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    实现一种多Lora权值集成切换+Zero-Finetune零微调增强的跨模型技术方案,LLM-Base+LLM-X+Alpaca,初期,LLM-Base为Chatglm6B底座模型,LLM-X是LLAMA增强模型。该方案简易高效,目标是使此类语言模型能够低能耗广泛部署,并最终在小模型的基座上发生“智能涌现”,力图最小计算代价达成ChatGPT、GPT4、ChatRWKV等人类友好亲和效果。当…

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    实现Blip2RWKV+QFormer的多模态图文对话大模型,使用Two-Step Cognitive Psychology Prompt方法,仅3B参数的模型便能够出现类人因果思维链。对标MiniGPT-4,ImageBind等图文对话大语言模型,力求以更小的算力和资源实现更好的智能效果。

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    修复Transformer官方库中RWKV的适配问题,支持RWKV所有系列模型在转换后,通过RingRWKV库,与其他transfomer模型一样简单方便地部署和微调。

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  4. ALM-Agent ALM-Agent Public

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    麋鹿(MiLu),基于GPT2+BERT的语言模型,以少量的纯中文语料从头训练,验证小模型在ChatGPT类似友好能力

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  6. HF-For-RWKVWorld-LoraAlpaca HF-For-RWKVWorld-LoraAlpaca Public

    将RWKV World/World-CHN系列模型由原生pth转为HF格式,并进行基于peft库的Lora增量微调+Alpaca全量微调。

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