| # Introduction | |
| [OpenBMB Technical Blog Series](https://openbmb.vercel.app/) | |
| The MiniCPM-MoE-8x2B is a decoder-only transformer-based generative language model. | |
| The MiniCPM-MoE-8x2B adopt a Mixture-of-Experts(MoE) architecture, which has 8 experts per layer and activates 2 of 8 experts for each token. | |
| # Usage | |
| This is a model version after instruction tuning but without other rlhf methods. Chat template is automatically applied. | |
| ``` python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| torch.manual_seed(0) | |
| path = 'openbmb/MiniCPM-MoE-8x2B' | |
| tokenizer = AutoTokenizer.from_pretrained(path) | |
| model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map='cuda', trust_remote_code=True) | |
| responds, history = model.chat(tokenizer, "山东省最高的山是哪座山, 它比黄山高还是矮？差距多少？", temperature=0.8, top_p=0.8) | |
| print(responds) | |
| ``` | |
| # Note | |
| 1. You can alse inference with [vLLM](https://github.com/vllm-project/vllm)(>=0.4.1), which is compatible with this repo and has a much higher inference throughput. | |
| 2. The precision of model weights in this repo is bfloat16. Manual convertion is needed for other kinds of dtype. | |
| 3. For more details, please refer to our [github repo](https://github.com/OpenBMB/MiniCPM). | |
| # Statement | |
| 1. As a language model, MiniCPM-MoE-8x2B generates content by learning from a vast amount of text. | |
| 2. However, it does not possess the ability to comprehend or express personal opinions or value judgments. | |
| 3. Any content generated by MiniCPM-MoE-8x2B does not represent the viewpoints or positions of the model developers. | |
| 4. Therefore, when using content generated by MiniCPM-MoE-8x2B, users should take full responsibility for evaluating and verifying it on their own. | |