transformers gguf conversational text-generation base_model:google/gemma-2-2b-it base_model:quantized:google/gemma-2-2b-it license:gemma endpoints_compatible region:us
---
base_model: google/gemma-2-2b-it
library_name: transformers
license: gemma
pipeline_tag: text-generation
tags:
- conversational
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
  agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
  Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
quantized_by: Davidqian123
---

## Gemma-2-2b-Instruct-GGUF Introduction

Gemma 2 Instruct is the latest addition to Google's Gemma family of lightweight, state-of-the-art open models. Built on Gemini technology, this 2 billion parameter model excels at various text generation tasks while being compact enough for edge and low-power computing environments.

## Key Features

- Based on Gemini technology
- 2 billion parameters
- Trained on 2 trillion tokens of web documents, code, and mathematics
- Suitable for edge devices and low-power compute
- Versatile for text generation, coding, and mathematical tasks
- Retains the large vocabulary from Gemma 1.1 for enhanced multilingual and coding capabilities

## Applications

Gemma 2 Instruct is designed for a wide range of applications, including:

- Content creation
- Chatbots and conversational AI
- Text summarization
- Code generation
- Mathematical problem-solving

For more details check out their blog post here: https://developers.googleblog.com/en/smaller-safer-more-transparent-advancing-responsible-ai-with-gemma/

# Quantized GGUF Models Benchmark

| Name                         | Quant method | Bits | Size     | Use Cases                           |
| ---------------------------- | ------------ | ---- | -------- | ----------------------------------- |
| gemma-2-2b-it-Q2_K.gguf   | Q2_K         | 2    | 1.23 GB  | fast but high loss, not recommended |
| gemma-2-2b-it-Q3_K_S.gguf | Q3_K_S       | 3    | 1.36 GB  | extremely not recommended           |
| gemma-2-2b-it-Q3_K_M.gguf | Q3_K_M       | 3    | 1.46 GB  | moderate loss, not very recommended |
| gemma-2-2b-it-Q3_K_L.gguf | Q3_K_L       | 3    | 1.55 GB  | not very recommended                |
| gemma-2-2b-it-Q4_0.gguf   | Q4_0         | 4    | 1.63 GB  | moderate speed, recommended         |
| gemma-2-2b-it-Q4_1.gguf   | Q4_1         | 4    | 1.76 GB  | moderate speed, recommended         |
| gemma-2-2b-it-Q4_K_S.gguf | Q4_K_S       | 4    | 1.64 GB  | fast and accurate, very recommended |
| gemma-2-2b-it-Q4_K_M.gguf | Q4_K_M       | 4    | 1.71 GB  | fast, recommended                   |
| gemma-2-2b-it-Q5_0.gguf   | Q5_0         | 5    | 1.88 GB  | fast, recommended                   |
| gemma-2-2b-it-Q5_1.gguf   | Q5_1         | 5    | 2.01 GB  | very big, prefer Q4                 |
| gemma-2-2b-it-Q5_K_S.gguf | Q5_K_S       | 5    | 1.88 GB  | big, recommended                    |
| gemma-2-2b-it-Q5_K_M.gguf | Q5_K_M       | 5    | 1.92 GB  | big, recommended                    |
| gemma-2-2b-it-Q6_K.gguf   | Q6_K         | 6    | 2.15 GB  | very big, not very recommended      |
| gemma-2-2b-it-Q8_0.gguf   | Q8_0         | 8    | 2.78 GB  | very big, not very recommended      |
| gemma-2-2b-it-F16.gguf    | F16          | 16   | 5.24 GB  | extremely big                       |

_Quantized with llama.cpp_


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