+ Always use full LoRa name with "vrtlxxxx" trigger in prompt like "Alba Baptista (vrtlalbabaptista) in a swimming pool". "Woman" or "1girl" will NOT work due to my way i do captions. + When using a Lora with multiple triggers (vrtlxx,vrtlyy) do not use the real character name but only trigger or a combination of it. "vrtlMain" always combines all trigger-words. + Previews made with settings: ZI Turbo Q8 / Euler / beta / 9 steps / CFG1 / auraflow 6 + Prompt for Previews: First Last (vrtlLora), , beautifully done head, modern casual styled hair, wearing a top crop knotted beige unbuttoned shirt with green four leaf clover pattern that shows a deep and wide cleavage, a embroidered badge with the shape of a ladybug that absolutely must have the black letters "ZI" below on shirt right side, upper body portraiture, centered, frontal, eye contact, blank dark gray gradient background.
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**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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**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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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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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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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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**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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**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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