DPS Build
一键安装 LlaMA 的工具来了! 一键安装 LLaMA 之后,在一台 M1 Macbook Air上跑起了 7B 的模型,速度还OK。大概吃了4G 内存。 这台机器有 16G 内存,8核的 M1 CPU。跑起来之后,CPU 会跑满。 具体安装步骤: 1. npm install npx (没有 npm 的同学可以先装 npm,js 的包管理工具) 2. npx dalai llama 3. npx dalai serve 它会自动安装相关的 python 包,并下载 7B 的 LLaMA 模型。…
用自己的数据集 fine tune 这个 LLaMA 模型,效果超过 GPT-3.5
https://twitter.com/iamgingertrash/status/1636180818606592000
https://twitter.com/iamgingertrash/status/1636180818606592000
X (formerly Twitter)
simp 4 satoshi on X
Here’s our LLaMA-13B fine tuned with RLHF & SFT
This has only been trained on 3% of our total dataset size, and no NSFW yet.
It is better than GPT3.5
We’re open sourcing all weights and inference code in a few days after training
This has only been trained on 3% of our total dataset size, and no NSFW yet.
It is better than GPT3.5
We’re open sourcing all weights and inference code in a few days after training
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朱老师用一系列 AI 工具创作了一本童书,总共花了二十小时。
当然,他也坦言,因为自己是设计师,所以懂排版;因为之前出过书,所以了解整个出版流程。如果没有这些经验,恐怕远远不止二十小时。
他用到的工具: ChatGPT3.5, New Bing, Midjourney V4, Figma, Blurb.
https://www.douban.com/note/846359765/
#ai
当然,他也坦言,因为自己是设计师,所以懂排版;因为之前出过书,所以了解整个出版流程。如果没有这些经验,恐怕远远不止二十小时。
他用到的工具: ChatGPT3.5, New Bing, Midjourney V4, Figma, Blurb.
https://www.douban.com/note/846359765/
#ai
豆瓣
我用AI做了一本童书
《环球旅行者的狗》,The World Traveler's Dog。问过ChatGPT了,换个词用Global也可以,这不重要。动手一共算是用了不到20小时,跨越一个月,其他时间都在思考。 这次用到的工具包括:ChatGPT3.5, New Bing, Midjourney V4, Figma, Blurb. 用到的经验包括:我出版过几本书,清楚拿书号和印刷发行的流程。我自己是设计师,排版对我来...
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DPS Build
OpenAI 刚刚发布了 GPT-4,以下四张图表说明了它的大幅提升: 1. GPT-4 模拟参与了各类考试,比如 LSAT 之类的律师执照考试,得到了 88 percentile 的高分,SAT 阅读写作得到了 93 percentile 的高分,GRE 词汇得了 99 percentile 的高分 2. 在各类公认的 NLP 测试上,GPT-4 也有着优良表现 3. 除了在英语数据上有着巨大提升 (MMLU 的测试中,GPT-4 从 GPT-3 的 70.1% 提高到了 85.5%),在其他语言上也有极大进步,比如中文到了…
GPT-4 技术报告的撰写用到了自己😂
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困扰了我大半年的 Pycharm 问题终于解决了。
因为之前使用 Homebrew 安装 Pycharm,后来莫名其妙地无法更新,无法卸载也无法重装,每次都遇到这个报错信息:
找到这个包含 meta info 的脚本,两段 XX 分别是日期和版本号,使用 tab 可以自动补全:
/opt/homebrew/Caskroom/pycharm/.metadata/XXXXXX/XXXXXX/Casks/pycharm.rb
然后修改里面的命令,变成:
因为之前使用 Homebrew 安装 Pycharm,后来莫名其妙地无法更新,无法卸载也无法重装,每次都遇到这个报错信息:
Error: No such file or directory @ rb_sysopen今天终于找到了解决方案:
找到这个包含 meta info 的脚本,两段 XX 分别是日期和版本号,使用 tab 可以自动补全:
/opt/homebrew/Caskroom/pycharm/.metadata/XXXXXX/XXXXXX/Casks/pycharm.rb
然后修改里面的命令,变成:
if File.readable?(path) && File.readlines(path).grep(/# see com.intellij.idea.SocketLock for the server side of this interface/).any?
改完之后,执行brew uninstall pycharm -dhttps://github.com/Homebrew/discussions/discussions/3517#discussioncomment-4811585
GitHub
Fail to upgrade goland: Error: No such file or directory @ rb_sysopen - /usr/bin/goland · Homebrew · Discussion #3517
I tried to upgrade goland, and brew say Error: No such file or directory @ rb_sysopen - /usr/bin/goland. The path /usr/bin/goland is actually not exists, i think the script would skip it instead of...
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估算大语言模型的训练成本:
Nvidia A100 跑一小时的电费大概是1美金
https://simonwillison.net/2023/Mar/17/beat-chatgpt-in-a-browser/
Nvidia A100 跑一小时的电费大概是1美金
https://simonwillison.net/2023/Mar/17/beat-chatgpt-in-a-browser/
Simon Willison’s Weblog
Could you train a ChatGPT-beating model for $85,000 and run it in a browser?
I think it’s now possible to train a large language model with similar functionality to GPT-3 for $85,000. And I think we might soon be able to run the resulting …
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DPS Build
在单机上可以跑得动 Meta 发布的 LLaMA 模型。 https://til.simonwillison.net/llms/llama-7b-m2 https://twitter.com/ggerganov/status/1634282694208114690 #ml
GitHub
GitHub - thomasantony/llamacpp-python: Python bindings for llama.cpp
Python bindings for llama.cpp. Contribute to thomasantony/llamacpp-python development by creating an account on GitHub.
DPS Build
第一个方案已经写完了,结果很迷。有的时候答案非常棒,有的时候完全找不到北。 目前可能的优化空间: 1. 把计算相似度的算法调整,默认是 cosine; 2. 把文本数据进一步清洗,尽可能去除噪音数据; 3. 调整 embedding 的 chunk 的大小 4. 准备更多高质量的文本数据。
手工写完一个方案后,看到有人把工具链搭出来了:
LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data.
https://github.com/jerryjliu/llama_index
LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data.
https://github.com/jerryjliu/llama_index
GitHub
GitHub - run-llama/llama_index: LlamaIndex is the leading framework for building LLM-powered agents over your data.
LlamaIndex is the leading framework for building LLM-powered agents over your data. - run-llama/llama_index
DPS Build
手工写完一个方案后,看到有人把工具链搭出来了: LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. https://github.com/jerryjliu/llama_index
将准备好的 embedding 放到 Redis 里,整体性能会优于专门的 vector db
https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/redis.html
https://twitter.com/tisoga/status/1637763047774388224
https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/redis.html
https://twitter.com/tisoga/status/1637763047774388224
HuggingFace 能够直接渲染 Jupyter notebooks 了,而且可以直接在 Google colab 打开。
https://huggingface.co/spaces/davanstrien/notebooks-on-the-hub/blob/main/welcome_notebook_on_the_hub.ipynb
https://huggingface.co/spaces/davanstrien/notebooks-on-the-hub/blob/main/welcome_notebook_on_the_hub.ipynb
huggingface.co
welcome_notebook_on_the_hub.ipynb · davanstrien/notebooks-on-the-hub at main
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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OpenAI 的 applied search lead - Lilian Weng 写的两篇关于 prompts 的长文,有非常多的细节
https://lilianweng.github.io/posts/2021-01-02-controllable-text-generation/
https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/
https://lilianweng.github.io/posts/2021-01-02-controllable-text-generation/
https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/
lilianweng.github.io
Controllable Neural Text Generation
[Updated on 2021-02-01: Updated to version 2.0 with several work added and many typos fixed.]
[Updated on 2021-05-26: Add P-tuning and Prompt Tuning in the “prompt design” section.]
[Updated on 2021-09-19: Add “unlikelihood training”.]
[Updated on 2021-05-26: Add P-tuning and Prompt Tuning in the “prompt design” section.]
[Updated on 2021-09-19: Add “unlikelihood training”.]