CLLMs: Consistency Large Language Models
In this post we dive into Consistency Large Language Models (CLLMs), a new family of models which can dramatically speedup LLMs inference!
In this post we dive into Consistency Large Language Models (CLLMs), a new family of models which can dramatically speedup LLMs inference!
Learn about Representation Finetuning (ReFT) by Stanford University, a method to fine-tune large language models (LLMs) efficiently.
ReFT: Representation Finetuning for Language Models Read More »
What if we could discover OpenAI models internal weights? In this post we dive into a paper which presents an attack that steals LLMs data.
In this post we dive into the era of 1-bit LLMs paper by Microsoft, which shows a promising direction for low cost large language models
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits Read More »
In this post we dive into the Self-Rewarding Language Models paper by Meta AI, which can possibly be a step towards open-source AGI
Diving into a research paper introducing an innovative method to enhance LLM inference efficiency using memory offloading
Fast Inference of Mixture-of-Experts Language Models with Offloading Read More »
In this post we dive into LLM in a flash paper by Apple, that introduces a method to run LLMs on devices that have limited memory
LLM in a flash: Efficient Large Language Model Inference with Limited Memory Read More »
Dive into Orca 2 research paper, the second version of the successful Orca small language model from Microsoft,
Orca 2: Teaching Small Language Models How to Reason Read More »
In this post we dive into Microsoft’s CODEFUSION, an approach to use diffusion models for code generation that achieves remarkable results
CODEFUSION: A Pre-trained Diffusion Model for Code Generation Read More »
In this post we dive into Table-GPT, a novel research by Microsoft, that empowers LLMs to understand tabular data