LLMs

How can we make interacting with conversational models feel more natural?

Every conversational interface to a language model adopts the same pattern:

A chat history sidebar, with each conversation lasting just a few turns

New sessions always begin in a brand-new thread

Every user query must always elicit exactly one response

None of these assumptions... See more

What is Substrate?

Substrate is an AI inference platform. In particular, it excels at enabling complex multi-model workloads . At its core, Substrate is 1) a collection of cutting-edge AI models – tuned for optimum performance, and 2) a set of composable APIs for relating these models to each other. We believe having both of these components in one... See more

Fine-Tuning for LLM Research by AI Hero

This repo contains the code that will be run inside the container. Alternatively, this code can also be run natively. The container is built and pushed to the repo using Github actions (see below). You can launch the fine tuning job using the examples in the https://github.com/ai-hero/llm-research-examples... See more

pair-preference-model-LLaMA3-8B by RLHFlow: Really strong reward model, trained to take in two inputs at once, which is the top open reward model on RewardBench (beating one of Cohere’s).

DeepSeek-V2 by deepseek-ai (21B active, 236B total param.): Another strong MoE base model from the DeepSeek team. Some people are questioning the very high MMLU... See more

• Multiple indices. Splitting the document corpus up into multiple indices and then routing queries based on some criteria. This means that the search is over a much smaller set of documents rather than the entire dataset. Again, it is not always useful, but it can be helpful for certain datasets. The same approach works with the LLMs themselves.

... See more

We generally lean towards picking more advanced commercial LLMs to quickly validate our ideas and obtain early feedback from users. Although they may be expensive, the general idea is that if problems can't be adequately solved with state-of-the-art foundational models like GPT-4, then more often than not, those problems may not be addressable... 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

GPT-4 Turbo can accept images as inputs in the Chat Completions API, enabling use cases such as generating captions, analyzing real world images in detail, and reading documents with figures. For example, BeMyEyes uses this technology to help people who are blind or have low vision with daily tasks like identifying a product or navigating a store.... See more