[AINews] Trust in GPTs at all time low • ButtondownTwitterTwitter

Detailed High-Level Discord Summaries

• The summary includes discussions on the low trust in GPTs, feedback from notable individuals like Nick Dobos, and the need for better context management in GPTs.
• A meta announcement welcomes the addition of a new Discord scraped, the CUDA MODE.
• The high-level summaries cover various Discord communities like TheBloke, Nous Research AI, Mistral, LM Studio, and more, highlighting key points and discussions in each community.
• Interesting topics such as model performance, debate on LLMs, incentives for innovation with Bittensor, model exploration, and collaboration announcements were also mentioned.
• Mistral Discord Summary includes discussions on dequantization success, API vs. local model performance debates, scrutiny on Mistral documentation, RAG integration for fact-hallucinations, and targeting homogeneity in low-resource language development.
• LM Studio Discord Summary discusses issues with the GGUF model, LM Studio quirks, and hardware demands for local LLMs.

Customizing Hybrid Search for RAG Systems:

• Tuning hybrid search within Retrieval-Augmented Generation (RAG) for different question types, with specific strategies for Web search queries and concept seeking.

Expanding RAG Knowledge with Multimodal Data:

• Sharing a YouTube video evaluation of multimodal RAG systems and support documentation.

Embedding Dumps and Cloud Queries Stoke Interest:

• Users seek assistance for integrating vector embeddings with Opensearch databases and finding cloud-based storage solutions.

API Usage and Server Endpoints Clarified:

• Queries address API choice differences and creating server REST endpoints, with references to create-llama from documentation.

Discussion on RAG Context Size and Code:

• Enquiries explore the effect of RAG context size on retrieved results and seek tutorials on RAG over code implementation.

Perplexity AI Discord Summary:

• Custom GPT curiosity discussed; inquiry about an Epub/PDF reader with AI integration; insider knowledge shared about Google’s future through Perplexity AI; odd model responses reported; conversation initiated on API uploads and source citations.

VFX Studios Eye AI Integration:

• Major VFX studios, including one owned by Netflix, are seeking professionals skilled in stable diffusion technologies. This shift underscores the importance of generative imaging and machine learning in revolutionizing storytelling.

New Paradigms in AI Job Requirements Emerge:

• AI technologies like Stable Diffusion and Midjourney humorously noted to potentially become standard demands in future job postings, reflecting a shift in employment standards within the tech landscape.

Efficiency Breakthroughs in LLM Training:

• A new paper by Quentin Anthony proposes hardware-utilization optimization during transformer model training to address prevalent inefficiencies.

Codeium's Leap to Series B Funding:

• Celebrating Codeium's progress to Series B funding, highlighting growing optimism and projections around the company's future.

Hardware-Aware Design Boosts LLM Speed:

• A hardware-aware design tweak outlined in a tweet and research paper yields a 20% throughput improvement for 2.7B parameter LLMs.

Treasure Trove of AI and NLP Knowledge Unveiled:

• A curated list shared by @ivanleomk offers resources for deepening understanding of AI models and their underpinnings.

• The Forgotten Art of File Management: @spottyluck highlighted a growing problem where many people lack understanding of a file system hierarchy or how to organize files, resulting in a 'big pile of search/scroll'.
• Generational File System Bewilderment: @spottyluck shared an article from The Verge discussing how students are increasingly unfamiliar with file systems, referencing a case where astrophysicist Catherine Garland noticed her students couldn't locate their project files or comprehend the concept of file directories.

The Mistral Discord community is abuzz with discussions surrounding topics like style transfer, quantized models, AI stress tests, watermarking LLMs, and model performance evaluations. Users debate the influences of training styles on LLM outputs, speculate about model fumbles under stress, and discuss the significance of slight differences in model performances. Additionally, conversations delve into hosting LLMs locally vs. using APIs, system prompts for Mistral models, model embedding token limits, and continuous pretraining challenges for low-resource languages. The community also shares the excitement of dataset releases, contributes to PRs, and seeks assistance with downloading models for completing tasks.

LM Studio General Messages:

• Users discussed issues like model confusion, macOS memory errors, local models behavior, AMD GPU support, and format frustrations. Links to helpful resources were shared.

LM Studio Models Discussion Chat:

• Users talked about training a local LLM, LM Studio compatibility with other tools, hardware resource usage, JSON formatted prompts, and quantized model performance.

LM Studio Feedback:

• The chat included inquiries about catalog updates, failed downloads cleanup, Chinese language support, error messages confusion, and model compatibility issues with LM Studio.

LM Studio Hardware Discussion:

• Conversations revolved around GPUs and memory management, budget PC builds for LLMs, power supply for multiple GPUs, LLM performance on different hardware, and mixing GPU generations.

OpenAI AI Discussions:

• Topics ranged from improving DALL-E faces, AI related book recommendations, training AI with questionable data, to creating AI art and scripting languages.

OpenAI GPT-4 Discussions:

• Discussions covered utilizing GPT for image analysis, identifying active GPT models, image display challenges, @ usage in GPT, and managing D&D GPTs across devices.

OpenAI Prompt Engineering:

• Discussions focused on portrait generation issues, prompt improvements, implementing feedback buttons in GPT, conversation structuring, and sharing insights across channels.

OpenAI API Discussions:

• Included topics on image orientation challenges, expectations for DALL-E updates, adding response buttons in GPT, structuring conversations for AI interactions, and sharing insights.

OpenAccess AI Collective (axolotl) General Chat:

• Users discussed training patterns, GPU acquisitions, software stack skepticism, problematic commits, and configuration improvements for the axolotl library.

OpenAccess AI Collective (axolotl) Development Chat:

• The chat focused on a CI breakdown post torch-2.2.0, seeking solutions for test failures without specified requirements, and considering torch compatibility for CI testing.

OpenAccess AI Collective (axolotl) General Help and Discussions

• Issues were addressed such as torch requirements, norm equilibrium improvement, version control PR, and checkpoint upload problems.
• Users encountered challenges with axolotl installation, understanding batch size, dataset configuration, merging models, and token counting script requests.
• Links to job logs, PRs, and GitHub repositories were provided for further information.

HuggingFace General Help and Discussions

• Topics discussed included model training challenges, specific language models, model efficiency, embedding logic, diffusion model integration, and community content publishing challenges.
• Links to ControlNet in Diffusers, Stable Diffusion Computing Network on GitHub, and image-to-image resources were shared for reference.

• LLaVA-1.6 release blog post highlighting improved features like higher image resolution and enhanced OCR, outperforming Gemini Pro in benchmarks.
• LLaVA-1.6 excels in Comic Test with positive feedback on various difficulty levels.
• Comparison between LLAVA and GT4-V on photographic landmark recognition shows varying performance.
• Discussion on style preservation in VLMs like Dall-E and its unique accuracy in reflecting specific styles.
• Introduction of SPARC for pretraining multimodal representations with fine-grained details without code or models availability yet.
• Perplexity AI general channel covering diverse topics like custom GPTs, PDF/EPUB reader queries, and unique notification sounds search.
• Insights on Perplexity AI Pro, prompt disappearance issues, and community queries in the Perplexity AI section.
• Eleuther's general channel discusses leaked model incident, cultural interpretations, diffusion model leaderboards search, and more.
• Research channel on Eleuther delves into scaling effects of transformer models, continuous pretraining of MoE models, and contradictions in ImageNet pre-training performance.

The conversations in the Discord channels covered a wide range of topics. One discussion centered around the necessity of token discretization during inference and its impact on language model inference processes. Another talk delved into the reevaluation of n-gram models and their performance when combined with neural models like Llama-2 70b. Additionally, there were exchanges on training models like llava for Visual Question Answering systems and the use of encoder-decoder models for text and image-to-text domains. The discourse also touched on CUDA development insights, advancements in multi-query attention kernels, and the deployment of nested tensors in PyTorch. These discussions showcased a blend of technical inquiries, model evaluations, and shared insights in the AI community.

DiscoResearch ▷ #general (10 messages🔥):

• Hermes 2 Dataset Unleashed: @teknium has shared their release of the Hermes 2 dataset with the community, accessible through Twitter. The dataset might be valuable for those interested in AI research and development.
• Community Love for the Dataset: In response to @teknium's post, @hammadkhan expressed gratitude for the Hermes 2 dataset release with a heartfelt thank you emoji.
• Practical Applause for Lilac Integration: @devnull0 complemented the lilac integration in the Hermes 2 dataset, indicating a well-received feature within the community.
• Apple Engineer's Crafting Conundrums Presented: @devnull0 shared a humorous prompt regarding the effort required by Apple engineers, found in a tweet by @cto_junior on Twitter.
• Mixtral's Impressive Performance Metrics Shared: @bjoernp highlighted the surprising speed of Mixtral, reaching 500 tokens per second, with a link to Groq's chat platform, sparking curiosity and a follow-up question by @sebastian.bodza on the company's use of custom AI accelerators.

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