LLMs

One interesting thing about LLMs is that they can actually recover (and without error loops). You can have a step that doesn't work right, and a later step can use its common-sense knowledge to ignore some of the missing results, conflicting information, etc. One of the problems with developing with LLMs is that the machine will often cover up... See more

First of all, I'd say you have a bigger problem where your company is trying to find nails with a hammer. That is where your sentiment comes from, and could be an obstacle for both you and the company. It's the same deal when I see people keep on talking about RAG, and nowadays "modular RAG", when really, you could treat everything as a software... See more

OpenAI is treating its new marketplace seriously now: The brand new GPT store will come with REVENUE SHARING.... (missing in the Plugins launch)

and launching a Stateful Assistants API:

- Persistent Threads (/api/openai/threads)

- Built in Retrieval (chunking etc done for you)

- Code Interpreter (RIP Adv Data Analysis?)

- Speech to Text and Text to... See more

A new v0.4.0 release of lm-evaluation-harness is available !

New updates and features include:

• Internal refactoring


• Config-based task creation and configuration


• Easier import and sharing of externally-defined task config YAMLs


• Support for Jinja2 prompt design, easy modification of prompts + prompt imports from Promptsource


• More advanced configuration

... See more

The need for better AI or LLM-specific infrastructure, along with the host of problems that come with non-deterministic of LLMs, means that there’s more software work ahead of us, not less. Abstraction layers like LLMs create more possibilities and thus, more work.

Is this a good thing or a bad thing? I’m not sure.

A great example of this is frontend... See more

One thing that is still confusing to me, is that we've been building products with machine learning pretty heavily for a decade now and somehow abandoned all that we have learned about the process now that we're building "AI".

The biggest thing any ML practitioner realizes when they step out of a research setting is that for most tasks accuracy has... See more

DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference

Table of Contents

1. Introduction


2. Key LLM Serving Techniques


3. Dynamic SplitFuse: A Novel Prompt and Generation Composition Strategy


4. Performance Evaluation


5. DeepSpeed-FastGen: Implementation and Usage


6. Try out DeepSpeed-FastGen


7. Acknowledgements

1. Introduction

Large... See more

Today, we’re releasing the Assistants API, our first step towards helping developers build agent-like experiences within their own applications. An assistant is a purpose-built AI that has specific instructions, leverages extra knowledge, and can call models and tools to perform tasks. The new Assistants API provides new capabilities such as Code... See more

The multiple cantilevered AI overhangs:

Compute overhang. We have much more compute than we are using. Scale can go much further.

Idea overhang. There are many obvious research ideas and combinations of ideas that haven’t been tried in earnest yet.

Capability overhang. Even if we stopped all research now, it would take ten years to digest the new... See more