Shipping first is all you need.

AI News for 12/11/2024-12/12/2024. We checked 7 subreddits, 433 Twitters and 31 Discords (207 channels, and 6137 messages) for you. Estimated reading time saved (at 200wpm): 616 minutes. You can now tag @smol_ai for AINews discussions!

OpenAI launched Realtime Video a day after expected, but it made less of a splash because Gemini got there first, with less cost, and less rate limiting.

The buzz is still solidly pro Gemini:

and we enjoy seeing some friendly sniping between undoubtedly SOTA, very hard working teams.

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.

Here are the key topics organized from the Twitter discussions:

AI Model Releases & Updates

• Google launched Gemini 2.0 Flash with major improvements in multimodal capabilities, real-time streaming, and performance metrics. @GoogleDeepMind noted developers can now use real-time audio/video streaming.
• OpenAI announced video capabilities for ChatGPT, including live video and screensharing in Advanced Voice mode.
• Anthropic released research on Clio, a system for analyzing real-world usage patterns of Claude across different languages and use cases.

AI Infrastructure & Development

• @bindureddy observed that "Anthropic is capturing the developer ecosystem, Gemini has AI enthusiast mindshare, ChatGPT reigns over AI dabblers"
• Together Computing acquired CodeSandbox to launch Together Code Interpreter for seamless code execution.
• @teortaxesTex noted that dropping Attention mechanisms means losing several key capabilities that rely on it.

Industry & Market Updates

• Scale AI and TIME launched TIME AI for Person of the Year coverage
• @far__el discussed comparisons between US and Chinese AI capabilities, suggesting the gap may be smaller than commonly believed.

Memes & Humor

• ChatGPT added Santa mode for holiday conversations
• Multiple jokes about AI outages and service disruptions
• Humorous takes on model comparisons and industry competition

AI Reddit Recap

/r/LocalLlama Recap

Theme 1. Meta's Llama 3.3-70B: Roleplaying and Prompt Handling Excellence

• Why is Llama 3.3-70B so immediately good at adopting personas based on the system prompt (and entering roleplay, even when not specified) (Score: 311, Comments: 83): Llama 3.3-70B is recognized for its proficiency in adopting personas and engaging in roleplay based on the system prompt, even when roleplay is not explicitly requested. This highlights its advanced ability to interpret and respond to nuanced prompts effectively.
  • Roleplay and Creative Writing: Llama 3.3-70B has been highlighted for its roleplay capabilities, with examples showing its ability to portray characters like Yoda and Jar Jar Binks effectively. Some users noted its creative potential in roleplay, although it still faces issues like repetition and short responses, particularly in quantized forms.
  • Comparison with Other Models: Discussions compared Llama 3.3 to other models like Mistral Large and GPT-4o, with some users noting that Llama 3.3 is more expressive and less censored. The model's ability to adopt personas is attributed to its training, possibly influenced by Meta's AI Studio and the diverse data from platforms like Facebook and Instagram.
  • Training and Censorship: The community speculated that Llama 3.3 was trained with a focus on roleplay and character portrayal due to Meta's strategic goals, unlike OpenAI's models, which are heavily censored. Users discussed how Meta's approach to training and data curation might have contributed to Llama 3.3's advanced roleplay abilities, with some attributing its success to the lack of fine-tuning constraints and diverse training data.

Theme 2. Microsoft's Phi-4: Small Model, Big Benchmark Results, Skepticism Remains

• Introducing Phi-4: Microsoft’s Newest Small Language Model Specializing in Complex Reasoning (Score: 217, Comments: 86): Microsoft has introduced Phi-4, a new small-language model designed to specialize in complex reasoning tasks. The post did not provide further details or context about the model's capabilities or applications.
  • Many users express skepticism about Phi models, stating they perform well on benchmarks but fall short in real-world applications. Synthetic training datasets are speculated to be a focus for Microsoft, potentially for licensing to other companies as an alternative to scraped data.
  • There is a humorous discussion about the 14B parameter model being considered small, with users noting that it requires significant GPU resources. Benchmark results for Phi-4 are impressive, but users remain cautious due to past experiences with Phi-3.
  • Some comments mention the availability of Phi-4 on Hugging Face next week, and there is a suggestion that earlier posts about Phi-3 were attempts at generating hype. The use of synthetic data for training is highlighted, particularly for tasks like math completion.

• Bro WTF?? (Score: 81, Comments: 38): Phi-4 demonstrates promising performance in benchmarks compared to other models like Phi-3, Qwen 2.5, GPT, and Llama-3.3, with evaluations conducted using OpenAI's SIMPLE-EVALS framework. The table categorizes results into "Small models" and "Large models," detailing metrics such as MMLU, GPQA, and MATH.
  • Phi-4's Performance: While Phi-4 shows promising benchmark results, users express skepticism about its real-world applicability, noting past Phi models' tendency to underperform outside controlled tests. There is a consensus that despite good reasoning abilities, the model struggles with factual data due to its smaller dataset.
  • Open Source and Synthetic Data: Discussions highlight open-source advancements, with some users noting Phi-4's potential to outperform models like GPT-4o mini in certain tests. There is also a debate on the efficacy of synthetic data versus broad internet data, with some users advocating for high-quality synthetic data for better model training.
  • Model Availability and Usage: The model is expected to be available on Hugging Face and is currently downloadable from Azure, though users report slow download speeds. Some users share their experiences with previous Phi models, emphasizing their utility in specific tasks like reasoning and single-turn interactions, despite being verbose and less effective in multi-turn chats.

Theme 3. OpenAI o1 vs Claude 3.5 Sonnet: Subscription Showdown

• OpenAI o1 vs Claude 3.5 Sonnet: Which gives the best bang for your $20? (Score: 139, Comments: 78): OpenAI's o1 excels in complex reasoning and mathematics, outperforming other models in the $20 tier, making it ideal for non-coding tasks. Claude 3.5 Sonnet is superior for coding, offering a better balance of speed and accuracy, despite the 50 messages/week limit. Claude is noted for its engaging personality, while o1 is recognized for its high IQ, making Claude preferable for coding and conversational tasks, and o1 for math and reasoning.
  • Users discuss the cost-effectiveness of different models, with 1M input tokens priced at $15 and output tokens at $60 per 1M, expressing concerns about the pricing structure. Some recommend using openrouter or OpenWebUI for flexibility in model selection without subscription costs.
  • Claude is favored for its coding capability and engaging personality, though some users report issues with hallucinations in code and overly consistent responses, while others find it indispensable for solving complex software bugs quickly. o1 is criticized for being overly agreeable, making it less effective for some tasks.
  • Gemini 2.0 and Qwen series are mentioned positively; Gemini is noted for its speed and being free, while Qwen is preferred for non-coding tasks over o1. There is a general sentiment that using APIs and avoiding subscriptions can be more efficient and cost-effective.

Theme 4. Gemini series shines in Math Benchmarks, Growing Cognitive Reputation

• U-MATH: New Uni-level math benchmark; Gemini is goat / Qwen is king (Score: 74, Comments: 21): Gemini and Qwen are highlighted for their exceptional performance on U-MATH, a new university-level math benchmark. The post suggests that Gemini is considered the greatest of all time (GOAT) in this context, while Qwen is recognized as the leading performer.
  • Gemini's Performance: Gemini is consistently recognized as the top-performing model across various benchmarks, including U-MATH, LiveBench, and FrontierMath, outperforming other models like GPT-4o and Claude. Google's focus on math and science through projects like AlphaZero, AlphaFold, and AlphaProof is speculated to contribute to Gemini's success.
  • Model Comparisons and Challenges: The discussion highlights the impressive performance of smaller models like 7b-Math, which closely match larger models such as 72b-Instruct. However, smaller models struggle with understanding contextual cues and "instructions following," often leading to hallucinations, as noted with Qwen models.
  • Benchmark Details and Updates: The U-MATH and μ-MATH benchmarks are the only ones testing LLMs at this complexity level, with Gemini Pro leading in solution and judgment abilities, despite lower hallucination rates in other models like GPT/Claude/Gemini Flash. The leaderboard and HuggingFace links provide additional insights into these evaluations.

Other AI Subreddit Recap

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

Theme 1. NeurIPS 2024 Sabotage Allegations Disrupt Research

• [D] The winner of the NeurIPS 2024 Best Paper Award sabotaged the other teams (Score: 327, Comments: 31): NeurIPS 2024 Best Paper Award controversy involves accusations against a ByteDance researcher for allegedly sabotaging other teams' research to gain an advantage. The claim includes the researcher attending meetings to debug colleagues' code, maintaining a competitive edge, and there's a call for his paper's withdrawal. Further details can be found in the integrity report.
  • Allegations Against Keyu Tian: Keyu Tian allegedly modified the PyTorch source code and disrupted training processes by hacking clusters and creating login backdoors, which allowed him to sabotage colleagues' experiments by altering model weights and terminating processes. This led to large-scale experiment failures, raising concerns about the integrity of his actions.
  • Legal and Institutional Reactions: ByteDance is reportedly suing Tian for damages, which could impact his NeurIPS 2024 Best Paper Award. There is speculation about the repercussions this incident might have on his academic and professional standing, with questions about whether NeurIPS has conduct policies that would affect his award.
  • Cultural and Competitive Context: Some commenters highlight the intense competitive pressure within Chinese academic environments, which might drive individuals to extreme actions to secure resources and recognition. This context might explain, though not justify, the alleged behavior, reflecting broader systemic issues in the field.

Theme 2. Controversial 'Stop Hiring Humans' Campaign in SF

• "Stop Hiring Humans" ads all over SF (Score: 237, Comments: 79): "Stop Hiring Humans" ads have been placed throughout San Francisco, generating significant attention and discussion. The campaign's provocative message suggests a shift towards automation and AI-driven solutions, raising questions about the future of human employment in tech-centric cities.
  • Many commenters, such as XbabajagaX and dasjati, noted that the ad campaign's provocative nature is a strategic move to gain attention and free press, highlighting its success in sparking widespread discussion and media coverage. Link to campaign analysis.
  • Discussions, including those by heavy-minium and umotex12, criticized the campaign for misleading claims about AI capabilities, arguing that it could desensitize the public to real AI advancements or prematurely accelerate societal conversations about AI.
  • Commenters like AI_Ship and Secure-Summer2552 pointed out the dystopian and tone-deaf nature of the ads, especially given the visible social issues such as homelessness in San Francisco, comparing it to a Black Mirror episode and suggesting a need for AI to support rather than replace humans.

Theme 3. ChatGPT's Santa Voice: Seasonal Gimmick or Revolutionary?

• ChatGPT Advanced Voice Mode adds a Santa Voice (Score: 128, Comments: 33): ChatGPT has introduced an Advanced Voice Mode featuring a Santa Voice option.
  • Users discuss the Santa Voice feature with mixed reactions; some find it fun and seasonal, while others encounter issues like difficulty switching back to standard voices or finding the feature creepy due to camera activation. surfer808 mentions an incident where the camera light was on, and the Santa Voice interacted with them, raising privacy concerns.
  • Zulakki reports a technical issue where the Santa Voice was initially available but then disappeared, causing inconvenience when trying to demonstrate it to family. This suggests a potential bug or limitation in the feature's availability.
  • There is a humorous debate about Santa's nationality, with comments suggesting he is from the UK, North Pole, or Canada, reflecting a light-hearted take on the feature's implementation and its cultural implications.

• 12 Days of OpenAI: Day 6 thread (Score: 126, Comments: 241): OpenAI's 12 Days event featured ChatGPT's Santa mode on Day 6, showcasing advanced voice capabilities with video. The live discussion was accessible via OpenAI's website and YouTube.
  • Advanced Voice Mode and Video Integration: Users are discussing the integration of video and screen sharing in Advanced Voice Mode (AVM), with some expressing concerns about AVM's ability to process video context effectively. Several comments highlight the delayed rollout in Europe, with speculation that capacity issues, rather than legal constraints, may be the cause.
  • Comparisons with Google Gemini: Users compare OpenAI's releases with Google's Gemini 2.0, noting Gemini's multimodal capabilities and voice mode features. Some users feel Google is ahead in terms of timely and effective feature releases, while others are excited about OpenAI's potential future updates, such as a rumored GPT-5 release.
  • User Experience and Accessibility: There is a mix of excitement and frustration regarding feature accessibility, with some users unable to access new features across all devices or regions. Comments also address the perceived patronizing tone of ChatGPT's voice responses, with suggestions for more natural interactions.

Theme 4. OpenAI’s 12 Days of Releases: Video in AVM

• OpenAI releases video to Advanced Voice Mode (Score: 105, Comments: 43): OpenAI has introduced video features to its Advanced Voice Mode, coinciding with the Gemini release.
  • OpenAI's new features include live video conversations and screen sharing in Advanced Voice Mode, with rollout starting today for Teams users and most Plus and Pro subscribers, while Enterprise and Edu users will access it early next year. The "Santa mode" is globally available wherever ChatGPT voice mode is accessible.
  • There is a discussion about the rollout timeline, with some users pointing out discrepancies in communication, as OpenAI stated the feature would roll out "today and over the next week", which some users compare to previous delays in feature releases.
  • Users are curious about availability, with some expressing frustration over the delayed access in Europe, while others inquire about how to access the new features, with a YouTube link provided as a resource.

AI Discord Recap

A summary of Summaries of Summaries by O1-mini

Theme 1. AI Model Showdowns: Gemini vs. Claude

• Claude Dominates Coding Tasks: Users consistently report that Claude outperforms Gemini 2.0 in coding accuracy, solidifying its position as the preferred choice for development workflows.
• Gemini 2.0 Flash Accelerates AI Speed: Gemini 2.0 Flash receives accolades for its enhanced speed and performance, although some bugs like real-time video reading issues are still being ironed out.
• Project Astra Targets OpenAI’s Throne: Project Astra is gaining traction as a formidable competitor to OpenAI, with the release of Gemini 2.0 potentially reshaping the AI industry landscape.

Theme 2. GPU Frenzy: New Launches and Scalping Wars

• 5090 GPU Launch Sparks Excitement: Anticipation peaks as the 5090 GPU is set to launch in early January, boasting an impressive 32GB VRAM that promises to boost AI computations.
• Scalpers Clash with Web Scrapers for GPUs: The rise of GPU scalpers forces users to adopt web scrapers and other tactics to secure coveted cards during high-demand launches.
• Intel ARC B580 vs. Nvidia RTX 3060: The Battle Continues: Debates rage over whether Intel’s B580 GPU with 12GB VRAM can outshine the popular RTX 3060, despite concerns over CUDA support.

Theme 3. AI Tool Turbulence: Updates, Bugs, and Integrations

• Codeium’s Windsurf Wave 1 Unveiled: Windsurf Wave 1 introduces autonomy upgrades like Cascade Memories and automated terminal commands, enhancing AI interaction through .windsurfrules.
• Aider Faces Installation Hurdles: Users grapple with global installation of Aider, finding workarounds like uv tool install aider-chat amidst OpenSSL compatibility warnings.
• Cohere Go SDK Needs Structural Fixes: Feedback highlights issues in Cohere’s Go SDK, particularly with StreamedChatResponseV2 fields, necessitating urgent structural adjustments for accurate parsing.

Theme 4. MLOps Marvels: Innovations in Training and Optimization

• Direct Preference Optimization Hits Llama 3.3: DPO successfully integrates with Llama 3.3, supported by comprehensive documentation, streamlining the fine-tuning process for users.
• SPDL Boosts AI Training Efficiency: SPDL leverages thread-based data loading to significantly reduce AI model training time, a game-changer for Reality Labs research.
• Training Jacobian Analysis Reveals Hidden Dynamics: A new paper delves into the training Jacobian, uncovering how initial parameters influence final outcomes and highlighting challenges in scaling the analysis to larger networks.

Theme 5. Community Catalysts: Hackathons, AMA Sessions, and Collaborative Tools

• LLM Agents MOOC Hackathon Deadline Looms: The LLM Agents MOOC Hackathon wraps up submissions on December 17th, transitioning from Devpost to Google Forms to streamline evaluations.
• Modular’s AMA Series Deepens Technical Insights: Ask Me Anything sessions hosted by experts like Joe and Steffi explore GPU programming with Mojo, fostering deeper community understanding.
• Early Access to Community Packages Launched: Modular unveils an early access preview of community packages, inviting users to participate in testing and expanding the package ecosystem collaboratively.

PART 1: High level Discord summaries

Codeium / Windsurf Discord

• Windsurf Wave 1 Launch: Codeium has launched Windsurf Wave 1, introducing significant autonomy upgrades including Cascade Memories and automated terminal command execution. Users can review the full changelog for detailed updates.
  • The release enhances AI interaction by guiding behavior through .windsurfrules, enabling more effective task management as users adapt to the new features.
• Cascade Memories Enhancement: Cascade Memories have been integrated into Windsurf, providing robust guidance for AI behavior via .windsurfrules. This feature aims to automate user interactions and improve task management.
  • Community feedback indicates that Cascade Memories significantly enrich AI functionalities, though some users have reported internal errors related to this feature.
• Gemini Models vs Claude Performance: Discussions highlight that Gemini 2.0 models may outperform Claude in coding tasks, with users expressing interest in deploying Gemini models on tools like Cursor.
  • Users report that models like Gemini-exp-1206 show superior performance metrics compared to others, stirring debates on optimal model selection for development workflows.
• Image Upload Capabilities Extension: Cascade image uploads in Windsurf now support files exceeding 1MB, enhancing flexibility in processing diverse file types. This upgrade addresses previous limitations in user experiences.
  • The expanded image upload capacity has been positively received, allowing users to engage with more complex datasets and media within the platform.
• Improved Python Support in Windsurf: Python support within Windsurf has been upgraded, promising a smoother and more fluent coding experience. Users can manage their upgrade plans via the Codeium plan page.
  • Enhanced Python integration aims to streamline development processes, though some users have reported challenges due to internal errors post-update.

aider (Paul Gauthier) Discord

• O1 Pro Excels as a Debugger: Users reported that O1 Pro effectively fixes issues in a single attempt, outperforming other models in handling repetitive or complex tasks.
  • Frustrations were shared regarding Sonnet, which often loops indefinitely on simple edits, highlighting O1 Pro's efficiency.
• Gemini 2.0 Flash Shines in Performance: Gemini 2.0 Flash is praised for its speed and accuracy, scoring well in edit modes and providing a substantial context window for coding tasks.
  • Despite some mixed results, many users find it suitable for practical applications, especially when combined with editor models.
• Aider Installation Hurdles and Solutions: Users faced challenges installing Aider globally, but found solutions like using uv tool install aider-chat effective.
  • Warnings such as OpenSSL compatibility issues during installation were discussed but considered ignorable.
• DeepSeek Faces Performance Issues: Users expressed frustration with DeepSeek accessed via OpenRouter, citing slow performance and frequent errors.
  • Despite these challenges, DeepSeek is noted for its accuracy, leading some users to continue its utilization.
• Gemini Model Response Discrepancies: Users reported that the Gemini model in Aider provides outdated sports scores compared to the web interface.
  • This suggests a lack of access to recent events through the API, highlighting concerns about information consistency.

Cursor IDE Discord

• Claude Remains Top Choice Over Gemini 2.0 for Coding: Users discussed Gemini 2.0 and Claude, with Claude leading in coding accuracy despite Gemini's recent enhancements.
  • Comparisons highlighted Claude's continued superiority in programming tasks, prompting users to maintain their preference.
• Users Express Concerns Over Cursor's Performance: Participants provided feedback on Cursor's recent updates, expressing frustrations with the performance and limitations of its chat and composer features.
  • Suggestions focused on optimizing AI rules to enhance querying and response capabilities within Cursor.
• AI Tools Pricing Sparks Community Debate: The cost-effectiveness of AI tools like Cursor and Gemini was debated, with users assessing their value in relation to the outputs they deliver.
  • Concerns were raised about subscription pricing and how it compares with other available options in the market.
• Developers Discuss Preferred Web Hosting Platforms: Users recommended platforms such as Railway and Cloudflare Workers for server hosting, emphasizing the importance of selecting based on project types.
  • Discussions highlighted the balance between cost and usability when choosing hosting solutions for various development projects.
• Gen Z's Coding Styles Under the Microscope: Lighthearted conversations emerged about generational programming styles, referencing humorous YouTube videos portraying Gen Z coders.
  • Participants voiced concerns over the potential future impact of these programming trends on coding quality and workplace interactions.

OpenAI Discord

• OpenAI Launches Santa Mode and Advanced Voice Features: On Day 6 of the 12 Days of OpenAI, Kevin Weil and team introduced the new Santa voice alongside video and screensharing capabilities in Advanced Voice.
  • The demo encouraged viewers to engage with the festive features, enhancing the interactive experience during the holiday-themed event.
• Project Astra Challenges OpenAI's Dominance: Project Astra is gaining attention as a potential competitor to OpenAI, with discussions highlighting its readiness to challenge OpenAI's offerings.
  • Some users believe that the upcoming release of Gemini 2.0 could significantly impact the competitive landscape in the AI industry.
• Gemini 2.0 Surpasses OpenAI Models with Mixed Feedback: Gemini 2.0 Flash is currently accessible on the web and has received positive feedback for its performance compared to OpenAI's models.
  • However, users have reported bugs affecting features like real-time video reading, indicating areas that require further refinement.
• Advancements in AI Image and Voice Technologies: ElevenLabs' voice AI technology is being tested for realism, with efforts to achieve indistinguishable outputs from human voices.
  • In the realm of AI image generation, tools like Hailuo and Sora are experiencing high demand due to free credits, though users have varied responses regarding output quality across different video formats.
• OpenAI Service Outage and Recovery Procedures: A service outage affected OpenAI from 3:16pm PST to 7:38pm PST on December 11, with API traffic recovery initiating around 5:40pm.
  • All services are now operational, and OpenAI is set to perform a root-cause analysis to prevent future incidents.

Perplexity AI Discord

• Gemini 1.5 Pro Deep Search Slower Than Perplexity: Users observed that Gemini 1.5 Pro Deep Search delivers more comprehensive research capabilities compared to Perplexity, but with significantly longer response times. Detailed benchmarks were shared to illustrate the performance differences.
  • One member highlighted that Gemini's thoroughness makes it suitable for intensive research tasks, despite the trade-off in speed, while others preferred Perplexity for its quicker responses in less demanding scenarios.
• Perplexity Deprecates O1 Reasoning Model: The O1 reasoning model has been removed from the Perplexity platform, prompting concerns about handling complex queries. @AravSrinivas mentioned that the model was deemed unnecessary as reasoning now auto-triggers for complex tasks.
  • Discussions emerged regarding the impact on Pro users who relied on the O1 model for advanced reasoning, with some questioning the decision and its effects on workflow efficiency.
• Perplexity Launches LinkedIn Verification: Perplexity introduced LinkedIn verification, allowing users to connect their profiles for enhanced functionality. The announcement has left the community curious about the feature's specific benefits.
  • Users speculated potential advantages such as improved credential verification or personalized user experiences, but Perplexity has yet to clarify the exact purpose of this integration.
• Advancements in GPR Devices Methodologies: A discussion on GPR devices and methodologies sparked interest among members, with this link highlighting recent advancements.
  • Participants engaged in conversations about the latest techniques and applications of GPR technology, emphasizing its growing role in various engineering fields.
• Perplexity API Encounters 3D Secure Issues: Users reported that adding a card via the Perplexity API causes the UI to freeze, followed by the swift appearance and disappearance of the bank's 3D Secure screen, preventing transaction authorization.
  • Discussions focused on the necessity of 3D Secure for security compliance and the lack of alternative solutions within the API, hindering seamless payment processes.

Unsloth AI (Daniel Han) Discord

• Direct Preference Optimization with Llama 3.3: Members confirmed that Direct Preference Optimization (DPO) successfully integrates with Llama 3.3, supported by comprehensive documentation and examples.
  • Theyruinedelise highlighted that the provided documentation enhances usability, facilitating smoother implementation for users.
• Challenges in Model Merging and Quantization: Discussions focused on the complexities of merging models, especially the drawbacks of merging into 4-bit, which risks degrading LoRA fine-tuned models.
  • Disgrace6161 advocated for merging into full precision first to maintain performance, emphasizing the importance of preserving model quality.
• Optimizing Fine-Tuning with LoRA Adapters: LoRA adapters were extensively discussed for their role in fine-tuning, highlighting their ability to optimize VRAM usage while maintaining model integrity.
  • Participants noted that higher ranks in LoRA can enhance performance, depending on task-specific requirements and dataset characteristics.
• SPDL Enhances AI Training Efficiency: The SPDL blog post outlined how SPDL accelerates AI model training through thread-based data loading, significantly reducing training time.
  • This method improves data management and throughput, proving essential for handling larger datasets in Reality Labs research.
• Release of OpenPlatypus Dataset: The OpenPlatypus dataset, comprising 25,000 samples, was released and evaluated against Qwen QwQ at temperature 0, incurring a cost of $30 on OpenRouter.
  • Recommendations include excluding responses outside the 100-5000 tokens range and applying k-means clustering post sample size reduction.

Stability.ai (Stable Diffusion) Discord

• Anticipation Builds for 5090 GPU Launch: Members are eagerly awaiting the 5090 GPU release scheduled for early January, highlighting its impressive 32GB VRAM capacity.
  • Humorous remarks like 'In AI time, that like years' reflect the community's excitement and anticipation for the new GPU.
• Combating GPU Scalpers with Web Scrapers: Discussions surfaced around the rise of scalpers acquiring GPUs, prompting users to explore web scrapers and other techniques to secure cards during launch.
  • Participants emphasized the added difficulty for those without a physical presence in the US, underscoring the challenges in obtaining GPUs.
• Top Models Recommended for Image Generation: Users recommended models such as Dream Shaper, Juggernaut, and SDXL for generating specialized content like spaceships, noting their effectiveness.
  • Some suggested leveraging LoRA training to enhance model performance, while others pointed out that 8GB VRAM may limit capabilities.
• Issues with Older Stable Diffusion Models: Members reported challenges with older models like WD1.4, which tend to produce anomalous results during image generation tasks.
  • Recommendations included captioning regularization images when training LoRA models to mitigate these issues and improve output quality.
• Recommended Discord Servers for Video AI Enthusiasts: A query about suitable Discord servers for discussing local video AI models mentioned platforms like Mochi, LTX, and HunYuanVideo.
  • The Banodoco Discord server was highlighted as a prime community for enthusiasts interested in these video AI models.

Eleuther Discord

• Training Jacobian Analysis Reveals Parameter Dependencies: A new paper on arXiv analyzes the training Jacobian, illustrating how final parameters are influenced by their initial values by transforming a small sphere in parameter space into an ellipsoid.
  • The study identifies distinct regions in the singular value spectrum, noting that training on white noise compresses the parameter space more aggressively than training on real data, and highlights computational challenges when scaling Jacobian analysis to larger networks.
• RWKV Models Release: Flock of Finches & QRWKV-6: The RWKV team announced Flock of Finches 37B-A11B and QRWKV-6 32B Instruct Preview, both demonstrating impressive benchmark results on multiple tasks.
  • Flock of Finches achieved competitive performance with only 109 billion tokens trained, while QRWKV-6 has already surpassed previous RWKV models in key metrics.
• Muon Optimizer Shows Promise Over AdamW: Consensus emerged that Muon might outperform existing optimizers like AdamW, with its gradient orthogonalization potentially relating to maximum manifold capacity loss and reinforcement learning regularization.
  • The Muon optimizer's underlying mathematics are considered insightful and plausible for enhancing performance, though discussions continue on its broader applicability.
• NeurIPS Prize Controversies and VAR Paper Misconduct Concerns: The ARC prize at NeurIPS sparked debates regarding goalpost shifting and potential manipulative tactics by organizers, casting doubt on the validity of its benchmarks.
  • Additionally, concerns were raised about Keyu Tian, the first author of a NeurIPS 2024 best paper, with allegations of misconduct and malicious code attacks during his internship at ByteDance, prompting calls to reassess the paper's accolades.
• Negative Attention Weights and Cog Attention: The introduction of Cog Attention proposes an attention mechanism that allows for negative weights, aiming to enhance model expressiveness by facilitating token deletion, copying, or retention.
  • While the concept is innovative, concerns about its effectiveness and potential learning difficulties remain, particularly in specific applications like Sudoku tasks.

LM Studio Discord

• GPU Grids: LM Studio's Multi-GPU Mastery: LM Studio efficiently spreads tasks across multiple GPUs, requiring them to be of the same 'type' but not necessarily the same model. Heyitsyorkie mentioned that GPU offload in LM Studio is a toggle that utilizes all available GPUs.
  • LM Studio users highlighted that this setup enhances performance scalability by leveraging the total computational power of connected GPUs.
• Mac Power: Running 70b LLMs on M4 Max: The M4 Pro chip can run 8b models with at least 16GB of RAM on Mac, while the M4 Max is capable of running 70b models, provided users prioritize RAM for flexibility.
  • Participants noted that larger models like 70b require significant memory, making the M4 Max a suitable choice for demanding AI tasks.
• GPU Showdown: Intel B580 vs Nvidia RTX 3060: Intel's B580 GPU offers affordability with 12GB of VRAM, but requires Vulkan support, leading to skepticism among users. In contrast, the RTX 3060 provides 12GB VRAM and is available second-hand between $150-$250.
  • mlengle emphasized a preference for Nvidia GPUs due to their CUDA support, which is lacking in Intel's offerings.
• Uncensored AI: Navigating Model Safety Cuts: A user expressed frustration in finding guidance to create an uncensored AI model, highlighting a lack of clear resources for removing safety features. They were advised to explore Unsloth finetuning guides and consider utilizing datasets aimed at achieving less restrictive models.
  • Participants suggested alternative approaches to model safety, citing the complexities involved in modifying existing LLMs.
• Fine-Tuning vs RAG: Choosing the Right LLM Strategy: Participants discussed the complexity of fine-tuning LLMs, especially with numerical data, suggesting it may not provide desired results. Alternatives like RAG (Retrieval-Augmented Generation) for data retrieval were recommended.
  • The community indicated that traditional analytical methods might yield better insights for specific use cases compared to fine-tuning.

Bolt.new / Stackblitz Discord

• Token Usage Shows 'NaN' After Minimal Use: Users reported that token usage displays 'NaN' after minimal usage, leading to confusion and inaccurate tracking.
  • Support suggested reloading tabs or contacting help if the issue persists, as the display problem was being addressed.
• Debugging in Bolt Causes Excessive Token Consumption: Users faced issues with debugging in Bolt, leading to excessive token consumption without effective results.
  • Recommendations included using more focused prompts and file pinning to prevent unwanted changes during complex tasks.
• Supabase Integration Set to Enhance Bolt Functionality: The community discussed the potential integration of Supabase into Bolt, which many believe will enhance functionality for building projects.
  • Users expressed optimism that this integration could significantly streamline workflows, particularly for those transitioning from services like Firebase.
• Feature Requests Focus on GitHub Integration and Full-Stack Support: Users voiced suggestions for features, including better GitHub integration and more support for full-stack applications.
  • The community emphasized approaching feature requests politely, directing them to the GitHub issues page for formal consideration.

Notebook LM Discord Discord

• NotebookLM UI Overhaul with Interactive Audio: NotebookLM is set to receive a revamped UI featuring separate sections for Sources, Chat, and Notes & Audio Overview, along with an Interactive Audio Beta enabling real-time interactions with hosts (Tweet).
  • This update aims to enhance user experience by improving navigation and usability, addressing current limitations in source management and audio interactions.
• Gemini 2.0 Enhances Performance: Gemini 2.0 is anticipated to outperform existing models with higher output token limits and advanced features (Tweet).
  • However, concerns have been raised regarding the potential limitations in context window size compared to previous iterations.
• Custom AI Voices Boost Podcast Personalization: Members discussed the integration of custom voices for podcasts, with Eleven Labs being suggested for voice cloning to meet the growing demand for personalized audio experiences.
  • One user emphasized the importance of utilizing professionally cloned voices to enhance listener engagement and content uniqueness.
• AI-driven TTRPG Adventures Gain Popularity: Interest surged in running TTRPG adventures using AI, drawing parallels to solo D&D games for more immersive storytelling.
  • Users reported varied success with this approach, noting it as an entertaining endeavor despite some challenges.
• AI-generated Video Podcasts Explore Deep Themes: A new AI-generated video podcast featuring a caveman and an AI chatbot delves into themes like The Meaning of Life, blending humor with profound conversations.
  • This innovative format showcases the dynamic between ancient and modern perspectives, attracting interest for its unique approach.

Nous Research AI Discord

• Hermes 3B Exceeds Benchmarking Expectations: Users are comparing benchmarks of Hermes 3B, Llama 3.2, Mistral 7B, and Qwen 2.5, with Hermes 3B demonstrating superior performance in various metrics.
  • Senor1854 highlighted the reliability of the new math benchmark dataset compared to established ones, emphasizing the importance of evolving evaluation techniques.
• QTIP Model Outperforms AQLM Without Retraining: The QTIP model has been reported to outperform AQLM without requiring retraining, as detailed in the QTIP GitHub repository.
  • Community reactions suggest a resurgence of signal processing techniques in machine learning, with members pointing to the research paper for deeper insights.
• Llama3 Faces Capacity Utilization Challenges: Llama3 has been noted to experience a drop in performance related to model capacity utilization, leading members to scrutinize the underlying model dynamics.
  • Members plan to examine the relevant research to understand the performance degradation, expressing interest in how model capacity affects Llama3's efficacy.
• Launch of New Math Benchmarks U-MATH and μ-MATH: Toloka announced the launch of U-MATH and μ-MATH, two new benchmarks designed to evaluate LLMs on university-level mathematics.
  • These benchmarks are expected to provide more reliable evaluations, contrasting with previous scoring systems and driving advancements in evaluation techniques.
• Pretraining Small Models with Big Model Hidden States: Kotykd proposed a novel training methodology using big model hidden states to pretrain smaller models in a different architecture for improved efficiency.
  • This idea has sparked discussions regarding the feasibility and potential of such methods, with members highlighting the need for further exploration and experimentation.

GPU MODE Discord

• Torch.compile Faces Dynamic Padding Penalties: A user reported that using torch.compile(mode='max-autotune') with dynamic=True led to significant performance penalties during the initial decoder iterations, specifically slower runs with new conditioning shapes.
  • Despite enabling dynamic padding, the performance issues persisted, prompting discussions on potential solutions to mitigate the penalties associated with variable-length inputs.
• Triton Enhances Matmul and Softmax with Fused Kernels: Members are developing a fused kernel for matmul and softmax in Triton, drawing parallels to existing point-wise activation fusions like ReLU.
  • Guidance was sought on utilizing the group-ordered matmul example from Triton's documentation to overcome challenges associated with fusing softmax operations.
• Float8 Training in TorchAO: Transitioning from DDP to FSDP: TorchAO's implementation of float8 training encounters errors when scaling to multi-GPU setups using DDP, despite running smoothly on single GPUs.
  • Community members recommended adopting FSDP for data parallelism and encouraged sharing code or reporting issues on TorchAO to facilitate troubleshooting and improvements.
• CUTLASS Emerges as Top GEMM Implementation Alternative: In discussions about optimal GEMM implementations excluding cuBLAS, CUTLASS was identified as the leading alternative option.
  • Participants compared various alternatives like pure CUDA and Triton, ultimately acknowledging CUTLASS for its superior performance in matrix multiplication tasks.
• GPU Glossary Launch and H100 Tensor Core Clarifications: The GPU Glossary was launched on Modal, detailing terms such as 'Streaming Multiprocessor' and addressing core counts and tensor core functionalities in the H100 GPU.
  • Discussions highlighted the need for accurate representations of GPU architectures, including the clarification that each SM in the H100 has 128 FP32 cores and the operational differences of tensor cores compared to CUDA cores.

Cohere Discord

• Cohere Support Responsiveness: When users reported issues, members emphasized contacting the support team at support@cohere.com for urgent matters.
  • Another user encouraged messaging directly for faster assistance, acknowledging the support team's presence.
• Rerank Timeout Issues: Multiple users experienced 504 gateway timeout errors while using the Rerank feature, with one reporting requests timing out after 40 seconds.
  • The issue appeared sporadic, as some members noted service restoration shortly after, with others still reporting challenges.
• FP8 Quantization Outperforms BnB on H100: A discussion on quantization techniques revealed that with H100 hardware, FP8 quantization outperforms BnB for fast inference under high user load.
  • Members agreed that traditional calibration datasets like WikiText often fall short in practical performance, especially for non-English languages.
• Cohere Go SDK Structural Fixes: Feedback indicated that the Cohere Go SDK's StreamedChatResponseV2 field related to tools calls is incorrectly structured.
  • Definitions for ToolPlanDelta and ToolCallDelta are missing necessary fields for accurate parsing.
• Aya Expanse Model Licensing Concerns: Users expressed a preference for using the Aya Expanse model in internal company settings, emphasizing the need for speed while avoiding potential data leaks.
  • Concerns over the CC-BY-NC license were raised, leading to a discussion on the implications of non-commercial use even within corporate environments.

LLM Agents (Berkeley MOOC) Discord

• LLM Agents Hackathon Deadline and Platform Change: The LLM Agents MOOC Hackathon submission deadline is approaching on December 17th. Submissions have transitioned from Devpost to Google Forms to ensure proper evaluation.
  • Winners will be announced in the first half of January 2025, and participants are encouraged to seek last-minute assistance through the chat and visit the hackathon website for more details.
• Advanced LLM Agents MOOC Launches in Spring 2025: The Advanced Large Language Model Agents MOOC is set to launch in Spring 2025, focusing on reasoning and AI for mathematics. Sign-ups are currently open at this link.
  • The syllabus is still in development, with more details expected from Prof Song. The course will run from mid January to early May, with additional information available on the MOOC website.
• Assignments and Quizzes Policies for MOOC: All assignments, including the written article, are due on December 12th, 2024, by 11:59 PM PST. Quizzes are graded on a completion basis, allowing participants to earn certificates without penalty.
  • The written article assignment requires a link to a social media post and can be submitted via Written Article Assignment Submission. Quizzes aim to facilitate learning rather than strict assessment.
• Ninja Tier Requirements for Hackathon: For the Ninja Tier in the hackathon, completing all quizzes and submitting the article assignment are essential, with labs being optional.
  • Participants are encouraged to write about their hackathon projects for the written article assignment, enhancing their contributions to the tier.

Interconnects (Nathan Lambert) Discord

• Google Launches Android XR: Google unveiled Android XR, a new mixed reality operating system designed for headsets and smart glasses, during a recent demo.
  • The platform features real-time translation with subtitles, reinforcing Google's strategic pivot towards augmented reality technologies.
• OpenAI vs Anthropic Market Rivalry: OpenAI and Anthropic are intensifying their competition for market leadership, with Anthropic achieving $1B in ARR by the end of 2024 compared to OpenAI's $4B revenue and $157B valuation.
  • This rivalry highlights Anthropic's growth in coding applications, prompting concerns among OpenAI executives about shifting strategies from safety to aggressive marketing.
• Advancements in MLLM Development: Community members are actively seeking quality sources for tracking MLLM developments, with some utilizing scraping techniques and Twitter feeds as potential resources.
  • Efforts to enhance information quality reflect the demand for up-to-date and reliable data in the MLLM space.
• Hugging Face's VLM Insights: Merve from Hugging Face is recommended as a key resource for VLM insights, with her informative posts accessible via Twitter.
  • Her content is considered valuable for those staying abreast of developments in Vision-Language Models.
• AI Model Creative Benchmarking: Discussions emerged around establishing meaningful benchmarks for measuring LLM capabilities in creative tasks, addressing the current lack of standards for diversity and creativity.
  • Claude-3, despite being favored by the community, often ranks lower in creative writing benchmarks, highlighting the need for improved evaluation metrics.

LlamaIndex Discord

• Calsoft Launches CalPitch Tool: Calsoft introduced CalPitch, a tool designed to assist their business development team in researching prospects and drafting outreach emails with human oversight.
  • This launch showcases how AI can enhance and speed up current workflows.
• Enhancements to RAG Agents with SharePoint and LlamaParse: A new feature enables building RAG agents that respect SharePoint permissions, addressing requests from Azure stack users to connect to enterprise data sources using LlamaParse for parsing unstructured PDF data.
  • Concerns about data privacy were addressed, ensuring that no data is retained beyond 48 hours.
• Google Gemini 2.0 Models Released: Google launched its latest Gemini 2.0 models, including day-0 support, accessible via pip install llama-index-llms-gemini or pip install llama-index-llms-vertex.
  • The Gemini 2.0 Flash model promises enhanced speed and capabilities, hailed as a game changer in the AI landscape.
• Personalizing Slack Bots with ReAct Agent: A user is developing a Slack bot using the ReAct Agent and seeking advice on incorporating personality without revealing it's an AI.
  • Community members suggested using FunctionCallingAgent with a system prompt to customize its personality.
• Integrating BGEM3 with Qdrant Database: A user inquired about integrating the BGEM3 model with a Qdrant database through LlamaIndex, seeking guidance on the process.
  • Resources related to BGEM3 were shared to assist in the integration.

Modular (Mojo 🔥) Discord

• Swag Challenge Winners Announced: We kicked off the week with a swag challenge on Monday, and winners were announced here. Ahmed also hosted an Ask Me Anything session about GPU programming with Mojo.
  • This initiative not only engaged the community but also provided an opportunity for participants to interact directly with experts on GPU programming using Mojo.
• AMA Sessions Deep Dive into Mojo: On Tuesday, Joe hosted an Ask Me Anything session on the standard library, providing valuable insights into the functionalities and features of the library.
  • Additionally, today features ask Steffi anything about async Mojo/coroutine implementation in MLIR and ask Weiwei anything about the Mojo optimization pipeline, aiming to deepen understanding of specific technical topics.
• Launch of Community Packages Early Access: Yesterday, we launched the early access preview of community packages, encouraging users to join and help test the packaging. Interested users can register in <#1098713770961944628> to gain access to the instructional <#1313164738116583454> channel.
  • This launch seeks to expand the package ecosystem by involving the community in testing and development.
• Async Mojo Implementation and Optimization Pipeline: Today's Ask Me Anything sessions include discussions on async Mojo/coroutine implementation in MLIR and the Mojo optimization pipeline.
  • These sessions aim to provide in-depth technical knowledge and foster engagement among AI engineers working with Mojo.

DSPy Discord

• DSPy Framework for LLMs: After introducing DSPy framework, DSPy significantly reduces time spent on prompting for programming language models.
  • The framework uses boilerplate prompting and task signatures, simplifying prompt creation and enhancing efficiency in LLM-powered applications.
• Focus on Text and Image Inputs: Members debated investing in video and audio inputs, with one member suggesting focusing on text and image inputs for now.
• Defining LLM Agents: A member initiated a discussion on the definition of 'LLM agents', sharing a thread that explores its metaphorical implications.
  • Participants humorously acknowledged the debate's controversial nature, stating "you've kicked the bee's nest now."
• Optimizing with Labeled Data: It was confirmed that optimizers can be used with labeled data, specifically gold standard input-output pairs.
  • This confirmation led to increased interest and engagement from members in optimizing using labeled datasets.
• AI as a Platypus in Technology: A member reflected on AI challenging existing technology categorizations, likening it to a 'platypus' in tech as described in The Platypus In The Room.
  • They emphasized that "Nearly every notable quality of AI and LLMs challenges our conventions, categories, and rulesets."

OpenInterpreter Discord

• Searching for Spider Verse Glitch Effect: A user is seeking a Spider Verse glitch effect they saw on a website to replicate the effect.
  • They expressed keen interest in the creative aspect of the effect.
• Docker Issues with Open Interpreter: A member reported that Open Interpreter running in Docker only returns the model's chat response instead of executing code.
  • They suggested that the application seems to pretend to execute code without actually doing so.
• GitHub Model I Tutorial Update: A user inquired about recent changes to the GitHub page for the model i tutorial, noting significant shifts in information.
  • It seems like the GitHub page updated and a lot of stuff is different now, indicating confusion over the documentation.
• Struggles with NVIDIA NIM Base URL Setup: A user sought assistance with setting up NVIDIA NIM base URL links, mentioning challenges without success.
  • They expressed frustration, stating they have been trying for ages but have had no luck.
• WebVoyager vs GPT 4V Preferences: A member asked for opinions on WebVoyager, indicating a preference to update the model to use GPT 01 instead of GPT 4V.
  • They are curious about testing it out and potentially switching models.

tinygrad (George Hotz) Discord

• Coverage.py Introduction: A member introduced Coverage.py as a tool for measuring Python code coverage, highlighting its ability to track executed code and analyze unexecuted parts.
  • The latest release, 7.6.9, launched on December 6, 2024, supports Python versions from 3.9 up to 3.14 alpha 2.
• gcov as Alternative Coverage Tool: A member recommended gcov for coverage analysis and inquired about more fine-grained options.
  • This sparked a broader conversation on the variety of available coverage tools and their respective advantages.
• George Hotz Endorses Coverage.py: George Hotz recognized Coverage.py as a good place to start for assessing test coverage, reflecting his confidence in its capability to enhance code quality.
  • His endorsement underscores the tool's effectiveness among engineers seeking to improve their testing processes.
• Seeking Test Coverage Expertise: A member requested assistance from proficient users of test coverage tools to identify dead code.
  • They emphasized that untested code should probably be deleted to maintain code quality.

Torchtune Discord

• QRWKV6-32B Model boosts compute efficiency: Recursal AI transformed the Qwen 32B Instruct model into the QRWKV6 architecture, maintaining the original 32B performance while achieving 1000x compute efficiency during inference.
  • This modification replaces transformer attention with RWKV-V6 attention, resulting in significant cost reductions in computation.
• AMD GPUs enable rapid training: Training of the QRWKV6 model was completed in just 8 hours using 16 AMD MI300X GPUs (192GB VRAM each), showcasing advancements in AI development speed.
  • Upcoming models like Q-RWKV-6 72B and RWKV-7 32B are in progress, promising enhanced capabilities.
• RWKV-V6 Attention enhances scalability: The linear attention mechanism in the QRWKV6 model proves to be highly efficient at scale, especially for processing long contexts.
  • Despite these improvements, the model's current context length is capped at 16k due to compute constraints, though it remains stable beyond this limit.
• Model transformation cuts retraining costs: The conversion process allows transforming any QKV Attention model to an RWKV variant without the need for full retraining, thereby reducing compute costs.
  • However, the model inherits language limitations from the Qwen model, supporting only approximately 30 languages compared to RWKV's typical 100+ languages.
• Community collaboration drives advancements: Training for the QRWKV6 model is sponsored by TensorWave, with significant contributions from EleutherAI and the RWKV community.
  • While the transformation process is innovative, some details remain undisclosed, leaving the community curious about the how-to aspects.

Gorilla LLM (Berkeley Function Calling) Discord

• Finetuning Gorilla LLM for Custom API: A user is seeking guidance on how to finetune Gorilla LLM to recognize a custom API, indicating previous difficulties in the process.
  • They specifically noted challenges in downloading the GoEx model from Hugging Face.
• Downloading GoEx Model Challenges: The user mentioned experiencing trouble while attempting to download the GoEx model to use in a Colab environment.
  • This highlights the need for clearer instructions or troubleshooting steps for model acquisition.
• Implementing Reversibility in Gorilla LLM: The user inquired about strategies for implementing reversibility within their Gorilla LLM project.
  • This suggests a broader interest in effective control mechanisms during development processes.
• Training Gorilla LLM in Colab: They are conducting training of Gorilla LLM in a Colab environment.
  • This approach may necessitate efficient resource management and clear training protocols.

Axolotl AI Discord

• PyTorch's PYTORCH_TUNABLEOP_ENABLED Flag: A member highlighted the use of PYTORCH_TUNABLEOP_ENABLED=1 in PyTorch to enable tunable operations, referring to the PyTorch GitHub repository.
  • This feature suggests optimizations in CUDA tunable operations, potentially enhancing efficiency for developers utilizing PyTorch.
• CUDA Tunability Boosts GPU Performance: The discussion centered around PYTORCH_TUNABLEOP_ENABLED=1 and its benefits for CUDA operations, indicating possible performance improvements in GPU computation tasks.
  • Members believe the tunable approach allows developers to customize operations more effectively, aligning with specific user requirements.

Mozilla AI Discord

• Mozilla Builders Demo Day Recap Released: The Mozilla Builders Demo Day Recap highlights how members gathered in person despite challenging weather conditions, showcasing incredible technology and participant connections.
  • The event included showcases of cutting-edge tech and fostered strong connections among participants.
• Acknowledgments to Key Contributors: Special thanks were extended to specific teams and contributors who made the event possible, detailed here.
  • Community members demonstrated remarkable resilience by attending despite difficult conditions, such as braving tsunamis.
• Social Media Buzz from Demo Day: Mozilla Builders shared their LinkedIn update and a tweet capturing the event as a spectacular confluence of amazing people and incredible technology.
  • The social media posts highlighted the event's success and the strong community engagement.
• Demo Day Highlights Video Available: A highlights video from the event, titled Demo_day.mp4, has been shared for those who missed the event.
  • The video showcases some of the presentations and interactions from the day, providing a comprehensive overview.

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

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

The AI21 Labs (Jamba) 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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