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--- a/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
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- chat
 ---
-### 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
+# InternLM3-8B-Instruct GGUF Model

-SDK下载
-```bash
-#安装ModelScope
-pip install modelscope
+## Introduction
+
+The `internlm3-8b-instruct` model in GGUF format can be utilized by [llama.cpp](https://github.com/ggerganov/llama.cpp), a highly popular open-source framework for Large Language Model (LLM) inference, across a variety of hardware platforms, both locally and in the cloud.
+This repository offers `internlm3-8b-instruct` models in GGUF format in both half precision and various low-bit quantized versions, including `q5_0`, `q5_k_m`, `q6_k`, and `q8_0`.
+
+In the subsequent sections, we will first present the installation procedure, followed by an explanation of the model download process. 
+And finally we will illustrate the methods for model inference and service deployment through specific examples.
+
+## Installation
+
+We recommend building `llama.cpp` from source. The following code snippet provides an example for the Linux CUDA platform. For instructions on other platforms, please refer to the [official guide](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#build).
+
+- Step 1: create a conda environment and install cmake
+
+```shell
+conda create --name internlm3 python=3.10 -y
+conda activate internlm3
+pip install cmake
 ```
+
+- Step 2: clone the source code and build the project 
+
+```shell
+git clone --depth=1 https://github.com/ggerganov/llama.cpp.git
+cd llama.cpp
+cmake -B build -DGGML_CUDA=ON
+cmake --build build --config Release -j
+```
+
+All the built targets can be found in the sub directory `build/bin`
+
+In the following sections, we assume that the working directory is at the root directory of `llama.cpp`.
+
+## Download models
+
+In the [introduction section](#introduction), we mentioned that this repository includes several models with varying levels of computational precision. You can download the appropriate model based on your requirements.
+For instance, `internlm3-8b-instruct-fp16.gguf` can be downloaded as below：
+
+```shell
+pip install huggingface-hub
+huggingface-cli download internlm/internlm3-8b-instruct-gguf internlm3-8b-instruct.gguf --local-dir . --local-dir-use-symlinks False
+```
+
+## Inference
+
+You can use `llama-cli` for conducting inference. For a detailed explanation of `llama-cli`, please refer to [this guide](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
+
+### chat example
+
+```shell
+build/bin/llama-cli \
+    --model internlm3-8b-instruct.gguf  \
+    --predict 512 \
+    --ctx-size 4096 \
+    --gpu-layers 48 \
+    --temp 0.8 \
+    --top-p 0.8 \
+    --top-k 50 \
+    --seed 1024 \
+    --color \
+    --prompt "<|im_start|>system\nYou are an AI assistant whose name is InternLM (书生·浦语).\n- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.<|im_end|>\n" \
+    --interactive \
+    --multiline-input \
+    --conversation \
+    --verbose \
+    --logdir workdir/logdir \
+    --in-prefix "<|im_start|>user\n" \
+    --in-suffix "<|im_end|>\n<|im_start|>assistant\n"
+```
+
+### Function call example
+
+`llama-cli` example:
+
+```shell
+build/bin/llama-cli \
+    --model internlm3-8b-instruct.gguf \
+    --predict 512 \
+    --ctx-size 4096 \
+    --gpu-layers 48 \
+    --temp 0.8 \
+    --top-p 0.8 \
+    --top-k 50 \
+    --seed 1024 \
+    --color \
+    --prompt '<|im_start|>system\nYou are InternLM-Chat, a harmless AI assistant.<|im_end|>\n<|im_start|>system name=<|plugin|>[{"name": "get_current_weather", "parameters": {"required": ["location"], "type": "object", "properties": {"location": {"type": "string", "description": "The city and state, e.g. San Francisco, CA"}, "unit": {"type": "string"}}}, "description": "Get the current weather in a given location"}]<|im_end|>\n<|im_start|>user\n' \
+    --interactive \
+    --multiline-input \
+    --conversation \
+    --verbose \
+    --in-suffix "<|im_end|>\n<|im_start|>assistant\n" \
+    --special
+```
+
+Conversation results:
+
+```text
+<s><|im_start|>system
+You are InternLM-Chat, a harmless AI assistant.<|im_end|>
+<|im_start|>system name=<|plugin|>[{"name": "get_current_weather", "parameters": {"required": ["location"], "type": "object", "properties": {"location": {"type": "string", "description": "The city and state, e.g. San Francisco, CA"}, "unit": {"type": "string"}}}, "description": "Get the current weather in a given location"}]<|im_end|>
+<|im_start|>user
+
+> I want to know today's weather in Shanghai
+I need to use the get_current_weather function to get the current weather in Shanghai.<|action_start|><|plugin|>
+{"name": "get_current_weather", "parameters": {"location": "Shanghai"}}<|action_end|>32
+<|im_end|>
+
+> <|im_start|>environment name=<|plugin|>\n{"temperature": 22}
+The current temperature in Shanghai is 22 degrees Celsius.<|im_end|>
+
+> 
+```
+
+## Serving
+
+`llama.cpp` provides an OpenAI API compatible server - `llama-server`. You can deploy `internlm3-8b-instruct.gguf` into a service like this:
+
+```shell
+./build/bin/llama-server -m ./internlm3-8b-instruct.gguf -ngl 48
+```
+
+At the client side, you can access the service through OpenAI API:
+
 ```python
-#SDK模型下载
-from modelscope import snapshot_download
-model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm3-8b-instruct-gguf')
+from openai import OpenAI
+client = OpenAI(
+    api_key='YOUR_API_KEY',
+    base_url='http://localhost:8080/v1'
+)
+model_name = client.models.list().data[0].id
+response = client.chat.completions.create(
+  model=model_name,
+  messages=[
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": " provide three suggestions about time management"},
+  ],
+  temperature=0.8,
+  top_p=0.8
+)
+print(response)
 ```
-Git下载
-```
-#Git模型下载
-git clone https://www.modelscope.cn/Shanghai_AI_Laboratory/internlm3-8b-instruct-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>
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