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--- |
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license: other |
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--- |
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![Aquila_logo](./log.jpeg) |
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<h4 align="center"> |
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<p> |
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<a href="https://huggingface.co/BAAI/AquilaChat2-34B/blob/main/README.md">English</a> |
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<b>简体中文</b> | |
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</p> |
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</h4> |
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<p align="center"> |
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<a href="https://github.com/FlagAI-Open/Aquila2" target="_blank">Github</a> • <a href="https://github.com/FlagAI-Open/Aquila2/blob/main/assets/wechat-qrcode.jpg" target="_blank">WeChat</a> <br> |
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</p> |
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# 悟道·天鹰(Aquila2) |
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我们开源了我们的 **Aquila2** 系列,现在包括基础语言模型 **Aquila2-7B** 和 **Aquila2-34B** ,对话模型 **AquilaChat2-7B** 和 **AquilaChat2-34B**,长文本对话模型**AquilaChat2-7B-16k** 和 **AquilaChat2-34B-16k** |
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悟道 · 天鹰 Aquila 模型的更多细节将在官方技术报告中呈现。请关注官方渠道更新。 |
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基于AquilaChat2-34B初始版本的开发经验,我们对AquilaChat2-34B进行了全面升级并发布1.2版本。评测结果显示, |
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AquilaChat2-34B-V1.2 模型在主观评测的8个二级能力维度上,均接近或超过 GPT3.5 水平。 |
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## 快速开始使用 AquilaChat2-34B |
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## 使用方式/How to use |
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### 1. 推理/Inference |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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device = torch.device("cuda") |
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model_info = "BAAI/AquilaChat2-34B" |
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tokenizer = AutoTokenizer.from_pretrained(model_info, trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained(model_info, trust_remote_code=True) |
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model.eval() |
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model.to(device) |
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text = "请给出10个要到北京旅游的理由。" |
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tokens = tokenizer.encode_plus(text)['input_ids'] |
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tokens = torch.tensor(tokens)[None,].to(device) |
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stop_tokens = ["###", "[UNK]", "</s>"] |
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with torch.no_grad(): |
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out = model.generate(tokens, do_sample=True, max_length=512, eos_token_id=100007, bad_words_ids=[[tokenizer.encode(token)[0] for token in stop_tokens]])[0] |
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out = tokenizer.decode(out.cpu().numpy().tolist()) |
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print(out) |
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``` |
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## 证书/License |
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Aquila2系列开源模型使用 [智源Aquila系列模型许可协议](https://huggingface.co/BAAI/AquilaChat2-34B/blob/main/BAAI-Aquila-Model-License%20-Agreement.pdf) |