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探索 AI 模型 47
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查看详情 ↗**This is the full Chinese-Alpaca-2-13B model，which can be loaded directly for inference and full-parameter training.** **Related models👇** * Long context base models * Chinese-LLaMA-2-7B-16K (full model) * Chinese-LLaMA-2-LoRA-7B-16K (LoRA model) * Chinese-LLaMA-2-13B-16K (full model) * Chinese-LLaMA-2-LoRA-13B-16K (LoRA model) * Base models * Chinese-LLaMA-2-7B (full model) * Chinese-LLaMA-2-LoRA-7B (LoRA model) * Chinese-LLaMA-2-13B (full model) * Chinese-LLaMA-2-LoRA-13B (LoRA model) * Instruction/Chat models * Chinese-Alpaca-2-7B (full model) * Chinese-Alpaca-2-LoRA-7B (LoRA model) * Chinese-Alpaca-2-13B (full model) * Chinese-Alpaca-2-LoRA-13B (LoRA model) This project is based on the Llama-2, released by Meta, and it is the second generation of the Chinese LLaMA & Alpaca LLM project. We open-source Chinese LLaMA-2 (foundation model) and Alpaca-2 (instruction-following model). These models have been expanded and optimized with Chinese vocabulary beyond the original Llama-2. We used large-scale Chinese data for incremental pre-training, which further improved the fundamental semantic understanding of the Chinese language, resulting in a significant performance improvement compared to the first-generation models. The relevant models support a 4K context and can be expanded up to 18K+ using the NTK method.
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查看详情 ↗**This is the full Chinese-LLaMA-2-13B-16K (context size 16K)，model，which can be loaded directly for inference and full-parameter training.** **Related models👇** * Long context base models (16K) * Chinese-LLaMA-2-7B-16K (full model) * Chinese-LLaMA-2-LoRA-7B-16K (LoRA model) * Chinese-LLaMA-2-13B-16K (full model) * Chinese-LLaMA-2-LoRA-13B-16K (LoRA model) * Long context Instruction/Chat models * Chinese-Alpaca-2-7B-16K (full model) * Chinese-Alpaca-2-LoRA-7B-16K (LoRA model) * Chinese-Alpaca-2-13B-16K (full model) * Chinese-Alpaca-2-LoRA-13B-16K (LoRA model) * Base models * Chinese-LLaMA-2-7B (full model) * Chinese-LLaMA-2-LoRA-7B (LoRA model) * Chinese-LLaMA-2-13B (full model) * Chinese-LLaMA-2-LoRA-13B (LoRA model) * Instruction/Chat models * Chinese-Alpaca-2-7B (full model) * Chinese-Alpaca-2-LoRA-7B (LoRA model) * Chinese-Alpaca-2-13B (full model) * Chinese-Alpaca-2-LoRA-13B (LoRA model) This project is based on the Llama-2, released by Meta, and it is the second generation of the Chinese LLaMA & Alpaca LLM project. We open-source Chinese LLaMA-2 (foundation model) and Alpaca-2 (instruction-following model). These models have been expanded and optimized with Chinese vocabulary beyond the original Llama-2. We used large-scale Chinese data for incremental pre-training, which further improved the fundamental semantic understanding of the Chinese language, resulting in a significant performance improvement compared to the first-generation models. The relevant models support a 4K context and can be expanded up to 18K+ using the NTK method.
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查看详情 ↗For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**. **Pre-Training with Whole Word Masking for Chinese BERT** Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu You may also interested in, - Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm - Chinese MacBERT: https://github.com/ymcui/MacBERT - Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA - Chinese XLNet: https://github.com/ymcui/Chinese-XLNet - Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
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查看详情 ↗For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**. **Pre-Training with Whole Word Masking for Chinese BERT** Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu You may also interested in, - Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm - Chinese MacBERT: https://github.com/ymcui/MacBERT - Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA - Chinese XLNet: https://github.com/ymcui/Chinese-XLNet - Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
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查看详情 ↗Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants. For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA. ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants. You may also interested in, - Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm - Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA - Chinese XLNet: https://github.com/ymcui/Chinese-XLNet - Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer If you find our resource or paper is useful, please consider including the following citation in your paper. - https://arxiv.org/abs/2004.13922
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查看详情 ↗**This is the full Chinese-LLaMA-2-13B model，which can be loaded directly for inference and full-parameter training.** **Related models👇** * Long context base models * Chinese-LLaMA-2-7B-16K (full model) * Chinese-LLaMA-2-LoRA-7B-16K (LoRA model) * Chinese-LLaMA-2-13B-16K (full model) * Chinese-LLaMA-2-LoRA-13B-16K (LoRA model) * Base models * Chinese-LLaMA-2-7B (full model) * Chinese-LLaMA-2-LoRA-7B (LoRA model) * Chinese-LLaMA-2-13B (full model) * Chinese-LLaMA-2-LoRA-13B (LoRA model) * Instruction/Chat models * Chinese-Alpaca-2-7B (full model) * Chinese-Alpaca-2-LoRA-7B (LoRA model) * Chinese-Alpaca-2-13B (full model) * Chinese-Alpaca-2-LoRA-13B (LoRA model) This project is based on the Llama-2, released by Meta, and it is the second generation of the Chinese LLaMA & Alpaca LLM project. We open-source Chinese LLaMA-2 (foundation model) and Alpaca-2 (instruction-following model). These models have been expanded and optimized with Chinese vocabulary beyond the original Llama-2. We used large-scale Chinese data for incremental pre-training, which further improved the fundamental semantic understanding of the Chinese language, resulting in a significant performance improvement compared to the first-generation models. The relevant models support a 4K context and can be expanded up to 18K+ using the NTK method.
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查看详情 ↗**This is the full Chinese-LLaMA-2-13B-16K (context size 16K)，model，which can be loaded directly for inference and full-parameter training.** **Related models👇** * Long context base models (16K) * Chinese-LLaMA-2-7B-16K (full model) * Chinese-LLaMA-2-LoRA-7B-16K (LoRA model) * Chinese-LLaMA-2-13B-16K (full model) * Chinese-LLaMA-2-LoRA-13B-16K (LoRA model) * Long context Instruction/Chat models * Chinese-Alpaca-2-7B-16K (full model) * Chinese-Alpaca-2-LoRA-7B-16K (LoRA model) * Chinese-Alpaca-2-13B-16K (full model) * Chinese-Alpaca-2-LoRA-13B-16K (LoRA model) * Base models * Chinese-LLaMA-2-7B (full model) * Chinese-LLaMA-2-LoRA-7B (LoRA model) * Chinese-LLaMA-2-13B (full model) * Chinese-LLaMA-2-LoRA-13B (LoRA model) * Instruction/Chat models * Chinese-Alpaca-2-7B (full model) * Chinese-Alpaca-2-LoRA-7B (LoRA model) * Chinese-Alpaca-2-13B (full model) * Chinese-Alpaca-2-LoRA-13B (LoRA model) This project is based on the Llama-2, released by Meta, and it is the second generation of the Chinese LLaMA & Alpaca LLM project. We open-source Chinese LLaMA-2 (foundation model) and Alpaca-2 (instruction-following model). These models have been expanded and optimized with Chinese vocabulary beyond the original Llama-2. We used large-scale Chinese data for incremental pre-training, which further improved the fundamental semantic understanding of the Chinese language, resulting in a significant performance improvement compared to the first-generation models. The relevant models support a 4K context and can be expanded up to 18K+ using the NTK method.
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查看详情 ↗This repository contains the resources in our paper **"Revisiting Pre-trained Models for Chinese Natural Language Processing"**, which will be published in "Findings of EMNLP". You can read our camera-ready paper through ACL Anthology or arXiv pre-print. **Revisiting Pre-trained Models for Chinese Natural Language Processing** *Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, Guoping Hu* You may also interested in, - Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm - Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA - Chinese XLNet: https://github.com/ymcui/Chinese-XLNet - Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。