[AINews] Less Lazy AI • ButtondownTwitterTwitter

Mistral Discord Summary

• LLama3 and Mistral Integration Insights: Community members speculated on the architecture and training data differences between LLama3 and other models. Performance comparisons between OpenHermes 2.5 and Mistral were also discussed.
• Model Hosting and Development Dilemmas: AI hosting on services like Hugging Face and Perplexity Labs was considered. Discussions on CPU inference for LMMs and Mistral's quantization were prominent.
• Fine-tuning Focuses and Financial Realities: Questions on fine-tuning for specific domains like energy market analysis were addressed.
• Showcasing AI in Creative Arenas: Users showcased applications such as novel writing with AI assistance and discussed tools for improving AI writing sessions.

TimesFM Training Clarified: A corrected sequence for TimesFM model training was shared to emphasize non-overlapping output paths based on the model's description. Meanwhile, the conversation about handling large contexts in LLMs spotlighted the YaRN paper, while a method for autoencoding called 'liturgical refinement' was proposed.

MoE-Mamba Delivers Impressive Results: According to a recent paper, 'MoE-Mamba' SSM model surpasses other models with fewer training steps. Strategies, such as adding a router loss to balance experts in MoE models and stabilizing gradients via techniques from the Encodec paper, were discussed for improving AI efficiency.

Interpretability Terms Defined: In the realm of interpretability, a distinction was noted between a 'direction' as a vector encoding monosemantic meaning and a 'feature' as the activation of a single neuron.

Organizing Thunderous Collaborations: A meeting schedule for Tuesday 6th at 5pm (UK time) was confirmed concerning topics like testing at scale, where Slurm was mentioned as a tool for queuing numerous jobs.

Multimodal MoE Models Explored: Discussions veered toward merging MoEs with VLMs and diffusion models for multimodal systems, aiming for deeper semantic and generative integration, and investigating alternatives like RNNs, CLIP, fast DINO, or fast SAM.

GPT-NeoX 'gas' Parameter Deprecated: An update on GPT-NeoX involves the deprecation of the 'gas' parameter as it was found non-functional and a duplicate of 'gradient_accumulation_steps', with the warning that past configurations may have used smaller batch sizes unintentionally. A review of the related pull request is underway.

Alignment Lab AI Discord Summary

• Diving into Training Data for Mistral-7B Open-Orca: An inquiry about replicating Mistral-7B Open-Orca with a curated filtered subset dataset for efficient training, called SlimOrca. The dataset was confirmed by a user and details about accessing the training configuration were discussed.
• Dataset Discovery and Confirmation: The use of SlimOrca dataset for Mistral-7B Open-Orca training was confirmed, and guidance on where to find the training configuration was provided.
• Commercial Contact Conundrum: A request for marketing contact details remained unanswered in the message history.

Skunkworks AI Discord Summary

• Skewed Perspectives in AI Discussions: Users discussed contrasting approaches to embedding techniques in AI, focusing on whole document text embeddings versus visual embedded techniques, particularly in the context of an encoder/decoder model reimplemented task.

LLM Perf Enthusiasts AI Discord Summary

• BentoML Eases Model Deployment: Positive feedback on deploying models with BentoML for a VLLM backend on AWS, highlighting the ease of the process.
• DSPy Framework Elevates Language Model Programming: The launch of DSPy, a Stanford initiative for transforming foundation model programming, was discussed. A related YouTube video provided insight into DSPy's capabilities.

AI Engineer Foundation Discord Summary

• AIEF Bulgaria Chapter Makes Waves: The AIEF Bulgaria Chapter's second monthly meet-up gathered 90 participants for 'Lightning Talks' on various AI topics, promoting networking within the community.
• Diverse Lightning Talks Spark Interest: Presentations covered QR Code Art, LMMs, Zayo, and strategies for building defensible businesses in the AI age, with recordings to be shared soon.
• Spotlight on ChatGPT Implementation Strategy: Details on 'ChatGPT Adoption Methodology' were shared, focusing on integrating ChatGPT into business processes; resources were linked via a Google Slides document.
• Sharing Success on Social Media: The AIEF Bulgaria lead posted highlights from the meet-up on LinkedIn, showcasing the community and technological advancements.
• Presentations Capturing Technical Innovation: Slide presentations from the event highlighted technical diversity and innovation within the community, including topics like QR Code Art and AI business models.

GPT-4's Lyric Quirks: @cccntu discussed the limitations of GPT-4 in generating lyrics accurately, mentioning that using perplexity with search yields better results than the AI. Greentext Generation Challenges: @euclaise suggested that 4chan's greentext format may be difficult for AI to learn due to lack of training data, while @teknium shared a snippet showcasing an AI's attempt to mimic a greentext narrative. Call for Indian Language AI Innovators: @stoicbatman invited developers and scientists working on AI for Indian languages to apply for GPU computing resources and infrastructure support provided by IIT. Llama2 Pretrained on 4chan Data?: @stefangliga claimed that 4chan content is part of llama2's pretraining set. Apple Accused of Creating Barriers for AR/VR Development: @nonameusr criticized Apple's restrictive practices hindering AR/VR advancement.

The LM Studio Hardware Discussion section covers various topics related to hardware compatibility, model performance, and GPU configurations. Users discuss seeking model advice for specific PC specs, updates on model versions, preferences between VS Code and IntelliJ, integration tools like Continue.dev, and queries on image generation models. Links to tools, hardware discussions, and troubleshooting tips are shared among users.

Mistral mishap with markdown: @drprimeg1 struggled with Mistral Instruct AWQ not outputting content inside JSON format when given a prompt with Markdown formatting. Their current approach to classification can be found here, but the model responds with placeholders instead of actual content. Markdown mayhem in models: @ethux suggested that @drprimeg1's problem could be due to the Markdown formatting, noting that the model tries to output JSON but ends up displaying Markdown syntax instead. GuardrailsAI to guide prompt effectiveness: @ethux recommended GuardrailsAI as a tool for ensuring correct output formats and mentioned its capability to force outputs and retry upon failure. They also included a reference to the tool at GuardrailsAI. Teacher forcing talk: @ethux mentioned that GuardrailsAI implements a form of teacher forcing by providing examples of what went wrong and how to correct it, while also being predefined. Instructor Introduction: As another recommendation for structured output generation, @ethux shared a link to Instructor, a tool powered by OpenAI's function calling API and Pydantic for data validation, described as simple and transparent. Additional insights and a community around the tool can be accessed at Instructor's website.

-face-and-deepgram?utm_source=ainews&utm_medium=email&utm_campaign=ainews-less-lazy-ai" target="blank">Sentiment Analysis with Hugging Face and Deepgram</a> - @lunarflu encouraged @imcoza1915 to draft a community blog post on Hugging Face Hub, @4gentbur3k shared a link discussing the integration of Hugging Face's transformers with Langchain, @wubs expressed amazement at AI-driven art generation enhancements, @andysingal shared progress on creating a fine-tuning models resource list. Links mentioned include blogs on agent-helper Langchain, Hugging Face Blog Explorers, and an AI art generation post from Art Forge Labs. They also referenced a GitHub repository for a fine-tuning models resource list.

The OpenAccess AI Collective discussions include topics like customizing GPT communication for human-like interactions, stability concerns in using different GPT models, challenges with over-moderation in creative writing, troubleshooting issues with GPUs and memory requirements, and exploring new optimization techniques. Users share tips on storytelling consistency, address policy violation messages, and seek advice on specific technical issues. The conversations also touch on running Axolotl on different machines, debating fine-tuning techniques, and maximizing data utilization. Links to various resources and tools are shared throughout the discussions.

Fast & Furious PyTorch Code Tip: @tantara shared a link to a PyTorch code section from gpt-fast repo, suggesting specifying the compiled layers when using the torch.compile API. Torch Compiler Fine-Grain Control Unveiled: @marksaroufim mentioned the use of torch.compiler.disable() and recommended the PyTorch documentation on finer grained APIs to control torch.compile.

Seeking Deeper Semantic and Generative Integration: @martianulcrizat discussed the potential for a tighter integration between semantic understanding and generative capabilities within a VLM by employing MoE frameworks.

Search for VLM and Diffusion Model Combination Techniques: @martianulcrizat inquired about approaches for combining VLMs with diffusion models beyond the conventional methods involving QFormer, Adaptor layers, and cross-attention with continuous token representations.

Acceptance of Shared Papers on Integration Methods: !BeastBlaze acknowledged the relevance of papers shared by @martianulcrizat which potentially could assist in VLM and diffusion model integration.

Alternative Simplifications to Combining VLMs with Diffusion Models: !BeastBlaze mentioned new literature, albeit not readily available, which suggests the feasibility of using simple RNNs and CBOW to achieve similar outcomes to that of large models like CLIP, thereby enabling leaner methods like fast DINO or fast SAM.

This section discusses various updates and challenges shared on the LlamaIndex blog. It includes details on YouTube tutorials for building a SMART portfolio website using Next.js 14 and more, frustration with LangChain tutorials, issues encountered with LangChain's Ollama model, and the release of a LangChain guide book. Additionally, it mentions a data scientist's background and tutorials on their Medium+YouTube channel. The section also covers RAG development challenges, hackathons, multimodal models, multilingual embedding optimization techniques, and the launch of LlamaIndex's Slack bot on Discord.

The Orca dataset is the subset used for training models, and it is mentioned that the training configuration for the model should be in the config subdirectory of the model's repository. Additionally, a request for marketing contacts was made by @tramojx, seeking contact for a listing and marketing proposal, but no response was provided. Links to datasets and information related to model training configurations were also shared.