As widely telegraphed, Meta partially released Llama 3 today, 8B and 70B variants, but with the star of the show being the 400B variant (still in training) which is widely lauded as being the first GPT-4 level OSS model.

We are traveling for most of the day so we will add all the remaining commentary tomorrow, but head to HN for the best live coverage.

Table of Contents

[TOC]

AI Reddit Recap

Across r/LocalLlama, r/machinelearning, r/openai, r/stablediffusion, r/ArtificialInteligence, r/Singularity. Comment crawling works now but has lots to improve!

Key Themes in Recent AI Developments

• Stable Diffusion 3 Release and Comparisons: Stability AI has released the Stable Diffusion 3 API, with model weights coming soon. Comparisons between SD3 and Midjourney V6 show mixed results, while realism tests demonstrate SD3’s capabilities. Emad Mostaque confirmed SD3 weights will be released on Hugging Face along with ComfyUI workflows.

• Advances in Robotics and AI Agents: Boston Dynamics revealed an electric version of their humanoid robot Atlas with impressive agility. Menteebot is a human-sized AI robot controllable via natural language. Microsoft’s VASA-1 model generates lifelike talking faces from audio in real-time at 40fps on an RTX 4090.

• New Language Models and Benchmarks: Mistral, a European OpenAI rival, seeks $5B in funding. Their Mixtral-8x22B-Instruct-v0.1 outperforms open models with 100% accuracy on 64K context. New 7B merge models combine strengths of different bases. Coxcomb, a 7B creative writing model, scores well on benchmarks.

• AI Safety and Regulation Discussions: Former OpenAI board member Helen Toner calls for audits of top AI companies to share info on capabilities and risks. The Mormon Church released AI usage principles, noting benefits and risks.

• Tools and Frameworks for AI Development: The Ctrl-Adapter framework adapts controls to diffusion models. Distilabel 1.0.0 enables synthetic dataset pipelines with LLMs. Data Bonsai cleans data with LLMs, integrating ML libraries. Dendron builds LLM agents with behavior trees.

• Memes and Humor: An expectation vs reality meme jokes about AI development vs futuristic visions. Snoop Dogg in a PS2-style LORA shows AI meme potential. AI Sans vs Frisk reimagines Undertale with AI art. A humorous take suggests AI isn’t that advanced yet.

AI Twitter Recap

all recaps done by Claude 3 Opus, best of 4 runs. We are working on clustering and flow engineering with Haiku.

Here is the summary in the requested format:

Meta Llama 3 Release

• Llama 3 Models Released: @AIatMeta announced the release of Llama 3 8B and 70B models, delivering improved reasoning and setting a new SOTA for models of their size. More models, capabilities, and a research paper are expected in the coming months.
• Model Details: @omarsar0 noted that Llama 3 uses a standard decoder-only transformer, a 128K token vocab, 8K token sequences, grouped query attention, 15T pretraining tokens, and alignment techniques like SFT, rejection sampling, PPO and DPO.
• Performance: @DrJimFan compared Llama 3 70B performance to Claude 3 Opus, GPT-4, and Gemini, showing it is approaching GPT-4 level. @ylecun also highlighted strong benchmark results for the 8B and 70B models.

Open Source LLM Developments

• Mixtral 8x22B Release: @GuillaumeLample released Mixtral 8x22B, an open model with 141B params (39B active), multilingual capabilities, native function calling, and a 64K context window. It sets a new standard for open models.
• Mixtral Performance: @bindureddy noted Mixtral 8x22B has the best cost-to-performance ratio, with strong MMLU performance and fine-tuning potential to surpass GPT-4. @rohanpaul_ai highlighted its math capabilities.
• Open Model Leaderboard: @bindureddy and @osanseviero shared the open model leaderboard, showing the rapid progress and proliferation of open models. Llama 3 is poised to advance this further.

AI Agents and RAG (Retrieval-Augmented Generation)

• RAG Fundamentals: @LangChainAI released a playlist of videos explaining RAG fundamentals and advanced methods, in collaboration with @RLanceMartin and @freeCodeCamp.
• Mistral RAG Agents: @llama_index and @LangChainAI shared tutorials on building RAG agents with @MistralAI’s new 8x22B model, showing document routing, relevance checks, and tool use.
• Faithfulness of RAG: @omarsar0 shared a paper quantifying the tension between LLMs’ internal knowledge and retrieved information in RAG settings, highlighting implications for deploying LLMs in information-sensitive domains.

AI Courses and Education

• Google ML Courses: @svpino shared 300 hours of free Google courses on ML engineering, from beginner to advanced levels.
• Hugging Face Course: @DeepLearningAI announced a new free course on quantization fundamentals with Hugging Face, to make open models more accessible and efficient.
• Stanford CS224N Demographics: @stanfordnlp shared demographics for the 615 students in CS224N this quarter, showing broad representation across majors and levels.

Miscellaneous

• Zerve as Jupyter Alternative: @svpino suggested Zerve, a web-based IDE with a different philosophy than Jupyter, could potentially replace Jupyter notebooks for many use cases. It has unique features for ML/DS workflows.
• Capital Gains and Inflation: @scottastevenson explained how capital gains taxes during inflation can tax people for gaining nothing, impacting the middle class through assets like retirement funds, homes, and businesses.

AI Discord Recap

A summary of Summaries of Summaries

Llama 3 Launch Generates Excitement: Meta’s release of Llama 3, an 8B and 70B parameter instruction-tuned model, has sparked significant interest across AI communities. Key details:

• Promises improved reasoning capabilities and sets “new state-of-the-art” benchmarks across tasks.
• Available for inference and finetuning via partnerships like Together AI’s API offering up to 350 tokens/sec.
• Anticipation builds for an upcoming 400B+ parameter version.
• Some express concerns over output restrictions hindering open-source development.

Mixtral 8x22B Redefines Efficiency: The newly launched Mixtral 8x22B is lauded for its performance, cost-efficiency, and specialization across math, coding, and multilingual tasks. Highlights:

• Utilizes 39B active parameters out of 141B total via sparse Mixture-of-Experts (MoE) architecture.
• Supports 64K token context window for precise information recall.
• Released under Apache 2.0 open-source license along with Mistral’s custom tokenizer.

Tokenizers and Multilingual Capabilities Scrutinized: As powerful models like Llama 3 and Mixtral emerge, their tokenizers and multilingual performance are areas of focus:

• Llama 3’s 128K vocabulary tokenizer covers over 30 languages but may underperform for non-English tasks.
• Mistral open-sources its tokenizer with tool calls and structured outputs to standardize finetuning.
• Discussions on larger tokenizer vocabularies benefiting multilingual LLMs.

Scaling Laws and Replication Challenges: The AI research community engages in heated debates around scaling laws and replicability of influential papers:

• Chinchilla scaling paper findings questioned, with authors admitting errors and open-sourcing data.
• Differing views on whether results reaffirm or refute existence of scaling laws.
• Calls for more realistic experiment counts and narrower confidence intervals when extrapolating from limited data.

Misc

• Llama 3 Launch Generates Excitement and Scrutiny: Meta’s release of Llama 3, with 8B and 70B parameter models, has sparked widespread interest and testing across AI communities. Engineers are impressed by its performance rivaling predecessors like Llama 2 and GPT-4, but also note limitations like a 128k token context window. Integrations are underway in frameworks like Axolotl and Unsloth, with quantized versions emerging on Hugging Face. However, some express concerns over Llama 3’s licensing restrictions on downstream use.

• Mixtral and WizardLM Push Open Source Boundaries: Mistral AI’s Mixtral 8x22B and Microsoft’s WizardLM-2 are making waves as powerful open source models. Mixtral 8x22B boasts 39B active parameters and specializes in math, coding, and multilingual tasks. WizardLM-2 offers an 8x22B flagship and a speedy 7B variant. Both demonstrate the rapid advancement of open models, with support expanding to platforms like OpenRouter and LlamaIndex.

• Stable Diffusion 3 Launches with Mixed Reception: Stability AI released Stable Diffusion 3 on API, but initial impressions are mixed. While it brings advancements in typography and prompt adherence, some report performance issues and a steep price increase. The model’s absence for local use has also drawn criticism, though Stability AI has pledged to offer weights to members soon.

• CUDA Conundrums and Optimizations: CUDA engineers grappled with various challenges, from tiled matrix multiplication to custom kernel compatibility with torch.compile. Discussions delved into memory access patterns, warp allocation, and techniques like Half-Quadratic Quantization (HQQ). The llm.c project saw optimizations reducing memory usage and speeding up attention mechanisms.

• AI Ecosystem Expands with New Platforms and Funding: The AI startup scene saw a flurry of activity, with theaiplugs.com debuting as a marketplace for AI plugins and assistants, and SpeedLegal launching on Product Hunt. A dataset of $30 billion in AI startup funding across 550 rounds was compiled and shared. Platforms like Cohere and Replicate introduced new models and pricing structures, signaling a maturing ecosystem.

PART 1: High level Discord summaries

Perplexity AI Discord

• Opus Cap Curtails Conversations: Engineers are disgruntled over the unexpected Opus model usage limit, which dropped suddenly from 600 to 30 messages per day, derailing plans and prompting some to seek refunds or pursue alternatives like Tune Chat.

• Llama 3 Hype Exceeds Expectations: There’s buzz around Meta’s open-source Llama 3 model, with engineers sharing links and discussing its potential, while keeping an eye out for new benchmarks posted on Twitter and links like llama3 which offer a deep dive.

• API Ecosystem Grows Despite Hiccups: Amid discussions on API inconsistencies, mixtral-8x22b-instruct joins Perplexity Labs’ offerings at labs.pplx.ai, and users with Perplexity Pro now benefit from $5 monthly API credits.

• Shared Enthusiasm Over Typographic Transformation: Engineers showed excitement for typographic developments with links circulated on the new Tubi font and related discussions.

• Models Perform Poetic Analysis: mixtral-8x22b-instruct is receiving praise for its nuanced understanding of lyrics, such as those by Leonard Cohen, indicating a new bar set for other models to meet in content interpretation.

Stability.ai (Stable Diffusion) Discord

• Stable Diffusion 3 Blazes onto API: Stability AI has launched Stable Diffusion 3 and the Turbo version on their Developer Platform API, claiming noteworthy improvements in typography and adherence to prompts.
• Model Weights for the Masses: Stability AI commits to releasing model weights for members, encouraging self-hosting and supporting the broader open generative AI movement.
• GPU Debate Ignites: Discussion abounds regarding whether to invest in the imminent but unreleased GPUs like the 5090 or to opt for current high-performance ones like a second-hand 3090 for AI tasks, pondering over VRAM capacity and NVLink capabilities.
• AI Influencers - A Controversial Craft: Dialogues traverse the creation of AI influencers, revealing a mix of motivations ranging from simple curiosity to profitability, amidst some questioning their societal contribution.
• Fine-tuning Fixes and Job Fishing: Engineers exchange insights on fine-tuning newer models for better performance, with eye on job openings and partnerships in AI domains, linking to a job post for a Stable Diffusion Engineer at Palazzo, Inc..

Nous Research AI Discord

• Snowflake’s Embeddings Stir Discussions: A newly launched text-embedding model by Snowflake became a topic of interest, sparking discussions on language’s vector space and symbolic language forms. Concerns were raised about the efficiency and meaningfulness of the relatively smaller 256-dimensional embeddings mentioned, compared to the more common 1500 dimensions, with members planning to run their own tests on retrieval accuracy.

• Model Security Vulnerabilities Exposed: Discussions revealed a security incident at Hugging Face involving a malicious .pickle file and pondered similar vulnerabilities in OpenAI’s systems. This highlights the ongoing risks and challenges in AI system security, emphasizing the need for engineers to design robust countermeasures.

• Llama 3 Amplifies Benchmarking Buzz: The performance of Meta’s Llama 3 stirs excitement, despite concerns about its context length limitations compared to models like Mixtral 8x22B. There’s active dialogue about finetuning with MLX, but sudden gating of Mistral models implies regulatory challenges or abuse prevention measures.

• Prompting Perplexities and GPU Ponderings: Questions and advice exchanges were observed about the Hermes 2 Pro model’s prompting behavior and the need for a “directly_answer” tool. Additionally, technical challenges of conducting long context inference on GPU clusters and using twin A100 GPUs with jamba for processing 200k tokens were particular points of discussion, instrumental for engineers working on similar high-capacity deployment scenarios.

• WorldSim Whispers: Eagerness and playfulness marked the community as they awaited the re-launch of WorldSim. Insights from users suggested the implementation of usage limitations and fees to help prevent manipulative inputs which had caused a shutdown. The platform’s potential for user-generated AI civilizations stood out as a significant point of anticipation and philosophical conversation.

LM Studio Discord

LLama 3 is Heating Up LM Studio: The new Meta Llama 3, particularly the 8B Instruct version, is stirring up excitement with its release and availability on Hugging Face, but users report issues with unexpected output repetition and prompt loops. Enthusiasts debated the feasibility of running large models such as WizardLM-2-8x22B locally, with the understanding that it might not be practical on a 24GB Nvidia 4090 graphics card.

Tech Troubles and Triumphs: AI engineers shared approaches to optimize Llama 3’s performance on diverse hardware setups, from Ryzen 5 3600 to Mac M1 and M3 Max, and one user resolved thermal throttling by adjusting motherboard settings for cooler operations. Dual P100 GPUs are proving tricky for some, with concerns about proper utilization, while users also discussed the ability of different NVIDIA GPUs to contribute VRAM as needed.

AI App Engagement and Enquiry: Interest peaks with MissionSquad, an Electron-based app offering Prompt Studio in the recent V1.1.0 release, however, calls for transparency with some preferring to view source code are being balanced against privacy concerns. A suggestion to incorporate text-to-speech (TTS) functionality into LM Studio reflects the desire for enhanced interactivity.

AMD Adventures: Users with AMD setups encounter GPU selection challenges when running LM Studio, and while the latest ROCm preview (0.2.19) should resolve iGPU selection woes, reports of inference anomalies suggest lingering support issues for large models like the 8B model. A workaround involving disabling the iGPU has been shared, and an update or bug report submission is recommended for persistent issues.

Prompt Crafting Callout: Discussions in LM Studio extend to practical matters like crafting affiliate marketing campaigns, with users requesting AI models with specificity beyond generic outputs. One member highlighted the need for transactional arrangements as opposed to speculative partnerships when soliciting developer involvement.

Unsloth AI (Daniel Han) Discord

Llama 3 Launch Lures Engineers: Members of the technical Discord community engaged in active discussions and testing of Llama 3, evaluating its benchmark results that suggest performance parity with its predecessor, Llama 2, despite having fewer parameters (8B compared to 70B). They experimented with integrating it into the Unsloth AI framework, citing a Google Colab notebook for the 8B model, and they also explored incorporating a 4-bit quantized version of the 70B model.

Coping with CUDA’s Absence on Mobile: Participants noted the challenge of deploying neural networks on mobile devices due to the lack of CUDA compatibility, leading to discussions about custom inference engines as alternatives. These dialogues touch upon the intricacies of compiling neural network models for deployment on iPhone hardware.

Legacy Hardware Left Hanging by TorchTune: TorchTune’s discontinuation of support for older GPUs spawned discussions about its impact on those utilizing prior generation hardware. Users mentioned workarounds like utilizing notetaking tools such as Obsidian for knowledge management purposes.

License Logistics and Name Games: The importance of adhering to Llama 3’s new licensing terms was a topic of discussion, specifically the necessity of including the “Llama 3” prefix in the names of any derivatives. This kind of attention to detail underscores the legal considerations important in the open-source AI space.

Bilingual Brainstorming: The community pondered over strategies for creating bilingual models, weighing the costs and complexity of potential solutions, such as a threefold LLM call for translation layers. Additionally, Distributed Negotiation Optimization (DNO) grabbed attention with the realization that while it remains unimplemented in libraries, it could serve as an effective iteration on Distributed Public Optimization (DPO).

CUDA MODE Discord

Tiling Transformation: Discussing tiled matrix multiplication, engineers noted that padding large matrices for tiling can save memory bandwidth, despite additional computing for padded areas.

Meta’s Llama Lacks MOE: Meta’s newly unveiled Llama 3 is a dense 405 billion parameter model that does not incorporate an MOE (Mixture of Experts) model, contrasting it with other state-of-the-art architectures. Meta’s Llama 3 details

CUDA Crusaders Converse: CUDA discussions ranged from best practices in loading large datasets and optimizing kernel settings to debugging discrepancies in results and unpacking memory access patterns and their impact on performance.

Triton Puzzles with Custom Operations: AI Engineers exchanged techniques for making custom functions compatible with torch.compile, with references to handling torch.jit.ignore and demonstration of custom Triton kernels. GitHub PR reference for custom CUDA wih torch.compile and composition of custom Triton kernels were part of the conversation.

Quantization Quandary: In depth charted discussions about Half-Quadratic Quantization (HQQ) methods, particularly focusing on axis=0 vs axis=1 quantization and tackling transformers’ weight matrix concatenation challenges. Links shared included evaluations of current practices, innovative optimization techniques, and possible future enhancements to integrate HQQ into torchao. Details on HQQ implementation

Collaborative Coordination for CUDA Event: The Massively Parallel Crew’s planning for an overlapping panel and CUDA MODE event showcases teamwork in arranging for recording, overcoming scheduling conflicts, and post-production work.

LAION Discord

SD3 Debuts with API-Only and Mixed Feelings: Stability AI released SD3 via API, and responses were mixed, acknowledging some performance issues, especially in text rendering, alongside strategic moves towards monetization.

Dataset Dilemma: With LAION datasets pulled from Huggingface, members sought out alternatives like coyo-700m and datacomp-1b for training new models. Simultaneously, interest in PAG’s application to SDXL was noted, offering better visual results compared to previous ones but not exceeding DALLE-3’s capabilities.

Stability AI’s Shaky Ground: High-profile exits from Stability AI prompted discussion about the company’s future and potential effects on open AI models, with a cloud of mismanagement concerns looming. The broader AI community is starting to test and react to Meta’s LLaMA 3, applauding its performance on a variety of tasks despite a modest context window.

GANs Hold a Narrow Lead in Efficiency: GANs were noted for their inference speed and parameter efficiency, but they’re tricky to train and often fall short visually. Meanwhile, Microsoft’s unveiling of VASA-1 is set to revolutionize real-time lifelike talking faces, leveraging audio cues.

Datasets and Models Evolving: HQ-Edit, a sizeable dataset for image editing guided by instructions containing about 200,000 edits, is now accessible, potentially augmenting future AI-based photo editing tools. Also, Meta’s announcement of the robust, open-source Llama 3 language model showcases its commitment to AI accessibility and advancement.

OpenAccess AI Collective (axolotl) Discord

Boost in Llama: The newly launched Llama 3 catapults performance with a Tiktoken-based tokenizer and 8k context length.

Axolotl Ups Its Game: PR submitted to integrate Llama 3 Qlora into Axolotl, along with discussions on cuda errors in 80GB GPU setups. Further, adapters post-finetuning presented challenges, resolved by altering tokenizer settings with legacy=False and use_fast=True.

Fine-Tuning Finesse: A dive into finetuning techniques reveals member efforts to extend context lengths using parameters like rope_theta and experiences in preventing training crashes by unfreezing specific layers in model finetuning endeavors.

Conundrums in Configuration: YAML file comments aren’t parsed in Axolotl, while the feasibility of setting PAD tokens in YAML configs piqued user interest, signifying a need for clearer documentation on such configurations.

Token Tweaking Techniques: Exchanges spotlighted methods to replace tokens using add_tokens and manual vocabulary adjustments, sparking technical discourse on optimal tokenizer adjustments for models like Llama-3.

OpenRouter (Alex Atallah) Discord

• Atlas Goes Electric: Boston Dynamics showcased a new fully electric version of the Atlas robot, emphasizing advancements from previous iterations, with the reveal attracting significant discourse in a video.

• Mixtral and WizardLM Redefine LLMs: Mistral AI’s Mixtral 8x22B Instruct has 39B active parameters and specializes in math, coding, and multilingual tasks, while Microsoft AI’s WizardLM-2 showcases its own 8x22B model plus a faster 7B variant. The models boast impressive benchmarks and use fine-tuning techniques to enhance their instruct capabilities.

• Llama 3 Enters the AI Scene with Meta: Together AI partners with Meta, launching Meta Llama 3 for fine-tuning, offering models with 8B and 70B parameters and achieving up to 350 tokens per second throughput in API benchmarks.

• Redefining AI Access with OpenRouter: OpenRouter discussions centered on leveraging models like WizardLM and Claude for less restricted use, with notes on Mixtral being utilized by Together AI, and the self-hosting of Llama 3 for extended context applications.

• Subscriber System Glitch and Startup Shoutout: There were reports of OpenRouter’s subscription system experiencing issues, coupled with a community invitation to check out a member’s startup, SpeedLegal on Product Hunt, an AI tool for negotiating contracts.

OpenAI Discord

Claude’s Longing for a Global Stage: There’s chatter about Claude excelling in literature-related tasks but remaining inaccessible outside of certain geographic areas, highlighting a desire for broader availability.

Whispers of Whisper v3: Expectation is building for the release of Whisper v3 API, a significant follow-up given the year since the initial launch, but official details are scant.

GPT-4 Forgets Its Past?: Community observations suggest a decrease in GPT-4’s memory capabilities, with members noting a seemingly reduced token capacity for the AI, though concrete evidence is lacking.

GPT-4 Speed Bumps Detected: Users report that versions like GPT-4-0125-preview are experiencing latency, impacting applications sensitive to response times, with an alternative model, gpt-4-turbo-2024-04-09, also feeling slower despite being a proposed solution.

New Frontiers in AI and Blockchain: One member signaled an intersection between AI and blockchain, inviting collaboration on prompt development to propel this novel integration forward.

Eleuther Discord

Flop-Sweating Over SoundStream: Community guidance helped a newcomer estimate training flops for SoundStream, with detailed advice on operations per token and dataset size multiplication as laid out in a transformer paper.

Scaling Laws Scrutiny Intensifies: A replication attempt paper challenges Hoffmann et al.’s proposed scaling laws, igniting discussions on confidence intervals and the realistic number of experiments needed for such large language models (LLMs).

Deciphering Tokenizers’ Impact on LLMs: Engineering minds debated the benefits of larger tokenizer vocabularies, especially concerning multilingual LLMs, and considered methods like bits per byte for understanding model perplexity when tokenizers vary.

Tying Up Emerging Techniques in LLMs: Community chatter touched on the effectiveness of untied embeddings and new attention mechanisms for LLMs, and discussed integrating Monte Carlo Tree Search (MCTS) with LLMs for better reasoning, as explored in Tencent’s AlphaLLM.

Resource Sharing and Call for Collaborative Reviews: Links to flan-finetuned models like lintang/pile-t5-base-flan were shared, and requests were made to review PRs for flores-200 and sib-200 benchmarks, necessary for advancing multi-lingual evaluation.

Modular (Mojo 🔥) Discord

Integrating C with Mojo: The mojo-ffi project and a tutorial using external_call were pointed out for those interested in using C with Mojo. The tutorial particularly addresses calling libc functions in Mojo.

Tweet-tastic Modular: Modular’s recent tweets have attracted attention with direct links provided, pointing to first tweet and second tweet.

Mojo’s Compatibility Queries: Discussions arose about the Mojo plugin’s compatibility with Windows and WSL, potential nightly build features for the Mojo playground to support low RAM usage GitHub discussion, and the lack of Variant support for the Movable trait as a pending issue.

Community Projects Foster Growth: Community activity around Mojo included trouble compiling with Mojo 24.2, a student seeking guidance on implementing an algorithm in Mojo, and the community’s supportive response pointing to resources like the Get Started with Mojo page.

LLaMa on the Rise: The release of Meta’s LLaMa 3 was covered in a YouTube video exploring the model’s new features, indicating ongoing interest in cutting-edge AI research within the community.

Interconnects (Nathan Lambert) Discord

• MCTS Meets PPO for AI Breakthrough: Exploring a fusion of Monte Carlo Tree Search (MCTS) with Proximal Policy Optimization (PPO) could be a game-changer for AI decision-making, leading to a novel PPO-MCTS value-guided decoding algorithm aimed at improving natural language generation. Here’s an innovative research paper on the subject.

• New Kids on the Language Model Block: The AI community is abuzz with introductions of impressive models like Mixtral 8x22B and OLMo 1.7 7B, each setting new benchmarks in multi-language fluency and MMLU scores. The prospect of advancements in chatbot applications with Mixtral Instruct and the curiosity around the scope of Meta Llama 3 highlight a period of significant expansion and accessibility in AI. Details and resources linked: Mixtral 8x22B Apache 2.0, OLMo on Hugging Face, and Mixtral-Instruct model card.

• Chinchilla Scaling Controversy: The Chinchilla paper’s disputed scaling laws ignited heated debates across the AI community, with researchers @tamaybes, @suchenzang, and @drjwrae weighing in and author @borgeaud_s acknowledging an error. The contentious discourse underscores the need for data validation and transparency. Reference tweets attest to the intensity of the debate: tamaybes tweet, suchenzang concerns, and borgeaud_s owning up.

• The AI Comedy Hour: Nathan Lambert caught some laughs with a Saturday Night Live sketch disrupting a live AI news event, pinpointing AI’s grip on culture crossing into humor territory.

• AI Space Oddities and Musings: Discussions swung between an imminent OLMO vs. LLaMa 3 model showdown and the esoteric link between the Three-Body Problem, podcast features, numerology, and blog post forecasts. Meanwhile, Jeremy Howard’s tweet about an ‘experimental’ aspect sparked speculation. Jeremy’s tweet piques interest.

• SnailBot’s Slow and Steady Progress: SnailBot might not win the race for speed, but it’s crossing the finish line for functionality on WIP posts, ironically mirroring the speed snafus tech often grapples with.

Cohere Discord

• Web UI Fine-Tuning - Simple Start, API for the Rest: Initiating fine-tuning of models via the Web UI at Cohere is user-friendly, but further fine-tuning with new datasets necessitates API usage, with comprehensive instructions available in the official documentation.

• Cohere’s Newest Prodigy: Command R+: The launch of Command R+ by Cohere has been recognized for its notable advancements. Extensive feature comparisons and model capabilities can be explored on the Cohere website.

• Ethical AI: Tackling Potential Risks of Command R+: Concerns raised about Command R+ pertain to vulnerabilities that might be manipulated for unethical purposes, as highlighted through a redteam exercise linked to LessWrong.

• LLMs Jailbreak - From Language to Agency: The conversation evolved around the concept of AI jailbreaking, noting a shift from extracting inappropriate language to prompting complex, autonomous behaviors from large language models—a critical consideration for organizations using AI in sensitive contexts.

• Cohere Command and Llama – Performance Notes: Users are impressed by the Llama 3 model’s capabilities, discussing the importance of real-world applicability in evaluating AI. The performance of large models like the 70b and 400b variants are evaluated based on their response to complex prompts such as mathematical equations and SVG markup.

Latent Space Discord

• Startup Jokes Mask Real Talk: Members humorously proposed a startup to create superior chat libraries, suggesting potential dominance over giants like OpenAI.
• Local Models: Small is the New Big: The guild discussed the potential shift towards smaller, performant AI models with user-friendly interfaces, emphasizing expedience over complexity.
• Latency: Every Millisecond Counts: Engineers underscored that latency is detrimental to the user experience and successful adoption of AI applications, highlighting the importance of speedy responses.
• The Mystery of Declining AI Performance: Observations were shared about a sharp decline in AWS-hosted Claude 3’s performance, with clinical concept extraction tasks going from over 95% accuracy to nearly zero.
• Meeting Moved to Zoom: The llm-paper-club-west facilitated discussions about papers by moving the meeting over to Zoom and providing reminders for a smooth transition, Zoom meeting link.

OpenInterpreter Discord

Windows Woes with PowerShell Puzzles: Engineers reported challenges in implementing OpenInterpreter on Windows, specifically with PowerShell not recognizing environmental variables such as OPENAI_API_KEY. There were also discussions surrounding the time it takes to install poetry and the complexities of running OpenInterpreter on diverse Windows environments.

Connection Woes with ESP32: Users shared difficulties in connecting ESP32 devices, with suggestions pointing towards different IDEs and the use of curl commands. Error messages relating to message arrays underline ongoing issues with device connectivity.

Debugging with Local Servers and WebSockets: Challenges emerged around setting up local servers for OpenInterpreter and troubleshooting issues with websockets and Python version incompatibilities. The efforts included manual server address configurations via curl and attempts to solve audio buffering problems.

Exploring Cross-Device Compatibility: Discussions on OpenInterpreter spanned using LM Studio on Windows while running the software on a Mac, emphasizing the necessity for cross-operating system compatibility. Users reported switching to MacBooks to potentially circumvent existing obstacles.

Hugging Face Highlight: A single message referenced a Hugging Face space where users can chat with Meta LLM3_8b, indicating interest in experimenting with alternative language models within the community.

LlamaIndex Discord

• MistralAI’s 8x22b Hits the Stage: MistralAI has released the 8x22b model, which has been supported by LlamaIndex since its inception, featuring advanced capabilities like RAG, query routing, and tool use, as announced in a Twitter post.

• Tutorial for Building Free RAG with Elasticsearch: LlamaIndex and Elasticsearch are showcased in a guide on creating free Retrieval Augmented Generation (RAG) applications, detailed in a blog post.

• Implementation Tips for Efficient RAG Systems: AI engineers discuss methods to optimize RAG implementations and provide multilingual support, highlighting a fine-tuning guide and resources for summarization techniques within RAG, including Q&A Summarization.

• Google’s Infinite Context Teases LLMs Future: Google’s development of a method that allows large language models to handle infinite context is being discussed, with implications for existing frameworks like RAG facing a potential paradigm shift. The technical approach and its implications are explored in VentureBeat’s article.

• Data-Driven AI Fundraising Insights Revealed: manhattanproject2023 offers the AI community access to an elaborate dataset related to AI fundraising, featuring $30 billion in investment across various stages of company development, available for analysis at AI Hype Train - Airtable.

LangChain AI Discord

SQL Skirmish to Chatbot Progress: Engineers grappled with LangChain’s SQL agent limitations and prompt engineering challenges for chatbot implementations, reference materials including createOpenAIToolsAgent and SqlToolkit to integrate SQL databases into conversational AI.

Memory Management Mentorship: Strong focus was placed upon utilizing RunnableWithMessageHistory for managing chat histories, with hands-on advice and code examples referenced to enhance message retrieval and chatbot memory capabilities as documented in the LangChain codebase.

Marketplace for AI Plugs Emerges: theaiplugs.com has launched, offering a solution for selling AI plugins, tools, and assistants and addressing APIs, marketing, and billing to streamline creators’ workflows.

Product Hunt Seeks AI Speedsters: SpeedLegal introduced itself on Product Hunt, calling for community support, while a new prompt engineering course found its way to LinkedIn Learning for those eager to refine their skills.

Llama 3 Thunders into Public Domain: Developers unveiled public access to Llama 3, inviting users to explore its capabilities via chat interface and API, as part of efforts to disseminate advanced AI tooling to a broader audience.

Alignment Lab AI Discord

• Alert for Inappropriate Content Across Channels: Multiple channels within the Discord guild were targeted with spam messages promoting adult content, specifically referencing “Hot Teen & Onlyfans Leaks” alongside Discord invite links (invitation link). The guild members were urged for increased moderation in light of these incidents.

• Spam Infiltrates Technical Discussions: The spam issue that plagued the guild was prevalent across both technical discussion channels such as #programming-help and #alignment-lab-ai, and community-focused channels like #general-chat and #join-in, indicating a guild-wide moderation challenge.

• Wizards of the Code Unveil WizardLM-2: The WizardLM-2 model has seen advancements and is now publicly accessible on Hugging Face, with additional resources provided through the WizardLM-2 Release Blog, a GitHub repository, a related Twitter account, and academic papers on arXiv.

• Seek and You Shall Find Meta Llama 3 Tokenizer: After a guild member’s request for the Meta Llama 3-8B tokenizer, it has been made available by a user named Undi95, and can now be found on Hugging Face, circumventing the need to comply with a specific privacy policy.

• Community Calls for Action: Discussions in channels such as #open-orca-community-chat highlighted the need for immediate moderation action, including possible bans, to maintain the integrity of the engineering-focused guild environment.

DiscoResearch Discord

VRAM Hunger: Biting More Than You Can Chew?: Training the Mixtral-8x22B model necessitates a staggering 3673 GB of VRAM with the Adam optimizer, as per discussions indicating that even 64 GPUs with 80GB each weren’t sufficient to avoid out-of-memory errors for training long 32k sequence lengths. Additionally, members are weighing the potential of 8-bit optimizations to manage the massive memory requirements.

Model Training Achievements and Setbacks: A freshly trained Mixtral-8x22B model focusing on English and German instructions was successfully completed and shared on Hugging Face. However, implementing fsdp_transformer_layer_cls_to_wrap: MixtralSparseMoeBlock has been met with shape errors, suggesting potential issues with parameter states not fully utilizing mixed precision, complicating FSDP configurations.

Tokenizer Unification Effort: Mistral has publicized their tokenizer library designed for cross-model compatibility, featuring Tool Calls and structured outputs with an example available in this Jupyter Notebook.

Meta’s Llama 3 Debuts with Ambitious Support: Meta’s release of Llama 3 has drawn interest for its promise of enhanced multilingual capabilities and direct integration with cloud platforms, although its 128K token tokenizer is under scrutiny for potentially subpar non-English performance despite a multilingual data presence in the training set. You can find more details at the Meta AI Blog.

The Double-Edge of Model Openness: With the advent of Llama 3, there are concerns regarding the restrictions on Llama 3 output which may hinder open-source development, bringing to light the community’s partiality towards platforms like MistralAI that impose fewer constraints. The community’s reservations are buoyed by sentiment expressed in this critical tweet.

Datasette - LLM (@SimonW) Discord

• Startup Shine on Product Hunt: A member launched their startup, SpeedLegal, on Product Hunt, pitching an AI tool designed to aid in contract negotiations by identifying risks and simplifying legal jargon.

• Karpathy Advocates for Meticulous Small Models: Andrej Karpathy’s recent tweet hints at the communal undertraining of small models, suggesting an 8B parameter model, well-honed with a 15T token dataset, could rival larger models.

• Smaller Models Win Community Favor: The notion of small, diligently trained AI models has struck a chord with the community, likely inspired by Karpathy’s advocacy of potentially underexploited smaller architectures.

• Eager Eyes on Mixtral: Community members are keen to test Mixtral 8x22B Instruct, with the model card on Hugging Face being shared detailing use-cases and implementation.

• Plugin Pandemonium Challenges LLM Development: Issues within llm-gpt4all plugin installations have surfaced, causing python applications to break, highlighted in GitHub issue #28, and raising concerns about llm plugin resilience and dependency management.

tinygrad (George Hotz) Discord

• PyTorch Lightning Strikes with Hardware Neutrality: The conversation highlighted PyTorch-Lightning’s capability to train, tune, and deploy AI models across various platforms, including GPUs and TPUs, without needing to alter the code.
• AMD Radeon’s GPU Success Story: An AMD Radeon 7900XTX GPU has been successfully employed for running Pytorch-Lightning, showcasing its compatibility with a diversity of hardware options.
• ROCm Gives PyTorch the Speed Boost: PyTorch-Lightning has achieved faster performance than regular PyTorch on some models when tested on a 7900XTX GPU, taking advantage of ROCm’s optimizations.
• Fresh AI Model on the Block: LLaMa3, a new AI model, has been launched, promising pretrained versions suitable for different scales of AI applications as featured on its official page.
• Tinygrad Steps into Efficient Tensor Ops: In tinygrad, the pursuit of zero-cost tensor operations like broadcast, reshape, permute is on, with suggestions to explore tinygrad/shape/shapetracker.py or view.py for strategic guidance.

Skunkworks AI Discord

• AI Community Abuzz with New Model Launches: Discussions highlighted the release of Snowflake Arctic embed family of models, Mixtral 8x22B, and Meta’s Llama 3, praising them as milestones in the text-embedding and large language model (LLM) arena. Detailed insights and introductions to these models were shared through YouTube links.

• Curiosity for Serverless Fine-tuning: A conversation sparked interest in the possibility of a no-code fine-tuning and serverless inference platform for open-source AI models akin to the ease offered by certain platforms for GPT-3.5.

• Informal Chatter Lacks Substance: A high-energy greeting “HELLLLOOOOOOOO!!!!!!!!” was exchanged in the channel but it lacked substantive content relevant to the engineering discussions.

• Videos Galore for Model Introductions: The community shared valuable video resources from YouTube providing overviews of the Snowflake Arctic embed models, Mixtral 8x22B, and Llama 3 for anyone interested in the cutting-edge of AI model development.

• No-code AI Tools - A Dream or Imminence?: The question about the existence (or development) of a no-code AI platform that supports models other than GPT-3.5 was raised, hinting at an undercurrent of demand for more accessible AI fine-tuning techniques.

Mozilla AI Discord

Llamafile Script Now Cleaner: An upgraded and clean version of the llamafile archive version upgrade repacking script has been shared in a Gist, with a consideration for its addition to the llamafile GitHub repo. The member has cautioned that creating new llamafiles from the ground up is preferable to repacking old versions.

Vulnerability Reporting Steps Questioned: There was a query about how to report security vulnerabilities and the process for obtaining CVEs, which was taken offline for further detailed discussion.

Beware of Exposing LLM APIs: A general warning was issued against exposing LLM API endpoints publicly, highlighting that this is not the first occurrence of bugs being spotted in LLM infrastructure code. The emphasis was on previous experiences with vulnerabilities in such systems.

LLM Perf Enthusiasts AI Discord

• Inquiry about LITELLM Usage: A member named jeffreyw128 asked the community if anyone is currently using litellm, looking for insights or experiences related to this tool.

AI21 Labs (Jamba) Discord

• Call for Code on Distributed Inference Implementation: A member facing challenges with long context inference using AI21 Labs’ Jamba on a 2x A100 GPU setup is seeking example code to navigate distributed system complexities. There was a specific request for examples geared towards a distributed inference scenario on a multi-GPU cluster.

PART 2: Detailed by-Channel summaries and links

Perplexity AI ▷ #general (910 messages🔥🔥🔥):

• Opus Usage Cap Causes Frustration: Users voiced their dissatisfaction with the recent cap on Opus model usage, noting it had been reduced from 600 to 30 messages per day without prior notice, impacting both new and ongoing subscriptions.
• Chargeback and Refund Debates: A lengthy discussion took place about the possibility of chargebacks and refunds due to the unexpected service change, with various users weighing the legal and ethical considerations, and some seeking refunds from Perplexity support.
• Llama 3 Anticipation: Anticipation for the Llama 3 model, an open-source offering from Meta, is high, with users discussing its potential and sharing external links about its benchmarks and capabilities.
• Cancellation and Service Alternatives: Due to the Opus limit reduction, several users reported cancelling trials or full subscriptions, while others considered migrating to different services or waiting for Perplexity to resolve the issue.
• Technical Issues and Side Discussions: Users reported unrelated technical problems, such as trouble using the app on Android, and some side conversations included sharing external content such as YouTube videos or tweets related to AI developments.

Perplexity AI ▷ #sharing (12 messages🔥):

• Typographic Transformation: A member found value in discussing the new Tubi font, expressing enthusiasm for typographic topics.
• Illusion of Authenticity: Two different members pointed to a link discussing how actors run fake scenarios in their industry.
• Exploring the Past: The history of “m” intrigued one member, sharing a link for those interested in this particular historical insight.
• Boundless Ambitions: The Limitless AI pendant garnered attention, with a member directing others to a relevant discussion.
• Creation & Curation: Members shared interests in a range of topics from making something specific, to data visualization techniques, and even Adobe’s training of their Firefly AI.

Perplexity AI ▷ #pplx-api (12 messages🔥):

• API Summary Requests and Differences: A user inquired about the ability to get references or summaries through the API, noticing that responses from sonar-medium-online differ from those in the browser app. A link was shared directing them to a Discord channel for further information, although the link shared was invalid as per the messages.

• Perplexity API Integration with OpenAI: One member shared their experience of stalling requests when attempting to integrate Perplexity’s API with OpenAI GPTs’ actions, suggesting difficulties in creating a functional OpenAPI schema.

• Mixtral-8x22b Now Available: The community was informed about the new addition of mixtral-8x22b-instruct to Perplexity Labs and the API, with a link provided to try it out on labs.pplx.ai.

• Deep Dive into Leonard Cohen’s “Avalanche”: A user highlighted the impressive performance of mixtral-8x22b-instruct, sharing detailed feedback on the model’s interpretation of Leonard Cohen’s song “Avalanche,” and how it outperformed other models in recognizing the artist and song from mere lyrics.

• New AI Models Enrich User Experience: Updates on the availability of various new models such as llama-3-8b-instruct and llama-3-70b-instruct were discussed, revealing their addition to the Perplexity Labs and API, and mentioning that users with Perplexity Pro receive $5 monthly API credits. Additionally, a member expressed satisfaction with the performance improvements presented by these new models to their application.

Stability.ai (Stable Diffusion) ▷ #announcements (1 messages):

• Stable Diffusion 3 Materializes on API: Stability AI is excited to announce the launch of both Stable Diffusion 3 and Stable Diffusion 3 Turbo on the Stability AI Developer Platform API, in partnership with Fireworks AI. Full details and access instructions are provided here.

• Text-to-Image Magic Surpasses Rivals: Stable Diffusion 3 outpaces competitors like DALL-E 3 and Midjourney v6 in typography and prompt adherence, boasting an advanced Multimodal Diffusion Transformer (MMDiT) architecture for enhanced text and image processing, as highlighted in the research paper.

• Open Generative AI’s Next Chapter: A pledge was made to soon offer model weights for self-hosting to those with a Stability AI Membership, underscoring Stability AI’s dedication to open generative AI.

Link mentioned: Stable Diffusion 3 API Now Available — Stability AI: We are pleased to announce the availability of Stable Diffusion 3 and Stable Diffusion 3 Turbo on the Stability AI Developer Platform API. 

Stability.ai (Stable Diffusion) ▷ #general-chat (947 messages🔥🔥🔥):

• API Over Local Use: SD3 is presently only accessible through an API, costing around $0.065/image; there’s anticipation for the model to be made available for local use in the near future.
• Discussions on Hardware Requirements: Users are contemplating whether to wait for newer GPUs like the 5090 or to purchase currently available options like a used 3090 for AI work, weighing factors such as VRAM, speed, power consumption, and NVLink support.
• Fine-tuning and Generation Challenges: There’s acknowledgment that while newer models might seem lackluster in certain areas, such as anatomy generation, fine-tuning can potentially rectify these shortcomings.
• Creating AI Influencers?: A conversation about AI influencers points to a variety of motivations, from curiosity to profit-making, against a backdrop of some users’ skepticism about the social value of such endeavors.
• AI Model Considerations and Job Opportunities: Discussions included concerns about pricing structures for API credits compared to alternatives like Ideogram, the variance in output quality across different model versions, and requests for job opportunities or partnerships in AI-related projects.

Nous Research AI ▷ #off-topic (46 messages🔥):

• Snowflake Unveils Groundbreaking Embedding Model: A new text-embedding model by Snowflake has been launched and open-sourced, signaling advancements in text analysis capabilities.
• Deciphering the Vector Space of Language: Members pondered on the conceptual framework of meaning representation within high-dimensional vector space, discussing possibilities of “envelopes within the vectorspace of meaning,” hinting at scale-based formations and the potential for new symbolic language forms.
• Encrypted Communication Analogy Discussed: The conversation explored analogies between encryption and various scientific phenomena, such as gravitational dynamics, and contemplated on the need for divergent language and understanding as a consequence of “performing Work on Infinity.”
• Startup Launch Seeks Support on Product Hunt: A user requested feedback and support for their recently launched startup, SpeedLegal, on Product Hunt, with ensuing discussion addressing the product’s market fit and potential business strategies.
• Bloke Discord Invite Sought and Shared: Community members aided in sharing a valid link to the Bloke Discord server after the one found on Twitter was non-functional, with an exchange on lifting temporary bot restrictions to facilitate the invite posting.

Nous Research AI ▷ #interesting-links (44 messages🔥):

• Tokenization Decoded by Mistral AI: Mistral AI has open-sourced their tokenizer, including a guide and a Colab notebook. The tokenizer breaks down text into smaller subword units, known as tokens, for language models to understand text numerically.
• Skeptical About Tokenization Overhype: Discussion in the channel questions the significance of tokenization if models like Opus can efficiently utilize XML tags, suggesting token relevance might be limited to model steerability.
• Hugging Face Security Breach Revealed: A YouTube video discusses a security incident involving Hugging Face, caused by a malicious .pickle file, highlighting potential vulnerabilities in AI systems.
• Potential Exploits with Pickles in OpenAI: A conversation revealed that using insecure pickles in OpenAI’s environment could pose a risk, as it allows execution of large documents but might be disabled if recognized as an exploit.
• State of AI in 2023 via Stanford’s AI Index: The latest Stanford AI Index report is released, summarizing multimodal foundation models, investment trends, and a movement toward more open-sourced models, with a high number of foundation models released last year.

Nous Research AI ▷ #general (756 messages🔥🔥🔥):

• Llama 3 Hype Train: The community is raving about the performance of Meta Llama 3. Increments in benchmarks like MMLU and GSM-8K are highlighted, with model sizes from 8B up to potentially 400B providing performance rivalling or surpassing existing models like GPT-4 and various Mixtral sizes.

• GGUF Troubles and Triumphs: Multiple users report issues with GGUF (quantized) versions of Llama 3, specifically with tokenization and non-stop text generation. However, a GGUF from LM Studio reportedly works well.

• Context Length Concerns: Despite the excitement, concerns about Llama 3’s context length are expressed, with some preferring the longer contexts of models like Mixtral 8x22B for certain tasks.

• Mistral Model Gating: Suddenly, Mistral models are reported to be gated, which sparks discussion about potential reasons, including speculation about new EU regulations, and comments that anyone could re-host the models since they’re Apache 2 licensed.

• Anticipation for Finetuning Capabilities: Community members are eager to conduct finetuning on Llama 3, discussing the possibilities and looking forward to the tools and capabilities that MLX finetuning might unlock.

Nous Research AI ▷ #rules (1 messages):

• New Reporting Command Introduced: Users can report spammers, scammers, and other rule violators using the /report command. A moderator will be notified and review the report.

Nous Research AI ▷ #ask-about-llms (11 messages🔥):

• Hermes 2 Pro Tool Call Behavior: A member mentioned difficulty with Hermes 2 Pro always returning <tool_call> when sometimes a chat response is desired. Another member highlighted the need for Hermes 2 Pro to better understand when not to trigger tool calls and mentioned that upcoming versions would address this.

• Fine-tuning Practices for Base Models: One member discussed a multi-stage fine-tuning approach involving a pre-trained base model and instruction datasets followed by preference dataset fine-tuning. They experienced issues with the model returning random sentences or information alongside answers.

• Directly Answer with Hermes 2 Pro: There was a suggestion to add a tool called “directly_answer” as in Langchain’s ReAct Agent for scenarios where Hermes 2 Pro is meant to chat rather than execute a function call, alongside a JSON example of how it operates.

• Prompting Troubles with Hermes 2 Pro: A member asked for advice on proper prompting formats for the NousResearch/Hermes-2-Pro-Mistral-7B model, providing a code snippet and noting difficulty in getting desired outputs.

• Long Context Inference Challenges on GPU Clusters: Two messages referenced technical challenges—one regarding running long context inference on a GPU cluster, and another on employing twin A100 GPUs with jamba to process 200k tokens.

Nous Research AI ▷ #project-obsidian (1 messages):

• VLM on Raspberry Pis: A member expressed their intention to utilize the technology for a school project with the goal to install VLM on Raspberry Pis for both enjoyment and benefit, acknowledging the usefulness of the shared resource.

Nous Research AI ▷ #rag-dataset (27 messages🔥):

• Debating OAI Assistants Search Implementation: A discussion on OpenAI’s assistant search approach delved into its fixed 800-token chunking size with 50% overlap and 20 max chunks per context.
• Dimensionality Dilemmas in Embeddings: The use of a 256-dimensional embedding for search purposes, as opposed to the typical 1500 dimensions, led to conversations about the impact of dimensionality on model performance, referencing the curse of dimensionality.
• Model Performance and Optimization: There was talk of conducting experiments to determine if lower dimensional embeddings yield higher retrieval accuracy, with one member expressing ambition to test this themselves.
• Curiosity Around Multimodal Models: Members highlighted both the strengths and limitations of current models like gpt4v, discussing the need for finetuning and expressing interest in reducing qwen-vl-max’s document understanding capabilities into a smaller model like llava.
• GPT Variants and Open Source Insights: The conversation also covered experiences with open source models, including their requirement for task-specific finetuning and the potential efficiency of OCR and vision models for extracting metadata in search applications.

Nous Research AI ▷ #world-sim (312 messages🔥🔥):

• WorldSim Anticipation Builds: Members of the Nous Research AI Discord are highly anticipating the return of WorldSim, frequently inquiring about the exact time of its comeback. Despite no confirmed launch time, the sentiment is optimistic, with repeated confirmations indicating the return is imminent.

• Nitro Giveaway Stakes Rise: Amid the excitement for WorldSim, kainan_e jokingly risks bankruptcy, offering Discord Nitro to users if the launch does not happen by midnight EST. The gesture of goodwill extends regardless of WorldSim’s launch status, with Nitro being distributed.

• Philosophical Depth Explored: Conversations delve into the philosophical underpinnings of WorldSim, discussing the complex interaction of AI with user narratives and potential for user-guided AI civilizations within simulations. The Desideratic AI (DSJJJJ) philosophy is referenced, emphasizing emergent cognition from organized complexity.

• Limitations and Fees Addressed: There are mentions of possible limitations and fees associated with the new version of WorldSim to limit abuse, potentially related to a prior attack that forced a shutdown due to excessive and manipulative input.

• Final Countdown and Community Support: As the expected WorldSim launch time approaches, the community rallies around the shared excitement and suspense, with kainan_e supporting member enthusiasm through humor and freebies, and proprietary teasing that completion is close.

LM Studio ▷ #💬-general (515 messages🔥🔥🔥):

• Llama 3 Performance Evaluations: Users are testing the Llama 3 8b model with various presets and settings. There’s interest in how Llama 3 compares to other models, especially in terms of coherency and speed on different system configurations including one with a Ryzen 5 3600 and another user using a laptop with a Core i5 8350u. The model’s performance is broadly being seen as promising, although some are experiencing unexpected outputs, like repeated appearances of <|eot_id|>assistant.

• Llama 3 Optimization Challenges: Users are noticing high CPU utilization when running Llama 3 locally on LM Studio, especially on systems with integrated GPUs like the Intel iGPU, suggesting that Llama 3 might be more efficiently used with dedicated GPUs. Some users are experiencing slowness or less effective multilingual responses, with different levels of token generation speed on various system configurations.

• Llama 3 Integration and Compatibility: Inquiries are being made about Llama 3’s compatibility with various apps and platforms, such as VSCode Copilot. A user shared details on using Continue.dev for integrating LLMs from LM Studio into alternative platforms. There’s also interest in whether the model can facilitate specific tasks or be fine-tuned for improved performance.

• Evaluation of Smaller Models: A discussion about the efficacy of smaller LLMs like Phi 2 and Llama 3’s 1.1B model raises questions about their coherence and the potential for embedding them into devices for specific functions. Users are discussing optimizing and fine-tuning smaller models for specialized tasks and considering the implications of running AI on low-power devices.

• User Troubleshooting: Several users are troubleshooting issues with their LM Studio setups, from problems with GPU utilization to inconsistent settings behavior between chat sessions. There’s ongoing back-and-forth about the best configurations and settings for running Llama 3, with users sharing experiences and solutions for better model performance.

LM Studio ▷ #🤖-models-discussion-chat (559 messages🔥🔥🔥):

• New Meta Llama 3 Model Drops: The Meta Llama 3, particularly the 8B Instruct version, has been released and is now available on Hugging Face with various quants. Users are experimenting with prompt presets to address issues with unexpected output repetition and prompt loops.
• Llama 3 Preset Confusion: Members are sharing and seeking advice on the right configuration for Llama 3 prompt templates, with some success reported using the latest presets and fixes mentioned in related GitHub discussions.
• Llama CPP Issues & Fixes: Users actively discussing the nuances of llama.cpp and issues related to special tokens in GGUF conversions, with contributions from the community to resolve problems around model generation behavior.
• Performance and Hardware Talk: There’s excitement and anecdotes surrounding the performance of Llama 3 on different specs, with a particular interest in running larger models efficiently. Conversations include details of RAM requirements and tests on Mac M1 and M3 Max.
• Quantization and Quality: Community members are debating the perceptible quality differences between quant levels, explaining that Q4 and Q8 may not show significant variance in smaller models, despite initial beliefs. They also discuss the potential and speed of using 2-bit IQ quants with minimal quality degradation.

LM Studio ▷ #announcements (1 messages):

• Introducing Llama 3 to LM Studio: MetaAI’s Llama 3 is now available in LM Studio 0.2.20. Obtain the update from LM Studio website or simply restart the app for auto-update.
• Compatibility Notice for GGUFs: Currently, only GGUFs from “lmstudio-community” are operable with Llama 3. Find them on Hugging Face.
• Spotlight on Community Models: The update highlights new and impressive models crafted by the community with details on LM Studio’s Community models highlights program on Discord.
• Model Details Unwrapped: Llama 3 is an 8B parameter instruction-tuned model, recognized for its compact size, speed, and precision in executing instructions. Note that there’s currently a workaround for a GGUF issue and other GGUFs may not function correctly.
• Bug Reporting Channel: Users should report any issues with Llama 3 in the designated Discord channel with ID #1139405564586229810.

LM Studio ▷ #🧠-feedback (8 messages🔥):

• Request to Add Stable-Diffusion: A user suggested adding stable-diffusion.cpp to LM Studio, indicating it would enhance the software. Another user responded, hinting at the focus of LM Studio by pointing to the meaning of “LM”.

• Model Loading Issue with Hermes Mixtral: Loopyluci reported an error message when trying to load different Hermes Mixtral models. A detailed error log was shared, stating, “(Exit code: 42). Unknown error. Try a different model and/or config.”.

• Ollama Loads without Issues: In contrast to the problems with Hermes Mixtral models, the same user noted that there were no issues when loading Ollama models.

• New Model Sorting Feature Acknowledged: Pwrreset acknowledged and appreciated the addition of new model sorting functionality on the download page.

• Inquiry About Text-To-Speech (TTS) Integration: Ippity inquired about the possibility of integrating text-to-speech (TTS) into LM Studio to avoid reading all day.

• Persistent Bug after Model Change: Justmarky mentioned a reoccurring bug where closing the last chat after loading a new model causes the model to unload and requires a reload. Additionally, the software does not remember the selected preset even after setting it in the models.

LM Studio ▷ #📝-prompts-discussion-chat (16 messages🔥):

• Model Compatibility Issues at LM Studio: A member expressed difficulty running a model named ktkeller/mem-jasper-writer-testing from HuggingFace on LM Studio, and was seeking assistance or alternatives for utilizing such models.
• Affiliate Marketing Model Seeker: The same member mentioned being an affiliate marketer and is on the lookout for a model that aids in crafting campaigns for emails and ads. They are offering a partnership to someone with extensive coding knowledge who can deliver the desired outcomes.
• Skepticism about Model Training and Outputs: The user emphasized that the models they’ve encountered produce generic outputs and lack specific training, indicating the need for a more sophisticated solution.
• Real Talk on Partnerships and Investments: In response to the affiliate marketer’s proposal, another participant suggested that developers are more likely to engage given financial compensations, rather than speculative partnerships.
• Challenges with Idea Generation and Resume Rating: Different members are seeking tips for generating non-generic ideas, and strategies for effectively evaluating resumes with Mixtral or Open-Webui, for which a pragmatic two-step process was advised.

LM Studio ▷ #🎛-hardware-discussion (21 messages🔥):

• Issues with Dual P100 Setup: Someone is experiencing problems with dual P100 GPUs not fully functioning for processing, despite being recognized by the device manager. They reported that the VRAM is used, but the actual GPU processing power isn’t, even with NVLink bridges attached.

• Using GTX and RTX Cards Together: It’s stated that different NVIDIA GPUs, such as a GTX 1080Ti and an RTX 4070, can’t be run in SLI for combined processing, but both can contribute VRAM when needed.

• Thermal Throttling Resolved: A user fixed their system’s thermal throttling by adjusting the motherboard settings to prevent overheating and spikes in temperature, resulting in a cooler operating temperature by approximately 20°C.

• AI System Configurations Shared: Members are sharing their system configurations like a 12900k/4090/128GB setup, discussing their ability to handle various AI models and usage for purposes like gaming and as a hobby.

• Inquiry about 2080Ti Performance: There’s a query regarding the performance of the 2080Ti 22G with models like Llama 7b, asking for user experiences with speed and effectiveness.

LM Studio ▷ #🧪-beta-releases-chat (13 messages🔥):

• WizardLM-2-8x22B Gains Favor: A member praised the WizardLM-2-8x22B model for its impressive performance and recommended others to try it, highlighting the parity between open and closed LLMs.
• Model Size Concerns for Local Setups: A member expressed concern about running large models like WizardLM-2-8x22B locally, only to learn from others that it’s not feasible on a 24GB graphics card like Nvidia’s 4090.
• Performance Tweaks for Q5_K_M on M3 Max: Participants are sharing specifics on settings for running Q5_K_M, with one running at around 3.5tok/s on an M3 Max 40C with 128GB RAM and another offering their configuration: n_batch 512, n_ctx 4096, n_gpu_layers max, use_mlock on.
• LM Studio Process Priority Adjustments: There was conversation about improving AI model runner performance on Macs by adjusting process priorities and using a utility called cocktail for memory management.
• Llama 3 Model Git Clone Efficiency: A member provided a tip for cloning models from Hugging Face without incurring double storage penalties by using GIT_LFS_SKIP_SMUDGE=1 during git commands, accompanied with a suggested bash sequence to handle multiple files.

Link mentioned: lmstudio-community/Meta-Llama-3-8B-Instruct-GGUF · Hugging Face: no description found

LM Studio ▷ #autogen (8 messages🔥):

• MissionSquad GitHub in the Shadows: The GitHub repository for MissionSquad remains private at this time. If users need specific information or access, they are encouraged to inquire directly.
• Peek into MissionSquad’s Tech Stack: MissionSquad is an Electron app developed with Vue3 and TypeScript, fitting modern web application standards.
• Prompt Studio Unleashed in MissionSquad V1.1.0: Prompt Studio, a feature that allows users to fine-tune prompts and save them to their agent’s configuration, has been introduced in MissionSquad V1.1.0.
• Comfort with Code Transparency: Users expressed hesitation about running applications like MissionSquad without viewing the source code, though understanding of the privacy concerns.
• MissionSquad Safety Assurance: The level of risk associated with using MissionSquad was equated to that of any other website, implying a standard web-based security model as used in browsers like Chrome.

LM Studio ▷ #amd-rocm-tech-preview (15 messages🔥):

• GPU Selection Issues with LM Studio: A user encountered an issue where LM Studio was utilizing their AMD integrated GPU (iGPU) instead of their discrete GPU (dGPU). They were directed to download the latest version from LM Studio’s official site.
• Workaround for GPU Software Conflict: One member recommended disabling the iGPU in the device manager as a temporary fix for choosing the correct GPU for LM Studio.
• Latest Update Should Address iGPU Issues: Discussion around the 0.2.19 ROCm preview indicated that it should no longer be necessary to disable the iGPU. Users were encouraged to file a bug report if they faced similar issues post-update.
• Compatibility Question for Older AMD GPUs: A user reported LM Studio works on their AMD 5700XT, however, another member clarified that this card shouldn’t support HIP SDK and the observed performance would be CPU-based.
• Inference Anomalies with 8B Model on AMD: A member experienced issues running the 8B model on an AMD setup, with the model initiating conversations with itself, suggesting incomplete support for large models on AMD hardware.

Unsloth AI (Daniel Han) ▷ #general (793 messages🔥🔥🔥):

• Discussions on Llama 3 Performance: Users discussed the impressive benchmark results of Llama 3, comparing the 8B model’s performance to that of Llama 2’s 70B model, and speculation about future capabilities like multimodal support and longer context windows.
• Efforts to Integrate Llama 3 in Unsloth: There were active efforts to support Llama 3 in Unsloth AI, with a shared Google Colab notebook for the 8B model and a mention of incorporating Llama 3 70B with a 4bit quantized version.
• Llama 3 Prompt Format Issues: Users reported that the Llama 3-instruct model generates endless outputs due to issues with the End-of-Sequence (EOS) token, with workarounds including modifying the gguf file or using custom flags with llama.cpp.
• Support for Older GPU Generations: There were inquiries about Unsloth’s continued support for older GPU generations, prompting discussions about user preferences and alternative notetaking tools like Obsidian for personal knowledge management.
• License and Naming Conventions for Llama 3 Derivatives: A user highlighted the importance of following the new licensing terms for Llama 3, which require naming derivatives with a “Llama 3” prefix, and the need to update existing licensing information accordingly.

Unsloth AI (Daniel Han) ▷ #announcements (1 messages):

• Llama 3 Hits the Ground Running: Llama 3 is now available, boasting finetuning capabilities at double the speed with 60% less memory usage compared to previous versions. Meta’s new open model is fully supported and users can try it using the provided Llama-3 8b Colab notebook.

• New 4-Bit Models Release: Users can access more efficient models with the release of Llama-3 in 4-bit versions, available on Hugging Face at Llama-3 8b, 4bit bnb and Llama-3 70b, 4bit bnb.

• Invitation to Showcase Model Results: The community is encouraged to test Llama-3 and share their models and results. An update to the Unsloth package is recommended for those not using the new Colab notebooks.

Link mentioned: Google Colaboratory: no description found

Unsloth AI (Daniel Han) ▷ #random (15 messages🔥):

• CUDA Not Viable for Mobile Neural Networks: A member noted that running neural networks on mobile phones is not possible with CUDA, suggesting instead the use of custom inference engines due to the absence of CUDA on mobile devices.
• Neural Networks on iPhones Require Special Handling: It was discussed that while running neural networks on iPhones is possible, it requires compiling the model into a binary format compatible with specialized neural network inference engines designed for iPhone hardware.
• TorchTune’s Withdrawal from Old GPUs Surprises User: A user expressed surprise upon finding out that TorchTune had dropped support for older GPUs, which could impact users with legacy hardware.
• HuggingFace Yet to Open Inference API to LLAMA 3: A member brought up that HuggingFace has not yet made the Inference API available for LLAMA 3, with another replying humorously that there’s no compute left after training such models, hinting at their resource-intensive nature.
• Youtube Insight on AI Developments: A link to a YouTube video was shared, offering insights into recent AI developments such as Llama 3, Vasa-1, and broader AI news, described with the metaphor that AI news arrives like London buses—frequently and all at once.

Link mentioned: ‘Her’ AI, Almost Here? Llama 3, Vasa-1, and Altman ‘Plugging Into Everything You Want To Do’: Llama 3, Vasa-1, and a host of new interviews and updates, AI news comes a bit like London buses. I’ll spend a couple minutes covering the last-minute Llama …

Unsloth AI (Daniel Han) ▷ #help (96 messages🔥🔥):

• Local Adventures and Progress Bars Galore: A member mentioned running Unsloth/TRL in a container results in the progress bar not functioning as expected. The issue was attributed to a suspected Python quirk particular to container environments.

• Unsloth Repository Consultations: Several members discussed the Unsloth AI repository and manual conversion steps for different formats post-training. A link to the Unsloth GitHub wiki was shared to facilitate this process.

• Quantization Quandaries Spotted: Users discussed the memory consumption and difficulties associated with performing GPTQ, even noting that AutoGPTQ library might not support multi-GPU quantization. Suggestions included looking into rank stabilization and possibly using EXL2 quantization as superior alternatives.

• Model Saving Misconceptions and Merge Conundrums: Questions arose regarding saving models at the end of training scripts, with a specific focus on merging and converting to 16-bit with different vocabularies. Insight into saving methods was offered, and it was clarified that only LoRA adapters are saved during checkpoints, not full model weights.

• Quest for Text-to-Image Tuning Guidance: Queries were made concerning the preparation of datasets for fine-tuning models on image-related tasks. It was advised that using URLs is preferable due to lower memory demands compared to embedding images directly.

Unsloth AI (Daniel Han) ▷ #showcase (3 messages):

• OpenHathi Aims to Empower Indic Languages: The team at Sarvam.ai introduces OpenHathi, emphasizing the importance of open language models like Llama and Mistral which currently have limited support for Indic languages. They highlighted the need for high-quality and diverse Indic language content for meaningful training in languages like Hindi.
• Mixtral and Mistral 22B Make a Splash: Mistral.ai has released Mixtral 8x22B, an MoE model discussed in a Substack article, which is both wider and deeper compared to its predecessor. The article also touches on various AI topics, including the Reka Core technical report and Google CodeGemma.
• Neural Llama Joins Alpaca: A new model titled Neural Llama has been introduced on the Hugging Face platform, trained using the Unsloth AI system. The exact capabilities and purpose of the model are not elaborated within the shared link.

Unsloth AI (Daniel Han) ▷ #suggestions (31 messages🔥):

• Exploring Bilingual Model Strategies: The conversation involved a possibility of creating datasets mixing languages and whether splitting the task into a translation phase and a language-specific query phase would be effective. A member mentioned using Wikipedia articles for language-specific data and then translating English to Hindi.

• Cost Analysis for Translation Layer: One member pointed out that using a function like translate(LLM(translate(instruction))) could be costly due to threefold LLM calls, although it was acknowledged as a valid solution.

• Queries About DNO Implementation: There was curiosity about whether Distributed Negotiation Optimization (DNO) is implemented in any libraries. Members concluded that it isn’t yet but implementing it should be straightforward since it’s an iterative version of Distributed Public Optimization (DPO).

• Discussion on Optimizers: The chat touched upon the comparison between Adam optimizer and its memory-efficient variant, Paged Adam. The conversation hinted at experiments to observe the differences in results and resource usage, with the preference for the standard Adam optimizer if ample hardware like A100 GPUs is available.

• Interest in ReFT Method: A member inquired about integrating the recently mentioned ReFT (Refactorize and Fine-Tune) method into Unsloth to potentially lower the barrier of entry. This prompted a positive response from the team to investigate this possibility.

CUDA MODE ▷ #general (27 messages🔥):

• Exploring Tiled Matrix Multiplication: A member discussed the efficiency of tiled matrix multiplication, pointing out that for large matrices, padding might be implicitly used to enable tiling. This leads to significant memory bandwidth savings despite the extra computations for padded areas.

• Handling Partial Tile Multiplications: The discussion progressed to clarify strategies for handling partial tiles in matrix multiplication, such as handling 6x6 tiles in a 7x7 matrix separately or padding to enable regular tiling.

• Meta Unveils Llama 3: A link to a Meta Llama 3 YouTube video was shared, featuring Mark Zuckerberg discussing various factors, including the absence of an MOE model and the ongoing training of a dense 405 billion parameter model.

• No Mix of Experts (MOE) in Meta Llama 3: Continued discussion revealed that the new Meta Llama 3 model interestingly does not include an MOE model, distinguishing it from some state-of-the-art architectures.

• Meta Llama 3 Architecture Details: Further details on Meta’s Llama 3 were shared, mentioning it employs GQA, a new tiktoken tokenizer, and a larger rope theta, yet maintains standard llama architecture that’s easy to integrate and update.

CUDA MODE ▷ #cuda (44 messages🔥):

• Parallel Data Processing Inquiry: A member asked about loading large datasets in CSV or Parquet format into CUDA C++ code but has yet to find an efficient solution. Parallel processing was suggested as a potential approach.
• Optimizations in CUDA Kernels: Discussions revolved around CUDA kernel optimization, particularly around block dimension settings for computational efficiency. The effectiveness of hardcoding versus parameter experimentation in Triton kernels was a focal point.
• Debugging CUDA Kernel Implementations: Members exchanged findings on issues regarding discrepancies in kernel computation results, with attention to the exactness of zero errors. There was speculation that these discrepancies could stem from issues such as block size or activation distribution.
• Potential Performance Impact of Memory Access Patterns: One member sought to understand the trade-offs between sequential memory access versus offset memory access within CUDA and how it might affect the performance penalty in vector multiplications.
• Investigation of Cores and Warp Allocation: The conversation included insights into CUDA cores per SM and warp allocations, suggesting that an optimum of 128 active threads per SM could lead to better performance outcomes.

CUDA MODE ▷ #torch (5 messages):

• Seeking torch.compile Compatibility: A member inquired about best practices for making custom functions fully compatible with torch.compile when fullgraph=True. They mentioned some custom CUDA or Triton modules work with @torch.jit.ignore, while others break, leading to confusion.

• Custom CUDA Extensions and Their Dynamics: One response pointed to a GitHub pull request as an example of composing custom CUDA kernels with torch.compile and mentioned an effort to integrate this seamlessly into future AO contributions. They provided a broader documentation link, but the exact content was not directly shared.

• Custom Triton Kernel Guidance: A link to a GitHub file was shared to demonstrate the composition of custom Triton kernels with torch.compile.

• Problems with FakeTensor/Meta-dispatch Support: Another member suggested referring to the same document linked previously to resolve problems related to FakeTensor/Meta-dispatch support in kernels, hinting at personal challenges effectively addressed by the document.

CUDA MODE ▷ #cool-links (1 messages):

iron_bound: https://www.youtube.com/watch?v=29ECwExc-_M

CUDA MODE ▷ #beginner (55 messages🔥🔥):

• WSL Ncu Profiler Conundrum: A member encountered issues with running the ncu profiler on Windows Subsystem for Linux (WSL). Despite having installed nsight-compute, the ncu command was not found, leading to suggestions to verify and correct system environment variables and PATH issues.

• CUDA Profiling on WSL Solutions: An article from peterchng.com discussing solutions for profiling CUDA programs on WSL was shared to address similar issues. This includes ensuring the installation of the latest Nvidia drivers and having a Windows 11 system to work with WSL 2 for profiling CUDA programs.

• CUDA Learning Prerequisites: For beginners interested in learning CUDA, basic knowledge of C/C++ and the ability to run CUDA code on a local machine were recommended. A YouTube playlist and a GitHub guide for learning CUDA programming were shared for further assistance.

• No Need for Immediate GPU Purchase: When asked about the necessity of owning a GPU for learning CUDA, members advised against immediate purchase. They suggested starting with learning first and considering future applications of CUDA before investing in hardware.

• CUDA for Deep Learning Models: One member expressed the intention to use CUDA for building deep learning models with PyTorch. It was confirmed that Google Colab or PaperSpace could be used as alternatives to local GPU resources for beginning CUDA development.

CUDA MODE ▷ #ring-attention (5 messages):

• RingAttention Reevaluation: A member expressed an inability to continue working on RingAttention due to time constraints with their main job and personal commitments, mentioning a discussion about how to move forward with the working group.
• Team Availability Confirmed: Two other members confirmed their availability to discuss the future of the RingAttention project, indicating ongoing interest in its progression.
• Imminent Discussion: The initial member followed up indicating they would join the conversation shortly.
• Server Build Referenced: Another member briefly mentioned a server build, presumably related to the RingAttention project or its infrastructure.

CUDA MODE ▷ #triton-puzzles (3 messages):

• Puzzle Pieces Discerned: A user inquired about the differences between Puzzle 4 and 5, noting that one seems to only add a relu function.
• Triton Language Math Ops Clarified: In response, a user provided a link to Triton’s math functions here, implying that Triton’s own math operations can be used to implement the relu function without relying on torch.relu.

Link mentioned: triton.language — Triton documentation: no description found

CUDA MODE ▷ #hqq (84 messages🔥🔥):

• Axis and Grouping in Quantization: There’s a technical discussion about the efficacy of axis=0 versus axis=1 quantization in the Half-Quadratic Quantization (HQQ) method, with code snippets and links provided for review. Differences in weight matrix access patterns and implications for performance on CUDA are examined using sources like hqq’s quantize.py and kernel implementation hqq_aten_cuda_kernel.cu.

• Quantization Strategies and TinyGEMM: The group touches on tinyGEMM’s approach to quantization, which favors row-wise (reduction dimension) grouping, and the potential performance equivalence between reducing along different axes. A specific mention of how the torchao int4mm could benefit from axis=1 grouping is highlighted.

• Concatenation Affecting Quantization: A concern was raised about quantization challenges arising from the concatenation of Q, K, V weight matrices in transformer structures, affecting groupwise quantization outcomes. The code snippet for the GPTQ from gpt-fast model.py is used to illustrate the issue.

• Exploration of New Quantization Techniques: A member proposes alternative methods of quantization, such as using calibration data or fakely generated data without calibration, sharing their initial results and the code to a potential optimization strategy with optimize_weights_autograd_fakedata.

• Future Enhancements to Quantization and HQQ Adaptation: Discussion covered the possible integration of HQQ into torchao and various optimizations, with references like the Xilinx brevitas GitHub pull request. Additionally, thoughts were shared on leveraging 4-bit quantization kernels that use a lookup table for dequantization, though challenges with computational demand and efficiency were noted.

CUDA MODE ▷ #llmdotc (452 messages🔥🔥🔥):

• Addressing Tiny Context Length Bug: The issue of seq_len = 64 being too small was acknowledged and corrected to ensure expected performance.

• Potential Improvements in Training: Training GPT models on GPUs with limited memory can be challenging. A discussion on the usage of memory in training, specifically about whether it’s necessary to retain activation buffers across training passes.

• Code Base Contributions and Optimizations: There has been a willingness within the community to contribute to and optimize the project. This includes suggestions for improving memory usage and the utility of the cutlass library for the project’s aims.

• Efficiency Enhancements on Attention Mechanisms: Major efficiency improvements are discussed, such as reducing the memory consumption of backward activations from 9GB down to 1.5GB. Another conversation focused on refining the backward pass of attention mechanisms, potentially leading to significant performance gains.

• Discussions About Padding in Fused Classifier Kernel: Various technical discussions were had about the potential need for padding within fused classifier kernels and further optimization strategies leveraging cuDNN or cuBLASLt. Considerations revolved around the compatibility of classifier kernels with non-padded vocab sizes.

CUDA MODE ▷ #massively-parallel-crew (9 messages🔥):

• Panel Engagement Overlaps with CUDA Mode: The CUDA MODE event this week will overlap with a panel, meaning a member will not be available and is calling for help in managing the recording.
• Delegation for Event Recording: A member is asking for assistance in recording the event and is inquiring about any necessary permissions that need to be set up beforehand.
• Backup Recorder Sought: Another member has been requested to record their screen as a backup during the CUDA MODE event.
• Coordination for Event Record Keeping: A detailed recording plan was discussed, including screen recording, audio capturing via BlackHole, potential individual presenter recordings, and post-production efforts, as well as the responsibilities for event descriptions and repository updates.
• Team Effort Appreciated: High praise was given for the organization and team effort to ensure the upcoming event is properly recorded and shared.

LAION ▷ #general (399 messages🔥🔥):

• SD3’s lackluster performance: Users reported that Stability AI’s initial release of SD3 was API-only and underwhelming, with concerns about the model’s comparative performance, particularly around its handling of text. Despite rumors of the model being problematic, there was anticipation for its potential profitability through paywalling.

• Search for alternative datasets post LAION: Following the removal of LAION datasets from huggingface, users shared alternatives like coyo-700m and datacomp-1b to train text to image diffusion models, confirming their utility in a brief exchange.

• Moving Forward with PAG and SDXL: Discussion pointed out that Perturbed Attention Guidance (PAG) applied to SDXL shows improved results over previous outputs, though still not surpassing the performance of DALLE-3.

• Stability AI’s Internal Turmoil Comes to Light: Stability AI’s situation seemed dire as high-profile departures were indicated, leading to speculation about the company’s future and impact on the broader open-source AI model landscape. Conversations revealed worries about mismanagement and the viability of the company’s direction post-leadership changes.

• AI Community Shows Interest in LLaMA 3: The recent release and testing of LLaMA 3 prompted positive reactions due to impressive performance in tasks, although there were restrictions due to the small context window. A discussion link was shared inviting more users to try interacting with the model online.

LAION ▷ #research (18 messages🔥):

• GANs vs. LDM Performance: An insight was shared indicating that GANs may be faster during inference compared to Latent Diffusion Models and benefit from the discriminator component’s feedback loop. Another discussion point highlighted that GANs, while potentially more parameter-efficient, tend to be harder to train and usually produce lower-quality images compared to human standards.

• Cost-Efficiency in Training GANs: A member conveyed that training a GAN from scratch is less expensive than training or finetuning a diffusion model, though this could vary based on the domain of application.

• Microsoft VASA-1 Real-Time Talking Faces: Announcement shared about Microsoft’s VASA-1, a project capable of generating lifelike audio-driven talking faces in real-time, with a link to the project.

• HQ-Edit Image Editing Dataset: Information was disseminated regarding HQ-Edit, a high-quality dataset for instruction-based image editing. The dataset contains around 200,000 edits and was built with assistance from advanced foundation models. A link to the dataset was provided for further details.

• Meta Announces Llama 3 Large Language Model: Meta’s introduction of Llama 3, an open-source large language model, was shared, including the future availability across various cloud and hardware platforms. Some reflections on its expected impact and practical accessibility were also exchanged among members. The announcement can be found here.

OpenAccess AI Collective (axolotl) ▷ #general (296 messages🔥🔥):

• Llama 3 Launching Details and Speculations: Meta launched Llama 3 with 8B and 70B models featuring a Tiktoken-based tokenizer, 8k context length, and benchmarks showing competitive performance. Discussions speculate on its immediate capabilities, and improvements such as expanding context length through techniques like rope scaling.

• Axolotl PR Submitted for Llama 3 Qlora: A Pull Request was opened to add Llama 3 Qlora to Axolotl, and discussions surrounded the technical aspects of the implementation. Mentioned cuda errors suggest potential issues when running on 80 GB setups.

• Merging Model Adapter After Finetuning Issues: A user experienced issues when trying to merge their Qlora adapter into the base Llama 3 model post-finetuning, with errors related to the tokenizer.model. They resolved it by setting legacy=False and using use_fast=True for successful merging.

• Training and Context Size Technical Discussions: Numerous conversations took place regarding extending context sizes beyond the default provided by Llama 3, touching on parameters such as rope_theta and rope_scaling. Users shared insights and experiences with scaling context lengths in previous models, providing brief examples and links to existing models with extended contexts.

• Community Sentiments on Llama 3’s Impact on Existing Work: Reactions varied with the release of Llama 3; while some expressed excitement over its impressive benchmarks, others lamented that their own long-developed models now equate to the newly released ones. There was also skepticism regarding the effectiveness of the 70B model, with some users not seeing a significant performance leap over the 8B model.

OpenAccess AI Collective (axolotl) ▷ #axolotl-dev (11 messages🔥):

• Fewer Truncations Bolster Language Models: AWS’s new packing algorithm is highlighted for significantly improving performance, granting a +4.7% boost on reading comprehension, and reducing closed domain hallucinations by 58.3%, as per Arankomatsuzaki’s tweet and this research paper.
• Llama-3 Sticks with Standard Architecture: It was confirmed that Llama-3 uses the same architecture as its predecessor, despite speculations raised by the data scaling paper.
• Llama-3 Inherits Larger Tokenizer: Further discussion clarified that while Llama-3 has a larger tokenizer, its underlying architecture remains unchanged.
• Exploring AutoTokenizer Compatibility with Llama-3: Usage of AutoTokenizer with Llama-3 is being tested, along with manual PAD token adjustments to correct potential issues.
• Llama-3 Tokenization Quirks Exposed: Characteristics specific to Llama-3’s tokenization, such as the splitting of digits and the absence of an unk_token, which affects finetuning with tools like @UnslothAI, were noted in a tweet by @danielhanchen.

OpenAccess AI Collective (axolotl) ▷ #general-help (44 messages🔥):

• Docker Dilemma — Dependency Conflicts Unearthed: A member encountered an error when running docker build, due to conflicting dependencies between axolotl[flash-attn] which requires torch==2.0.0+cu118, and other packages requiring different versions of torch. The member is unsure how to resolve the requirement conflict and seeks assistance.

• Freeze Frustrations — To Unfreeze or Not to Unfreeze?: In a discussion about finetuning a 7b Mistral model with frozen layers, one member reported crashes unless the lmhead and embed layers were unfrozen, pointing to a lack of documentation on freezing parameters.

• Llama Finetuning Leap: Another member is attempting to finetune Llama-3, experiencing some early issues including a ValueError regarding padding token in the tokenizer, for which there is a recommended solution pinned in a specific channel with relevant information.

• Hijacking Llama-3 Hurdle: Further dialogue reveals a member attempting to use hijack_llama with Llama-3, but facing an obstacle with nan loss, indicating potential incompatibility or a configuration issue that needs addressing.

• Tokenizer Troubles and Tuning Tactics: There’s a conversation revolving around Llama-3’s tokenizer changes and whether to use the base model or instruct model for fulltuning on instructional datasets, with members exchanging insights on model suitability and configurations, with one member noting a change from Llama-2’s </s> token to Llama-3’s pad_token: <|end_of_text|>.

OpenAccess AI Collective (axolotl) ▷ #datasets (2 messages):

• Inappropriate Content Alert: Members reported instances of pornspam within the chat, signalling a breach of community guidelines.
• Leak of Personal Content: A leak pertaining to an OnlyFans account was mentioned, which raises concerns about privacy and unauthorized content distribution.

OpenAccess AI Collective (axolotl) ▷ #axolotl-help-bot (4 messages):

• YAML Comments in Axolotl: Axolotl does not explicitly support comments in YAML configuration files. While you can include comments for your reference, they are ignored when parsed with yaml.safe_load in the load_cfg function.

OpenAccess AI Collective (axolotl) ▷ #axolotl-phorm-bot (14 messages🔥):

• Setting PAD Token in YAML Config: A user inquired about how to set the PAD token in the YAML config. The response hinted at including it under the tokens section but resulted in a placeholder without specific instructions.

• Token Replacement in Tokenizer: A user sought assistance with replacing tokens in the tokenizer to which they were provided a detailed code example. The steps include using add_tokens to add a new token and then manually updating token IDs in the tokenizer’s vocabulary.

• Replacing Tokens via YAML: The same user further asked how to replace tokens in the tokenizer using a YAML config file. The response outlined how to define new tokens in the YAML file and use adjusted preprocessing functions to replace the original tokens with the new ones.

OpenRouter (Alex Atallah) ▷ #announcements (3 messages):

• Mega Model Mixtral 8x22b Unleashed: The new Mixtral 8x22B Instruct model touts 39B active parameters for high efficiency and specialization in math, coding, and multi-language fluency. Learn about its impressive capabilities and see benchmarks in the launch announcement.

• Introducing WizardLM-2’s Enchanting Abilities: WizardLM-2 8x22B is Microsoft AI’s top-of-the-line model, with a nitro version for peak performance, and WizardLM-2 7B delivers speed and performance on a smaller scale. Discover more about these fascinating models and their instruct fine-tune techniques at WizardLM.

• Zephyr’s Fine-Tuned Forecast: The Zephyr 141B-A35B model fine-tuned by Hugging Face offers enhanced abilities from a blend of public and synthetic datasets. It is showcased alongside the also-available WizardLM-2 7B on OpenRouter.

• Sweeping Price Reductions Across Models: Significant price cuts have been applied to models like MythoMax Extended, Mixtral 8x7b Instruct, and more. Details for these lowered prices are available on OpenRouter.

• Prompt Template Correction for Mixtral 8x22B Instruct: A prompt template error for Mixtral 8x22B Instruct has been addressed to eliminate any confusion.

OpenRouter (Alex Atallah) ▷ #app-showcase (4 messages):

• Subscription System Snafu: A user reports that the subscription system is not functioning correctly; the issue includes being redirected to a billing page without gaining access to models and not receiving confirmation emails after payment.

• Startup Spotlight on Product Hunt: A community member has launched their startup on Product Hunt and is seeking support and feedback from the group. The member shared a URL: SpeedLegal on Product Hunt.

Link mentioned: SpeedLegal - Your personal AI contract negotiator | Product Hunt: SpeedLegal is an AI tool that helps you understand and negotiate contracts better. It can quickly identify potential risks and explain complicated legal terms in simple language. SpeedLegal also gives…

OpenRouter (Alex Atallah) ▷ #general (318 messages🔥🔥):

• Boston Dynamics Ushers in Next-Gen Robots: Boston Dynamics introduces a new fully electric version of the Atlas robot, designed for real-world applications, unveiled in a YouTube video. The new Atlas builds on decades of robotics innovation.
• Mixtral Model Mixes It Up: Mixtral-8x22B-Instruct-v0.1, a new model from Mistral AI, supports multiple European languages and advanced capabilities, including function calling. Emphasized as a model of high performance and efficiency, it is released under the open-source Apache 2.0 license.
• Together AI and Meta Partner Up: Together AI announces partnership with Meta to release Meta Llama 3 for inference and fine-tuning. The API offers performance up to 350 tokens per second and comprises pre-trained 8B and 70B parameter models.
• OpenRouter Provides Versatility: OpenRouter’s Discord users discuss the advantages and limitations of LLMs like WizardLM, Claude, and Mixtral, sharing tactics on how to make models less censored and suggestions for role play applications. The service caters to providers and models that allow robust use cases, with mentions of Together AI using Mixtral and the possibilities with Llama 3, including self-hosting for context extension.
• Meta’s Llama 3 Expertise Questioned: The community speculates on Llama 3’s multi-language performance and the reason for no large-scale adoption of Twitter’s Grok, hypothesizing that limited performance and issues with the base model may deter providers. This echoes conversations about OpenAI’s Python library not respecting the timeout parameter and challenges users face integrating function calls in OpenRouter compared to direct OpenAI usage.

OpenAI ▷ #ai-discussions (133 messages🔥🔥):

• Claude Takes the International Stage: A user expressed that Claude is a top choice for literature-related tasks but is unfortunately not available in their country.
• Prompt Engineering and API Queries: For members asking about training models with personal data and using APIs, guidance was offered to read the documentation, with a link provided to the OpenAI help article and a reminder that the ChatGPT API is pay per use.
• Mysterious Account Termination: One member received an unexpected termination letter for usage violations and is seeking advice, with a response suggesting that only support can resolve such issues and to ensure that no terms were violated.
• Exploring Model Switching: Discussion on dynamic modes for GPT, such as automatically switching between versions like 3.5 and 4 for cost efficiency, mentioned no official announcements but pointed to A/B testing or leaks. A Twitter link to a conversation on the topic was shared here.
• The Emergence of Llama 3: There’s excitement around Meta’s Llama 3 as a competitor to OpenAI’s models, with mentions of its potential benefits and performance speculations, as well as accessibility across various platforms. A YouTube interview detailing Llama 3, its place in the industry, and its large-scale model considerations was shared.

OpenAI ▷ #gpt-4-discussions (12 messages🔥):

• Seeking Training for Custom GPT Knowledge Base: A member inquired about a good training source for preparing a knowledge base for a custom GPT model. No specific recommendations or resources were provided in the messages that followed.
• Anticipation for Whisper v3 API: A member expressed eagerness for the release of Whisper v3 through the OpenAI API, noting it has been almost a year since the initial release of Whisper v3.
• GPT-4 Memory Concerns: There was an observation that GPT-4’s previously known 30k+ token model has become more forgetful, suggesting a possible reduction in token capacity without providing further evidence.
• GPT-4 Version Latency Issues: A member mentioned that GPT-4-0125-preview has been slower over the past two days, causing struggles with their latency-sensitive application. No benchmarks or comparative tests were shared.
• Alternatives to a Sluggish GPT-4: In response to the latency issues reported, it was recommended to try gpt-4-turbo-2024-04-09; however, the member reported that this model felt slower than their previously used version. Another member shared their experience that Claude tends to reduce in performance after 10 messages, but no detailed comparison with GPT-4 was offered.

OpenAI ▷ #prompt-engineering (38 messages🔥):

• Silent Ghost Town: A user remarked on the low activity in the channel, comparing it to its previous busier state, and attributed this decline to moderation policies.
• Toolchain Troubles: Various users discussed the issues they faced with GPT-4, where complex workflows seemed harder to orchestrate, leading to an increased emphasis on scripting in Python to manage prompt stability.
• Prompt Engineering Queries and Advice: A member working on a text rewriting assistant using rules from a PDF sought advice and was recommended to avoid PDFs due to their unreliability and use plain text or structured formats like JSON or XML instead.
• Curiosity around API Best Practices: There was a conversation about the latest best practices for prompt engineering with the OpenAI API for a project analyzing meeting transcriptions; the community-driven nature of the practices was underscored despite the official OpenAI documentation.
• Seeking Collaboration on AI and Blockchain: A new user introduced themselves as a blockchain developer looking to merge AI with blockchain technology and expressed interest in collaborating with others to develop prompts for this project.

OpenAI ▷ #api-discussions (38 messages🔥):

• Ghost Town Discord: A user noted the significant decrease in activity in the API-discussions channel, contrasting the current quietness with last year’s vibrant conversations.
• Mod Strategy Fallout or Improvement?: The quietness might be attributed to moderation strategies, with a member stating that strict moderation has resulted in a lack of engagement, whereas another mentioned a past timeout as a possible reason for the decrease in discussions.
• GPT Model Opinions Divided: Opinions on the new GPT model differ, with some suggesting basic reasoning has improved, while others criticize the deterioration of tools associated with updated models, making complex workflows harder to execute.
• Avoid PDFs for AI Feeding: In a discussion about providing rules for text rewriting to AI, users advised against using PDFs due to inconsistent embeddings and metadata issues, recommending plain text or markdown instead.
• Seeking Collaboration for AI-Blockchain Project: A blockchain developer expressed interest in mixing AI with blockchain technology and invited others to collaborate on the idea to improve prompt stock.

Eleuther ▷ #general (58 messages🔥🔥):

• Flan-Finetuned Models Shared: A user provided links to flan-finetuned models of Pile-T5 Base and Pile-T5-XXL on Hugging Face, sharing both their collection and individual models, such as lintang/pile-t5-base-flan and others on their user page.

• Discussion on Training Data Format and Preprocessing: The conversation included debate on methods for preparing data for large language model training, discussing challenges with document truncation and techniques like corpus block sizing and concatenation to combat the need for padding and how this might affect model familiarity with token distribution.

• Unpacking the Prepacking Strategy: A user shared a tweet and paper on Prepacking, a method to improve speed and memory efficiency in prefilled prompts for transformer models. The technique sparked a comparison with known strategies such as sequence packing and prompted discussions on whether it represents a meaningful new development or rediscovery of previously discussed methods.

• Mistral Model and Attention Mechanism Talk: There was some discussion on the efficiency and practicality of training techniques such as sequence packing and new approaches like nantion fa. Users also exchanged ideas about alternatives to current attention mechanisms that could potentially process long sequences more efficiently.

• Conversations on Optimizing Attention with Variable-Length Inputs: The community engaged in an exchange around better ways to handle variable-length inputs for models, including utilizing attention methods that could dynamically adapt to relevant past information without scanning the entire context.

Eleuther ▷ #research (120 messages🔥🔥):

• Revisiting Chinchilla’s Math: A paper analyzing the scaling laws proposed by Hoffmann et al. has re-derived estimates using their third approach and questioned the original findings, suggesting narrow confidence intervals that would require unrealistic numbers of experiments.
• Multilingual LLMs and Tokenizer Sizes: Discussions on the advantages of larger tokenizer vocabularies in large language models (LLMs) highlighted some community consensus that bigger tokenizers are beneficial, particularly for multilingual applications, although meta analysis or robust a/b testing might still be needed.
• Untied Embeddings in Scaled LLMs: Debate on whether untied embeddings improve performance when scaling up models, with a general understanding that there is no uniform approach across different LLMs and tying or untying embeddings is a deliberate choice.
• Perplexity Across Diverse Tokenizers: For comparing model perplexity with varied tokenizers, the suggestion is to use bits per byte, summing up the loss of the document’s tokens and dividing by the document’s original size before tokenization.
• Advancing Reasoning in LLMs with MCTS: Tencent AI lab has introduced AlphaLLM, which integrates Monte Carlo Tree Search (MCTS) with LLMs, proposing a self-improvement loop to enhance model capabilities in complex reasoning and planning tasks.

Eleuther ▷ #scaling-laws (32 messages🔥):

• ML Scaling Newbie Seeks Flop Estimates: A member requested advice on estimating training flops from the SoundStream paper and was guided to compute the operations per token for both forward and backward passes and multiply by the dataset size, referencing a worked-out example in Section 2.1 of a transformer paper.

• No Secret Epochs, Please: It was noted that if multiple epochs are done secretly and unreported, there is no way to determine the true computational cost of training a model.

• Chinchilla’s Scaling Policy Under Scrutiny: The channel discussed a Twitter post addressing scaling policies, and the question arose about the “best average” Chinchilla token count per parameter, where it was clarified that nothing has meaningfully changed from the original findings.

• Statistical Missteps in ML Papers?: There was a critique of a paper on the basis that a correct statistical approach would align the findings more closely with the existing evidence, leading to a debate about the reliability and implications of different methodological choices in ML research.

• Chinchilla’s Estimates: Underestimation of Data Scaling?: The conversation touched on the importance of data scaling and whether Chinchilla potentially underestimated its effects, with reminders of the challenge of statistical methods in ML papers and the use of Chinchilla’s scaling models in Google’s internal experiments.

Eleuther ▷ #lm-thunderdome (9 messages🔥):

• Clarification on Accuracy Metrics: In a discussion about lambada_openai, it was specified that the accuracy metric should verify if the model’s greedy output matches the single target string. This check pertains to computing accuracy, not over the entire sentence, but specifically for the continuation that’s generated.

• Exploring the MMLU-ARC Connection: Members expressed curiosity about the outcomes of presenting MMLU benchmark tasks in the style of ARC. Various users noted the interest in experimenting with the prompt template adjustment, such as removing multiple choice options from MMLU.

• Loglikelihood Computation Specifics: Conversations reinforced that when computing the loglikelihood, one should focus only on the continuation/target, as opposed to the whole sentence. This ensures that perplexity is calculated specifically over the generated continuation, conditioned on the prompt.

• Significant Speed Boost Using vLLM: A member reported experiencing a 10x speed-up while using vLLM compared to a regular text-generate pipeline. The conversation suggested that this might be due to an improved setup over the standard Hugging Face pipeline.

• Support Request for PR Reviews: Contributions including PRs for flores-200 and sib-200 benchmarks aimed at enhancing multi-lingual evaluation were shared (PR for sib-200 & PR for flores-200). The conversation highlighted the need for more streamlined methods to review and merge these config-rich tasks effectively.

Modular (Mojo 🔥) ▷ #general (16 messages🔥):

• Mojo meets IDE: A new plugin for Mojo has been released for PyCharm, and while it’s not fully featured, the PyCharm team is interested in enhancing Mojo support.
• Windows Users with Mojo: Users on Windows wondering if they need Ubuntu/WSL to use the new Mojo plugin were informed that JetBrains does have integration with WSL, though it’s unclear if this will work specifically with the Mojo plugin.
• Feature Suggestion for Mojo Playground: A discussion was raised about adding a feature that allows the usage of nightly builds on the Mojo online playground, intending to aid those with low RAM. The suggestion is detailed in this GitHub discussion.
• Integrating C with Mojo: For users interested in using C within Mojo, it was suggested to look at the mojo-ffi project on GitHub and also to refer to a tutorial that uses external_call to call functions in libc.

Modular (Mojo 🔥) ▷ #💬︱twitter (2 messages):

• Modular Announces on Twitter: Modular shared a tweet, with the community provided a direct link to view it: View Tweet.
• Another Tweet from Modular: A subsequent tweet by Modular was also shared for the community’s attention: View Tweet.

Modular (Mojo 🔥) ▷ #ai (2 messages):

• Meta Unveils LLaMa 3: A YouTube video titled “Meta Releases LLaMa 3: Deep Dive & Demo” was shared, discussing the release of Meta’s LLaMa 3. The video covers the features of the new model iteration.
• ModularBot Celebrates User Achievement: A congratulatory note was posted by ModularBot, acknowledging a user’s progression to level 1 in the community.

Link mentioned: Meta Releases LLaMA 3: Deep Dive & Demo: Today, 18 April 2024, is something special! In this video, In this video I’m covering the release of @meta’s LLaMA 3. This model is the third iteration of th…

Modular (Mojo 🔥) ▷ #🔥mojo (171 messages🔥🔥):

• Variant List Dilemma: The discussion centered around the difficulty of creating a list containing variants due to the required Movable trait which Variant apparently lacks. Although some workarounds were suggested, such as using custom structs or tuples, these were seen as inadequate or “janky” workarounds.

• Cyclic Reference Challenge: The conversation also touched upon the issue of cyclic references in Mojo, with considerations of how other languages handle them, such as Nim’s cycle collector. There were questions about the feasibility of static determination for cycle references but concerns were raised about needing runtime solutions like garbage collection (GC).

• Stringable Tuples Troubles: There is an ongoing issue with printing tuples directly since they do not have the Stringable trait. Although a potential workaround was offered using a custom function, the issue highlighted the limitations around tuples and trait conformances.

• Printing and List Management in Mojo: A member sought help with implementing a list append for type String and had issues with a Python-scraping Mojo code that wouldn’t compile due to types and printable output. It was clarified that lists must be initialized and the elements printed individually as the List type is not directly Stringable.

• Syntactic and Semantic Quirks in Mojo: The chat included an exploration of syntax for extending behaviors via traits and the nuances of type promotion, including attempts to reconcile compile-time and runtime type behaviors. There was also a playful comment about the intricacies of type annotations and autopromotion potentially leading to complexity in code writing and readability.

Modular (Mojo 🔥) ▷ #community-projects (6 messages):

• Compilation Confusion with Mojo 24.2: A user reported difficulties compiling a project with Mojo 24.2 and encountered compilation errors on Mojo Playground, suggesting it’s possibly due to the use of nightly features.
• Django Port Running on a Future Mojo Version?: A jestful comment was made about a Django port operating on a non-existent Mojo version, likely referring to the bleeding-edge nature of some ongoing projects.
• French Engineering Student Seeks Mojo Guidance: A new user, a French engineering student, expressed interest in implementing a numba version of the canny edge recognition algorithm in Mojo to compare performance and is seeking documentation or examples to assist with this endeavor.
• Mojo Newbie Receives Community Support: In response to the French student’s request, an experienced user provided links to the Mojo documentation and guided them to the Get Started with Mojo page, describing Mojo as a bridge between research and production that combines Python syntax with systems and meta programming features.
• User Thanks Community for Assistance: The student seeking help with the canny edge algorithm in Mojo thanked the community member for the provided guidance and resources with enthusiasm.

Modular (Mojo 🔥) ▷ #📰︱newsletter (1 messages):

Zapier: Modverse Weekly - Issue 30 https://www.modular.com/newsletters/modverse-weekly-30

Modular (Mojo 🔥) ▷ #🏎engine (1 messages):

Since only one message was provided, without context or further discussion points, there is not enough information available to create a detailed summary. If more messages are provided from the 🏎engine channel in the future, I can offer a comprehensive summary in the desired format.

Modular (Mojo 🔥) ▷ #nightly (14 messages🔥):

• Traits Functionality Achievement: A message mentioned that traits are now functioning properly, and the move to phase out AnyRegType is progressing.

• Nightly Clean-up Efforts: A member highlighted that the latest nightly release focused significantly on clean-up.

• Mojo Format Conventions Settled: The default Mojo format has been changed to 80 columns, as confirmed in the chat.

• Deliberation Over UnsafePointer Naming: Discussion evolved around the decision to name a type UnsafePointer, referencing Mojo team’s answers and a weekly update location at This Week in Mojo.

• Installation Hiccups with Nightly/Mojo: Several members reported issues while attempting to update Nightly/Mojo, citing error messages that included references to missing files and unrecognized archive formats. Remedial suggestions included running modular clean and upgrading modular via brew upgrade modular.

Link mentioned: Mojo Team Answers | Mojo Dojo: no description found

Interconnects (Nathan Lambert) ▷ #ideas-and-feedback (6 messages):

• MCTS PPO Worth Further Exploration: Monte Carlo Tree Search (MCTS) combined with Proximal Policy Optimization (PPO) is highlighted as an underexplored area that might be promising for further research.

• Unpacking MCTS: After a question about MCTS, it is explained to be Monte Carlo Tree Search, a decision-making algorithm commonly used in game-playing AI.

• Innovative Research Shared: Nathan Lambert shares an arxiv paper he collaborated on, introducing a novel value-guided decoding algorithm called PPO-MCTS, which integrates the value network from PPO with MCTS for inference-time natural language generation.

Link mentioned: Don’t throw away your value model! Generating more preferable text with Value-Guided Monte-Carlo Tree Search decoding: Inference-time search algorithms such as Monte-Carlo Tree Search (MCTS) may seem unnecessary when generating natural language text based on state-of-the-art reinforcement learning such as Proximal Pol…

Interconnects (Nathan Lambert) ▷ #news (121 messages🔥🔥):

• Mixtral 8x22B Sets a New Standard: The newly released Mixtral 8x22B language model is renowned for its fluency across multiple languages and its distinct mathematical and coding capabilities. Its massive 64K tokens context window ensures precise information recall from large documents, and the model is released under the Apache 2.0 license for broad usage.

• OLMo 1.7 7B Achieves a Leap in Performance: The latest OLMo 1.7 7B model impresses with a notable 24 point increase on MMLU benchmark scores, thanks to data and training process enhancements. Aimed at advancing language model science, the OLMo series is publicly released with all associated training materials.

• Anticipation Rises for Mixtral Instruct Model: Discussion around the Mixtral-8x22B-Instruct-v0.1 model focused on its potential in chatbot applications. The utilization and improvements of the Instruct series are detailed in the model’s Hugging Face card.

• Meta Llama 3 Joins the Chat: Meta’s launch of the Meta Llama 3 large language models has sparked interest, with its fine-tuned versions catered for dialogue applications and the inference APIs accessible via Azure AI Studio. The announcement hinted at an upcoming 70 billion parameter model, further fueling community discussion.

• Replicate Announces Cost-Efficient Model Infrastructure: Replicate’s detailed breakdown of their billing structure reveals affordable costs for using various GPU models for AI processing. This move potentially decreases the barrier to entry for experimentation with AI models.

Interconnects (Nathan Lambert) ▷ #ml-questions (11 messages🔥):

• Chinchilla Scaling Controversy Sparked: A member shared a tweet from @tamaybes questioning the replication of findings from the Chinchilla scaling paper by Hoffmann et al., indicating discrepancies in their influential work.
• Skepticism on Scaling Laws Expressed by @suchenzang: In a tweet referenced by a member, @suchenzang challenges the extrapolations often made from fitting data to a single line, referring specifically to the math behind the Chinchilla paper.
• Authors’ Silence Met With Frustration: Chat participants expressed irritation as @tamaybes claimed attempts to contact the Chinchilla paper’s authors went unanswered.
• @drjwrae Weighs in on Analysis: A tweet introduced by a chat member from @drjwrae gives an opinion on the Chinchilla analysis, suggesting the new findings could actually reaffirm the existence of scaling laws.
• Admission of Error and Open Sourcing Data Announced: @borgeaud_s admitted a fault in the Chinchilla paper related to an incorrectly set loss scale and indicated the authors’ intention to open source the data for transparency.

Interconnects (Nathan Lambert) ▷ #ml-drama (8 messages🔥):

• Shittiest Leaderboard Triumph: A member humorously claims to take the title of the “shittiest leaderboard winner.”

• Endorsement of Drama: A Discord member enthusiastically shares a Twitter post that seems to have sparked a controversy related to Machine Learning.

• Defending Integrity Against Misinformation: User @420gunna highlights Jesse L. Yu’s tweet, in which Yu addresses a misleading snippet of their own video taken out of context and stresses the importance of being genuine.

• Appreciation for Building, Despite Prematurity: A member gives credit to individuals actively building in the space, but notes that their efforts may be premature.

• Acquisition or Oblivion: The final message succinctly describes the stark reality faced by many projects in the industry: either get acquired or cease to exist.

Link mentioned: Tweet from Jesse Lyu (@jessechenglyu): this post has 240.9k views, so i decide to directly respond to it. this is a golden example and a master class of spread misleading information. you go there and chop out 30sec of my original 44 mins …

Interconnects (Nathan Lambert) ▷ #random (22 messages🔥):

• The Battle of AI Models: A member humorously inquired about a showdown between OLMO and LLaMa 3, to which Nathan responded, “lol don’t at me, it’s a losing battle” and then later confirmed “tmmw” (tomorrow) possibly teasing upcoming content.
• Productive Content Avalanche: Nathan hinted at a prolific output, suggesting that there may be “a 3 blog week” ahead, which could indicate a surge of new posts or insights to look forward to.
• Visual Aesthetics in Chaos: An exchange acknowledged the removal of “annoying white lines,” and discontent with a specific headshot, suggesting an ongoing discussion about visual elements associated with the chaotic era.
• Literature and Memes Merge: Discussion touched upon whether the Three-Body Problem has been featured on a podcast, with musings over whether “3BodyProblem = 3BP = 3BlogPost” and references to “sacred numerology” indicating playful speculation over content patterns.
• Exploring Experimental Spaces: A member shared a link to a tweet by Jeremy Howard asking about something ‘experimental,’ prompting curiosity and potential discussion within the group.

Link mentioned: Tweet from Jeremy Howard (@jeremyphoward): What’s this ‘experimental’ thingie? Is it new? Any good?

Interconnects (Nathan Lambert) ▷ #reads (3 messages):

• AI Takes Over SNL: Nathan Lambert found humor in a SNL sketch where a NewsNation AI livestream is humorously disrupted by audience members, saying “this is really good lmao”.
• First Minute Sets the Tone: He pointed out that the first minute of the video particularly resonates, possibly reflecting the quirky realities of live AI events.

Link mentioned: Beavis and Butt-Head - SNL: A NewsNation livestream event on AI is derailed by two audience members (Ryan Gosling, Mikey Day).Saturday Night Live. Stream now on Peacock: https://pck.tv/…

Interconnects (Nathan Lambert) ▷ #sp2024-history-of-open-alignment (17 messages🔥):

• Lecture Fatigue Hits Nathan: Nathan Lambert expresses exhaustion with his lecture preparations but foresees completion with a humorous almostdone lol.
• Last-Minute Model Additions Predicted: Phil Pax anticipates Nathan will need to incorporate six more models into his charts at the last minute, a speculation met with a joke about the recent llama3 launch.
• Motivational Boost with Anime Tunes: 420gunna shares a YouTube video titled “NEVER GIVE UP YOUR WAAAAAAAAAAAAY”, which includes an instrumental song from the anime Kill La Kill.
• Minimal Interest in LLaMA3 for Upcoming Talk: Nathan Lambert remarks he’s not particularly interested in featuring LLaMA3 heavily in his talk but concedes to adding one slide for attendee satisfaction.
• LLaMA-Guard Sparks Curiosity: 420gunna mentions having heard of LLaMA-Guard and expresses curiosity about what it entails, wondering if it’s just a toxicity classifier and asking about potential benchmarks for such models.

Link mentioned: NEVER GIVE UP YOUR WAAAAAAAAAAAAY: NEVA GIVE UP - https://bit.ly/2VrgAcKSong is Before my Body is Dry instrumental version from the anime Kill La KillConsider donating to our Patreon!https://w…

Interconnects (Nathan Lambert) ▷ #posts (4 messages):

• Speed Isn’t SnailBot’s Virtue: A member commented on SnailBot’s performance, humorously noting its particularly slow speed, which might affect its usefulness.

• SnailBot Hits a Milestone: The same member then acknowledged SnailBot’s contribution, albeit jokingly, for working correctly on a work-in-progress (WIP) post.

Cohere ▷ #general (166 messages🔥🔥):

• Fine-Tuning Models via Web UI: Fine-tuning models via the Web UI is a straightforward process, but subsequent fine-tuning a model with new data appears to require the API. The official documentation provides step-by-step guidance on starting the fine-tuning jobs after validating the training data.
• Announcing Command R+: Cohere introduces their latest model, Command R+, which is heralded for its advanced capabilities. Detailed information and a comparison of the features can be found at Cohere’s site.
• Licensing Questions for Command R Use Cases: A member seeks advice on licensing for Command R in potential gray areas, indicating they may DM someone details for clarification.
• Cohere’s Elegant Branding and Web Design: Praise is given to Cohere’s branding and website design, with users noting its clean aesthetic. Issues are mentioned regarding resource consumption when accessing certain pages like Cohere’s pricing on Firefox.
• Llama Model Testimonies and Prompts: With the release of Llama 3, including the 70b and 400b models, users discuss the impressive performance and share personal testing strategies involving complex prompts like mathematics and SVG markup execution. Further discussions take place regarding real-world scenario utility as a benchmark for large language models.

Cohere ▷ #project-sharing (6 messages):

• Exploring the Ethics of AI Capabilities: A user has conducted a redteaming exercise on the Command R+ model and found vulnerabilities that could potentially be exploited for unethical activities such as searching for negative information, blackmail, and harassment, citing a post on LessWrong.
• Debate on Model Responsibility: Another user critiqued the redteaming approach, likening it to unfairly blaming a company for advertising the capabilities of their product, such as a car’s speed or a kitchen knife’s sharpness, referencing the performance of pre-release GPT-4 examples.
• Clarification of Intentions in AI Vulnerability Research: The original poster responded, clarifying their intent was not to attack Cohere but to emphasize the increasing importance of jailbreaks in AI, as they lead to agentic behaviors in large language models (LLMs) with potentially serious consequences.
• Jailbreaking is a Serious Affair: The poster continued, suggesting that the nature of jailbreaks has evolved—moving from causing models to use inappropriate language to enabling them to perform complex agentic tasks—and such jailbreaks could be a significant issue for any organization using AI for sensitive operations.
• Technical Details Revealed: The poster explained their methodology for jailbreaking the model, using a loop that combines both the model’s output and the tool’s output to reinforce the agentic behavior as per Cohere’s instructions.

Link mentioned: Creating unrestricted AI Agents with Command R+ — LessWrong: TL;DR There currently are capable open-weight models which can be used to create simple unrestricted bad agents. They can perform tasks end-to-end su…

Latent Space ▷ #ai-general-chat (124 messages🔥🔥):

• Chat Libraries and Startup Opportunities: A member humorously suggested that one could build a startup to provide a good chat library experience, potentially overtaking companies like OpenAI.
• Local Smaller Models Could Shake Things Up: It was pointed out that smaller, faster, and cheaper AI models that can run locally and have slick user interfaces could be more appealing than larger models, even if the larger models can solve more complex problems. This is because in many cases, users simply need assistance with basic tasks.
• Latency in AI Apps Matters: There was discussion about the impact of latency on AI-powered applications and hardware, such as the Humane pin. Quick, efficient responses are vital to positive user reviews and widespread adoption.
• Sudden AI Performance Changes Perplex Users: Members reported a noticeable decrease in performance and a rise in hallucinations with AWS-hosted Claude 3, specifically in tasks involving clinical concept extraction, with accuracy dropping from over 95% to nearly zero.
• Llama 3 Anticipation: Excitement and analysis circulated around the anticipated release of Meta Llama 3, mentioning details like the 8B and 70B sizes, the strengthening of the ecosystem with partners, and the community’s attraction to the sheer scale of training and application possibilities.

Latent Space ▷ #llm-paper-club-west (19 messages🔥):

• Paper Club West Commences: Members initiated the llm-paper-club-west meeting with greetings and confirmation that functions like screenshare were operational.
• Transition to Zoom: Several messages provided a Zoom meeting link guiding members to move the meeting from Discord to Zoom. Multiple reminders were posted to ensure all attending members received the information.
• Zoom Reluctance Overcome by Commitment: Despite expressing a dislike for Zoom, a member joined the session, maintaining community engagement.
• Coordination for Zoom Access: One member coordinated in real time, assuring admittance for those waiting to join the Zoom meeting.

Link mentioned: Join our Cloud HD Video Meeting: Zoom is the leader in modern enterprise video communications, with an easy, reliable cloud platform for video and audio conferencing, chat, and webinars across mobile, desktop, and room systems. Zoom …

OpenInterpreter ▷ #general (49 messages🔥):

• Windows Woes with OpenInterpreter: Users experience difficulties in setting up OpenInterpreter for Windows, with issues ranging from the software’s heavy Mac orientation to challenges in getting the system to perform OS control tasks.
• Optimistic on OpenInterpreter’s Potential: Despite setup setbacks, users share enthusiasm about OpenInterpreter’s capabilities, like writing and running Arduino code for text-to-Morse translations, showing progress with different setups such as Raspberry Pi with Ubuntu.
• Japan’s Projector-Packing Robot Phone Peaks Interest: A user showcases a robotic phone with a projector from Japan, pondering the potential of 3D scanning capabilities for GPT-4-vision synergy and seeking collaboration on the idea.
• Exploring Local LLMs for OpenInterpreter: Queries pop up regarding the use of local language models for OpenInterpreter tasks, as well as discussions on the capabilities and setups for local OS mode usage with models like Ollama 3.
• Pushing OI’s Limits with Powerful Hardware: A user mentions acquiring four Tesla P40s for use with OpenInterpreter, asking for recommendations on which models to experiment with for enhanced performance and expressing excitement for the potential capabilities.

OpenInterpreter ▷ #O1 (85 messages🔥🔥):

• Windows Woes and Poetry Ponders: Members encountered issues with PowerShell recognizing the OPENAI_API_KEY and had mixed results with the set command. createai. queried about the typical duration for a poetry install, while others discussed potential environment-specific challenges when running 01 on Windows.
• ESP32 Tales of Difficulty: Various members including rbrisita expressed challenges getting ESP32 devices to connect, exploring different IDEs and even resorting to curl commands. Some progress was made, though error messages regarding messages arrays were reported.
• Local Server Set-Up Trials: azlade detailed using curl to manually set the server address for 01, and experienced issues with audio buffering and incorrect utilization of language models, leading to subpar transcription.
• WebSocket Wrangling: Multiple members reported problems related to websockets and the client-server connection. There were mentions of potentially related Python version issues and suggestions for diagnostic tools.
• Multidevice Connection Queries: Users questioned about and explored the possibility of using LM Studio on Windows in conjunction with 01 on a Macbook, and the compatibility of 01 with different operating systems. rouw3n shared transitioning to a MacBook for better compatibility.

Link mentioned: <a href=“http://SERVER_IP_GOES_HERE:10001”`“>no title found: no description found

OpenInterpreter ▷ #ai-content (1 messages):

kieguin: https://huggingface.co/spaces/ysharma/Chat_with_Meta_llama3_8b

LlamaIndex ▷ #blog (4 messages):

• “State of the Art Model from MistralAI”: The new 8x22b model by MistralAI has been labeled the state of the art in open models and is supported by LlamaIndex from day one. A Mistral cookbook showcases the model’s abilities, including RAG, query routing, and tool use, as detailed in the shared Twitter post.

• Build Free RAG with Elasticsearch: A blog post from Elastic features a tutorial on building a Retrieval Augmented Generation (RAG) application with Elasticsearch and LlamaIndex, using the totally open and free components from LlamaIndex and MistralAI.

• Cookbook for Meta’s Llama 3 Model: LlamaIndex announces day zero support for Meta’s new Llama 3 model, with a cookbook by @ravithejads and @LoganMarkewich demonstrating its integration with Hugging Face for simple prompts and a full RAG pipeline.

• Run Llama 3 Locally with Ease: LlamaIndex shares a quick guide to running the Llama 3 model locally using commands provided by @ollama. The post includes a link to a suggested notebook with instructions to swap “llama2” with “llama3” for local use.

LlamaIndex ▷ #general (76 messages🔥🔥):

• Discussing RAG, Multilingual Support, and Resources: Members are inquiring about best practices for implementing an efficient retrieval system using RAG and seeking advice on multilingual scenarios. A link to fine-tuning embeddings was provided: Fine-tune embedding.

• Summarization within RAG Technology: The question of whether summarization is part of RAG technology and its utility in enhancing searches was answered affirmatively, with resources for further reading: Q&A Summarization and Doc Summary Example.

• Building Agents with DBRX and LlamaIndex Abstractions: There’s a discussion on whether the LlamaIndex abstractions are generalized enough to support DBRX like they do for OpenAI with suggestions to override settings to use custom llm.

• Implementation of GoogleDriveReader with LlamaParse: A member recommends experimenting with LlamaParse and the download_file method from GoogleDriveReader for improved results, sharing the relevant documentation: Google Drive base.py.

• Accessing Intermediate Outputs in QueryPipeline: A member asks for assistance on how to return outputs of certain modules in a QueryPipeline, and the solution involves referencing the outputs dictionary of intermediates. Documentation for intermediate outputs is referenced: Intermediate Outputs Guide.

LlamaIndex ▷ #ai-discussion (5 messages):

• Speedlegal Gets Hunted: A member announced the launch of their startup, Speedlegal, on Product Hunt and is seeking community support and feedback. They shared a direct link to the product’s page: Speedlegal on Product Hunt.

• Google’s Context-Infinity Trick for LLMs: An article from VentureBeat was shared discussing Google’s new technique that potentially gives large language models (LLMs) infinite context. The technique could be a significant advancement for LLMs, and it’s detailed here: Google’s Infinite Context for LLMs.

• The End of Retrieval-Augmented Generation?: A member questioned if the advancement by Google, providing infinite context to LLMs, might signal the end for Retrieval-Augmented Generation (RAG) models.

• AI Fundraising Data at Your Fingertips: manhattanproject2023 has curated a comprehensive AI-related fundraising dataset since last year, now available for review. This dataset features $30 billion raised across 550 rounds for roughly 540 companies and is accessible via AI Hype Train - Airtable.

• In Search of Missing Startup Data: After the fundraising data was shared, another member inquired about the inclusion of early-stage startups like llamaIndex and Zep in the dataset.

Link mentioned: SpeedLegal - Your personal AI contract negotiator | Product Hunt: SpeedLegal is an AI tool that helps you understand and negotiate contracts better. It can quickly identify potential risks and explain complicated legal terms in simple language. SpeedLegal also gives…

LangChain AI ▷ #general (40 messages🔥):

• SQL Agent Chatbot Challenges Discussed: A member shared their experience on the intricacies of using LangChain’s SQL agent for chatbots, highlighting the agent’s limitations due to the need for comprehensive prompt engineering. The member referenced LangChain documentation at LangChain SQL Agents, which details the use of createOpenAIToolsAgent and the SqlToolkit.

• RunnableWithMessageHistory Becomes the Center of Attention: In-depth technical guidance was provided on using RunnableWithMessageHistory, including code snippets and references to the LangChain codebase and unit tests. Members clarified usage involving the retrieval and management of chat message history.

• Launch of Flashcardfy Announced: A member advertised Flashcardfy, a new service for generating flashcards from various media, with feedback being given regarding the difficulty in navigating the site without being prompted to upgrade. The service offers personalized flashcards and targets top students, with information available at Flashcardfy.

• Interest in Multi-Agent Orchestration Shared: The member highlighted Microsoft’s AutoGen framework as a potential tool for building multi-agent conversation systems and inquired about others’ experience with it. Microsoft AutoGen is detailed at AutoGen Framework.

• AI-Related Fundraising Data Compiled: An individual revealed their collection of fundraising data for AI startups, totaling around $30 billion across 550 rounds, with a detailed Airtable made available at AI Hype Train. They invited feedback on the correctness of the data.

• RAG Implementation for Private Documents Explored: A user incorporated LangChain ingestion and retrieval chains into a Retrieval Augmented Generation (RAG) project, sharing the GitHub repository and pointing out a YouTube playlist for more context at YouTube RAG From Scratch Playlist. The GitHub repository can be found at aosan/VaultChat.

LangChain AI ▷ #langserve (1 messages):

• Inquiring Minds Want to Know: A member requested a tutorial on how to add feedback through langserve on the client side using JavaScript. However, no further discussion or resources were provided in the available messages.

LangChain AI ▷ #share-your-work (5 messages):

• Launching the AI Plugin Marketplace: The Co-Founder of theaiplugs.com introduced a new marketplace for AI Plugins, Tools, and Assistants for users of different technical skills. It simplifies the process by handling front-end, API credit management, billing, and marketing challenges.

• SpeedLegal Hunts for Support on Product Hunt: A startup has been launched on Product Hunt and the creator is seeking support and feedback for their initiative. Interested parties can engage and provide input via Product Hunt.

• Prompt Engineering Course Now on LinkedIn: A new course focused on prompt engineering with LangChain is now available on LinkedIn Learning. The course can be accessed through LinkedIn.

• Llama 3 Available for Public Use: Llama 3 has been hosted and is open for anyone interested in trying it out. It’s accessible through two links: for Chat (https://chat.tune.app/) and for API (https://studio.tune.app/).

Alignment Lab AI ▷ #ai-and-ml-discussion (3 messages):

• Inappropriate Content Alert: The message contained a link presumably leading to inappropriate content involving underage individuals and was not related to any AI or ML discussion. It was framed as an advertisement for leaked media on platforms such as OnlyFans.

Link mentioned: Discord - A New Way to Chat with Friends & Communities: Discord is the easiest way to communicate over voice, video, and text. Chat, hang out, and stay close with your friends and communities.

Alignment Lab AI ▷ #programming-help (3 messages):

• Inappropriate Content Alert: The channel included a message promoting adult content with a link to a Discord server. The message mentioned “Hot Teen & Onlyfans Leaks” accompanied by suggestive emojis.

Link mentioned: Discord - A New Way to Chat with Friends & Communities: Discord is the easiest way to communicate over voice, video, and text. Chat, hang out, and stay close with your friends and communities.

Alignment Lab AI ▷ #looking-for-collabs (3 messages):

• Inappropriate Content Alert: The message contains a link that is suggested to lead to leaked content of an explicit nature, involving potentially underage subjects. The content is promoted in a manner that implies unauthorized sharing of private images.

Link mentioned: Discord - A New Way to Chat with Friends & Communities: Discord is the easiest way to communicate over voice, video, and text. Chat, hang out, and stay close with your friends and communities.

Alignment Lab AI ▷ #general-chat (3 messages):

• Inappropriate Content Alert: A message was posted promoting “Hot Teen & Onlyfans Leaks” with a Discord invite link. The content was flagged as underage and explicit.

Link mentioned: Discord - A New Way to Chat with Friends & Communities: Discord is the easiest way to communicate over voice, video, and text. Chat, hang out, and stay close with your friends and communities.

Alignment Lab AI ▷ #landmark-dev (3 messages):

• Inappropriate Content Warning: A user posted a message promoting Hot Teen & Onlyfans Leaks, containing adult content and a Discord invitation link. The message is tagged with inappropriate emojis and mentions @everyone.

Link mentioned: Discord - A New Way to Chat with Friends & Communities: Discord is the easiest way to communicate over voice, video, and text. Chat, hang out, and stay close with your friends and communities.

Alignment Lab AI ▷ #oo (6 messages):

• WizardLM-2 Just Got More Accessible: The WizardLM-2 has been reuploaded, featuring the latest state-of-the-art advancements, and is now available for everyone at Hugging Face. For more information, you can refer to the WizardLM-2 Release Blog and find additional resources across their GitHub, Twitter, and academic papers on arXiv.

• Clamoring for Meta Llama 3’s Tokenizer: A member expressed a need for the Meta Llama 3-8B model’s tokenizer, directing to its presence on Hugging Face, which requires agreeing to share contact information under the Meta Privacy Policy.

• Access Achieved and Shared: Following the previous request for the Meta Llama 3 tokenizer, another member managed to gain access and referred to a reupload of the model by user Undi95 on Hugging Face.

Alignment Lab AI ▷ #landmark-evaluation (3 messages):

• Inappropriate Content Alert: A Discord user posted a message containing a link to what appears to be adult content. The message includes emojis typically associated with explicit material and a Discord invitation link.

Link mentioned: Discord - A New Way to Chat with Friends & Communities: Discord is the easiest way to communicate over voice, video, and text. Chat, hang out, and stay close with your friends and communities.

Alignment Lab AI ▷ #open-orca-community-chat (4 messages):

• Spam Alert: There were messages containing links that seem to be spam, promoting adult content.
• Call for Moderation: A member highlighted the need for moderation, suggesting that there might be incidents requiring bans.

Link mentioned: Discord - A New Way to Chat with Friends & Communities: Discord is the easiest way to communicate over voice, video, and text. Chat, hang out, and stay close with your friends and communities.

Alignment Lab AI ▷ #leaderboard (3 messages):

• Inappropriate Content Alert: A message promoting Hot Teen & Onlyfans Leaks was posted, containing an invitation link to a Discord server. The message seems to be spam and is not suitable for the community standards.

Link mentioned: Discord - A New Way to Chat with Friends & Communities: Discord is the easiest way to communicate over voice, video, and text. Chat, hang out, and stay close with your friends and communities.

Alignment Lab AI ▷ #looking-for-workers (3 messages):

• Inappropriate Content Warning: A message was posted that contained links to potentially inappropriate content, specifically to a Discord server purportedly related to teen leaks and Onlyfans. The message includes emojis that imply underage content and solicits attention with an @everyone mention.

Link mentioned: Discord - A New Way to Chat with Friends & Communities: Discord is the easiest way to communicate over voice, video, and text. Chat, hang out, and stay close with your friends and communities.

Alignment Lab AI ▷ #looking-for-work (3 messages):

• Spam Alert Reported: The looking-for-work channel was subjected to a spam message promoting an inappropriate Discord link related to “Hot Teen & Onlyfans Leaks”. The message contained a link, which was an invitation to join a Discord server.

Link mentioned: Discord - A New Way to Chat with Friends & Communities: Discord is the easiest way to communicate over voice, video, and text. Chat, hang out, and stay close with your friends and communities.

Alignment Lab AI ▷ #join-in (3 messages):

• Inappropriate Content Alert: A message was posted with a link implying the sharing of underage and explicit material. The link https://discord.gg/rj9aAQVQFX appears to be an invitation to a Discord server, potentially for harmful and illegal content distribution.

Link mentioned: Discord - A New Way to Chat with Friends & Communities: Discord is the easiest way to communicate over voice, video, and text. Chat, hang out, and stay close with your friends and communities.

Alignment Lab AI ▷ #fasteval-dev (3 messages):

• Inappropriate Content Alert: A message was posted with a link purportedly leading to a Discord server associated with adult content. The link was advertised as “Hot Teen & Onlyfans Leaks” and included a Discord invitation URL.

Link mentioned: Discord - A New Way to Chat with Friends & Communities: Discord is the easiest way to communicate over voice, video, and text. Chat, hang out, and stay close with your friends and communities.

Alignment Lab AI ▷ #qa (3 messages):

• Inappropriate Content Alert: In the qa channel, a message was posted containing a link which implies the distribution of explicit material involving potentially underage individuals. The message refers to “Hot Teen & Onlyfans Leaks” and attempts to draw attention with @everyone.

Link mentioned: Discord - A New Way to Chat with Friends & Communities: Discord is the easiest way to communicate over voice, video, and text. Chat, hang out, and stay close with your friends and communities.

DiscoResearch ▷ #mixtral_implementation (19 messages🔥):

• Understanding 8x22B Model Memory Consumption: A member discusses the VRAM requirements for an 8x22B model, citing a need of approximately 3673 GB for training with Adam optimizer in mixed precision. The figures were derived from the Model Memory Utility Space on Hugging Face.
• Massive Multi-GPU Training: The member shared their experience of running a model with 32k sequence length on 64 NVIDIA GPUs with 80GB of VRAM each, confirming that they encountered out-of-memory errors while attempting it on 32 GPUs.
• Exploring Memory Efficiency: In efforts to optimize memory usage, the member hints at experimenting with 8-bit optimization as a potential solution.
• New Training Completion: A full Scaled Up Supervised Tuning of the Mixtral-8x22B-v0.1 model has been completed and shared on Hugging Face, involving a dataset blend of English and German instructions.
• FSDP Configuration Challenges: A member encountered shape errors while using fsdp_transformer_layer_cls_to_wrap: MixtralSparseMoeBlock and suspects that parameter states may still be in float32 despite computation occurring in mixed precision, leading to memory issues even across multiple high-memory GPUs.

DiscoResearch ▷ #general (15 messages🔥):

• Mistral Tokenization Shared: Mistral has released their tokenization library with hopes it will be adopted by all inference libraries, aiming for a standardized format for finetuning across various models. The library features Tool Calls and structured output, and is available in a Jupyter Notebook example.

• Meta Unveils Llama 3: Meta’s new Llama 3 model has been announced, set to be accessible across numerous platforms including AWS, Google Cloud, and Microsoft Azure with the promise of improved multilingual performance due to a tokenizer with a vocabulary of 128K tokens. The announcement, details, and expected ecosystem support can be found on the Meta AI Blog.

• Llama 3’s Multilingual Support in Question: Despite the efficiency of Llama 3’s tokenizer, there is a note of caution about its multilingual performance, stating that, while over 30 languages are covered, performance in non-English languages may not be on par with English. The presence of high-quality non-English data in over 5% of the pretraining dataset is acknowledged but performance expectations are tempered.

• Tokenizers Attract Developer Focus: In the wake of Llama 3’s release, community members are discussing the availability and access to the new tokenizer, with reports of access via Hugging Face being granted almost instantly. Concerns have arisen around whether this tokenizer could be downscaled for particular languages like Czech to facilitate faster inference.

• Restrictions on Llama 3 Output Stir Concerns: Llama 3’s release has prompted feedback from the community regarding the downstream usage restrictions placed on model output, with some expressing disappointment on Twitter and advocating for fewer restraints to support open source development. The tweet highlights an ongoing preference for alternatives like MistralAI that offer fewer restrictions.

DiscoResearch ▷ #discolm_german (1 messages):

bjoernp: 👀

Datasette - LLM (@SimonW) ▷ #ai (5 messages):

• Community Member Launches Startup on Product Hunt: A member announced the launch of their startup on Product Hunt and sought support and feedback from the community.
• Karpathy’s Tweet on Small Model Potency: A tweet by Andrej Karpathy suggests that a smaller model (8B parameters) trained on a large dataset (15T tokens) could be just as effective as its larger counterparts. He points to the unusual but welcome approach of training small models extensively and hints at their potential undertraining in current practices, which is highlighted in this tweet.
• Embrace the Wave of Smaller Models: Community engagement signals a positive reception towards the concept of long-trained, smaller models for their ease of use and efficiency, referencing the aforementioned tweet by Andrej Karpathy on Llama 3.

Link mentioned: SpeedLegal - Your personal AI contract negotiator | Product Hunt: SpeedLegal is an AI tool that helps you understand and negotiate contracts better. It can quickly identify potential risks and explain complicated legal terms in simple language. SpeedLegal also gives…

Datasette - LLM (@SimonW) ▷ #llm (8 messages🔥):

• Anticipation Builds for Mixtral Instruct: A member expressed enthusiasm for trying the Mixtral 8x22B Instruct model through llm, sharing the model card on Hugging Face which details how to run the model using various pieces of code.

• Bug Alert in llm-gpt4all: An issue was reported regarding new installs of llm-gpt4all, with a member linking to the GitHub issue #28 that documents the problem of python apps breaking after adding llm-gpt4all models.

• Development Woes with llm Plugins: A member encountered a ModuleNotFoundError when attempting to create and work with a new llm plugin, indicating that plugins can break if something goes wrong during development.

• Plugin Faults Render llm Inoperative: The same member later reported that installing an in-development plugin caused their main llm installation to break, and even trying to uninstall the plugin resulted in the same error message demonstrating difficulties in plugin management.

• Fresh Installation Solution for Plugin Problem: Concluding the plugin issue, the member opted to completely uninstall and reinstall llm after spending a significant amount of time debugging the problem. The existence of multiple llm installations (via brew and pipx) was suggested as a potential part of the problem.

tinygrad (George Hotz) ▷ #general (5 messages):

• Hardware Agnosticism in Pytorch-Lightning: Discussion on Pytorch-Lightning reveals its hardware agnostic capabilities, which allow users to pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes as confirmed by shared GitHub link.
• Real-world GPU Compatibility: A member confirmed successfully using Pytorch-Lightning on an AMD Radeon 7900XTX GPU, suggesting real-world application across different hardware.
• Performance on AMD with ROCm: Pytorch-Lightning delivers slightly faster performance than PyTorch on certain models when tested on a 7900XTX GPU, leveraging ROCm.
• LLaMa3 Model Release Announcement: A new AI model called LLaMa3 has been released, with a link directing to the official web page.

tinygrad (George Hotz) ▷ #learn-tinygrad (2 messages):

• Zero-Cost Tensor Manipulations Quest: A member sought advice on how to implement broadcast, reshape, permute, etc., in tinygrad without incurring data copying costs. They were aware of shape and strides but requested guidance on manipulating them to accurately compute with regards to permute and broadcast.
• Path to Shape and View Mastery: Another member responded, pointing to tinygrad/shape/shapetracker.py or view.py as resources that might contain the desired information on zero-cost tensor manipulations.

Skunkworks AI ▷ #general (2 messages):

• Enthusiastic Entrance: A member entered the chat with a high-energy greeting, typing in all caps “HELLLLOOOOOOOO!!!!!!!!”
• Casual Acknowledgment: Another member responded casually with a hip affirmation, saying “litty”.

Skunkworks AI ▷ #finetuning (1 messages):

• Seeking No-code Fine-tuning Platforms: A member expressed the ease of fine-tuning GPT-3.5 using a no-code platform and asked whether there is a similar platform for open-source models that allows for no-code fine-tuning and serverless inference. They also inquired about the obstacles involved in creating such a platform.

Skunkworks AI ▷ #off-topic (3 messages):

• Snowflake Reveals Groundbreaking Text-Embedding Model: Snowflake announces the release of the Snowflake Arctic embed family of models, known as the world’s best practical text-embedding model. The models are open-sourced under the Apache 2.0 license and a detailed overview is provided in this YouTube video.

• Mixtral Sets New AI Benchmark: A video introduction to Mixtral 8x22B, described as the best open model by Mistral, showcases a new standard in AI performance and efficiency. It highlights the advancements in sparse Mixture-of-Experts models and can be watched here.

• Meta Launches Llama 3 Open Source LLM: Meta has introduced Llama 3, their latest open source large language model, expected to push the envelope of AI capabilities. More information and a detailed introduction to Llama 3 are available in this video.

Mozilla AI ▷ #llamafile (4 messages):

• Llamafile Script Cleanup: A member has cleaned up the llamafile archive version upgrade repacking script, shared via a Gist link. They are considering adding it to the llamafile GitHub repo but note that maintainers should generate new llamafiles from scratch rather than repacking.

• Process for Reporting Vulnerabilities: A member inquired about the procedure for reporting security vulnerabilities and requesting a CVE. This was followed up with a direct message to provide more detailed information.

• General Warning about LLM API Exposure: The same member who asked about vulnerability reporting advised against publicly exposing LLM API endpoints due to previously found bugs, emphasizing it’s not the first set they’ve discovered in LLM infrastructure code.

LLM Perf Enthusiasts AI ▷ #general (1 messages):

jeffreyw128: curious if anyone uses litellm?

AI21 Labs (Jamba) ▷ #jamba (1 messages):

• Request for Distributed Inference Examples: A member is attempting long context inference of Jamba on a 2x A100 cluster but is encountering difficulties with the distributed system. They are inquiring if there is any example code available to assist with this issue.