下载链接：https://github.com/baichuan-inc/Baichuan2 在所有主流中英文通用榜单上，Baichuan 2全面领先Llama 2，而Baichuan2-13B更是秒杀所有同尺寸开源模型。毫不夸张地说，Baichuan2-13B是目前同尺寸性能最好的中文开源模型。而在过去一个月里，Baichuan系列的下载量在Hugging Face等开源社区已经超过了347万次，是...
最近尝试了一下Baichuan2-13B微调,把一些经验和困惑在此记录一下,欢迎各位大佬一起交流。 1. 环境配置 1.1 下载Baichuan2代码库&预训练模型 lfs安装,可自行搜索;如果预训练模型下载慢,可以手动下载,网上很多教程,不再赘述。 git clone https://github.com/baichuan-inc/Baichuan2.git git lfs install git clone...
But, it's ok. As you know, LLM runtime of the ITREX has been rebuilt to this repo:https://github.com/intel/neural-speed I checked the baichuan output. Please check this PRintel/neural-speed#80and branch zhenzhong/APIissues. Please git clone https://github.com/intel/neural-speed git ...
since the doc indicates from autoawq to run quantize for models, but from the official https://github.com/casper-hansen/AutoAWQ not support baichuan models can you just pls show me a simple demo on how to run awq for baichuan2-13b and deploy in vllm, sincerely thx. 👀 1 Author...
参考GitHub - baichuan-inc/Baichuan2: A series of large language models developed by Baichuan Intelligent Technology @st.cache_resource def init_model(): model = AutoModelForCausalLM.from_pretrained( "/data/text-generation-webui/models/baichuan-inc/Baichuan2-13B-Chat-4bits", ...
但是这些框架大部分支持的模型都很少,或只支持英文模型,支持中文模型的更少,目前社区最活跃的就是伯克利大学出的这个VLLM框架了,目前github 9.1k星,但当前这个框架支持的模型有一部分https://github.com/vllm-project/vllm ,虽支持百川,但定睛一看支持baichuan不支持baichuan2,这两者有些差别,我仍然不死心,用它来...
AIO Benchmark Baichuan2-13B 1024 in 1024 out OOM with the following config. accelerate 0.30.0 bigdl-core-xe-21 2.5.0b20240508 bigdl-core-xe-esimd-21 2.5.0b20240508 elastic-transport 7.16.0 intel-extension-for-pytorch 2.1.30+xpu ipex-llm ...
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项目地址:github.com/Baichuan-inc 预训练模型:huggingface.co/baichuan 对话模型:huggingface.co/baichuan Model Scope:modelscope.cn/models/Ba Baichuan-13B 有如下几个特点: 更大尺寸、更多数据:Baichuan-13B 在 Baichuan-7B 的基础上进一步扩大参数量到 130 亿,并且在高质量的语料上训练了 1.4 万亿 tokens,超过...
Baichuan 2下载地址：https://github.com/baichuan-inc/Baichuan2 一直备受瞩目的百川智能，自成立以来平均 28 天就能发布一款大模型。如果只是在速度上持续领先，或许可以理解为本就是一个“明星”创业公司的“分内之事”。但如果保持研发速度的同时，在质量上还完成了对LLaMA2的超越甚至是吊打，那必须值得称赞一番...