AI News Bytes: ChatGPT 4; Kosmos-1; Amazon outperforms GPT-3.5 by 16%; ChatLLaMA; ChatGPT Alternative Released.....

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Microsoft will launch ChatGPT 4 with AI videos next week:?The news was revealed by Andreas Braun, Chief Technology Officer at Microsoft Germany, at a recent event titled “AI in Focus — Digital Kickoff” (via?Heise). According to Braun, “We will introduce GPT-4 next week … we will have multimodal models that will offer completely different possibilities — for example videos.”

The first open-source ChatGPT alternative got released:?TogetherCompute has released a 20B chat-GPT model called?OpenChatKit?under the Apache-2.0 license, which is available for free on Hugging Face. A demo and announcement are available online. The researchers collaborated with?LAION?and?Ontocord?to create the training dataset.

What happens when we train the largest vision-language model and add in robot experiences??Meet PaLM-E, a 562-billion parameter, general-purpose, embodied visual-language generalist - across robotics, vision, and language. PaLM-E enables robot planning directly from pixels – all in a single model, trained end-to-end. PaLM-E is the largest VLM reported to date.

Is ChatGPT really 175 Billion Parameters???? This?blog post?from?Owen?concretely disproves this theory with publicly available information and verifiable, reproducible analysis. It is typical to store LLM weights as 8-bit integers in the INT8 format for lower latency inferencing, higher throughput and a 2x lower memory footprint compared to storing them in the float16 format. It takes 1 byte to store each INT8 parameter. Simple math shows the model will take 175GB of space to store.

Microsoft introduces Kosmos-1:?Kosmos-1?is a Multimodal Large Language Model (MLLM) that can perceive general modalities, learn in context (i.e., few-shot), and follow instructions (i.e., zero-shot). Specifically, they trained KOSMOS-1 from scratch on web-scale multimodal corpora, including arbitrarily interleaved text and images, image-caption pairs, and text data. It achieves great performance on language understanding, OCR-free NLP, perception-language tasks, visual QA, and more.

Meet?ChatLLaMA:?The First Open-Source Implementation of LLaMA Based on Reinforcement Learning from Human Feedback (RLHF): Meta has recently released LLaMA, a collection of foundational large language models ranging from 7 to 65 billion parameters. LLaMA is creating a lot of excitement because it is smaller than GPT-3 but has better performance. For example, LLaMA’s 13B architecture outperforms GPT-3 despite being 10 times smaller. This new collection of fundamental models opens the door to faster inference performance and chatGPT-like real-time assistants while being cost-effective and running on a single GPU. However, LLaMA was not fine-tuned for instruction tasks with a Reinforcement Learning from Human Feedback (RLHF) training process.?The good news is that today?Nebuly?has introduced?ChatLLaMA, the first open-source implementation of LLaMA based on RLHF.

Amazon outperforms GPT-3.5 by 16%:?Amazon's new?framework?called Multimodal-CoT, trained with 1 billion parameters, has surpassed the previous state-of-the-art LLM (GPT-3.5) by 16%, achieving a remarkable accuracy rate of 91.68% compared to the GPT's rate of 75.17%. The?framework?divides the reasoning process into two phases: rationale generation and answer inference. The model produces more persuasive arguments by including the vision aspects in both stages, which helps to create more precise answer inferences. This work is the first of its kind that studies CoT reasoning in different modalities.

Thinking about LLM caching service?: Although Langchain already has its caching service, for others, there were none. The Helicone team has started?caching support to Helicone. Now you can easily cache your OpenAI requests with their proxy so that duplicate requests don't drive up your bill. This also makes testing and developing a lot easier, faster, and cheaper.

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