Update models

2024-10-30 02:50:57 +08:00
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--- a/README.md
+++ b/README.md
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---
-license: Apache License 2.0
-
-#model-type:
-##如 gpt、phi、llama、chatglm、baichuan 等
-#- gpt
-
-#domain:
-##如 nlp、cv、audio、multi-modal
-#- nlp
-
-#language:
-##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
-#- cn 
-
-#metrics:
-##如 CIDEr、Blue、ROUGE 等
-#- CIDEr
-
-#tags:
-##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
-#- pretrained
-
-#tools:
-##如 vllm、fastchat、llamacpp、AdaSeq 等
-#- vllm
---
-### 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。
-#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
-
-SDK下载
-```bash
-#安装ModelScope
-pip install modelscope
-```
-```python
-#SDK模型下载
-from modelscope import snapshot_download
-model_dir = snapshot_download('second-state/datagemma-rag-27b-it-GGUF')
-```
-Git下载
-```
-#Git模型下载
-git clone https://www.modelscope.cn/second-state/datagemma-rag-27b-it-GGUF.git
-```
-
-<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>
+---
+base_model: google/datagemma-rag-27b-it
+inference: false
+license: gemma
+library_name: transformers
+pipeline_tag: text-generation
+model_creator: Google
+model_name: datagemma-rag-27b-it
+quantized_by: Second State Inc.
+tags:
+- conversational
+---
+
+<!-- header start -->
+<!-- 200823 -->
+<div style="width: auto; margin-left: auto; margin-right: auto">
+<img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;">
+</div>
+<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
+<!-- header end -->
+
+# Datagemma-rag-27b-it-GGUF
+
+## Original Model
+
+[google/datagemma-rag-27b-it](https://huggingface.co/google/datagemma-rag-27b-it)
+
+## Run with LlamaEdge
+
+- LlamaEdge version: [v0.14.3](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.14.3) and above
+
+- Prompt template
+
+  - Prompt type: `gemma-instruct`
+
+  - Prompt string
+
+    ```text
+    <bos><start_of_turn>user
+    {user_message}<end_of_turn>
+    <start_of_turn>model
+    {model_message}<end_of_turn>model
+    ```
+
+- Context size: `8192`
+
+- Run as LlamaEdge service
+
+  ```bash
+  wasmedge --dir .:. --nn-preload default:GGML:AUTO:datagemma-rag-27b-it-Q5_K_M.gguf \
+    llama-api-server.wasm \
+    --prompt-template gemma-instruct \
+    --ctx-size 8192 \
+    --model-name gemma-2-27b
+  ```
+
+- Run as LlamaEdge command app
+
+  ```bash
+  wasmedge --dir .:. \
+    --nn-preload default:GGML:AUTO:datagemma-rag-27b-it-Q5_K_M.gguf \
+    llama-chat.wasm \
+    --prompt-template gemma-instruct \
+    --ctx-size 8192
+  ```
+
+## Quantized GGUF Models
+
+| Name | Quant method | Bits | Size | Use case |
+| ---- | ---- | ---- | ---- | ----- |
+| [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 |
+| [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 |
+| [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 |
+| [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 |
+| [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 |
+| [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 |
+| [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 |
+| [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 |
+| [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 |
+| [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 |
+| [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 |
+| [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 |
+| [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|  |
+| [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|  |
+
+*Quantized with llama.cpp b3664*
--- a/config.json
+++ b/config.json
@@ -0,0 +1,32 @@
+{
+  "_name_or_path": "rag_27b_transformers_checkpoint_15000",
+  "architectures": [
+    "Gemma2ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": 50.0,
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "eos_token_id": 1,
+  "final_logit_softcapping": 30.0,
+  "head_dim": 128,
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 4608,
+  "initializer_range": 0.02,
+  "intermediate_size": 36864,
+  "max_position_embeddings": 8192,
+  "model_type": "gemma2",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 46,
+  "num_key_value_heads": 16,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 144,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.45.0.dev0",
+  "use_cache": true,
+  "vocab_size": 256000
+}
--- a/configuration.json
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				`{"framework": "pytorch", "task": "text-generation", "allow_remote": true}`