Please welcome K2-65B🏔️, the most performant fully-open LLM released to date. As a blueprint for open-source AGI, we release all model checkpoints, code, logs, and data. About K2: 🧠65 billion parameters 🪟Fully transparent & reproducible 🔓Apache 2.0 📈Outperforms Llama 2 70B

No shade to Runway obviously, but in today's video I'm taking a look at what the future of AI Video Motion Control might look like via Maitrix.org's Pandora. PLUS...

Check out #K2, a fully-open 65B LLM released by LLM360 Matching the performance of #Llama2 70B, #K2 is among the most powerful LLMs made fully transparent! Over the past 6 months, LLM360 has made open a series of LLMs across different tiers, all with open weights,

Missed this.

Everyone is talking about Sora and Kling. Another model is on the horizon. Learn about Pandora from Maitrix.org!

🔥Excited that Redcoast won the #NAACL2024 best demo runner up! Redcoast is a super easy-to-use tool ☕️ for automated distributed training of #LLMs, diffusion, reinforcement learning, meta-learning, etc. Users just write three functions (collate, loss, predict), and Redcoast

💡Divergence thinking💡 is a hallmark of human creativity and problem-solving 🤖Can LLMs also do divergent reasoning to generate diverse solutions🤔? Introducing Flow-of-Reasoning (FoR) 🌊, a data-efficient way of training LLM policy to generate diverse, high-quality reasoning

"With long context LLMs comes long prompts"👇 People typically just write 1- or 2-sentence quick prompts when using an LLM for a task. How to create 1- or 2-page long prompts to boost performance? 🔥PromptAgent automatically writes long prompts for you!🔥 Without need of the

Optimizing pages-long expert-level prompts automatically 👇 It's fascinating that _prompt optimization_ can be formulated as a _planning_ problem: - Treat the LLM as a world model🌎 - We want a prompt, as a plan trajectory, that thrives in this world - So we do strategic

Sasha Rush We have a new work at #ICML2024 to learn latent auto-encoding for text. It improves VAEs and other DGMs quite a bit: arxiv.org/abs/2402.19009 The idea is to augment diffusion with parameterized encoder-decoder (e.g., pretrained LLMs) Or in an alternative view, it replaces the

Excited to share that our paper “LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Models” is accepted to Conference on Language Modeling 2024! arxiv.org/abs/2404.05221 #LLMReasoners #COLM #COLM24

Happy to share that our agent engineering framework OpenAgents (github.com/xlang-ai/OpenA…) and LM tool-using survey(arxiv.org/2403.15452) are accepted by #COLM! See you at UPenn. And on a personal update, I feel excited and lucky that I will start my PhD at UCSD this fall

Introducing FLUTE, a CUDA kernel for non-uniformly quantized (via a lookup table) LLM Inference. It accelerates QLoRA's NormalFloat (NF) out of the box and more. As an application, we extended NF4 and are releasing quantized models for LLaMA-3 (8B/70B) and Gemma-2 (9B/27B).

🥳Our work on Multi-Model Theory of Mind evaluation won the #ACL2024 Outstanding Paper Award! How well can machines 🤖 form a coherent mental picture🧠of humans from vision-language observations? Can machines understand humans' goals and beliefs? Our MMToM-QA shows models

Excited to announce the official leaderboard for our MMToM-QA benchmark hosted on Maitrix.org

Enjoyed this paper on LMs, world models and agent models by Zhiting Hu and Tianmin Shu TLDR: for reasoning tasks, it’s a useful abstraction to treat LMs as simulators (“backends”) that simulate agent models and world models arxiv.org/abs/2312.05230

Very interesting work of simulating digital game with a DOOM "world model"! 🌴Pandora we created earlier is a general-domain world model aiming to simulate diverse worlds, including digital games, interactively controlled by natural language. A larger and better version is