Update models
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ai-modelscope
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README.md
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README.md
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---
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license: Apache License 2.0
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#model-type:
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##如 gpt、phi、llama、chatglm、baichuan 等
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#- gpt
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#domain:
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##如 nlp、cv、audio、multi-modal
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#- nlp
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#language:
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##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
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#- cn
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#metrics:
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##如 CIDEr、Blue、ROUGE 等
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#- CIDEr
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#tags:
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##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
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#- pretrained
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#tools:
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##如 vllm、fastchat、llamacpp、AdaSeq 等
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#- vllm
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---
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### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
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#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
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SDK下载
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```bash
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#安装ModelScope
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pip install modelscope
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```
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```python
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#SDK模型下载
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from modelscope import snapshot_download
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model_dir = snapshot_download('second-state/datagemma-rag-27b-it-GGUF')
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```
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Git下载
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```
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#Git模型下载
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git clone https://www.modelscope.cn/second-state/datagemma-rag-27b-it-GGUF.git
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```
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<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
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---
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base_model: google/datagemma-rag-27b-it
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inference: false
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license: gemma
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library_name: transformers
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pipeline_tag: text-generation
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model_creator: Google
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model_name: datagemma-rag-27b-it
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quantized_by: Second State Inc.
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tags:
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- conversational
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---
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<!-- header start -->
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<!-- 200823 -->
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<div style="width: auto; margin-left: auto; margin-right: auto">
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<img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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</div>
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<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
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<!-- header end -->
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# Datagemma-rag-27b-it-GGUF
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## Original Model
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[google/datagemma-rag-27b-it](https://huggingface.co/google/datagemma-rag-27b-it)
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## Run with LlamaEdge
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- LlamaEdge version: [v0.14.3](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.14.3) and above
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- Prompt template
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- Prompt type: `gemma-instruct`
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- Prompt string
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```text
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<bos><start_of_turn>user
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{user_message}<end_of_turn>
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<start_of_turn>model
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{model_message}<end_of_turn>model
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```
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- Context size: `8192`
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- Run as LlamaEdge service
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```bash
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wasmedge --dir .:. --nn-preload default:GGML:AUTO:datagemma-rag-27b-it-Q5_K_M.gguf \
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llama-api-server.wasm \
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--prompt-template gemma-instruct \
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--ctx-size 8192 \
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--model-name gemma-2-27b
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```
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- Run as LlamaEdge command app
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```bash
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wasmedge --dir .:. \
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--nn-preload default:GGML:AUTO:datagemma-rag-27b-it-Q5_K_M.gguf \
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llama-chat.wasm \
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--prompt-template gemma-instruct \
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--ctx-size 8192
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```
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## Quantized GGUF Models
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| Name | Quant method | Bits | Size | Use case |
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| ---- | ---- | ---- | ---- | ----- |
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| [datagemma-rag-27b-it-Q2_K.gguf](https://huggingface.co/second-state/datagemma-rag-27b-it-GGUF/blob/main/datagemma-rag-27b-it-Q2_K.gguf) | Q2_K | 2 | 10.4 GB| smallest, significant quality loss - not recommended for most purposes |
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| [datagemma-rag-27b-it-Q3_K_L.gguf](https://huggingface.co/second-state/datagemma-rag-27b-it-GGUF/blob/main/datagemma-rag-27b-it-Q3_K_L.gguf) | Q3_K_L | 3 | 14.5 GB| small, substantial quality loss |
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| [datagemma-rag-27b-it-Q3_K_M.gguf](https://huggingface.co/second-state/datagemma-rag-27b-it-GGUF/blob/main/datagemma-rag-27b-it-Q3_K_M.gguf) | Q3_K_M | 3 | 13.4 GB| very small, high quality loss |
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| [datagemma-rag-27b-it-Q3_K_S.gguf](https://huggingface.co/second-state/datagemma-rag-27b-it-GGUF/blob/main/datagemma-rag-27b-it-Q3_K_S.gguf) | Q3_K_S | 3 | 12.2 GB| very small, high quality loss |
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| [datagemma-rag-27b-it-Q4_0.gguf](https://huggingface.co/second-state/datagemma-rag-27b-it-GGUF/blob/main/datagemma-rag-27b-it-Q4_0.gguf) | Q4_0 | 4 | 15.6 GB| legacy; small, very high quality loss - prefer using Q3_K_M |
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| [datagemma-rag-27b-it-Q4_K_M.gguf](https://huggingface.co/second-state/datagemma-rag-27b-it-GGUF/blob/main/datagemma-rag-27b-it-Q4_K_M.gguf) | Q4_K_M | 4 | 16.6 GB| medium, balanced quality - recommended |
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| [datagemma-rag-27b-it-Q4_K_S.gguf](https://huggingface.co/second-state/datagemma-rag-27b-it-GGUF/blob/main/datagemma-rag-27b-it-Q4_K_S.gguf) | Q4_K_S | 4 | 15.7 GB| small, greater quality loss |
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| [datagemma-rag-27b-it-Q5_0.gguf](https://huggingface.co/second-state/datagemma-rag-27b-it-GGUF/blob/main/datagemma-rag-27b-it-Q5_0.gguf) | Q5_0 | 5 | 18.9 GB| legacy; medium, balanced quality - prefer using Q4_K_M |
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| [datagemma-rag-27b-it-Q5_K_M.gguf](https://huggingface.co/second-state/datagemma-rag-27b-it-GGUF/blob/main/datagemma-rag-27b-it-Q5_K_M.gguf) | Q5_K_M | 5 | 19.4 GB| large, very low quality loss - recommended |
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| [datagemma-rag-27b-it-Q5_K_S.gguf](https://huggingface.co/second-state/datagemma-rag-27b-it-GGUF/blob/main/datagemma-rag-27b-it-Q5_K_S.gguf) | Q5_K_S | 5 | 18.9 GB| large, low quality loss - recommended |
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| [datagemma-rag-27b-it-Q6_K.gguf](https://huggingface.co/second-state/datagemma-rag-27b-it-GGUF/blob/main/datagemma-rag-27b-it-Q6_K.gguf) | Q6_K | 6 | 22.3 GB| very large, extremely low quality loss |
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| [datagemma-rag-27b-it-Q8_0.gguf](https://huggingface.co/second-state/datagemma-rag-27b-it-GGUF/blob/main/datagemma-rag-27b-it-Q8_0.gguf) | Q8_0 | 8 | 28.9 GB| very large, extremely low quality loss - not recommended |
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| [datagemma-rag-27b-it-f16-00001-of-00002.gguf](https://huggingface.co/second-state/datagemma-rag-27b-it-GGUF/blob/main/datagemma-rag-27b-it-f16-00001-of-00002.gguf) | f16 | 16 | 29.9 GB| |
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| [datagemma-rag-27b-it-f16-00002-of-00002.gguf](https://huggingface.co/second-state/datagemma-rag-27b-it-GGUF/blob/main/datagemma-rag-27b-it-f16-00002-of-00002.gguf) | f16 | 16 | 24.6 GB| |
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*Quantized with llama.cpp b3664*
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