags:
  - gguf
---

# gemma-2-2b-GGUF

> gemma-2-2b-GGUF - LLM-Research 在 ModelScope 开源的模型。This model card corresponds to the 2b pretrained version of the Gemma 2 model in GGUF Format. The weights here are float32.

LLM-Research/gemma-2-2b-GGUF 是 ModelScope 魔搭社区上的text-generation模型，采用 gemma 许可。

- **Repository**: LLM-Research/gemma-2-2b-GGUF
- **License**: gemma
- **Tasks**: text-generation
- **Tags**: gguf
- **Downloads**: 2394
- **Stars**: 2
- **Last updated**: 2024-08-02

Source: https://www.modelscope.cn/models/LLM-Research/gemma-2-2b-GGUF

---

# Gemma Model Card

**Model Page**: [Gemma](https://ai.google.dev/gemma/docs)

This model card corresponds to the 2b pretrained version of the Gemma 2 model in GGUF Format. The weights here are **float32**.

> [!IMPORTANT]
>
> In llama.cpp, and other related tools such as Ollama and LM Studio, please make sure that you have these flags set correctly, especially **`repeat-penalty`**. Georgi Gerganov (llama.cpp's author) shared his experience in https://huggingface.co/google/gemma-7b-it/discussions/38#65d7b14adb51f7c160769fa1.

You can also visit the model card of the [2B instruct v2 model GGUF](https://huggingface.co/google/gemma-2-2b-it-GGUF). 

**Resources and Technical Documentation**:

* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
* [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
* [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335?version=gemma-2b-it-gg-hf)

**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent/verify/huggingface?returnModelRepoId=google/gemma-2-2b-it-GGUF)

**Authors**: Google

## Model Information

Summary description and brief definition of inputs and outputs.

### Description

Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
They are text-to-text, decoder-only large language models, available in English,
with open weights, pre-trained variants, and instruction-tuned variants. Gemma
models are well-suited for a variety of text generation tasks, including
question answering, summarization, and reasoning. Their relatively small size
makes it possible to deploy them in environments with limited resources such as
a laptop, desktop or your own cloud infrastructure, democratizing access to
state of the art AI models and helping foster innovation for everyone.