[AINews] Companies liable for AI hallucination is Good Actually for AI Engineers • ButtondownTwitterTwitter

The section discusses significant developments in AI technologies, such as AI model optimization, emerging frameworks like Mistral-Next and Sora for video editing, and community-driven collaborations. It also delves into the legal implications related to AI, particularly through a ruling involving Air Canada's chatbot misinformation leading to liability. The section emphasizes the importance of AI ethics, data management challenges, and the promotion of open-source frameworks within the AI community.

Boosting Token Generation to Maximize Speed: Engineers optimize GPU utilization to enhance token generation speed, exploring settings that push an RTX 4050 and Ryzen 7 up to 34 tokens/s. A user is looking to exceed this performance by offloading 50 layers and seeks advice on further improvements, while .gguf models are being fine-tuned for more human-like responses and censorship removal.

Hardware Tweaks and Multi-GPU Musings: Intel cores are being leveraged for KVMs on macOS and Windows, with an eye on upgrading from a 3090 to a 5090 GPU for better performance. The community is also sharing insights on multi-GPU configurations, power, space considerations, and tooling for optimized VRAM utilization across mismatched graphics cards.

LM Studio Model Recommendations and Quantization Insights: For users seeking the best 7b models with 32k context support, check TheBloke's repositories and sort by downloads in LMStudio's Model Explorer. Discussions point to Q5_K_M models for efficiency, and a Reddit post was highlighted for in-depth quantization method comparison.

LM Studio Autogen and CrewAI Starting Points: A beginner's tutorial on using Autogen with Local AI Agent was shared, while a broken link in the autogen channel was reported. The pin regarding the link was successfully removed after a user's suggestion.

LM Studio Integration and Tech Troubleshooting: Discussion on integrating LM Studio with Flowise and LangFlow was initiated, with users sharing attempts to connect using http_client and tackling server connection issues. Configuration insights were shared, involving manual settings introduction to achieve functional integration.

AI's Disputed Autonomy

• The legal implications of AI identifying as separate entities were discussed, with Air Canada's chatbot asserting its own refund policy. A judge rejected the framing of the chatbot as a separate legal entity.

Guardrails in the Spotlight

• The necessity of AI guardrails became a topic of humor and caution, as seen in the tale of Air Canada's chatbot creating a refund policy, implying a push for businesses to take AI guardrails more seriously.

BERT's Brisk Overview

• A 3-minute BERT discussion was presented by @ivanleomk, while others debated BERT’s impact on Google's search algorithms and its bidirectional nature.

LLM Paper Club Goes Global

• Swyxio invited AI enthusiasts to join the LLM Paper Club (Asia Edition!) and shared a recent podcast episode featuring insights on serverless infrastructure for AI.

AI and Agents in Harmony

• A vibrant discussion unfolded around AI agents and state machines, referencing resources such as CrewAI and MagickML.

OpenMoE Lacks Data

• A paper on OpenMoE, a mixture-of-experts model, was critiqued for its lack of performance due to training on less data than anticipated, and its efficiency in inference time was called into question by members such as @swyxio.

TheBloke Coding

• Exploring Vision Transformers for Contextual Awareness: Users discussed the limitations of autoregressive token prediction in building full world context through V-JEPA project on self-supervised learning.

• Discovering the Secrets of SIMD with Mojo: Discussions on understanding Mojo's types as SIMD abstractions and challenges faced in incorporating async functions into non-async code.

• The 'Autocommit' Timesaver: Introduction of a tool named autocommit that uses AI to generate commit messages based on diffs.

• Treading the Async and Sync Bridge in Python: Advice on working with async functions in sync code in Python and resources like nest_asyncio and asyncio-bridge.

• Promoting an Open-Source Typescript Agent Framework: Mention of an open-source project bazed-af and seeking suggestions to share it for feedback.

The Eleuther Discord channels discuss various topics such as interpretability, LM evaluation harness, and GPT-NeoX development. These discussions include highlighting the causal scrubbing method, appreciating influential work, sharing MMLU task repositories, dealing with issues in fine-tuning logprobs, and exploring architectural flexibility in GPT-NeoX. Users also seek advice on optimizing GPU utilization for LLMs, local LLM setup extensibility, platform and hardware discussions, local LLM capabilities, and support for LLM issues.

The LM Studio Models Discussion section covers various topics related to the utilization and exploration of different language models. Users discuss seeking long-context models, model exploration tips, model size versus performance considerations, preferences for language translation models, and the choice of quantized models. The section also involves discussions on factory reset confusion, preset folder location, UI panel behavior inconsistencies, and bug frustrations. Additionally, there are conversations about hardware configurations, multi-GPU setups, GPU adapter challenges, and AVX2 support validation. The section also touches on suggestions for improving chat etiquette, ensuring clarity in queries, and sharing experiences with operating systems. Finally, the section includes interactions on autogen tools, coding responses, and LM Studio integration. Links to related tools and resources are shared throughout the discussions.

Mistral ▷ #finetuning (6 messages):

• SLEB: A Technique for Accelerating LLMs: @alex096170 introduced SLEB, a method to prune LLMs for better inference speed without compromising performance.

• In Search of the Best LLM Pretraining Framework: @remek1972 asked about the best framework for pretraining large language models.

• Framework Recommendations for Different Scales: @quicksort suggested frameworks like Accelerate with deepspeed for multi-node environments.

• Scaling Up LLM Pretraining Advice: @quicksort recommended resources for pretraining LLMs on multiple nodes.

• Gratitude Expressed for Pretraining Guidance: @remek1972 thanked for helpful suggestions.

Mistral Random (2 messages):

• Elqano is Hiring AI Talent: @thomas_saulou announced a job opportunity at Elqano for a developer in generative AI.

• AI Launchpad Opportunity for Pre-Seed Startups: @deedubs__ shared an opportunity for pre-seed AI startups to gain exposure at Data Council presented by Zero Prime Ventures.

Mistral La Plateforme (24 messages🔥):

• Mistral vs GPT-4 for Coding: Comparing Mistral Medium and GPT-4 for coding ability.

• TDD Integration Approach with AI: Unique workflow integrating test-driven development (TDD) with AI assistance.

• Collaboration Offer for Open Source: Offer to share compute resources for open-source projects.

• Augmentoolkit Contribution and Dataset Cleanup: Contribution to augmentoolkit and discussion on large dataset cleanup.

• Data Cleaning and Testing Discussions: Exchange of ideas on data cleaning and testing.

Links mentioned

• In Search of RAM Efficiency for Adam: User @hexani raised a question about the amount of RAM needed for fine-tuning a 7B model using Adam due to its high memory requirement (8X weight copies). They also asked for methods to reduce this RAM usage but did not receive a direct response within the given messages.
• Batch Size Queries Unanswered: User @hexani inquired about the typical batch size used for training with GPUs like a 4090 or an H100 when fine-tuning models, but this question went unanswered.
• PyTorch Over Jax for LLM: User @carsonpoole mentioned they are in the process of converting an LLM to PyTorch format from Jax, describing the process as unpleasant.
• Axolotl Fine-tuning Tutorial Request Goes Unmet: User @pncdd sought a complete step-by-step tutorial on fine-tuning using axolotl, from dataset handling to execution, but no responses with such a guide appeared.
• Longform Text Generation Challenges: User @benh.1984 asked for advice on generating very long text (~20,000 tokens) using llms without success, and .ben.com suggested banning the end-of-sentence token to encourage continuance, though this might lead to degraded quality when the model wants to conclude.

In this section, members discuss the challenges of redacting personally identifiable information (PII) before summarization. Suggestions are made to use Python libraries for PII detection and to avoid direct AI detection of PII. Additionally, there are conversations about JSON formatting struggles, prompt crafting strategies, frustrations with unstructured categories, teaching AI with human-like training, and issues with knowledge base retrieval. Links to Discord and other relevant resources are also shared.

• Intel Unveils Text to 3D Model Converter: Intel introduced the LDM3D-VR model for converting text to 3D, focusing on virtual reality development.
• Detecting Deepfake Faces with a Web App: A web app utilizing XAI to identify deepfake images was promoted, with plans for future training enhancements.
• Enhancing AI Face Recognition: Conversations on limitations and future improvements for a model detecting AI-generated faces, including expanding the training dataset and applying transfer learning.
• Databricks: Accelerating AI Infrastructure: An article discussing Databricks' impact on generative AI and its growth directions in the industry.

Translation Models Grasping Nuanced Meaning:

• Translation Models Understanding Nuances
• Interest in Fine-tuning WikiSQL on Smaller Models
• Tools for Dataset Deduplication Needed
• Technical Issue with Python Import: Addressing an ImportError when importing from the transformers library.

In this section, users engage in discussions related to BERT's impact on Google search, the wonders of BERT's bidirectionality, and Swyxio's internet woes during the Latent Space LLM Paper Club (Asia Edition) session. The users also delve into topics like model training and quality, anticipation for the next paper, and the ongoing exploration of AI agents and state machines. Additionally, resources related to agents, including tools like CrewAI and MagickML, are shared and discussed. The community also celebrates Latent Space's one-year anniversary and plans for live testing and streaming. Various links are mentioned throughout the discussions, providing additional resources and insights.

This section discusses the implementation insights and collaboration efforts related to RingAttention and CUDA development. It includes discussions on the use of JAX in Large World Model, scheduling meetups for collaboration, creating project-focused channels, and initiating the development of CUDA RingAttention. The section also covers recommendations for resources on CUDA programming, exploration of Groq's inferencing speed, practical code examples request, and sharing of new CUDA for Python video releases. Overall, the focus is on technical discussions, coordination for research, exploration, and setting up collaboration channels for optimized CUDA kernels for RingAttention.

Perplexity Measure for Difficulty: @dreamgen explored using perplexity to gauge the difficulty of examples with a baseline model. @suikamelon shared a paper introducing 'learnability' for Supervised Fine-Tuning (SFT) of Large Language Models (LLMs). ### SPIN Implementation: @nruaif provided a GitHub link to the official implementation of Self-Play Fine-Tuning (SPIN). ### Torch Update Deliberation: @nanobitz discussed potential PyTorch update to 2.2.x. ### New Optimizers Integration: @yamashi expressed confusion about integrating new optimizers into the system. Despite challenges, a workaround was found. ### RAG API Guidance Request: User @mamo7410 inquired about implementing a RAG API with langserv, seeking help on obtaining streaming, runtime ID, and context documents for the frontend.

This section provides updates on various discussions and events in the AI community, including deep dives into OpenAI's Whisper model, tutorials on LangChain AI, discussions on training losses, pretraining frameworks, and embedding databases, as well as workshops and hackathons organized by the AI Engineer Foundation.

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