---
title: gemma-2-9b-it
canonical_url: "https://www.modelscope.cn/models/LLM-Research/gemma-2-9b-it"
md_url: "https://www.modelscope.cn/models/LLM-Research/gemma-2-9b-it.md"
repository: LLM-Research/gemma-2-9b-it
last_updated: 2024-09-03
license: gemma
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - gemma2
architectures:
  - Gemma2ForCausalLM
parameters: 9.2B
tensor_type:
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - Pytorch
language:
  - en
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 62589
stars: 19
tags:
  - unsloth
  - transformers
  - gemma2
  - gemma
---

# gemma-2-9b-it

> gemma-2-9b-it - LLM-Research 在 ModelScope 开源的模型。Reminder to use the dev version Transformers: pip install git+https://github.com/huggingface/transformers.git

LLM-Research/gemma-2-9b-it 是 ModelScope 魔搭社区上的 9.2B 参数text-generation模型，采用 gemma 许可，可用 deploy_task text/emb、lmdeploy 0.9.1、sglang 0.5.2 部署。

- **Repository**: LLM-Research/gemma-2-9b-it
- **License**: gemma
- **Tasks**: text-generation
- **Parameters**: 9.2B
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: unsloth, transformers, gemma2, gemma
- **Downloads**: 62589
- **Stars**: 19
- **Last updated**: 2024-09-03

Source: https://www.modelscope.cn/models/LLM-Research/gemma-2-9b-it

---

## Reminder to use the dev version Transformers:
`pip install git+https://github.com/huggingface/transformers.git`

# Finetune Gemma, Llama 3, Mistral 2-5x faster with 70% less memory via Unsloth!

Directly quantized 4bit model with `bitsandbytes`.

We have a Google Colab Tesla T4 notebook for **Gemma 2 (9B)** here: https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/u54VK8m8tk)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/buy%20me%20a%20coffee%20button.png" width="200"/>](https://ko-fi.com/unsloth)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

## ✨ Finetune for Free

All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.

| Unsloth supports          |    Free Notebooks                                                                                           | Performance | Memory use |
|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
| **Llama 3 (8B)**      | [▶️ Start on Colab](https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing)               | 2.4x faster | 58% less |
| **Gemma 2 (9B)**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing)               | 2x faster | 63% less |
| **Mistral (9B)**    | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)               | 2.2x faster | 62% less |
| **Phi 3 (mini)**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1lN6hPQveB_mHSnTOYifygFcrO8C1bxq4?usp=sharing)               | 2x faster | 63% less |
| **TinyLlama**  | [▶️ Start on Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)              | 3.9x faster | 74% less |
| **CodeLlama (34B)** A100   | [▶️ Start on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing)              | 1.9x faster | 27% less |
| **Mistral (7B)** 1xT4  | [▶️ Start on Kaggle](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook) | 5x faster\* | 62% less |
| **DPO - Zephyr**     | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)               | 1.9x faster | 19% less |

- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates.
- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
- \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.