初始化项目，由ModelHub XC社区提供模型

Model: KwaiKEG/kagentlms_qwen_7b_mat
Source: Original Platform

README.md

@@ -0,0 +1,86 @@
+---
+license: cc-by-nc-nd-4.0
+datasets:
+- kwaikeg/KAgentInstruct
+- kwaikeg/KAgentBench
+language:
+- en
+- zh
+pipeline_tag: text-generation
+---
+
+
+KwaiAgents ([Github](https://github.com/KwaiKEG/KwaiAgents)) is a series of Agent-related works open-sourced by the [KwaiKEG](https://github.com/KwaiKEG) from [Kuaishou Technology](https://www.kuaishou.com/en). The open-sourced content includes:
+
+1. **KAgentSys-Lite**: An experimental Agent Loop implemented based on open-source search engines, browsers, time, calendar, weather, and other tools, which is only missing the memory mechanism and some search capabilities compared to the system in the paper.
+2. **KAgentLMs**: A series of large language models with Agent capabilities such as planning, reflection, and tool-use, acquired through the Meta-agent tuning proposed in the paper.
+3. **KAgentInstruct**: Fine-tuned data of instructions generated by the Meta-agent in the paper.
+4. **KAgentBench**: Over 3,000 human-edited, automated evaluation data for testing Agent capabilities, with evaluation dimensions including planning, tool-use, reflection, concluding, and profiling.
+
+
+## User Guide
+
+### Direct usage
+
+Tutorial can refer to [QwenLM/Qwen](https://github.com/QwenLM/Qwen)
+```python
+from modelscope import AutoModelForCausalLM, AutoTokenizer
+from modelscope import GenerationConfig
+
+tokenizer = AutoTokenizer.from_pretrained("KwaiKEG/kagentlms_qwen_7b_mat", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    "KwaiKEG/kagentlms_qwen_7b_mat",
+    device_map="auto",
+    trust_remote_code=True
+).eval()
+
+response, history = model.chat(tokenizer, "你好", history=None)
+print(response)
+```
+
+### AgentLMs as service
+We recommend using [vLLM](https://github.com/vllm-project/vllm) and [FastChat](https://github.com/lm-sys/FastChat) to deploy the model inference service. First, you need to install the corresponding packages (for detailed usage, please refer to the documentation of the two projects):
+```bash
+pip install vllm
+pip install "fschat[model_worker,webui]"
+```
+To deploy KAgentLMs, you first need to start the controller in one terminal.
+```bash
+python -m fastchat.serve.controller
+```
+Secondly, you should use the following command in another terminal for single-gpu inference service deployment:
+```bash
+python -m fastchat.serve.vllm_worker --model-path $model_path --trust-remote-code
+```
+Where `$model_path` is the local path of the model downloaded. If the GPU does not support Bfloat16, you can add `--dtype half` to the command line.
+
+Thirdly, start the REST API server in the third terminal.
+```bash
+python -m fastchat.serve.openai_api_server --host localhost --port 8888
+```
+
+Finally, you can use the curl command to invoke the model same as the OpenAI calling format. Here's an example:
+```bash
+curl http://localhost:8888/v1/chat/completions \
+-H "Content-Type: application/json" \
+-d '{"model": "kagentlms_qwen_7b_mat", "messages": [{"role": "user", "content": "Who is Andy Lau"}]}'
+```
+
+### Citation
+```
+@article{pan2023kwaiagents,
+  author    = {Haojie Pan and
+               Zepeng Zhai and
+               Hao Yuan and
+               Yaojia Lv and
+               Ruiji Fu and
+               Ming Liu and
+               Zhongyuan Wang and
+               Bing Qin
+               },
+  title     = {KwaiAgents: Generalized Information-seeking Agent System with Large Language Models},
+  journal   = {CoRR},
+  volume    = {abs/2312.04889},
+  year      = {2023}
+}
+```