初始化项目，由ModelHub XC社区提供模型

Model: yondong/AMchat
Source: Original Platform

README.md

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+---
+license: Apache License 2.0
+pipeline_tag: text-generation
+---
+
+
+<p align="center">
+    <img src="logo.png" width="200"/>
+<p>
+
+<p align="center">
+    <b><font size="6">AMchat</font></b> 
+<p>
+
+<div align="center">
+
+[💻Github Repo](https://github.com/AXYZdong/AMchat)
+
+</div>
+
+AM (Advanced Mathematics) Chat is a large-scale language model that integrates mathematical knowledge, advanced mathematics problems, and their solutions. This model utilizes a dataset that combines Math and advanced mathematics problems with their analyses. It is based on the InternLM2-Math-7B model and has been fine-tuned with xtuner, specifically designed to solve advanced mathematics problems.
+
+If you find this project helpful, feel free to ⭐ Star it and help more people discover it!
+
+### Import from Transformers
+To load the AMchat model using Transformers, use the following code:
+```python
+from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_dir = snapshot_download("Shanghai_AI_Laboratory/internlm2-math-7b")
+tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto", trust_remote_code=True)
+
+# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
+model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto",  trust_remote_code=True, torch_dtype=torch.float16)
+model = model.eval()
+response, history = model.chat(tokenizer, "1+1=", history=[], meta_instruction="")
+print(response)
+```
+
+
+AM (Advanced Mathematics) chat 是一个集成了数学知识和高等数学习题及其解答的大语言模型。该模型使用 Math 和高等数学习题及其解析融合的数据集，基于 InternLM2-Math-7B 模型，通过 xtuner 微调，专门设计用于解答高等数学问题。
+
+如果你觉得这个项目对你有帮助，欢迎 ⭐ Star，让更多的人发现它！
+
+### 通过 Transformers 加载
+通过以下的代码加载 AMchat  模型
+
+```python
+from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_dir = snapshot_download("Shanghai_AI_Laboratory/internlm2-math-7b")
+tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto", trust_remote_code=True)
+
+# `torch_dtype=torch.float16` 可以令模型以 float16 精度加载，否则 transformers 会将模型加载为 float32，导致显存不足
+model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto",  trust_remote_code=True, torch_dtype=torch.float16)
+model = model.eval()
+response, history = model.chat(tokenizer, "1+1=", history=[], meta_instruction="")
+print(response)
+```