RedPajama + Big Code Can it Take on Vicuna and StableLM in the LLM Space

RedPajama + Big-Code: Can it Take on Vicuna and StableLM in the LLM Space

The past week has been a momentous one for the open-source AI community with the announcement of several new language models, including Free Dolly, Open Assistant, RedPajama, and StableLM. These models have been designed to provide more and better options to researchers, developers, and enthusiasts in the face of growing concerns around the control and censorship of AI by corporations.

While some users have expressed disappointment with the performance of certain models, such as the 7B model, which still needs improvement, others have praised the context size of 4096 in StableLM, which could give it a significant advantage over other LLMs like Llama and GPT-NeoX. However, the use of CC-BY-NC licensed datasets has been a source of concern for some users, who believe it limits their commercial use. Nonetheless, the emergence of open standards and free software in AI is seen as crucial to ensuring our freedom in the future.

As exciting as these releases are, there are still some questions that need to be addressed. For example, users are curious about the possibility of training StableLM on code as well, which could put it ahead of the competition. Some have also raised concerns about the legal implications of using the CC BY-SA-4.0 license, which could cause problems for companies in the LLM space. Furthermore, there are still no benchmarks or comparisons available to give users an idea of how these models stack up against each other.

Overall, the continued release of open models is a promising development for the AI community. However, users will need to wait and see how these models perform in real-world applications and how they evolve over time.

Tags: Open-source, AI, language models, Free Dolly, Open Assistant, RedPajama, StableLM, CC-BY-NC, Llama, GPT-NeoX, CC BY-SA-4.0.

LLAMA-style LLMs and LangChain: A Solution to Long-Term Memory Problem

LLAMA-style Long-Form Memory (LLM) models are gaining popularity in solving long-term memory (LTM) problems. However, the creation of LLMs requires a fully manual process. Users may wonder whether any existing GPT-powered applications perform similar tasks. A project called gpt-llama.cpp, which uses llama.cpp and mocks an OpenAI endpoint, has been proposed to support GPT-powered applications with llama.cpp, which supports Vicuna.

LangChain, a framework for building agents, provides a solution to the LTM problem by combining LLMs, tools, and memory. … click here to read

Stack Llama and Vicuna-13B Comparison

Stack Llama, available on the TRL Library, is a RLHF model that works well with logical tasks, similar to the performance of normal Vicuna-13B 1.1 in initial testing. However, it requires about 25.2GB of dedicated GPU VRAM and takes approximately 12 seconds to load.

The Stack Llama model was trained using the StableLM training method, which aims to improve the stability of the model's training and make it more robust to the effects of noisy data. The model was also trained on a … click here to read

Magi LLM and Exllama: A Powerful Combination

Magi LLM is a versatile language model that has gained popularity among developers and researchers. It supports Exllama as a backend, offering enhanced capabilities for text generation and synthesis.

Exllama, available at https://github.com/shinomakoi/magi_llm_gui , is a powerful tool that comes with a basic WebUI. This integration allows users to leverage both Exllama and the latest version of Llamacpp for blazing-fast text synthesis.

One of the key advantages of using Exllama is its speed. Users … click here to read

StableCode LLM: Advancing Generative AI Coding

Exciting news for the coding community! StableCode, a revolutionary AI coding solution, has just been announced by Stability AI .

This innovation comes as a boon to developers seeking efficient and creative coding assistance. StableCode leverages the power of Generative AI to enhance the coding experience.

If you're interested in exploring the capabilities of StableCode, the official announcement has all the details you need.

For those ready to … click here to read

LMFlow - Fast and Extensible Toolkit for Finetuning and Inference of Large Foundation Models

Some recommends LMFlow , a fast and extensible toolkit for finetuning and inference of large foundation models. It just takes 5 hours on a 3090 GPU for fine-tuning llama-7B.

LMFlow is a powerful toolkit designed to streamline the process of finetuning and performing inference with large foundation models. It provides efficient and scalable solutions for handling large-scale language models. With LMFlow, you can easily experiment with different data sets, … click here to read

LLaVA: Large Language and Vision Assistant

The paper presents the first attempt to use language-only GPT-4 to generate multimodal language-image instruction-following data. By instruction tuning on such generated data, the authors introduce LLaVA, an end-to-end trained large multimodal model that connects a vision encoder and LLM for general-purpose visual and language understanding.

LLaVA demonstrates impressive multimodel chat abilities and yields an 85.1% relative score compared with GPT-4 on a synthetic multimodal instruction-following dataset. When fine-tuned on Science QA, the synergy of LLaVA and … click here to read

Comparing Large Language Models: WizardLM 7B, Alpaca 65B, and More

A recent comparison of large language models, including WizardLM 7B , Alpaca 65B , Vicuna 13B, and others, showcases their performance across various tasks. The analysis highlights how the models perform despite their differences in parameter count. The GPT4-X-Alpaca 30B model, for instance, gets close to the performance of Alpaca 65B. Furthermore, the Vicuna 13B and 7B models demonstrate impressive results, given their lower parameter numbers.

Some users … click here to read

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