[AINews] not much happened today • ButtondownTwitterTwitter

This section provides recaps of AI-related discussions and developments on Twitter and Reddit. On Twitter, updates on AI models, API enhancements, new AI models, model performance, AI research, and industry trends are highlighted. Memes and humor related to AI are also covered. The Reddit recap section delves into advancements in small and efficient LLMs, new model releases, and benchmarks. Discussions on themes like small model improvements, llama.cpp evolution, GGUF model usage, and the release of Hermes 3 from NousResearch are outlined.

Online services such as Perplexity, Anthropic, and OpenAI's ChatGPT are experiencing outages, highlighting the advantage of using local Large Language Models (LLMs) during disruptions. Additionally, a DIY setup for running LLMs with a Ryzen 7950X CPU, 128GB DDR5 RAM, and 4090 GPU capable of running models with up to 70B parameters is described. Another section discusses how Large Language Models (LLMs) are developing their own understanding of reality as their language capabilities improve, potentially leading to more advanced reasoning and problem-solving abilities.

ForgeUI Adds Full Precision Support for Flux-dev:

ForgeUI now supports Flux-dev at full precision using GGUF checkpoints. It's currently unclear if this support will extend to other platforms such as automatic1111 or ComfyUI.

Evaluating Fine-Tuned Models with Quantization:

A user is seeking advice on evaluating their fine-tuned model after observing that a quantized version using GPTQ performs better than the original model. However, when using GGUF or AWQ for quantization, performance decreases, prompting a discussion about LM Studio's capabilities for private bug reporting.

LM Studio Server Setup and Connectivity Issues:

A user encountered an error attempting to connect LM Studio to Obsidian. The discussion identified potential issues related to LM Studio's server running on the LM Studio side and the need for CORS configuration.

P40 Power Consumption: Myths Debunked:

A common misconception about multiple P40s consuming 1kW for inference is false. When used for LLMs, they draw power sequentially, resulting in a total consumption close to a single GPU (around 250W).

Tensor Split & GPU Bottlenecks:

Disabling offload to the GTX with tensor split (set to 0,1 or the opposite in the configuration file) is crucial, as a 2GB GTX will bottleneck a T4 with 4GB combined memory. Search for 'tensor split' to learn more about this configuration option.

WeKnow-RAG integrates Web search and Knowledge Graphs into a "Retrieval-Augmented Generation (RAG)" system to enhance the accuracy and reliability of LLM responses. It combines the structured representation of Knowledge Graphs with dense vector retrieval, improving LLM responses by utilizing both structured and unstructured information.

The section discusses the Nous Research AI Discord channel covering topics related to the RAG dataset and reasoning tasks master list. It includes discussions on the RAG dataset, Charlie Marsh learning about RAG, and reasoning tasks master list. Members share insights on reasoning tasks, examples, and how to prompt large language models effectively. The section also covers the benefits of OpenAI's reasoning task examples, the use of weak models in Aider, and structuring responses using the Instructor library. Additionally, there are links to relevant GitHub repositories and other helpful resources in the AI domain.

HuggingFace Discord server has active discussions on various topics related to AI and machine learning. Some key highlights include: HawkEye, an AI-powered tool for CCTV surveillance analysis; new tokens introduced in Hermes 3 report; advancements in mobile AI with Google Pixel 9 smartphones; Hyperspace P2P AI network accessibility; and guidance on deploying YOLO models on robots. The community also explores topics like LLMTIL, Python 3.14, theorem proving models, and more. Stay tuned for the latest developments and insights in the world of AI on HuggingFace Discord channels!

This section discusses various topics related to OpenAI's GPT-4 model and its use. Users share experiences with custom GPTs displaying 'updates pending' messages, hypothesize about the cause of this issue, and express the need for clearer communication from OpenAI regarding this matter.

The discussions in the Latent Space AI ​In-Action Club focused on various topics such as commercialization of DSPy, Cursor Alpha development, LangChain, prompting vs. fine-tuning, and model distillation. Members shared insights about projects and companies like DSPy and Cursor, highlighting ongoing developments and potential advancements in the AI field. Collaborative efforts and technical details were discussed, showing a vibrant exchange of ideas and information among the club participants.

• DSPy's Local Performance: DSPy's ability to produce local models as good as GPT-4 was discussed. Members noted the ease of trying out DSPy and switching models.
• DSPy's Approach to Fine-tuning: DSPy aims to bridge prompting and fine-tuning, allowing easy model switches and data retuning.
• DSPy's Ability to Prompt Models: Claims of DSPy being better at prompting models than humans were addressed, emphasizing the importance of human engineering in prompting.
• Cohere Discussions: Discussions on Cohere's Startup Program aiding AI-driven startups and leveraging Cohere for Oracle Fusion SaaS were highlighted.

Jala: Automated Text Data Labeling

Jala provides an automated interface for text data labeling, leveraging advanced AI technologies for high accuracy and efficiency. It supports various text data types (e.g., CSV, JSON, TXT, XML) and offers scalable solutions for large datasets, easily integrating with existing workflows.

Jala's Use Cases: NLP, Machine Learning, and More:

• Jala is ideal for various industries and applications, including Natural Language Processing (NLP), Machine Learning and AI model training, and data annotation for research and development.

The tool offers automated content categorization capabilities, making it versatile for data-driven tasks. Users can join the waitlist for Jala to be among the first to experience its power. Signing up provides updates on its progress and early access to this innovative data labeling solution.