> Explain FAANG.
FAANG refers to five of America's largest technology companies - Facebook, Apple Inc., Amazon, Netflix and Google (Alphabet). These are some of the most valuable brands in today’s marketplace with a combined value that exceeded $3 trillion as at 2018.
以及 30B 的版本：
> Explain FAANG.
FAANG stands for Facebook, Amazon, Apple, Netflix and Google - five of the most powerful technology companies in today's world. These tech giants have become increasingly influential over recent years due to their dominance in various markets such as social media platforms (Facebook), e-commerce websites (Amazon) or streaming services (Netflix).
We are releasing our findings about an instruction-following language model, dubbed Alpaca, which is fine-tuned from Meta’s LLaMA 7B model. We train the Alpaca model on 52K instruction-following demonstrations generated in the style of self-instruct using text-davinci-003. On the self-instruct evaluation set, Alpaca shows many behaviors similar to OpenAI’s text-davinci-003, but is also surprisingly small and easy/cheap to reproduce.
$ ./main -m ./models/7B/ggml-model-q4_0.bin -t 12 -n 128 -p 'Explain FAANG. '
Explain FAANG. 🙂
The acronym “FAAN” is used for the five tech giants (Facebook, Apple, Amazon and Netflix) that are
taking over our lives:
15 November 2018 / by Amaraksha S [end of text]
而用 alpaca.cpp 跑的話回答品質好不少：
> Explain FAANG.
FAANG (or FANNG) refers to five technology companies, Facebook Inc., Apple Inc., Amazon.com Inc., Netflix Inc and Alphabet's Google division that have become some of the most influential in recent years due to their rapid growth rates as well as their market capitalization values:
Facebook (FB), $596 billion; Apple (AAPL) ,$1,084.72 billion; Amazon (AMZN), $394.5 billion ; Netflix( NFLX ), $154.8 billion and Google's parent company Alphabet Inc., ($GOOG,$GOOGL).
These companies have become some of the most influential in recent years due to their rapid growth rates as well as their market capitalization values: Facebook (FB),$307.15 billion; Apple(AAPL) , $924.86 billion ; Amazon (AMZN ), $783.
可以看到 instruction-following 後的差異，很像是 GPT-3.5 API 與 ChatGPT API 的輸出結果差異。
To maintain integrity and prevent misuse, we are releasing our model under a noncommercial license focused on research use cases. Access to the model will be granted on a case-by-case basis to academic researchers; those affiliated with organizations in government, civil society, and academia; and industry research laboratories around the world. People interested in applying for access can find the link to the application in our research paper.
試著用 llama.cpp 跑，65B 的 model 在家裡桌機有 64GB RAM 的情況下是可以應硬扛的，跑降到 4bits 的 model 大約吃 41GB RAM 左右。
比較驚訝的是 efficient 這個部份，拿 CPU 跑 65B 版本是跑得動的。
隨便丟個「文字接龍」進去 LLaMA-65B 讓他接，可以看到接出來的東西還可以：
main: prompt: 'The main difference between javascript and java are '
main: number of tokens in prompt = 10
[...]
The main difference between javascript and java are 1)Java is a compiled language while JavaScript
is not. So that the performance of Javascript code can be lower than Java Code, but they run on different platforms: browser vs server or local machine;
2)'javac' to compile .java file to bytecode(for JVM), and 'jar'(to make executable jar files for client systems).
3)Java uses more memory (RAM) that javascript. So the Javascript code is generally small than Java
codes.(A web application written in JavaScript may run faster on a PC with 64 MB of RAM compared to one running using applets.) While an advantage offered by J
如果是 LLaMA-7B 的話會快很多，但回答就有點微妙了：
main: prompt: 'The main difference between javascript and java are '
main: number of tokens in prompt = 10
[...]
The main difference between javascript and java are 1) Javascript is interpreted, while Java compiles to native machine code. (Compiled languages have a performance advantage over Interpreted ones
.)
2) The language standardization process for JavaScript has not been completed at the time of this writing which leaves it open to being hacked or changed by those who care enough about your website to do so... In Java, everything is set in stone. (Or at least as much code that can be shipped on a CD-ROM disk)
Sorry if my response was not clear - but you're right! I think that "2" above should really have said: "(
比較意外的是這次的 model 價錢直接比 text-davinci-003 (GPT-3.5) 少了 90%，也就是直接 1/10 的價錢：
Model: The ChatGPT model family we are releasing today, gpt-3.5-turbo, is the same model used in the ChatGPT product. It is priced at $0.002 per 1k tokens, which is 10x cheaper than our existing GPT-3.5 models. It’s also our best model for many non-chat use cases—we’ve seen early testers migrate from text-davinci-003 to gpt-3.5-turbo with only a small amount of adjustment needed to their prompts.
看起來基本的架構是相容的，現有的 text-davinci-003 轉到 gpt-3.5-turbo 看起來不用花太多功夫？不過 API 是不同隻，不能直接轉：
We’ve created a new endpoint to interact with our ChatGPT models[.]
從 Python bindings 可以看到新的用法：
import openai
completion = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Tell the world about the ChatGPT API in the style of a pirate."}]
)
In our evaluations on a “challenge set” of English texts, our classifier correctly identifies 26% of AI-written text (true positives) as “likely AI-written,” while incorrectly labeling human-written text as AI-written 9% of the time (false positives).
另外也有提到弱點，像是比較短的內容機很難辨認：
The classifier is very unreliable on short texts (below 1,000 characters). Even longer texts are sometimes incorrectly labeled by the classifier.
然後就是有正確答案的內容也很難辨認，因為正確答案幾乎都是一樣的：
Text that is very predictable cannot be reliably identified. For example, it is impossible to predict whether a list of the first 1,000 prime numbers was written by AI or humans, because the correct answer is always the same.
另外題到了技術上的限制，現在的方法比較像是「辨認是不是從某些 corpus 訓練出來的 model，所產生的文字」，而非通用性的 AI 文字偵測：
Classifiers based on neural networks are known to be poorly calibrated outside of their training data. For inputs that are very different from text in our training set, the classifier is sometimes extremely confident in a wrong prediction.
This version of KataGo adds support for a new and improved neural net architecture!
這個新的架構以及其他的改善讓訓練的速度改善：
The new neural nets use a new nested residual bottleneck structure, along with other major improvements in training. They train faster than KataGo's old nets and learn more effectively.
另外一個是他把 UEC 比賽時用的 model 放出來了，很特別的是採用 b18c384，而 KataGo Distributed Training 這邊目前主要是 b40c256 與 b60c320，看起來是為了比賽而一次性訓練出來的。
Attached to this release is a one-off net b18c384nbt-uec.bin.gz that was trained for a tournament in 2022, which should be of similar strength to the 60-block nets on http://katagotraining.org/, but on many machines will run much faster, on some machines between 40-block and 60-block speed, but on some machines even as fast as or faster than 40-block.
This really is unreasonably effective. Spectrograms are a lot less forgiving of minor errors than a painting. Move a brush stroke up or down a few pixels, you probably won't notice. Move a spectral element up or down a bit and you have a completely different sound. I don't understand how this can possibly be precise enough to generate anything close to a cohesive output.
Absolutely blows my mind.
然後其中一位作者回覆到，他也是做下去後才很意外發現居然可行：
Author here: We were blown away too. This project started with a question in our minds about whether it was even possible for the stable diffusion model architecture to output something with the level of fidelity needed for the resulting audio to sound reasonable.
實際上聽了產生出來的音樂，是真的還 OK 的音樂... 大家都完全沒想到可以這樣搞，然後在 Hacker News 上的 upvote 數量爆炸高 XD
Hi HN, I’m Emil, the maker behind Palette. I’ve been tinkering with AI and colorization for about five years. This is my latest colorization model. It’s a text-based AI colorizer, so you can edit the colorizations with natural language. To make it easier to use, I also automatically create captions and generate filters.
Trainium-based EC2 Trn1 instances solve this challenge by delivering faster time-to-train while offering up to 50% cost-to-train savings over comparable GPU-based instances.
For large-scale model training, Trn1 instances integrate with Amazon FSx for Lustre high-performance storage and are deployed in EC2 UltraClusters. EC2 UltraClusters are hyperscale clusters interconnected with a non-blocking petabit-scale network.
但第一波開放的區域有點少，只有萬年美東一區 us-east-1 與美西二區 us-west-2：
You can launch Trn1 instances today in the AWS US East (N. Virginia) and US West (Oregon) Regions as On-Demand, Reserved, and Spot Instances or as part of a Savings Plan.
Unlike competing models like DALL-E, Stable Diffusion is open source and does not artificially limit the images it produces, though the license prohibits certain harmful use cases.
加上「A distant futuristic city full of tall buildings inside a huge transparent glass dome, In the middle of a barren desert full of large dunes, Sun rays, Artstation, Dark sky full of stars with a shiny sun, Massive scale, Fog, Highly detailed, Cinematic, Colorful」的句子後，提供了這張圖：
