a quiet day is all you need

AI News for 9/26/2024-9/27/2024. We checked 7 subreddits, 433 Twitters and 31 Discords (224 channels, and 2635 messages) for you. Estimated reading time saved (at 200wpm): 288 minutes. You can now tag @smol_ai for AINews discussions!

Just a lot of non-headline news today:

• GDM announced AlphaChip
• FTC crackdown on deceptive AI claims
• Copilot now on GitHub.com in browser
• Looots of reporting on OpenAI drama
• GGML starting to monetize thru HuggingFace

You could tune in to the latest Latent Space with Shunyu Yao and Harrison Chase while you browse the news below!

If you are in SF for DevDay, consider bringing your demos and hot takes to our DevDay pregame on Monday.

The Table of Contents and Channel Summaries have been moved to the web version of this email: !

AI Twitter Recap

all recaps done by Claude 3.5 Sonnet, best of 4 runs.

AI Model Releases and Developments

• Llama 3.2 Release: Meta released Llama 3.2, including lightweight 1B and 3B models for on-device AI applications. @AIatMeta noted these models enable developers to build personalized, on-device agentic applications with capabilities like summarization, tool use, and RAG where data never leaves the device. @awnihannun demonstrated Llama 3.2 1B in 4-bit running at ~60 tokens/sec on an iPhone 15 pro.

• Molmo Multimodal Model: A new multimodal model called Molmo was released, with @osanseviero highlighting its data pipeline and training process. The model uses a dense captioning dataset of 712k images/1.3M captions and various datasets for supervised fine-tuning.

• AlphaChip: Google DeepMind announced AlphaChip, an AI method for chip design. @GoogleDeepMind stated it has transformed the way they design microchips, from TPUs for AI models to CPUs in data centers. @demishassabis noted the feedback loop where AlphaChip is used to design better AI chips, which are then used to train better models.

AI Infrastructure and Platforms

• Hugging Face Milestone: @ClementDelangue announced that Hugging Face crossed 1,000,000 free public models, emphasizing the importance of smaller, specialized models for specific use cases.

• RAG Applications: @svpino discussed challenges with scaling RAG applications, noting that more data can make them worse due to limitations of vector similarity search. He highlighted research showing accuracy degradation as knowledge bases grow.

• On-Device AI: Several tweets discussed the potential of on-device AI, with @cognitivecompai predicting that in another year, ChatGPT-level AI will be running on mobile/embedded devices.

AI Research and Benchmarks

• Reliability in LLMs: @omarsar0 shared insights from a Nature paper suggesting that larger and more instructable LLMs may become less reliable, with issues in difficulty concordance, task avoidance, and prompting stability.

• Elo Benchmarking: @sarahookr announced the acceptance of work on Elo benchmarking in NLP at NeurIPS 2024, addressing reliability issues in this widely used evaluation method.

AI Ethics and Regulation

• Free Speech and AI: @ylecun emphasized responsible use of free speech, warning about potential legal consequences for spreading harmful conspiracy theories.

• AI Regulation: Several tweets discussed SB-1047, a bill in California that could impact open-source AI development. @ylecun expressed hope that Governor Gavin Newsom would veto it.

AI Development Tools and Techniques

• PyTorch Optimization: @cHHillee discussed performance improvements in PyTorch for reinforcement learning workloads using cudagraphs and torch.compile, reporting >5x speedups.

• Web Scraping: @AlphaSignalAI shared a GitHub repo for easily scraping web pages and outputting in LLM-friendly formats like JSON, cleaned HTML, and markdown.

AI Reddit Recap

/r/LocalLlama Recap

Theme 1. Llama 3.2: Performance Gains and EU Regulatory Challenges

• Llama 3.2 Vision Models image pixel limits (Score: 40, Comments: 3): The new Llama 3.2 Vision Models have a maximum image size of 1120x1120 pixels for both the 11B and 90B versions, with a 2048 token output limit and 128k context length. These models support gif, jpeg, png, and webp image file types, information which was not readily available in official documentation and required extensive testing to determine.
  • The maximum image size for Llama 3.2 Vision Models is actually 4 560x560 images, as revealed in the preprocessor config on Hugging Face. This configuration specifies "max_image_tiles": 4 and image dimensions of 560x560.
• Users appreciated the information provided about the model's capabilities, noting its usefulness for practical applications.
• Running Llama 3.2 on Android via ChatterUI (Score: 39, Comments: 9): The post announces the release of ChatterUI v0.8.0-beta3, which now supports running Llama 3.2 models on Android devices. Using a Snapdragon 7 Gen 2 processor, the app achieves 50 tokens per second for prompt processing and 10 tokens per second for text generation, demonstrating good performance on modern Android hardware. The author provides a link to the beta release and invites feedback, particularly on character list and chat history changes.
  • Users expressed interest in larger models for mobile devices, with one user finding the Llama release disappointing compared to bigger models they're already running.
  • There's interest in an iOS version of ChatterUI, but the developer cited the cost of a Mac as a barrier to publishing on the App Store.
  • The app's performance on Android devices with Llama 3.2 models was noted, achieving 50 tokens per second for prompt processing and 10 tokens per second for generation.
• Is Llama 3.2 Banned to Use in EU? (Score: 71, Comments: 132): The Llama 3.2 license on Huggingface reportedly restricts license rights for EU-based individuals and companies to use multimodal models, though this restriction is not present in the GitHub license. This discrepancy raises questions about potential data collection and user fingerprinting in new Llama multimodal versions, possibly in response to EU data protection laws.
  • The EU AI Act and GDPR are cited as reasons for Meta's restrictions on Llama 3.2 in the EU, with concerns about training on personal data without consent. The AI Act's implementation starts in February 2025, raising questions about Meta's preemptive actions.
  • Discussions centered on the implications of EU regulations for AI models, particularly regarding biometric categorization and copyright issues. Some users expressed frustration with EU regulations, while others defended their importance for data protection.
  • There's debate about whether running AI models locally exempts them from EU regulations. The "household exemption" in GDPR was mentioned, but uncertainty remains about how regulators and courts will interpret these laws for open-source AI models.

Theme 2. Next-Gen Hardware for AI: NVIDIA RTX 5090 Specs Leaked

• RTX 5090 will feature 32GB of GDDR7 (1568 GB/s) memory (Score: 87, Comments: 40): The RTX 5090 is rumored to feature 32GB of GDDR7 memory with a bandwidth of 1568 GB/s. This represents a significant upgrade from the current generation, potentially offering substantial performance improvements for AI and graphics-intensive applications.
  • Pricing discussions dominate, with users speculating the RTX 5090 could cost $3500 or even $5090. Some hope for cheaper previous-gen cards, but 3090s prices have remained steady or increased in some regions.
  • The card's 600W power consumption raises concerns about power limits. Users debate the significance of the 32GB memory upgrade, with some calling it "huge" while others argue it's insufficient after three generations of 24GB.
  • Memory bandwidth calculations were scrutinized, with users suggesting the correct figure should be 1792 GB/s instead of 1568 GB/s. The potential for running 70B models and possibly the 90B Llama 3.2 on a single card was noted.

Theme 3. Quantization and Performance Analysis of Large Language Models

• Estimating Performance Loss: Qwen2.5 32B Q4_K_M vs BF16 MMLU PRO evaluation results (Score: 79, Comments: 15): The post compares the performance of Qwen2.5 32B model in Q4_K_M quantization against its BF16 version using an incomplete MMLU PRO evaluation. Despite the limitations of an incomplete dataset, the comparison provides a rough estimate of performance degradation due to quantization, showing results across various subject categories and an overall performance drop from 66.58% (BF16) to 64.23% (Q4_K_M). The evaluation was conducted using Ollama as the backend and a GitHub-hosted evaluation tool, with specific configuration details provided.
  • Qwen2.5 32B model's performance on the MMLU-Pro leaderboard was discussed, with users noting its close performance to the 72B version. The leaderboard allows self-reported results via JSON uploads, raising questions about submission sources.
  • Users expressed interest in comparing Q4_K_M quantization to other formats like IQ4_XS / NL with suitable calibration data. Some suggested creating a sorted bar plot to better visualize performance differences between quantizations.
  • The Q4_K_M quantization showed unexpected improvements in certain categories like history, which was attributed to potential "lucky dice rolls" in quantization. Users also discussed the minimal performance loss compared to BF16, considering it a valuable trade-off for reduced resource requirements.
• Running inference on the new Llama 3.2 1B model at 21 tok/s on an 8-core laptop with Rust (Score: 58, Comments: 8): The author extended their Rust-based project to support inference on the new Llama 3.2 1B and 3B models, achieving 21 tokens per second on an 8-core laptop without using ML libraries. The project, available on GitHub, now includes a light WebUI as an alternative to the terminal chat interface for local CPU inference.
  • Users praised the project's performance, comparing it to iPhone capabilities. The author emphasized the learning experience of building from scratch, describing it as a "mix of pain and reward when you finally get it right".
  • Requests for a Windows GUI chat executable were discussed. The author acknowledged this as a requested feature and suggested adapting the backend to be compatible with existing frontends that support multiple operating systems.
  • Debate arose over using web browsers versus native applications for the GUI. Web browsers were criticized for high RAM consumption and lower CPU/GPU performance compared to native apps.

Theme 4. Advancements in Creative Writing and Roleplay AI Models

• This is the model some of you have been waiting for - Mistral-Small-22B-ArliAI-RPMax-v1.1 (Score: 36, Comments: 22): Mistral-Small-22B-ArliAI-RPMax-v1.1, a new AI model for creative writing and roleplay, has been released. This model is based on Mistral's 22B parameter foundation and is designed to excel at character-based interactions, offering improved coherence and creativity compared to previous versions.
  • The Mistral Small 22B ArliAI RPMax v1.1 model achieved a training and eval loss below 1.0, surpassing the Llama 3.1 70B version. This performance suggests the model may excel at creative writing and roleplay tasks despite its smaller size.
  • The RPMax dataset is curated to eliminate repetitions and synthetic generations, focusing on quality over quantity. The training approach uses a single epoch, low gradient accumulation, and higher learning rate to prevent overfitting to specific character tropes or stories.
  • Users expressed interest in the model's performance for short story writing and requested public release of the dataset. Some inquired about VRAM requirements and EXL2 quantization options for running the model on systems with limited resources.

• Abliteration doesn't only effect how the model behaves and responds, but also effects how its fictional written characters think and respond as well (Score: 58, Comments: 15): The post discusses an unexpected consequence of "abliteration" on AI language models, noting that it not only affects the model's direct responses but also influences the behavior of fictional characters created by the model. The author observes that abliterated models tend to produce characters who react more positively and agreeably in situations where they would typically display anger, defiance, or upset, effectively removing refusal behaviors from both the model and its fictional creations.
  • Users tested abliterated models with system prompts, finding they can still be steered to refuse requests. Some argue these models are better suited as work tools, particularly in fields like healthcare where compliance is crucial.
  • The impact of abliteration varies depending on the extent applied. Some models, like Gemma 2 9b, showed unexpected behaviors (e.g., "homicidal bias") even when vanilla. The EQ Bench creative writing table suggests Gemma2 finetunes perform well in this area.
  • Some users noted that abliterated models may still have censorship, but express it through misunderstanding or reinterpretation of requests. This behavior might extend to roleplay contexts, affecting how fictional characters respond.

Theme 5. Hugging Face Milestone: 1 Million Models

• Hugging Face just passed 1,000,000 models (Score: 167, Comments: 18): Hugging Face has reached a significant milestone, surpassing 1,000,000 models available on their platform. This achievement was announced by Julian Bilcke on X (formerly Twitter) and can be verified on the Hugging Face models page, showcasing the platform's extensive collection of machine learning models.
  • Duplicate models are prevalent on Hugging Face, with users noting multiple uploads of the same model (e.g., Llama-3.2-1B-Instruct.Q4_K_M.gguf) and questionable fine-tuning claims. SomeOddCodeGuy mentioned seeing "5-15 q4 or q5 gguf repos" for older models.
  • Users discussed the potential for evolutionary AI development, with balcell suggesting treating weights as DNA and introducing genetic algorithm properties. An example of a successful small-scale evolutionary simulation was shared by involviert.
  • Concerns were raised about model quality and functionality, with remyxai noting that "half the time there is no model card" when querying the hub APIs. Others questioned how many models actually perform their intended functions.

Other AI Subreddit Recap

r/machinelearning, r/openai, r/stablediffusion, r/ArtificialInteligence, /r/LLMDevs, /r/Singularity

AI Research and Model Developments

• Google DeepMind's AlphaChip transforms microchip design: Google DeepMind announced that their AI-powered chip design method AlphaChip has significantly improved the process of designing microchips. This advancement could accelerate AI hardware development.

• New "blueberry" image generation model emerges: A mysterious new image generation model called "blueberry" has appeared on leaderboards, outperforming existing models like FLUX.1. Its origin is unknown but some speculate it could be from OpenAI.

• Google's NotebookLM adds audio and video input: Google's NotebookLM tool now allows users to submit YouTube videos and audio files as knowledge sources, expanding its multimodal capabilities.

AI Industry and Company News

• OpenAI leadership changes: Several high-profile departures from OpenAI have occurred recently, including Mira Murati, Bob McGrew, and Barret Zoph. This has sparked discussion about potential internal issues at the company.

• OpenAI plans massive data centers: OpenAI has asked the US government to approve 5GW data centers, highlighting the enormous computing power needed for advanced AI development.

• Sam Altman pushes for rapid breakthroughs: Reports indicate Sam Altman is pressuring OpenAI employees to quickly turn research breakthroughs into public releases, potentially accelerating AI progress.

AI Policy and Societal Impact

• UN prioritizes AI governance: The United Nations is calling for AI to be treated with the same urgency as climate change, signaling growing global concern about AI's societal impact.

• US government forms AI infrastructure task force: The Biden administration has created a task force to coordinate policy on AI data center infrastructure, demonstrating increased government involvement in AI development.

AI Model Releases and Improvements

• Flux.1 Dev adds ControlNet Outpainting: The Flux.1 Dev model now supports ControlNet Outpainting in ComfyUI, expanding its image generation capabilities.

• Elektroschutz LoRA released: A new Stable Diffusion LoRA called Elektroschutz has been released, demonstrating continued innovation in open-source AI models.

AI Discord Recap

A summary of Summaries of Summaries by O1-mini

Theme 1. Language Model Performance and New Releases

• ColQwen2 Dominates Vidore Leaderboard: The ColQwen2 model, powered by a Qwen2-VL backbone, achieves a remarkable +5.1 nDCG@5 score, surpassing colpali-v1.1 on the Vidore Leaderboard.

• Phi-3.5's Censorship Sparks Community Debate: Microsoft's Phi-3.5 model is criticized for its extensive censorship, leading users to explore an uncensored version on Hugging Face.

• Llama 3.2 Enhances Vision and Token Handling: Llama 3.2 11B Vision model now supports up to 128k tokens and introduces improved vision features, though performance benchmarks show mixed results.

Theme 2. Tooling, Integrations and New Features

• Aider Launches Architect/Editor Mode for Efficient Coding: The new architect/editor mode in Aider streamlines coding workflows, enabling faster bug fixes with models like o1-preview and Claude 3.5.

• OpenInterpreter Debuts Electron Frontend: OpenInterpreter unveils an Electron frontend, enhancing user experience and fostering greater community engagement.

• LangChain Integrates Langfuse and PostHog for MistralAI Tracking: A tutorial demonstrates setting up Langfuse within LangChain for comprehensive LLM application monitoring and user analytics via PostHog.

Theme 3. Hardware and GPU Performance in AI Workloads

• NVIDIA RTX 5090 Rumored to Feature 32GB VRAM: Speculations suggest the upcoming NVIDIA RTX 5090 will include a 32GB VRAM variant, while the RTX 5080 might receive a 24GB upgrade post its initial 16GB release.

• TensorWave Offers MI300X GPUs to Community: Darrick from TensorWave announces the availability of MI300X units for community members, aiming to boost GPU adoption and educational initiatives.

• AMD GPUs Underperform in AI Benchmarks: AMD GPUs, such as the 5700 XT and 7900 XTX, are reported to lag behind NVIDIA 3070 in productivity tasks like Stable Diffusion and Blender, highlighting performance discrepancies.

Theme 4. Deployment Updates and API Enhancements

• Cohere Releases API v2 with Enhanced Chat Capabilities: Cohere's API v2 introduces new endpoints like v2/chat with features including a messages parameter and system message support, enhancing chat interactions.

• OpenRouter Shifts to Token-Based Pricing for Gemini Models: OpenRouter transitions to counting tokens instead of characters for Gemini models, adjusting pricing to offer an estimated 50% cost reduction for Flash and 1.5 Pro models.

• Meta's Orion AR Glasses Integrated into Perplexity AI: Meta's Orion AR Glasses are incorporated into Perplexity AI, aiming to revolutionize user interactions within augmented reality environments.

Theme 5. Model Training and Optimization Techniques

• DSPy Integrates with Langtrace for Advanced Experiment Management: DSPy now supports Langtrace, enabling automatic capture of traces, checkpoints, and evaluation score visualizations, significantly enhancing AI experiment workflows.

• Fine-Tuning Llama Models Raises Overfitting Concerns: Users report challenges with fine-tuning Llama 3.2-3B, highlighting risks of overfitting with low training losses and emphasizing the need for proper data handling and tokenizer adjustments.

• LoRA+ Optimizations Improve Model Training Efficiency: LoRA+ optimization parameters are updated to fix default learning rate issues, enhancing the efficiency and stability of model training processes.

PART 1: High level Discord summaries

aider (Paul Gauthier) Discord

• Architect/Editor Mode Streamlines Coding: The new architect/editor mode in Aider enhances coding workflows, promoting faster bug fixes with models like o1-preview and Claude 3.5.
  • Users suggest leveraging Sonnet 3.5 for design tasks to maximize efficiency.
• Model Performance Benchmarking Encouraged: Users are prompted to benchmark various model combinations like o1-preview with o1-mini and Sonnet 3.5 to optimize performance.
  • Performance may vary depending on project size and editing context, suggesting that tailored setups deliver the best results.
• New /copy Command Proposed: A proposal for a new /copy command aims to let users easily copy the last LLM output into the clipboard for further use.
  • This feature enhances workflow, particularly for those utilizing the /ask command frequently.
• Streamlit's Interactivity Limitations Discussed: Members noted that Streamlit has limitations for Aider use cases, suggesting redesign necessities for improved interactivity.
  • While potential redesign was acknowledged, it's not currently deemed a priority by the group.
• Observations on Token Usage: Discussion centered around token usage in Aider, with advice to keep files minimized to 1-3 to avoid performance hits.
  • Members were recommended to use /tokens to monitor usage, as exceeding 30k tokens could lead to unpredictable behavior.

LM Studio Discord

• Molmo's Compatibility with LM Studio: New Vision models won't be supported in LM Studio for a while because they are incompatible with llama.cpp.
  • Users noted the Llama 3.2 11b is similar to the 3.1 8b but adds parameters to enhance vision features.
• Queries on Llama 3.2's Text Generation: The community has raised questions about Llama 3.2's token support, with claims it can handle up to 128k tokens.
  • Mixed reports have emerged about the model's performance and issues related to buggy integrations.
• Upgrade Concerns for LM Studio: Users expressed unease around upgrading from version 0.2.31 to 0.3.x regarding model compatibility and retention of settings.
  • It was confirmed that transitioning to 0.3.x would not lead to data loss, though it would replace previous versions.
• NVIDIA GPU Rumors Heat Up: Rumors indicate the upcoming NVIDIA RTX 5090 might feature 32GB VRAM, while the RTX 5080 could have a 24GB variant after its 16GB launch.
  • Skepticism abounds regarding the 5080's capabilities, with users claiming it's not equipped for current gaming and AI demands.
• Load Testing LLM Performance Recommendations: For effective load testing, users recommend employing local server API calls in LM Studio to manage multiple requests efficiently.
  • One member is creating a tutorial focused on these load testing methods, emphasizing the use of custom datasets.

GPU MODE Discord

• Call to Form Group for Llama 3.2 Models: A proposal has been made to create a working group to integrate Llama 3.2 vision models into llama.cpp, as discussed in this GitHub issue.
  • The issue notes that multimodal support can be reinstated once related components are refactored.
• Interest in Optimizing Code for Cerebras Chips: Discussions around optimizing code for Cerebras chips have highlighted the community's desire for insights on effective usage.
  • Members are intrigued about reaching out to connections at Cerebras for additional guidance on this hardware.
• Seeking Latest Triton Wheel for Windows: A member is searching for the latest compiled Triton wheel for Windows that works with Python 3.10, reflecting broader compatibility needs.
  • Community engagement around installation issues continues to be a focal point for Triton users on multiple platforms.
• M2 Pro Benchmarks shared: A member expressed enthusiasm over their M2 Pro benchmarks and cited DiffusionKit for performing on-device inference of diffusion models.
  • They included visuals that reinforce the benchmark capabilities of the M2 Pro in a practical context.
• TensorWave Offers MI300X to Boost Adoption: Darrick from TensorWave announced potential availability of MI300X units for community members, aimed at enhancing education on its use.
  • This opportunity has sparked positive engagement, with members expressing excitement over the offer.

Unsloth AI (Daniel Han) Discord

• Fine-tuning Llama Models Spark Confusion: Users discussed the nuances of fine-tuning Llama models, noting confusion over data formats like chatml and the necessity of adjusting tokenizer settings for special tokens.
  • Concerns arose regarding overfitting, with members warning against low training losses that signal potential model memorization traps.
• Model Checkpoint Loading Errors Emerge: A user encountered a data mismatch error while trying to load the unsloth/Llama-3.2-3B-Instruct-bnb-4bit model, spotlighting specific exceptions.
  • This initiated troubleshooting discussions, with suggestions focusing on sizing and configuration settings as possible culprits.
• Speculation Surrounds New Graphics Cards: Community members debated the specs and rumored release of upcoming GPUs like the 5090, projecting a likely 32GB VRAM option despite skepticism.
  • Opinions varied widely, demonstrating that while rumors circulate, actual benchmarks are needed to settle disputes.
• Data Packing Enhances Training Efficiency: Members highlighted that packing data allows training frameworks to manage unrelated parts, streamlining the process and enabling predictions of subsequent tokens efficiently.
  • This technique was noted to significantly improve training dynamics through effective management of multiple examples.
• Updates on Transformers and Model Compatibility: Users confirmed having the latest transformers version (4.45.1) installed, suggesting ongoing efforts to refine their model implementations.
  • Discussion around quantization challenges, particularly with Phi3.5, showcased the need for alternative strategies due to fatal vocab size mismatch errors.

HuggingFace Discord

• Challenges with Uncensored Models: Users noted that certain Hugging Face models are censored, making it difficult to create a game bot using a 12B chat model, suggesting alternatives like Venice.ai.
  • This discussion emphasized the need for uncensored models for broader creative applications.
• Neuralink's CUDA Implementation Explored: One participant shared insights on the use of CUDA with Neuralink to enhance model performance in advanced GPU programming.
  • This has implications for improving execution efficiency across various AI applications.
• Alibaba Introduces MIMO Technology: Alibaba launched MIMO, a new AI capable of creating realistic character videos from simple inputs, showcased through 10 demos including Interactive Scene Control.
  • This technology illustrates the potential for new immersive experiences in AI-generated content.
• Seeking Repos for Text-to-Video Model Training: A request was made for repositories focused on distributed GPU training for text-to-video (T2V) models, indicating a need for enhanced training resources.
  • Suggestions like checking out the CogVideo SAT finetuning have been made to aid this pursuit.
• Cybersecurity Services Offered by Expert Hacking: An individual emerged as an expert hacker offering various cybersecurity courses and services, inviting collaboration in these areas.
  • This highlights an interesting intersection of AI and cybersecurity, which is increasingly relevant in today's tech landscape.

OpenRouter (Alex Atallah) Discord

• Gemini Tokens Count Change: OpenRouter will transition to counting tokens instead of characters for Gemini models, decreasing token counts by about fourfold on the /activity page.
  • This adjustment leads to a pricing change where costs double, but offers an estimated 50% cost reduction for Flash and 1.5 Pro models.
• Llama 3.2 Vision Parameters Discussion: Users questioned the parameters for Llama 3.2 vision to avoid rejection, particularly in attractiveness evaluations.
  • The consensus suggested safety-focused training could prevent the model from responding adequately to such queries.
• Database Upgrade Downtime Annulled: The planned downtime for a database upgrade was cancelled, allowing services to remain operational.
  • Further scheduling updates for the upgrade will be communicated once determined.
• Chatroom UI Gets a Major Upgrade: OpenRouter announced a revamped UI for the Chatroom that displays model responses with reasoning collapsed by default, enhancing clarity.
  • Further UI enhancements are promised, aiming for a better user interface experience.
• OpenRouter Hits Rate Limits: Users reported encountering a 429 Resource Exhausted error, signaling the model can't process requests due to rate limit breaches.
  • Efforts to negotiate higher rate limits with Google are ongoing to alleviate these issues.

Cohere Discord

• Cohere Channel Etiquette Clarified: Miscommunication arose about posting in the wrong channel, prompting brief clarification on channel appropriateness. Some members still enjoyed non-Cohere content being shared.
  • A member expressed optimism for their project’s launch, thanking the community for helpful posting direction.
• Embed-English-v3 Model Finetuning In Limbo: Inquiries about finetuning the embed-english-v3 model led to the realization that currently, no embedder can be finetuned.
  • Suggestions were made to utilize custom embedding models from Hugging Face for those needing specific adjustments.
• API v2 Endpoints Make Their Debut: New API v2 endpoints have launched, notably enhancing Chat V2 with new features like a messages parameter. More info can be found in the API Reference.
  • Users discussed the implications of trial key rate limits, clarifying they're account-based, thus cutting down the benefits of rotating keys.
• Cultural Multilingual LMM Benchmark Gathers Momentum: The MBZUAI team is building a Cultural Multilingual LMM Benchmark covering 100 languages, aiming to improve their multimodal dataset.
  • Volunteers aiding in translations will be invited as co-authors for a submission to CVPR'2025, creating a community-driven effort.

Stability.ai (Stable Diffusion) Discord

• Tiled Upscale Offers Slower Alternative to ADetailer: Tiled Upscale can replace ADetailer with similar effects, but it works about 50 times slower as it processes the entire image.
  • This slower alternative raises questions about efficiency when detailed area-specific upscaling is needed.
• AMD GPUs Struggle in Productivity: Discussion explored how AMD GPUs, like the 5700 XT, falter in Stable Diffusion and Blender tasks, proving more effective for gaming.
  • Users reported a 3070 outperforming a 7900 XTX in productivity benchmarks, highlighting GPU performance discrepancies.
• Refurbished GPUs Gaining Favor: The advantages of choosing refurbished GPUs over used ones sparked lively debate, focusing on improved reliability through repairs and checks.
  • One user celebrated their experience with a refurbished 3090 TI, emphasizing it performed nearly as well as a new card.
• SSD Proven Essential for Load Times: Confirmed findings suggest using an SSD for Stable Diffusion can cut model load times by 10x or more compared to traditional HDDs.
  • Members noted that models running on M.2 SSDs greatly enhance image generation speed over older technologies.
• Creative Prompting for Object Sizes: Participants shared insights on effective prompting techniques for sizing objects in image generation, suggesting various descriptive terms.
  • Humorous phrases like 'yuge' and 'bigly' were jokingly proposed, though simpler terms were ultimately preferred.

Perplexity AI Discord

• Perplexity UI Issues Frustrate Users: Multiple users faced errors on the Perplexity website, reporting that interactions resulted in net::ERR_BLOCKED_BY_CLIENT errors while the Android app remained functional.
  • This led to significant frustration among users, particularly as the issue persisted across both desktop and mobile browsers.
• API Functionality Sparks Queries: Users expressed a desire to access the latest news on generative AI through the Perplexity API, questioning current limitations on specific API functionalities.
  • Concerns were raised about the robustness of existing solutions and the need to explore improvements.
• Subscription Promotions Cause Confusion: Frustration mounted as a user struggled to redeem a promotional code for a pro subscription without gaining access, sparking further queries about account transfers.
  • Others chimed in to clarify the steps involved in transferring subscriptions.
• Meta's Orion AR Glasses Enhance Experiences: Meta's recent announcement about Orion AR Glasses aims to revolutionize user interactions in augmented reality.
  • Initial feedback suggests the potential for significant shifts in how users engage in virtual environments.
• OpenAI Shifts Toward For-Profit Future: OpenAI's for-profit pivot potentially reshapes its funding strategies amidst competitive pressures in AI.
  • This shift raises questions about the implications for its operational strategies going forward.

Nous Research AI Discord

• GPU Memory Size Discrepancy Sparks Debate: Discussions highlighted the differences in memory sizes between 5080 and 5070 GPUs, with the 5080 model suggested to have nearly 20GB.
  • Members noted a trend of doubling memory sizes across generations, referencing the 3080 and 3090 models.
• Buzz Builds for DisTrO Paper Release: Curiosity surrounds the release date of the DisTrO paper, with members eager for insights, especially from a recent talk.
  • Helpful links to the full talk were shared after requests for easier access were made.
• Knowledge Graphs and Bitcoin Ordinal Theory Converge: A member discussed their work with knowledge graphs and unique embeddings derived from Bitcoin Ordinal Theory.
  • They proposed that LLMs form graph-based representations from semantic richness, hinting at possible avenues for emergent intelligence.
• Claude Sonnet 3.5 Delivers Improved Reasoning: Progress was noted in the reasoning capabilities of Claude Sonnet 3.5, attributed to utilizing example reasoning traces.
  • A standout example demonstrated improvements, indicating future directions for further exploration of reasoning enhancements.
• Hermes Available for Local Run on 4090: A member confirmed that Hermes can be run locally on a 4090 GPU using LMStudio, which supports any GGUF version.
  • This allows users an easy way to find and utilize Hermes without needing API access.

OpenAI Discord

• Agentic Search Project Faces Budget Cuts: A developer shared their Agentic Search project's failure due to costly compute and token usage, prompting them to consider fine-tuning a smaller model like Llama 3b.
  • This shift highlights the resource constraints larger models impose on development teams in the AI landscape.
• AI Adoption in Academia Skyrockets: Discussion revealed that over 50% of master's students are using AI-generated content for assignments, stirring debates on productivity vs. academic integrity.
  • Participants expressed concern over the potential long-term impacts on learning as AI becomes more ingrained in educational settings.
• AI's Energy Use Sparks Debate: Questions surfaced regarding AI systems' energy consumption, highlighting increasing awareness of their environmental impact.
  • Members discussed the need for sustainable practices as AI technologies become more prevalent in various industries.
• Game-Changing Tools for Developers: A member recommended the ChatGPT Toolbox Chrome extension, featuring chat history search and prompt management to boost productivity with ChatGPT.
  • Attention also turned to the anticipated Orion model, expected to introduce powerful new tools that could revolutionize the development process.
• Future Generations at Risk of Skill Loss: Concerns emerged that future generations may lose traditional skills like writing by hand due to increasing technology reliance.
  • Participants humorously speculated on societal views of basic skills in a tech-dominated future, raising questions about the evolution of learning tools.

Eleuther Discord

• Exploring Sponsorship for Open Source Models: A member inquired if Eleuther offers any sponsorship programs for open source models, expressing a lack of resources to fully train their projects.
  • This raises a discussion about community support for such initiatives within the open source realm.
• Innovations in LLM Search Space Simulation: A concept was proposed involving an abstract search space for LLMs, utilizing Monte Carlo tree search to simulate continuous thinking with text diffusion.
  • This method aims to rank the most coherent thoughts during the computational process, suggesting potential advancements in LLM architecture.
• Comparing Weight Distributions Pre and Post FP6: Discussion revolved around comparing weight distributions of models before and after FP6, with hints at using libraries like seaborn for visualization.
  • The goal is to see if any anomalies arise, as members suggested experimenting with multiple plotting libraries.
• ColQwen2 Makes Waves: A new model, ColQwen2, was announced as a top visual retriever, surpassing colpali-v1.1 with a +5.1 nDCG@5 score on the Vidore Leaderboard.
  • This model utilizes a Qwen2-VL backbone, promising superior performance in visual retrieval tasks, as noted in this post.
• Testing on H100s for Small Models: A member expressed willingness to assist with testing on H100s for small models, indicating confidence in their ability to contribute.
  • This sparked enthusiasm and appreciation from others in the discussion.

DSPy Discord

• Langtrace Enhances DSPy Experiment Management: Langtrace now supports running DSPy experiments with automatic capture of traces, checkpoints, and eval score visualizations, significantly improving management workflows.
  • Users can create individual projects for each pipeline block, allowing for targeted optimizations and effortless deployment of checkpointed prompts.
• MIPROv2 Compilation Runs Encounter Issues: Users reported challenges in tracking evaluation data during MIPROv2 compilation runs despite visible traces in the logs, suggesting a configuration mishap.
  • Troubleshooting revealed the need for proper attributes during the compile() call to ensure accurate data tracking.
• DSPy Optimization Tools Spark Discussion: Members expressed curiosity about DSPy's optimization tools, similar to Tensorboard, for tracking metrics efficiently in AI workflows.
  • They shared insights about tools such as the DSPy Visualizer and additional support available via Langtrace.
• Exploring DSPy ReAct Agents for RAG: Members inquired about examples of using DSPy ReAct agents, especially in conjunction with a LlamaIndex retriever for ReAct RAG implementations.
  • Other users pointed to existing examples in the repo (examples/agents/) and pledged to add more comprehensive examples soon.
• Feature Requests for RAG Agents Optimization: There were requests for integrating more vector databases like Qdrant and LanceDB with DSPy RAG agents, capturing a trend towards hybrid search capabilities.
  • The discussion about multimodal RAG pipeline optimization received confirmation of forthcoming developments in this area.

Modular (Mojo 🔥) Discord

• Poll on Mojo MAX Desktop Backgrounds: A member initiated a poll for Mojo / MAX branded desktop backgrounds inviting votes featuring adorable Mojo flames and MAX astronauts.
  • Reaction was mixed, with one member simply stating, 'Bruh', indicating surprise or disinterest.
• Verification Now Required for Posting: Verification is now a necessity for posting in all channels except a few specific ones listed, enhancing control.
  • Members are directed to visit the verification channel, where a demo GIF explains the process.
• Error Handling Needs in Mojo: Members discussed how the current error messages in Mojo do not reference user code, which hampers debugging.
  • There's concern about improvements in this area due to the limitations of the existing implementation.
• Proposing Safe Tagged Union for Variant Type: A member proposed evolving the Variant type into a safe tagged union to enhance pattern matching capabilities.
  • The discourse centered around ensuring compatibility with existing models and expectations in pattern matching.
• Call for Enhanced Mojo Documentation: Members agreed on the urgent need for improved documentation on Mojo and MLIR dialects to clarify user ambiguities.
  • Confusion over existing constructs has hindered development, necessitating clearer guidelines.

Latent Space Discord

• FTC Crackdown on Misleading AI Marketing: The FTC initiated a crackdown on misleading claims related to AI tools, particularly affecting companies like Do Not Pay, cited in their complaint PDF.
  • Concerns emerged regarding the FTC's definition of AI, with community members worried it may lead to scrutiny of many startups.
• Sustainability of Generative AI Under Fire: An article discussed the potentially unsustainable nature of the current generative AI boom, predicting a major collapse that might impact big tech, linked in their newsletter.
  • Critics argued that tools like GitHub Copilot showcase clear business value, which counters claims of unsustainability.
• Geohot's AMD Discontent: Geohot expressed dissatisfaction with AMD, questioning the company's innovative trajectory after noting no significant products post-RDNA3.
  • This frustration is symptomatic of a wider community concern regarding stagnation and motivation within AMD's technical advancements.
• Launch of ColQwen2 Model: The community cheered the introduction of the ColQwen2 model, which integrates a Qwen2-VL backbone for enhanced performance and efficiency.
  • This launch marked a major improvement in visual recognition capabilities, celebrated for its significant impact on the Vidore Leaderboard.
• AI Engineering Interviews Generate Excitement: A member shared enthusiasm about securing an interview opportunity leading to a potential AI Engineering role.
  • “Had an interview that could transition into an AI Engineering role so me be happy.”

LlamaIndex Discord

• Paragon Builds a Feature-Packed Chatbot: A blog post and video from useparagon illustrate their use of create-llama from LlamaIndex to create a chatbot interfacing with customer data from Slack, Google Drive, and Notion.
  • It ingests data continuously and in real time, making the integration highly effective.
• Langfuse and PostHog Enhance MistralAI: A tutorial shared in a Jupyter notebook explains how to set up Langfuse for tracking LLM applications and integrates PostHog for user analytics.
  • This setup enables comprehensive monitoring and analytics for AI applications, streamlining the development process.
• NLTK's punkt resource missing: A user reported encountering a Resource punkt not found error while using NLTK. Another member suggested checking the version of llama-index as the latest versions utilize punkt_tab.
  • Resource-related issues with NLTK's punkt hinted at potential compatibility concerns.
• Challenges Loading Fine-tuned Models: A user struggled to load their locally fine-tuned Llama3.1-8B for the Text2SQL task onto their GPU. Members recommended manually loading the model and tokenizer, ensuring it was on the GPU.
  • A detailed code snippet was shared, illustrating how to set up the model using quantization for optimized performance.
• Optimizing Vector Search for Customer Support: A proposed strategy for optimizing vector search involved storing questions in the vector chunk while keeping answers in metadata. This method aimed to enhance accuracy by focusing on question semantics during searches.
  • The user sought validation and welcomed suggestions for further improvements to their approach.

Interconnects (Nathan Lambert) Discord

• OpenAI rushed GPT-4o release amid concerns: Executives aimed to debut GPT-4o ahead of Google’s developer conference, resulting in a rushed release with incomplete safety data that later marked the model as too risky to deploy. Staff reportedly endured 20-hour days to meet this tight deadline while managing safety evaluations.
  • An article by Garrison Lovely sheds light on the intense pressure faced by safety teams during this high-stakes launch.
• OpenAI grapples with compensation demands: As outlined in The Information, OpenAI faces ongoing employee grievances over compensation as its valuation skyrockets. Staff have cashed out over $1.2 billion from profit units, spurring researchers to threaten resignation amidst fierce competition for talent.
  • New CFO Sarah Friar now navigates this turbulent environment, where many researchers demand substantial pay increases to remain amid leadership turnover.
• OpenAI's leadership instability: The recent departures of key figures Mira, Bob, and Barret add to ongoing leadership instability at OpenAI, raising concerns over its long-term direction. The emotional response from team members reflects the broader challenges of retaining talent in a competitive landscape.
  • In promoting transparency, one intern humorously likened their resignation to experiencing the bittersweet nature of cherishing a newborn.
• Substack taps into iPhone IAP subscriptions: As a Substack best seller, there is newfound access to iPhone In-App Purchase subscriptions, indicating a shift toward digital publishing on mobile devices. This opens channels for content creators to monetize their works more effectively on popular platforms.
  • The implications for content creators in the mobile market are significant, paving the way for increased engagement and revenue opportunities.
• Apple App Store management challenges revealed: Members share captivating insights into the Apple App Store, often viewed as a horror show by app developers, discussing the complexities of its management. The conversation highlights the necessity for developers to navigate the challenging landscape created by App Store policies.
  • While the realities can be daunting, the discussion brings to light potential strategies that developers can employ to manage the intricate workings of their app distribution.

OpenAccess AI Collective (axolotl) Discord

• Open Source Community Lags in Multimodal Support: A member highlighted that the open-source community is falling behind in adopting multimodal support, while the broader industry shifts in that direction.
  • This sentiment reflects a growing concern about the speed of innovation in the community.
• Understanding Area Chair Roles: A member explained that AC refers to a meta reviewer known as an area chair, who plays a critical role in the review process.
  • This insight underscores the importance of organization in academic and collaborative environments.
• Python Snippet for Training Conversation Splitting: A user presented a Python snippet aimed at splitting conversations for training purposes, ensuring conversations do not exceed maximum sequence length.
  • They emphasized its utility, particularly for handling long conversations while retaining context in training datasets.
• Flex Attention for Optimization Discussion: A member highlighted Flex Attention as a new optimized implementation that provides flexibility compared to previous attention methods.
  • Several resources were shared, including a link to the PyTorch blog detailing its design.
• Update on LoRA+ Optimization Parameters: A member requested the setting of loraplus_lr_embedding to a specific value, referencing a fix in a recent GitHub PR.
  • They explained that the fix was essential due to the failure to use a default value for this parameter.

tinygrad (George Hotz) Discord

• IOMMU's Role in Nvidia P2P: A user inquired why IOMMU must be disabled for Nvidia P2P support when using the tinygrad GPU modules, signaling a need for further technical insight.
  • This uncertainty highlights an area ripe for discussion as users seek to clarify critical hardware interactions.
• GPU Cloud Pricing Competition Sparks Discussion: George Hotz suggested a competitive rate of $0.50/hr for GPUs, leading to comparisons with options from providers like salad.com and vast.ai.
  • Participants raised concerns about whether this price incorporates VAT and reflects true market competitiveness.
• CLOUD=1 Features Debated: Debates flared over whether CLOUD=1 includes CPU resources; discomfort among users was expressed about mandatory device connectivity.
  • They emphasized that saving costs needs to be complemented by robust solutions to justify the service model.
• Challenges with Data Upload for ML Tasks: A member highlighted severe issues with connecting and uploading large datasets for training, hoping tinygrad could alleviate these frustrations.
  • The discussion noted that the data-compute ratio is crucial for efficiency, particularly in smaller models like mini LLMs and CNNs.
• Considerations on Persistent Storage Costs: Concerns emerged over persistent storage billing, with questions about whether tinygrad would address such charges, as many cloud providers have separate fees.
  • This points to a broader conversation on cost management in cloud service architecture.

LAION Discord

• Llama 3.2 11B Vision available for free: TogetherCompute partnered with AIatMeta to offer Llama 3.2 11B Vision for free, allowing developers to experiment with open-source multimodal AI. Access this innovative tool here.
  • For enhanced performance, paid Turbo endpoints for Llama 3.2 11B & 90B are also provided.
• Unlimited Access Sparks Ideas: Members discussed the implications of unlimited access to Llama 3.2, suggesting it might humorously caption the entire LAION dataset. This led to light-hearted community engagement around creative applications.
  • The playful conversation emphasized a collective enthusiasm for pushing the creative boundaries of AI tools.
• Concerns Arise Over Family Photo Generation: A member inquired about the effectiveness of a specific app for generating family photos, highlighting the keen interest in AI-driven personalized content. This discussion underscored the growing push for practical applications in daily life.
  • The inquiry reflects an ongoing curiosity about the capabilities of AI in generating relatable imagery.
• Victory in Copyright Enforcement Celebrated: A member shared a LinkedIn post celebrating a successful win in copyright enforcement, emphasizing that the good guys won this round. This was hailed as a significant victory for integrity within the community.
  • The sentiment contributed to a positive atmosphere, reaffirming the community's commitment to ethical practices.
• Discussion on Positional Information in Neural Networks: Members expressed confusion over how positional information integrates into the feature vector of latent pixels, noting the absence of positional encoding in CLIP text embeddings. They emphasized that self-attention steps in models also contribute to this process.
  • This led to constructive insight on the importance of convolution edges in yielding positional data for attention comparisons.

LLM Agents (Berkeley MOOC) Discord

• Lectures Set for LLM Safety Focus: Concerns arose about lectures addressing social alignment in relation to LLM agents, given the previous focus on AI safety. Prof. Dawn Song is expected to touch on this during her talk scheduled for December 2.
  • This indicates an ongoing dialogue about balancing safety and alignment in educational content.
• Course Sign-Up Process Confirmed: Clarifications on course enrollment confirmed that filling out the Google form ensures access to all course materials with assignment deadlines noted as December 12, 2024. Participants expressed gratitude for this clear communication.
  • This highlights the importance of clarity in administrative processes for a smooth learning experience.
• Confusion with Assignment Deadlines: A participant questioned discrepancies in assignment due dates between Berkeley and MOOC students, with confirmations that all assignments are due on December 12, 2024. Provisions for uniform deadlines improve course accessibility.
  • It’s crucial for students to have clear timelines, as confusion can affect focus and performance.
• Qquiz 3 Availability Muddled: Participants struggled to locate Qquiz 3, prompting discussions about its accessibility, confirming that it remains live on the MOOC students' website. This has led to more inquiries regarding quiz structuring.
  • Ensuring all students can access quizzes is essential for fostering an equitable learning environment.
• Lab Assignment Release Timeline Questioned: A user inquired about the timeline for lab assignment releases, noticing a gap in information on the MOOC website. Continued discussions around course clarity remain key for students tracking assignments.
  • Effective communication about assignment schedules will enhance student engagement and preparedness.

OpenInterpreter Discord

• OpenInterpreter showcases on-chain analytics prowess: A member demonstrated how to use OpenInterpreter to transition from code that's probably working to fully functional code for on-chain analytics with a shared Google Colab link.
  • This shift in approach was well-received, inviting further reposts from the community.
• Multimodal Support Hiccups in LLaMA: Discussion emerged around the removal of multimodal support in the LLaMA project since #5882, with updates contingent on the refactoring of llava.
  • A tracking thread was created, consolidating insights and links to relevant issues for any follow-up.
• Electrifying Frontend Development Buzz: Excitement grew over the development of an Electron frontend for OpenInterpreter, as a member highlighted its potential.
  • The enthusiasm reflects a positive sentiment about ongoing development within the OpenInterpreter community.
• HF's Latest with 90b Vision Model: HF announced an update introducing a 90b vision model, now available for various vision tasks.
  • This update is expected to considerably enhance real-world applications in related tasks.
• OpenInterpreter's Heartwarming Impact: A member shared how OpenInterpreter transformed their life, allowing them to forge incredible friendships and explore the A.I. landscape, reflecting gratitude towards the community.
  • They quoted a viral demo from a year back, underscoring the project's transformational potential in their journey.

LangChain AI Discord

• Optimizing vector search for customer support: A new strategy optimizing vector search aims to store questions in the vector chunk and answers in metadata, enhancing precision in question matches.
  • This method focuses on the semantics of questions, streamlining search results by filtering out irrelevant information.
• Challenges extracting context from Excel: A member reported struggles with contextual extraction from complex Excel files to generate meaningful outputs for LLMs.
  • Despite thorough searching, they haven't found effective methods to tackle this issue.
• CF Booking Chatbot simplifies conference room management: The newly built CF Booking Chatbot helps manage conference rooms by checking availability and booking, with a demo video showcasing its features.
  • Plans are underway to integrate Google Calendar for automatic syncing, further streamlining the process.
• Unize Storage generates high-quality knowledge graphs: Introducing Unize Storage, an AI system that creates accurate knowledge graphs from any input text, outperforming existing systems like LangChain's LLMGraphTransformer with an 85% accuracy on larger inputs.
  • This showcases a significant leap over LangChain’s 55% accuracy, pushing the boundaries of graph generation.
• Free API access with Unize Storage: The Unize API offers free credits and a chance for users to experiment with the new Unize Storage system, allowing for the visualization of generated knowledge graphs.
  • Interested users can start interacting with the system using this Playground.

Torchtune Discord

• Enforcing PackedDataset Size Limits: A member proposed to enforce that the packed size cannot exceed 2x the dataset max length to prevent errors when processing sequences.
  • This suggestion emerged as a potential safeguard against runtime inconsistencies.
• Max Sequence Length Failure Case Uncovered: It was demonstrated that the current implementation can fail even with a single input exceeding max_seq_len, especially with mismatched configurations.
  • A fix using explicit gating for token length was suggested to prevent these runtime errors.
• GitHub Error Discussion Highlights: The conversation pointed to a GitHub error indicating a possible decision to allow sequences greater than max_seq_len.
  • This link potentially clarifies the reasoning behind the current handling of packed dataset sizes.
• Collaboration Mandate for Review: A member suggested that another user should review the content of this discussion upon their return, emphasizing its importance.
  • This highlights the collaborative nature of the troubleshooting process.

Gorilla LLM (Berkeley Function Calling) Discord

• User Confusion on Function Calling Evaluation: A user expressed confusion about the function calling evaluation process and inquired if they could use their own evaluation dataset for analysis with a structure of , , .
  • They are specifically interested in a package for effective error breakdown analysis.
• Local LLM Deployment Interest: Another point raised was the desire for functionalities supporting a locally deployed LLM to extract error metrics with personal datasets.
  • The user requested recommendations for codebases suited for function calling capabilities in this context.
• Integration of LLMs in Applications: The conversation highlighted the integration of Large Language Models (LLMs) in applications such as Langchain and AutoGPT, referencing models like GPT, Gemini, Llama, and Mistral.
  • Their advanced function calling abilities in powering software solutions were recognized as a growing trend.
• Valuable Resource: Berkeley Function-Calling Leaderboard: The user highlighted the Berkeley Function-Calling Leaderboard as a resource for evaluating LLM function calling capabilities.
  • They noted that the leaderboard is based on user-centric function calling use cases.

AI21 Labs (Jamba) Discord

• Exploring OpenAI SDK Use with Jamba: A user inquired about how to utilize the OpenAI SDK with Jamba, questioning its feasibility.
  • This inquiry highlights curiosity about integrating different AI tools for enhanced functionalities within the Jamba framework.
• Jamba's Integration Queries Pile Up: The conversation around Jamba is buzzing, particularly on how to streamline processes with the OpenAI SDK.
  • Such discussions indicate a growing interest among developers to connect frameworks and enhance their project capabilities.

The Alignment Lab AI Discord has no new messages. If this guild has been quiet for too long, let us know and we will remove it.

The LLM Finetuning (Hamel + Dan) Discord has no new messages. If this guild has been quiet for too long, let us know and we will remove it.

The MLOps @Chipro Discord has no new messages. If this guild has been quiet for too long, let us know and we will remove it.

The Mozilla AI Discord has no new messages. If this guild has been quiet for too long, let us know and we will remove it.

The DiscoResearch Discord has no new messages. If this guild has been quiet for too long, let us know and we will remove it.

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