61 lines
2.6 KiB
Markdown
61 lines
2.6 KiB
Markdown
---
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license: Apache License 2.0
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pipeline_tag: text-generation
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---
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<p align="center">
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<img src="logo.png" width="200"/>
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<p>
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<p align="center">
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<b><font size="6">AMchat</font></b>
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<p>
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<div align="center">
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[💻Github Repo](https://github.com/AXYZdong/AMchat)
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</div>
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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.
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If you find this project helpful, feel free to ⭐ Star it and help more people discover it!
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### Import from Transformers
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To load the AMchat model using Transformers, use the following code:
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```python
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from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
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import torch
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model_dir = snapshot_download("Shanghai_AI_Laboratory/internlm2-math-7b")
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tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto", trust_remote_code=True)
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# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
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model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, torch_dtype=torch.float16)
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model = model.eval()
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response, history = model.chat(tokenizer, "1+1=", history=[], meta_instruction="")
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print(response)
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```
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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。
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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！
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### 通过 Transformers 加载
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通过以下的代码加载 AMchat 模型
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```python
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from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
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import torch
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model_dir = snapshot_download("Shanghai_AI_Laboratory/internlm2-math-7b")
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tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto", trust_remote_code=True)
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# `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
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model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, torch_dtype=torch.float16)
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model = model.eval()
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response, history = model.chat(tokenizer, "1+1=", history=[], meta_instruction="")
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print(response)
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``` |