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The Full Stack Profile picture
@full_stack_dl
Dec 29, 2020 • 14 tweets • 4 min read • Read on X
🛠️Tooling Tuesdays: Thread of Threads🛠️

Every week, we share a useful tool for full stack machine learning. Follow along, and please share your suggestions!

1/N
2/N: DeepNote

3/N: DVC

4/N: PyTorch Lightning

5/N: Dagster

6/N: Conda + Pip-Tools

7/N: PyTorch Transformer

8/N: tools we use for teaching bit.ly/berkeleyfsdl

9/N: LabelStudio, a great open-source solution for data labeling

10/N: Apache TVM, an End-to-End ML compiler framework for CPUs, GPUs and accelerators

11/N: Hugging Face, an absolute unit of Transformers research and production

12/N: Keepsake.ai from @replicateai, an experiment and model versioning solution.

13/N: Easy hyperparameter sweeps with Weights & Biases.

14/N: Static type checking for Python with MyPy

• • •

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More from @full_stack_dl

The Full Stack Profile picture
Jul 25, 2023
Is it the revenge of recurrent nets? Is it a subquadratic Transformer?

It's both, it's neither, it's RWKV: @BlinkDL_AI's novel architecture that infers efficiently like an RNN but matches Transformer quality -- so far.

Deep dive by @charles_irl:

fullstackdeeplearning.com/blog/posts/rwk…
> What is RWKV?

Typical RNNs are like a for loop that can't be vectorized, which hurts parallelization during training.

RWKV cleverly resolves this with a layer that works like an RNN cell when it's run step by step, but can be computed all at once like Transformer attention.
And unlike many other alternatives to Transformers, it gets comparable language modeling performance up to the largest scales tested: 14B params, 300B tokens. Image
Read 9 tweets
The Full Stack Profile picture
May 25, 2023
🆕 LLM Bootcamp videos are now available!

Check out our awesome invited speakers:

🏋🏻 @truerezashabani walks us through training LLMs at @Replit
🕵🏽 @hwchase17 talks about building agents with @LangChainAI
🔥 @npew talks about the path to @OpenAI ChatGPT
@truerezashabani led the team that trained the new bespoke code completion models at @Replit.

He breaks down
· The Modern LLM Stack™️
· What makes a good "LLM engineer"
· The importance of knowing and cleaning your data

fullstackdeeplearning.com/llm-bootcamp/s…
In the "agent" pattern, LLMs are given memory, access to tools, and goals.

@hwchase17, founder of the most popular LLM framework @LangChainAI, shares exciting recent research results and the gnarly challenges facing agents in production.

fullstackdeeplearning.com/llm-bootcamp/s…
Read 5 tweets
The Full Stack Profile picture
May 23, 2023
🥞🦜 LLM Bootcamp 🦜🥞

Today, let's talk about UX.

tl;dr: LLMs unlock new user interaction design patterns based on language user interfaces (LUIs). But the same principles of user-centered design still apply! Image
Since the inception of computing programmers & designers have dreamed of interfacing with computers via language as naturally as we interface with each other.

Proof-of-concepts for such language user interfaces date back to the 60s and recur repeatedly.

LLMs make LUIs possible. Image
A paradigm shift in user interfaces makes for a great time to build ambitious applications!

But because language models (and ML in general) come from the math-ier side of engineering, lots of folks are less familiar with the principles that guide user interaction design.
Read 16 tweets
The Full Stack Profile picture
May 16, 2023
🥞🦜 LLM Bootcamp 🦜🥞

Today, let's talk about prompt engineering.

tl;dr Effective prompting requires some intuition about language models, but there's an emerging playbook of general techniques.
First off: What is a "prompt"? What is "prompt engineering"?

The prompt is the text that goes into your language model.

Prompt engineering is the design of that text: how is it formatted, what information is in it, and what "magic words" are included.
So, what are some high-level intuitions for prompting?

First of all, the idea that LMs are "just statistical models of text," while literally true, leads to bad intuition that underestimates what they can do. Image
Read 13 tweets
The Full Stack Profile picture
Apr 17, 2023
🦜 LLM Lit Review 🦜

Over the last two weeks, we tweeted out twelve papers we love in the world of language modeling, from agent simulation and browser automation to BERTology and artificial cognitive science.

Here they are, collected in a single 🧵 for your convenience.
1/12 - Reynolds and McDonell, 2021. "Prompt Programming for LLMs: Beyond the Few-Shot Paradigm"

The OG Prompt Engineering paper -- formatting ticks, agent sim, and chain-of-thought, before they were cool
2/12 - Nye, Andreassen et al., 2021. "Show Your Work: Scratchpads for Intermediate Computation with LMs"

An early demonstration of unlocking LM capabilities by giving them control of their context
Read 14 tweets
The Full Stack Profile picture
Feb 21, 2023
Whatever our thoughts on chat _bots_, we enjoyed our chat with @hwchase17 of @LangChainAI on the most recent FSDL Tool Talk!
@charles_irl started us off with an overview of why we need LLM frameworks, then after a demo of how to use LangChain to do Q&A over the LangChain docs we did some live Q&A -- humans only.
You can catch the whole video on YouTube here:

Read 4 tweets

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