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Co-authored-by: Chen_01AI <Chen-01AI@users.noreply.huggingface.co>
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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
base_model: 01-ai/Yi-1.5-9B-Chat-16K
inference: false
model_creator: 01-ai
model_name: Yi-1.5-9B-Chat-16
model_type: yi
pipeline_tag: text-generation
quantized_by: Second State Inc.
license: apache-2.0
---
### 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/Yi-1.5-9B-Chat-16K-GGUF')
```
Git下载
```
#Git模型下载
git clone https://www.modelscope.cn/second-state/Yi-1.5-9B-Chat-16K-GGUF.git
```
<!-- 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 -->
<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>
# Yi-1.5-9B-Chat-16K-GGUF
## Original Model
[01-ai/Yi-1.5-9B-Chat-16K](https://huggingface.co/01-ai/Yi-1.5-9B-Chat-16K)
## Run with LlamaEdge
<!-- - LlamaEdge version: [v0.10.0](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.10.0) and above -->
- LlamaEdge version: coming soon
-
- Prompt template
- Prompt type: `chatml`
- Prompt string
```text
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
- Context size: `16384`
- Run as LlamaEdge service
```bash
wasmedge --dir .:. --nn-preload default:GGML:AUTO:Yi-1.5-9B-Chat-16K-Q5_K_M.gguf \
llama-api-server.wasm \
--prompt-template chatml \
--reverse-prompt "<|im_end|>" \
--ctx-size 16384 \
--model-name Yi-1.5-9B-Chat-16K
```
- Run as LlamaEdge command app
```bash
wasmedge --dir .:. --nn-preload default:GGML:AUTO:Yi-1.5-9B-Chat-16K-Q5_K_M.gguf \
llama-chat.wasm \
--prompt-template chatml \
--reverse-prompt "<|im_end|>" \
--ctx-size 16384
```
## Quantized GGUF Models
| Name | Quant method | Bits | Size | Use case |
| ---- | ---- | ---- | ---- | ----- |
| [Yi-1.5-9B-Chat-16K-Q2_K.gguf](https://huggingface.co/second-state/Yi-1.5-9B-Chat-16K-GGUF/blob/main/Yi-1.5-9B-Chat-16K-Q2_K.gguf) | Q2_K | 2 | 3.35 GB| smallest, significant quality loss - not recommended for most purposes |
| [Yi-1.5-9B-Chat-16K-Q3_K_L.gguf](https://huggingface.co/second-state/Yi-1.5-9B-Chat-16K-GGUF/blob/main/Yi-1.5-9B-Chat-16K-Q3_K_L.gguf) | Q3_K_L | 3 | 4.69 GB| small, substantial quality loss |
| [Yi-1.5-9B-Chat-16K-Q3_K_M.gguf](https://huggingface.co/second-state/Yi-1.5-9B-Chat-16K-GGUF/blob/main/Yi-1.5-9B-Chat-16K-Q3_K_M.gguf) | Q3_K_M | 3 | 4.32 GB| very small, high quality loss |
| [Yi-1.5-9B-Chat-16K-Q3_K_S.gguf](https://huggingface.co/second-state/Yi-1.5-9B-Chat-16K-GGUF/blob/main/Yi-1.5-9B-Chat-16K-Q3_K_S.gguf) | Q3_K_S | 3 | 3.9 GB| very small, high quality loss |
| [Yi-1.5-9B-Chat-16K-Q4_0.gguf](https://huggingface.co/second-state/Yi-1.5-9B-Chat-16K-GGUF/blob/main/Yi-1.5-9B-Chat-16K-Q4_0.gguf) | Q4_0 | 4 | 5.04 GB| legacy; small, very high quality loss - prefer using Q3_K_M |
| [Yi-1.5-9B-Chat-16K-Q4_K_M.gguf](https://huggingface.co/second-state/Yi-1.5-9B-Chat-16K-GGUF/blob/main/Yi-1.5-9B-Chat-16K-Q4_K_M.gguf) | Q4_K_M | 4 | 5.33 GB| medium, balanced quality - recommended |
| [Yi-1.5-9B-Chat-16K-Q4_K_S.gguf](https://huggingface.co/second-state/Yi-1.5-9B-Chat-16K-GGUF/blob/main/Yi-1.5-9B-Chat-16K-Q4_K_S.gguf) | Q4_K_S | 4 | 5.07 GB| small, greater quality loss |
| [Yi-1.5-9B-Chat-16K-Q5_0.gguf](https://huggingface.co/second-state/Yi-1.5-9B-Chat-16K-GGUF/blob/main/Yi-1.5-9B-Chat-16K-Q5_0.gguf) | Q5_0 | 5 | 6.11 GB| legacy; medium, balanced quality - prefer using Q4_K_M |
| [Yi-1.5-9B-Chat-16K-Q5_K_M.gguf](https://huggingface.co/second-state/Yi-1.5-9B-Chat-16K-GGUF/blob/main/Yi-1.5-9B-Chat-16K-Q5_K_M.gguf) | Q5_K_M | 5 | 6.26 GB| large, very low quality loss - recommended |
| [Yi-1.5-9B-Chat-16K-Q5_K_S.gguf](https://huggingface.co/second-state/Yi-1.5-9B-Chat-16K-GGUF/blob/main/Yi-1.5-9B-Chat-16K-Q5_K_S.gguf) | Q5_K_S | 5 | 6.11 GB| large, low quality loss - recommended |
| [Yi-1.5-9B-Chat-16K-Q6_K.gguf](https://huggingface.co/second-state/Yi-1.5-9B-Chat-16K-GGUF/blob/main/Yi-1.5-9B-Chat-16K-Q6_K.gguf) | Q6_K | 6 | 7.25 GB| very large, extremely low quality loss |
| [Yi-1.5-9B-Chat-16K-Q8_0.gguf](https://huggingface.co/second-state/Yi-1.5-9B-Chat-16K-GGUF/blob/main/Yi-1.5-9B-Chat-16K-Q8_0.gguf) | Q8_0 | 8 | 9.38 GB| very large, extremely low quality loss - not recommended |
| [Yi-1.5-9B-Chat-16K-f16.gguf](https://huggingface.co/second-state/Yi-1.5-9B-Chat-16K-GGUF/blob/main/Yi-1.5-9B-Chat-16K-f16.gguf) | f16 | 16 | 17.7 GB| |
*Quantized with llama.cpp b3135*

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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}