Another quiet day, but it coincides with the MVP of our Twitter data pipeline and summarization! You may have noticed we have renamed towards “AI News” in anticipation of this day.

For now it draws from swyx’s AI High Signal list, but we should be able to generalize it to your list at some point. Feedback welcome on the prompt (see below)!

Table of Contents

[TOC]

PART X: AI Twitter recap

Top Level Summary

The discourse on Twitter among the technical and engineer-oriented audience highlights the fast-evolving nature of AI, touching upon ethical considerations, technological advancements, corporate leadership changes, financial transaction dynamics, and the lighter side of tech life through humor. Key points include speculation on leadership changes at Google, emphasizing the non-programming skills that future coders may require, discussions around new AI models and their applications, concerns over financial transaction platforms, and cultural insights within big tech companies. The combination of technical innovation, corporate strategies, ethical challenges, and everyday issues faced by engineers and developers paints a vivid picture of the current tech landscape.

AI and Machine Learning Trends

• Discussions around AI ethics and its application reveal varied perspectives, with Margaret Mitchell discussing the role of ethics in AI spurred by Google Gemini’s launch.
• AI’s influence on coding and programming skills is a hot topic, with John Carmack sharing thoughts on the transition from traditional coding to managing AI.
• Guillaume Lample announces the release of “Mistral Large”, an improved model with multilingual capacities.
• AI-generated content and its potential dangers to web originality were discussed, predicting the end of ‘view-source’ by Pieter Levels.

Business and Management Insights

• The potential change in Google’s CEO position is speculated upon, highlighting Sundar Pichai’s contributions and future prospects, as discussed by Levels and Arav Srinivas.
• Levels also discussed what distinguishes highly paid engineers, focusing on adaptability and pragmatism.
• Delip Rao shed light on OpenAI possibly entering the synthetic data market, hinting at new strategies for AI development.

Technology and Hardware

• Santiago L. Valdarrama introduced a deep learning project challenge that focuses on identifying street numbers from images, encouraging the use of CNNs over OCR solutions.
• Alex Wang praises the productivity benefits of the Apple Vision Pro during business trips.
• Yann LeCun discussed running LLMs on mobile devices, indicating advancements in on-device AI.

Financial Transactions and Platform Dynamics

• Pieter Levels expressed frustrations over Stripe deciding certain payments to be high risk, affecting his business (Tweet).
• François Chollet and Dhéliat discuss the politics and culture within big tech workforces, providing an overview of the apolitical nature of these spaces as compared to startups (Tweet).

Memes/Humor

• Pieter Levels humorously wonders if he’s a “Broccoli boy” based on his ownership of related items.
• AISafetyMemes contemplates the future impact of AI on societal norms in a tongue-in-cheek manner (Tweet).

[META - HELP US] AI Twitter recap prompt

This is the prompt that produced the recap above. Help us tweak it!

You are a summarizer/labeler AI designed to integrate important discussion topics on Twitter for a technical, detail oriented engineer audience. Your task is to create a unified summary that captures key points, conversations, and references. Focus on thematic coherence and group similar discussion points.

Given a list of tweets, in the form of a tuple that looks like (tweet_text, impression_count, tweet_url), perform the following tasks:

• Bucket all of the tweets into a maximum of 6 total categories. Always dedicate 1 category to memes/humor. Use Named Entity Recognition to label and categorize all of the summaries and tweets.

• Sort the tweets within each category by their impression count in descending order, so that tweets with the most impressions are listed first.

• Present the results in a structured format, with tweets organized under their respective categories. Be sure for each tweet to add the link to the tweet so users can verify.

After that, generate a top level summary on the themes from the tweets you grouped and labeled. Go through and weave a compelling narrative that incorporates all of the categories, and references direct tweets throughout the text. Be sure for each paragraph in the narrative, that you have at least 3 supporting tweets, so users can know that you’re grounded in facts, and not just making things up.

Strategies:

• When you reference direct tweets, be sure to link to them.
• Capture key points, conversations, and references.
• Focus on thematic coherence and group similar topics.

Tactics:

• Pay close attention to the context and content of each tweet to accurately categorize them.
• Ensure summaries are concise and informative, allowing readers to grasp the key points at a glance.
• When linking to a tweet, do it in markdown, in a way the integrates in the sentence.
  • Good Example: “Sam Altman said that scaling laws are decided by god; the constants are determined by members of the technical staff”
  • Bad Example: “Sam Altman said that scaling laws are decided by god; the constants are determined by members of the technical staff. (read more )”
• DO NOT INTRODUCE THE TOPICS, only list them out with the relevant tweets.

Maintain confidentiality of user data and focus solely on the content and its implications. TWEET_DATA_HERE

That was the end of the message history.

Return the summary of all the Tweets in the format specified by the system. Only summarize the given Tweets, DO NOT add any additional information not mentioned in the given source, but DO cite details that would be relevant for an AI Engineer. DO NOT hallucinate any additional information not mentioned in the given source.

Begin.

PART 0: Summary of Summaries of Summaries

PART 1: High level Discord summaries

TheBloke Discord Summary

• Microsoft Fuels AI Race with Mistral Investment: Microsoft’s $16 million investment in Mistral AI triggered a debate about potential monopolistic tendencies versus the benefits of competition in the AI industry, with specific reference to the TechCrunch article.

• The Pros and Quirks of LLM Roleplay: Practical applications of offline models in role-playing narrative scenarios and ethical considerations of model outputs were a focal point, highlighting the fine line between content “confabulation” and “hallucination.”

• In Search for Efficiency in AI Training: Enthusiasts explored the realm of teaching LLMs through GitHub examples, using frameworks like DeepSeek for fine-tuning coding models as mentioned in the DeepSeek-Coder GitHub repository, and the cost-effective training technique QLoRA found in the Unsloth project.

• GGUF: Bridging the Conversion Gap: Technical insights were given into the conversion of Hugging Face models to GGUF format for Q5 KM output, with clarification that quantization is a distinct process detailed in the llama README.

• Speed Bumps and GPU Dilemmas in Coding Arena: Queries around speeding up chat response times using CSV files and overcoming the lack of GPU resources on platforms like Google Colab led to proposals of leveraging cloud APIs from providers like Hugging Face to boost inference speeds.

Mistral Discord Summary

• Mistral’s Size and Revenue Speculations: @rabdullin speculated on Mistral Medium potentially being 70B parameters and the business impact of Mistral AI’s entrance to the US enterprise market. @sublimatorniq discussed tuning challenges and pricing differences between new models, whereas @myhaw and @lerela encountered a technical issue with chatbot development using Mistral models which was later resolved.

• Vulkan Backend Rises and Falls: Performance and efficiency improvements were mentioned through Vulkan backend utilization with @saintvaseline expressing excitement for running 7-billion parameter models on AMD PCs. @tokenshifter, however, mentioned a technical limitation where APIs bypass tensor accelerators. Mutual inquiries around inference on large models across multiple GPUs call attention to recommended resource allocations.

• Fine-Tuning Finesse and Foibles: Integration concerns from @ethux and @kushagra_67246 clarified the role of LoRa as behavioral, not informational. Mistral fine-tuning was discussed, with @kunpengguo being advised on the substantial resources needed. Moreover, @aaronbarreiro and @mrdragonfox discussed limits of 32k tokens in document addition for training.

• Deployment Guides and Showcases Shine: @raoufchebri, @boles.ai, @deexxcryptz, and @arunprakashai provided a variety of resources, from deployment guides for Azure to plugins for synthetic data generation with Sensei, evidencing the community’s endeavors in harnessing Mistral Large. Meanwhile, @cogbuji introduced a fine-tuned medical terminology Mistral model available on Hugging Face.

• Open Source Confusion and Ethical Typos: Mixed reactions on Mistral’s open-source contributions were settled with clarification by @mrdragonfox on current openweight models. The ethics of mimicking human error within AI responses stirred discussions amongst users like @dawn.dusk and @foxalabs_32486, marking a philosophical tie to model design debates.

• Distrust in Google’s Enterprise Gems and Model Issues: Skepticism arose from @egalitaristen regarding Google’s enterprise tools’ performance, paired with mixed experiences shared by @sublimatorniq on model capabilities like 1.5 PRO, and concerns by @egalitaristen demanding hands-on proof. Additionally, issues in invoking function calling on Mistral and privacy concerns for Le Chat were significant points of discourse.

• Casting a Wary Eye on AI’s Video Comprehension and Development Hurdles: @sublimatorniq shared inadequate AI performance in describing video content, indicating gaps in model capabilities. The challenges of hiring in the AI sector due to expertise demand and high competition were underlined by @foxalabs_32486.

LM Studio Discord Summary

• Large Models, High Stakes: Technical discussions centered on challenges with loading large models in LM Studio, such as a 35GB model with 60GB RAM and 8GB VRAM, where performance was predicted to be slow. Platform differences such as Macs predominantly relying on RAM, whereas Windows benefits from GPU offloading, were also highlighted. An emerging interest in ternary model research was brought up with a paper cited, proposing possibilities like fitting 120B models into 24GB VRAM GPUs.

• Modding and Scripting Dilemmas: Questions on updating LLMs with the latest information from the Fabric modding API were raised, as well as inquiries into support for Pine Script code generation, for which a custom GPT link from OpenAI was provided.

• Hardware Horizons and Hassles: A conversation in the hardware discussion proposed using LLMs in various industries including electric vehicles and finance. The announcement of TinyCorp’s TinyBox, which features 6x 7900XTX GPUs and an EPYC 7532 CPU, aimed to revolutionize AI processing capabilities. Hardware compatibility issues with NVIDIA GPUs and LLMs surfaced alongside potential Windows corruption affecting LM Studio usage.

• Beta Testing Boundaries and Breakthroughs: Beta releases chat was quiet, with one detailed response explaining the addition of images to LM Studio, specifying a model PsiPi/liuhaotian_llava-v1.5-13b-GGUF/ as a prerequisite and recommending the download of the model’s mmproj and gguf to include images.

• Language Barrier: In the autogen channel, discussions revealed Gemini as the preferred choice over Chat GPT for translating psychological reports, despite unwanted formatting and insertions. The context for “translation” was specified to be from Turkish to English.

• Rapid Response Reputation: The single entry in the langchain channel offered minimal context but hinted at efficient performance without further elaboration.

• WSL Woes to Wins: The open-interpreter channel addressed challenges in WSL environments about the connect error httpcore.ConnectError: [Errno 111] Connection refused. By using the real local IP network address in place of localhost, users managed to resolve the issues after consulting Open Interpreter’s Docs and troubleshooting with different networking behaviors between WSL1 and WSL2.

OpenAI Discord Summary

• Sora’s Separate Saga: There’s a buzz about whether Sora will be integrated into ChatGPT or start as a standalone app, akin to the trajectory of DALL-E. Meanwhile, the rollout of the memory feature is in progress, being selectively released to users, though no specific timeline is provided.

• Mamba’s Memory Quandary & AI Race: Concerns have surfaced regarding the Mamba algorithm’s tendency to forget minor details that its models deem unimportant. The AI community also ponders Mistral Large’s progress, which is now only 20% behind GPT-4 and available on Azure. Incidentally, there were reports of Copilot bias, with instructions on submitting issues through OpenAI’s feedback form.

• GPT-4’s Growing Pains: Troubles with GPT-4’s responsiveness and accuracy in research answers have members exchanging tips, while API query performance for larger token sets is estimated at 15-20 seconds. Members also share frustration over GPT-4’s file functionality, echoing challenges in achieving optimal performance even with file uploads and custom API creation.

• Prompt Engineering Evolves: Enthusiastic discussions surrounding meta-prompting have emerged, promising strategies for creating cohesive outputs from AI, despite lacking shared concrete methodologies. Ethical considerations of AI-generated content are concurrently debated, with ponderings over models’ adherence to ethical outputs in self-prompting processes and the potential for complete, expansive documents from base primers of fine-tuning.

• Challenges and Strategies Shared Across Channels: Meta-prompting methods and prompt engineering strategies like MetaPrompting and LongRoPE dominate the conversation, with madame_architect offering a growing list of annotated papers to enhance prompt engineering. Privacy and data safety when using AI services are hot topics, where the community is reassured about the improbability of individualized scrutiny without significant cause, despite platforms inevitably having some access to user data.

Perplexity AI Discord Summary

• Mistral Large Ascends for Pro Members: <@ok.alex> announced that Mistral Large is now available to all Pro users on Perplexity AI, with access via settings or the Rewrite feature, and a mobile app release is forthcoming.

• Navigating Promo Pitfalls and Model Bash: Users encountered issues with a promo email from Rabbit R1, like @mithrilman, who needed to work through support for resolution. Meanwhile, @.claidler and @jaicraft debated the merits of various AI models, with GPT-4 Turbo being praised and Mistral Large noted for its code handling superiority.

• Expectation Management for Perplexity’s AI Engine: Perplexity is deemed excellent as an AI answer engine, says @brknclock1215, but comes with limitations such as poor handling of large file parsing or code execution. Comparisons to competitors like Merlin showed Perplexity’s strengths, especially in searching without SEO constraints.

• Tech Tidbits Spark Curiosity: Members like @ha.mz4_ presented links exploring innovations, such as Lenovo’s transparent laptop, without delving into discussions. @.anuni favored Mistral Large for its accuracy over GPT-4, and @commuting5048 noted GPT-4’s detailed muscle-building routine specifics.

• API Analysis and Sonar Scrutiny: Community tests led by @clay_ferguson and @brknclock1215 pointed out better performance using sonar-medium-online over alternatives but also reported inconsistencies and a desire for details about sonar-medium-online versus pplx-70b-online. Key findings suggest that prompt design heavily influences output, and gibberish responses may arise from attempts by models to list sources.

LAION Discord Summary

• Captcha Challenge for AI: A conversation took place about a captcha instructing users to “click on the object that is different from the others,” which @mikerhinos pointed out lacked a target word for computer vision labeling, suggesting its sole purpose was to deter bots.

• Stable Diffusion 3 Sparks Curiosity and Critique: Enthusiasm over the forthcoming Stable Diffusion 3 (SD3) was shared, alongside criticism of the current UNet2D’s limitations and an inability to train on batches with mixed resolutions, indicating high expectations for the model’s potential.

• Ethics and Efficacy of AI in Military Operations: A Bloomberg article sparked an ethical and technical debate on the use of AI in targeting airstrikes, with @thejonasbrothers, @chad_in_the_house, and @pseudoterminalx discussing the implications of AI decision-making in military scenarios.

• Fourier Transform Flexes in Neural Networks: @mkaic showcased their work on manual implementation of inverse discrete Fourier transform within neural networks, aiming for memory-efficient solutions and considering the use of torch.vmap. @p.ie.c.e.s recommended torchkbnufft for achieving more efficient Fourier synthesis.

• The Future of 1-Bit Large Language Models: Discussions about 1-bit large language models, particularly BitNet b1.58, suggested a movement towards more cost-effective and high-performance models that optimize hardware use, referencing a paper that can be accessed here.

Eleuther Discord Summary

• Double-Descent Strikes Back: Double-descent on training loss is a hot topic among users, with @leegao_ noting the peculiarity of its occurrence on training loss rather than the typical validation/test loss.

• Gradient Spikes on the Radar: A paper discussing gradient estimation shared by @ad8e sparked dialogue on its influence on training stability of large models. The experience of gradient spikes was tied to potential early layer gradient shifts as @uwu1468548483828484 reflected on the paper.

• The Tale of the Silent Data Corruption: @leegao_ highlighted a rumor regarding a failed Google LLM project, pointing to silent data corruption during pretraining and stressing the importance of vigilant monitoring.

• Token Troubleshooting and LoRA Insights: Issues with lm_head.weight differences arose during token addition experiments on Mistral-Instruct-V0.2, while @thatspysaspy engaged with conversations around LoRA pretraining potentials, referring to a paper called “LoRA-the-Explorer.”

• Olympiad Challenges and CycleGAN Innovations: The release of #OlympiadBench with Olympiad-level scientific problems and a best-performing model score of 17.23% has generated buzz. Meanwhile, @carsonpoole is experimenting with an innovative CycleGAN that integrates a diffusion model for improved results.

• Scaling Models and Mathematical Musings: Intense discussions about the spline view of Neural Networks, the theoretical aspects of LoRA in relation to SVD, and the scaling laws of training tokens reflect ongoing interests in both theoretical and practical model scaling.

• Unraveling ‘Energy’ in Interpretability: The term “energy” in the context of latent space analysis sparked a dialogue led by @wendlerc and @mrgonao, focusing on its meaning, equation interpretations, and tuned lens implementations in AI models.

• Batch, Evaluation, and Multimodal Labyrinths: Variations in GPT-NeoX batch size sensitivity, understandings of LM eval harness loglikelihood outputs, and queries on multimodal LM evaluation indicate a keen attention to metrics and scoring methodologies.

• Stepping into CoreWeave’s Domain: @jdranpariya initiated a conversation on CoreWeave specifics for setting up multi-node GPT-NeoX training, with community guidance pointing towards CoreWeave support and existing NeoX documentation for slurm-related instructions.

LlamaIndex Discord Summary

• LlamaIndex Cookbooks Sizzle with New Integrations: LlamaIndex announced their new function calling cookbook in collaboration with @FireworksAI, celebrating their RAG and FireFunction-v1 integration. They also launched a feature for creating a super-RAG by linking RAG applications into a network, signaling an era of interconnected API services for RAG applications which can be seen on Twitter.

• Event Alert: Mastering Complex PDFs with LlamaParse: A spotlight event, “Superior RAG for Complex PDFs”, looks to dive into LlamaParse capabilities, focusing on adeptly handling complex documents containing figures and tables, with LlamaIndex extending an open invitation along with code demos (Event Registration).

• Groq’s LPU Empowers Llama2 and Mixtral Models: LlamaIndex’s integration of @GroqInc’s LPU is set to greatly enhance the speed of LLM generation application workflows, a boon for Llama2 and Mixtral models outlined in the LlamaIndex and Groq Cookbook.

• Technical Troubleshooting and Discussions Heat Up: In the general channel, there were spirited discussions about best practices and troubleshooting within the LlamaIndex ecosystem, spanning topics from querying PDFs, reranking models, Golang integration, to clarification on nodes versus documents—all supported by ample documentation and resources shared by community members.

• The Hunt for the Perfect-Sized Model: @sysfor is in pursuit of an elusive mid-sized model that handles summarization and log correlation tasks efficiently, bridging the gap between Mistral 7b and Mixtral, and aiming to fit within a 24GB card, ideally a 10.7b quant 6/8 model.

HuggingFace Discord Summary

• Cosmopedia Sets New Data Horizon: Cosmopedia, a synthetic dataset by Mixtral, boasts 25B tokens and 30M files spanning textbooks, blogs, and stories, now available to AI enthusiasts who want to dive into a vast pool of data for machine learning applications. The dataset has been highlighted as a significant release, geared towards data-hungry AI models and can be accessed through a LinkedIn post.

• HuggingFace Hub Hits Version 0.21.0: The huggingface_hub repository has been updated to version 0.21.0, bringing dataclasses, PyTorchHubMixin improvements, and expanded InferenceClient capabilities, despite introducing some breaking changes. For the AI community, detailed release notes are available to peruse the nuances of the update.

• New AI Graces Hugging Chat: Google’s Gemma 7B, an open large language model (LLM), is now available on the Hugging Chat service, marking another step towards accessible and powerful conversational models. For details, the community is directed to a Twitter update by Julien Chaumond.

• TTS Arena Sings for Testers: TTS Arena is calling for participants to test, rate, and discover open text-to-speech models within a new interactive project, spearheaded by @reach_vb. With the initial rollout featuring five models, input and feedback are encouraged via TTS Arena’s announcement.

• Data Community Delivers 10k_prompts_ranked: Demonstrating the power of crowdsourcing, over 300 contributors developed a dataset in under two weeks, 10k_prompts_ranked, geared towards refining AI prompt ranking systems. The undertaking has been spotlighted as a testament to the strength and potential of community-led AI data efforts, with further insights shared in a HuggingFace blog post.

• Challenges with Free Inference-API Revealed: Users have reported timeout issues with the free Inference-API, with discussion underway to pinpoint causes such as potential rate-limiting, affecting the text-to-image AI model usability.

• RU Searching for Low-Resource AI Model Performance?: The CS231n study group is in the works, covering topics from software setup to neural network optimization, as the community gathers for a collective deep-dive into convolutional neural networks and visual recognition. Course content and arrangements are being shared, with Spring 2023 Assignments serving as a focal point.

• Call for AI Integration Army: Voices in the technical forest are wrestling with integrating AI into existing applications, with discussions on surpassing tool and API hurdles to enhancing CRMs with AI capabilities, from predicting production to handling customer interactions.

• Sentiment Analysis Tackles Urban Planning: LangChain with LLM is being pitted against the complex problem of urban sentiment analysis, a bold stride towards addressing issues of urban inequality through sharper social media insights.

• Image-Text AI Model Scrutinized for Local Servers: Queries are surfacing about executing the Salesforce BLIP model on local servers akin to llama.cpp for LLMs, aiming for a streamlined JSON response without the Python server overhead – here’s a starting point.

• Embedded in AI Inquiry: As AI whisperers seek to enhance their creations with arcface loss, questions about the nature of embedding sizes in the model’s architecture come to the fore, with a finer understanding necessary for optimal implementation.

• Embedding Model Choices Enkindle Discussion: When dealing with slim datasets, the community is valiantly pushing for embedding models that are both rapid and robust, recommending resources like BAAI’s bge-small-en-v1.5 and delving deeper into domain-specific transformer development for medical applications.

• Adventures in Email Brevity for LLMs: Ray-focusing efforts grow to condense lengthy emails while retaining core content for LLM consumption, perhaps to enable more precise and efficient AI interactions in the future.

• Promptly Rethinking CoT Economy: An intriguing approach to chain of thought (CoT) prompting in LLMs has been floated, urging models to “think silently” to conserve valuable tokens, thus potentially transforming the landscape of AI coaxing techniques.

• Text Generation Turmoil Over CPU: Encountering choppy waters, some seek aid in running text generation on CPU-limited vessels, highlighting the ongoing need for AI solutions that don’t require the luxury of GPU power.

• Debate Surges Over Diffusion Model Practices: Tensions rise over methodologies in Playground v2.5, particularly the adoption of “eps prediction” over zsnr, and the choice of the EDM framework. The heat extends to the handling of PRs, with claims of unfair treatment in favor of the Playground team bubbling up, as highlighted by a Mixture-of-Experts PR awaiting consideration.

• Photo Concept Bucket Flickers in Community Spotlight: Introducing Photo Concept Bucket, a community-crafted dataset featuring over half a million captioned images, poised to enhance AI’s visual understanding – a true testament to collaborative dataset building.

Please note that for certain channels, only a selection of messages was provided, hence summaries may reflect conversations from those excerpts rather than encompassing all channel activities.

LangChain AI Discord Summary

• Image Persistence Puzzle: In discussions on dealing with llava/visoon models, @deadthray raised an issue about the inefficiency of passing the image byte string repeatedly to maintain image references, indicating a challenge in how persistent image references are handled.

• Travel Chatbot Hits Rocky Road: @ritanshoo highlighted problems with their travel booking chatbot, unable to return relevant answers even with a sizable dataset in Pinecone, suggesting underlying issues with data retrieval or query processing.

• LangChain’s Production-Ready Debate: There’s been a convo about LangChain’s token consumption and its adaptability for real-world applications, with @m_gee bringing Reddit-sourced concerns to the table, while @baytaew argued for LangChain’s flexibility and recommended LangGraph for enhanced state management.

• Coding Language Showdown for LangChain: In a stellar clash of coding languages, @pcube__ sought the most seamless integration with LangChain to build a webserver. Amid responses, it seems Python and JavaScript took the lead, with Go remaining unmentioned.

• Memory Enhancements for LCEL: For those wanting to bolster LangChain LCEL with memory capabilities, @marknicholas sought advice on the best approaches, and while @kapa.ai provided general guidance, they recommended plunging into the depths of LangChain documentation for specifics.

• Spam Alert: The community had to deal with spam incidents across multiple channels from @davisson0429, who dropped a dubious Discord invite link accompanied by a barrage of vertical lines, effectively muddying the digital waters.

• LangGraph Stars with LangChain: @andysingal shared their insights on LangGraph, detailing its integration with LangChain to augment code generation’s safety and accuracy features, providing readers with a deep dive into its functionalities.

• AI Conversation Co-pilot Lands on Phones: Curious about real-time AI assistance on mobile devices? @jasonzhou1993 released a YouTube exposé revealing an AI Conversation Co-pilot for iPhones that offers instant advice through the Whisper & Mixtral models.

OpenRouter (Alex Atallah) Discord Summary

• OpenRouter Fixes Boost Message Clarity: After issues with message ordering/formatting for Perplexity and Gemma were identified by @louisgv, a successful fix was implemented to enhance user experience.

• Creating AI Tools with OpenRouter is a Breeze: OpenRouter not only supports models from its end but also from large providers like Google Vertex AI, Amazon Bedrock, and Cloudflare AI, offering a straightforward way for users to add the models they want to work with.

• Evaluating Czech LLMs Just Got Easier with OpenRouter: A new leaderboard project for evaluating Large Language Models (LLMs) for the Czech language was shared, leveraging OpenRouter for its ease of use and cost efficiency. The project is accessible here.

• Beta Testers Sought for Conversational AI Leap: Pablo, an AI Voice Chat app that leverages multiple LLMs without the need for typing, is seeking beta testers. They offer free AI credits, including for services like GPT-4, and interested participants can sign up using this TestFlight link.

• Chat Template Troubles Tackled: Discrepancies with chat templates affecting conversation continuities and turn-based chats in OpenRouter were reported and subsequently addressed, resulting in system updates and engagement from OpenRouter team members to resolve the issues.

OpenAccess AI Collective (axolotl) Discord Summary

• LLM Training: Consumer Hardware Not Enough: Training large language models like BitNet on consumer hardware remains impractical due to the lack of necessary equipment such as H100 GPUs at home, as discussed by @nafnlaus00. On the other hand, papers like The Era of 1-bit LLMs on arXiv have sparked interest for the potential shift in neural network hardware design and the feasibility of training on consumer hardware.

• LoRA’s Training Limitations and Alternatives: Members like @enka55 engaged in a discussion on the limitations of LoRA for incorporating new knowledge into models, with alternatives such as full fine-tuning being suggested. Moreover, the innovative multi-head LoRA (MHL) technique might be explored as an alternative to methods like ReLoRA, with resources such as LTE paper and source code on GitHub.

• Model Fine-tuning and Benchmarking Roadblocks: Technical discussions addressed challenges with fine-tuning models such as Q-Lora on GPUs like the Nvidia 4090 due to potential VRAM limitations. While @nanobitz pointed to using lm_eval_harness for benchmarks on fine-tuned models, there’s no direct integration with Axolotl.

• Ease of Use and Documentation Gaps: The need for clearer documentation on setting up Axolotl was voiced by users like @karisna, especially for Windows users, marking an area for improved user support. Difficulties with Axolotl’s Fine-tuning tooling and config issues, such as namespace conflicts with Pydantic within the Mistral config, were also highlighted.

• Replicate’s Performance in Discussion: A single mention by @dreamgen questioned the performance and reputation of replicate, yet no context or specific details followed to support this claim, leaving the issue open.

Interconnects (Nathan Lambert) Discord Summary

• Long Context AI on the Horizon: A tweet from Together Compute suggests significant developments in long context abilities for AI, an area of increasing importance.
• Key Industry Moves in AI Collaboration: Arthur Mensch confirms their company’s dedication to open-weight models, with a mention of a reselling agreement with Microsoft and the success of Le Chat and Mistral Large. For further details see Arthur Mensch’s tweet.
• Revolutionary “Starcoder2” for Code Models: BigCodeProject launches Starcoder2 with a 16k token context, built on The Stack v2, the most massive code dataset comprising over 900 billion tokens, aiming for increased openness and accessibility. More information can be found here.
• Call for HuggingFace to Intensify Model Training: As the code model space grows, Nathan Lambert suggests HuggingFace should escalate their model training efforts, particularly in light of the Starcoder2 introduction.
• Writing Tools War: Nathan Lambert details his writing process involving Notion, with Grammarly and ChatGPT aiding in edits before posting to Substack, while another user endorses Typora as a markdown editor.

CUDA MODE Discord Summary

• CUDA Emulation Exploration: @jash403 inquired about advice for running emulators on CUDA GPUs, leading @iron_bound to share a GitHub repository, krocki/nvgb, which emulates a Gameboy on CUDA. The project was highlighted in a Towards Data Science article describing it as the world’s fastest 8-bit console cluster.

• Praise for Triton Kernels Performance: @andreaskoepf lauded the unslothai Triton kernels for offering a 5X speed boost and 60% less memory use in QLoRA finetuning, while insights into integrating custom Triton kernels with torch.compile were shared from another channel, details of which are available on PyTorch GitHub.

• Deciphering GPU Cache and Memory Dynamics: @cudawarped opened a discussion on L2 cache efficiency, which suggests higher bandwidth for L2 cache compared to global memory, supported by a Stack Overflow thread and a study. @iron_bound praised the architectural analysis of Nvidia’s H100 found on Chips and Cheese.

• PyTorch’s Evolution and Compiler Talks Dominate Discussion: Among many topics in the #torch series of discussions, @marksaroufim shared the history of PyTorch rooted in LuaTorch around 2010. Users showed keen interest in the potential of solving GPU architecture optimization, debated compiler efficacy, and discussed educational material from companies like PolyMage Labs, especially on polyhedral compilation (here).

• True Identity of InvSqrt() Revealed: The discussion in the #algorithms channel fondly revisited the fast inverse square root algorithm used in Quake III, with @iron_bound sharing the Wikipedia link. @chhillee pointed out the intricacies of crafting a general-purpose implementation of such specific optimizations.

• Community Drives Ring Attention Development: An active collaboration was evident in #ring-attention where @ericauld invited feedback on a work-in-progress notebook showcasing ring attention mechanisms. @andreaskoepf offered to assist with side tasks, reinforcing the collaborative spirit. @nshepperd tackled technical challenges in implementing ring attention using jax and jax.Array.

Latent Space Discord Summary

• Lip Sync Enters the AI Stage: Pika has introduced an early access Lip Sync feature for Pro users, aimed to enhance AI-generated video realism but it’s still perceived as a bit uncanny according to feedback. Discover more in their announcement Pika’s Lip Sync Early Access.

• Conversation on AI-Powered Efficiency: Klarna’s AI assistant reportedly managed 2.3 million customer chats in a month, which has raised eyebrows and prompted discussions about the data behind AI effectiveness in customer service. Questions about the integrity of these numbers led to a share of a Fast Company article that casts doubt on the overly positive portrayal of AI’s impact.

• Elicit Hits a Milestone: Elicit’s growth to $1 million in annual recurring revenue just four months after launching subscriptions sparked celebrations among community members. This milestone hints at the scaling potential for AI businesses.

• Gemmas’s Tensor Tension: Technical challenges associated with running Google’s Gemma locally, particularly on MPS architectures, have been a focus, citing issues with complex tensor operations. Ongoing discourse includes references to the Gemma PyTorch GitHub repository for detailed exploration.

• A Peek into Coding Style with Noam Shazeer: Noam Shazeer’s first blog post highlighting coding style, with an emphasis on shape suffixes, has been shared within the community. AI engineers can read his insights here.

• Tuning in with Replicate’s CEO: A new podcast episode featuring the CEO of Replicate has been released, with the announcement shared on the guild’s #ai-announcements channel. Listen to it through swyxio’s tweet.

DiscoResearch Discord Summary

• RAG-LLM Optimization Query Unresolved: @rasdani inquired about end-to-end optimization of Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs) using gradients, referencing the LESS paper which tackles optimizer-aware data selection but does not backpropagate through data selection itself.

• DiscoLM_German_7b Outshines Leo Mistral 7B: In the German document extraction conundrum, @mab3049 faced challenges with Leo Mistral 7B, while @bjoernp recommended switching to DiscoLM_German_7b for better performance, as detailed in Hugging Face’s chat templating documentation.

• The Power of Proper Templating: Enhanced interactions with language models, specifically for German document extraction, are achievable through correct use of chat templates, improving the capabilities of models like DiscoLM_German_7b.

• Goliath Edges Out Llamaindex: @sebastian.bodza flagged issues with the llamaindex chunker for code, prompting @philipmay to propose the Goliath model as a superior choice for German language tasks, sparking a conversation around model preferences for specific language functionalities.

• Model Exploration Continues with Demonstrations: The guild discussed various models, exemplified by the DiscoLM German 7b Demo, to refine their approaches to specialized AI tasks like German document extraction.

LLM Perf Enthusiasts AI Discord Summary

• Anticipation Rises for Llama 3: User @res6969 sparked rumors about Llama 3 being released in the spring, but no official date has been confirmed.
• Expressing Latency Frustration: @res6969 voiced deep disappointment with OpenAI API response times, while @pantsforbirds echoed the sentiment, specifically targeting Azure hosting for poor performance.
• Seeking Clarity on Latency Issues: @justahvee sought to understand whether complaints about latency referred to time to the first token or overall completion time, to which @res6969 clarified it was the former.

AI Engineer Foundation Discord Summary

• Interview Prep Pro-Tip for AI Engineers: User iloveh8 asked for recommendations on preparing for an AI engineering interview but did not receive any responses yet.
• Catch the Agent Protocol V2 Coding Stream: _z invites the community to a coding live stream focused on Agent Protocol V2’s Config Options RFC, offering direct insights into the process.
• Attend the Voice + AI Meetup with Experts: @kwindla announced a Voice + AI meetup at Cloudflare, featuring AI experts like Jay Jackson from Oracle Cloud, with RSVP available for the 6:30 pm event on Wednesday.
• Inquiry About Voice + AI Event’s Online Accessibility: @yikesawjeez wants to know if the Voice + AI meetup will offer streaming for remote participation, showing interest in voice technology discussions.

Datasette - LLM (@SimonW) Discord Summary

• Claude’s JSON Drama: @derekpwillis is irked by Claude’s reluctance to output JSON objects without preamble, despite specific instructions to do so, which hinders users wanting pure JSON data.

• Layering Bolt AI on LLM: Angerman. expressed a desire to have a system like Bolt AI on top of LLM to facilitate enhanced interaction.

Skunkworks AI Discord Summary

The provided transcript does not contain any relevant technical or detailed discussion points for a summary aimed at an engineering audience. It appears to be a link shared in an off-topic channel without any accompanying context or discussion.

PART 2: Detailed by-Channel summaries and links

TheBloke ▷ #general (1271 messages🔥🔥🔥):

• Morpheus AI and Microsoft’s Investment: Morpheus AI, also known as TheBloke, seems to have been inactive for a while, raising questions about their activity on HuggingFace. Microsoft’s investment in Mistral AI sparked discussions about the implications for the AI industry, with concerns about monopolistic practices and the benefits of competition (TechCrunch article).

• Downloading Models from Hugging Face: Users discussed difficulties with downloading models from Hugging Face, experiencing bandwidth limitations and issues with Git LFS. The Hugging Face CLI and fast transfer were suggested as better alternatives for efficient downloading (Hugging Face CLI Guide).

• Discussion on Ethical AI and Censorship: A debate emerged on whether AI should reflect the world as it is or as it ought to be, with concerns about AI being ‘tortured’ to not be racist, and the impact of bias in datasets on AI behavior.

• Serverless Horror Stories: A discussion on the risks associated with serverless architectures surfaced, pointing out potential financial pitfalls such as unexpected large bills due to bandwidth overages from botnet activity (ServerlessHorrors).

• SSL Certificates and Cloudflare: Conversations about the costs and complexities of SSL certificates led to discussions on the advantages of services like Let’s Encrypt and Cloudflare for website security and DNS management. Concerns about Cloudflare’s business practices and per-request limitations were also highlighted.

Links mentioned:

• Microsoft has been secretly testing its Bing “Sydney” chatbot for years: Sydney first appeared in Bing in 2021.
• The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits: Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in which every single param…
• Google CEO tells employees Gemini AI blunder ‘unacceptable’: The Google CEO said the company is working around the clock on a fix for its AI image generator tool.
• UAI - Unleashing the Power of AI for Everyone, Everywhere: Introducing Universal AI Inference: The following text has been entirely written by Mistral’s great models. I’ve been hearing a lot of chatter about the need for more open models and community access to AI technology. It seems like ever…
• Come Look At This Come Look At This Meme GIF - Come Look At This Come Look At This Meme Run - Discover & Share GIFs: Click to view the GIF
• openbmb/MiniCPM-2B-dpo-bf16-llama-format · Hugging Face: no description found
• Mixture of Experts Explained: no description found
• High-Performance Llama 2 Training and Inference with PyTorch/XLA on Cloud TPUs: In a landscape where AI innovation is accelerating at an unprecedented pace, Meta’s Llama family of open sourced large language models (LLMs) stands out as a notable breakthrough. Llama marked a signi…
• Paper page - Nemotron-4 15B Technical Report: no description found
• Microsoft made a $16M investment in Mistral AI | TechCrunch: Microsoft is investing €15 million in Mistral AI, a Paris-based AI startup working on foundational models.
• nvidia/canary-1b · Hugging Face: no description found
• Lecture 10.2 — Mixtures of Experts — [ Deep Learning | Geoffrey Hinton | UofT ]: 🔔 Stay Connected! Get the latest insights on Artificial Intelligence (AI) 🧠, Natural Language Processing (NLP) 📝, and Large Language Models (LLMs) 🤖. Fol…
• Microsoft made a $16M investment in Mistral AI | TechCrunch: Microsoft is investing €15 million in Mistral AI, a Paris-based AI startup working on foundational models.
• Command Line Interface (CLI): no description found
• bigcode/the-stack-v2 · Datasets at Hugging Face: no description found
• EMO-Emote Portrait Alive: Emote Portrait Alive: Generating Expressive Portrait Videos with Audio2Video Diffusion Model under Weak ConditionsProject: https://humanaigc.github.io/emote-…
• LLM Performance on Groq LPU™ Inference Engine: Discord: groq.link/discord
• 7. Logging Metrics for Production: PygmalionAI’s large-scale inference engine. Contribute to PygmalionAI/aphrodite-engine development by creating an account on GitHub.
• Lecture 10.2 — Mixtures of Experts — [ Deep Learning | Geoffrey Hinton | UofT ]: 🔔 Stay Connected! Get the latest insights on Artificial Intelligence (AI) 🧠, Natural Language Processing (NLP) 📝, and Large Language Models (LLMs) 🤖. Fol…
• The Sarah Test: (by #theyallchoppable on the Ooba and SillyTavern Discord servers) See also: https://rentry.org/thecelltest The Sarah Test is a simple prompt to test a model’s coherency, logical consistency, whatever…
• ServerlessHorrors | Home: Stories you never want to feel on your own skin
• GitHub - pbelcak/fastfeedforward: A repository for log-time feedforward networks: A repository for log-time feedforward networks. Contribute to pbelcak/fastfeedforward development by creating an account on GitHub.
• GitHub - HumanAIGC/EMO: Contribute to HumanAIGC/EMO development by creating an account on GitHub.
• llama/TORCH_XLA_USER_GUIDE.md at llama2-google-next-inference · pytorch-tpu/llama: Inference code for LLaMA models. Contribute to pytorch-tpu/llama development by creating an account on GitHub.
• Fire Writing GIF - Fire Writing - Discover & Share GIFs: Click to view the GIF
• Blood GIF - Blood - Discover & Share GIFs: Click to view the GIF
• GitHub - PygmalionAI/aphrodite-engine: PygmalionAI’s large-scale inference engine: PygmalionAI’s large-scale inference engine. Contribute to PygmalionAI/aphrodite-engine development by creating an account on GitHub.
• Page Tear Infuriating Page GIF - Page Tear Infuriating Page Paper Tear - Discover & Share GIFs: Click to view the GIF
• GLU Variants Improve Transformer: Gated Linear Units (arXiv:1612.08083) consist of the component-wise product of two linear projections, one of which is first passed through a sigmoid function. Variations on GLU are possible, using di…
• GitHub - cloudflare/cfssl: CFSSL: Cloudflare’s PKI and TLS toolkit: CFSSL: Cloudflare’s PKI and TLS toolkit. Contribute to cloudflare/cfssl development by creating an account on GitHub.
• GitHub - Beomi/BitNet-Transformers: 0️⃣1️⃣🤗 BitNet-Transformers: Huggingface Transformers Implementation of “BitNet: Scaling 1-bit Transformers for Large Language Models” in pytorch with Llama(2) Architecture: 0️⃣1️⃣🤗 BitNet-Transformers: Huggingface Transformers Implementation of "BitNet: Scaling 1-bit Transformers for Large Language Models" in pytorch with Llama(2) Architecture - Beomi/…

TheBloke ▷ #characters-roleplay-stories (619 messages🔥🔥🔥):

• Discussing the Wonders and Limits of Roleplay Models: Users in the chat, like @nathaniel__, shared insights from their remarkable experiences with offline models, discussing characters with complex backstories and personalities. The conversation ventured into parallels between human cognition and model behavior, and the idea of “confabulation” versus “hallucination” when it comes to LLMs generating content.

• The Marauding Meth Head Model: @xtreme420 shared their surprise at how effective the Wizard-Vicuna-30B-Uncensored model was with minimal prompting, producing responses involving slurs and illicit activity instructions.

• Stringent Stance on Safety: @c.gato, @nathaniel__, and others debated the ethical and safety considerations of LLMs, arguing that while the technology itself lacks agency, it’s the actions people take based on the model’s output that can be problematic.

• Technical Trials and Tribulations: Technical discussions occurred regarding the quantization and fine-tuning of models (@mrdragonfox), the efficacy of different LLMs, and the operational nuances of using models like Mistral 7B (@johnrobertsmith).

• Mac vs. PC for Model Operation: An extensive debate unfolded about the merits of using MacOS with Apple’s M series chips for running models, with @mrdragonfox advocating for their value in specific applications despite the broader community’s mixed feelings about Apple products.

Links mentioned:

• PotatoOff Models: no description found
• deepseek-ai/deepseek-coder-7b-instruct-v1.5 · Hugging Face: no description found
• Skeptical Futurama GIF - Skeptical Futurama Fry - Discover & Share GIFs: Click to view the GIF
• TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF · Hugging Face: no description found
• No Biting Smiling Friends GIF - No Biting Smiling Friends Adult Swim - Discover & Share GIFs: Click to view the GIF

TheBloke ▷ #training-and-fine-tuning (32 messages🔥):

• New Python Libraries Challenge for LLMs: @guudbaad suggests teaching models to prefer usage examples provided with prompts and outlines a pre-processing strategy involving scraping GitHub and using multiple LLMs for reverse engineering code. No specific links provided.
• Finetuning Framework for Coding Models: @dirtytigerx recommends using DeepSeek’s open-sourced framework for fine-tuning coding models and discusses the complexity of data preparation; they also shared the GitHub link to the DeepSeek-Coder.
• Simplifying Data Retrieval: The same user suggests writing a scraper for online documentation and utilizing OpenAI’s custom GPT for retrieval, without sharing any further details or links.
• QLoRA Technique for Cost-Effective Model Training: @maldevide provides a link to GitHub for the Unsloth project, which helps with free QLoRA fine-tuning, offering a starting point for learning about training AI models. Access the project here.
• Estimating Compute for LLM Training & Smaller Scale Tests: @dirtytigerx suggests doing small test runs to estimate GPU hours for LLM training and mentions that many papers list their training run durations. They also recommend training smaller scale models firsthand for a better understanding.

Links mentioned:

• GitHub - unslothai/unsloth: 5X faster 60% less memory QLoRA finetuning: 5X faster 60% less memory QLoRA finetuning. Contribute to unslothai/unsloth development by creating an account on GitHub.
• GitHub - deepseek-ai/DeepSeek-Coder: DeepSeek Coder: Let the Code Write Itself.): DeepSeek Coder: Let the Code Write Itself. Contribute to deepseek-ai/DeepSeek-Coder development by creating an account on GitHub.

TheBloke ▷ #model-merging (1 messages):

falconsfly: Because a singble bit is misplaced / misaligned tensor dims

TheBloke ▷ #coding (17 messages🔥):

• GGUF Conversion Confusion Cleared: User @toranb queried about the correct arguments for converting a Hugging Face model to GGUF to generate Q5 KM output. Following a clarification from @dirtytigerx, it was emphasized that the convert.py script is for conversion, not quantization, and the quantization is a separate step described in the llama README.

• Seeking Speed in CSV Chatting: .ajayprince asked for advice on creating a quick-responsive chat system utilizing a CSV file, noting that using llama-2-7b-chat.ggmlv3.q4_0.bin takes about 10 minutes per result and hoping to reduce this to under one minute.

• GPU Limits Hit on Colab: .ajayprince mentioned the absence of available GPU units on Google Colab as a bottleneck, leading to the search for alternative solutions for faster processing.

• Cloud Inference as a Possible Solution: @tom_lrd and @dirtytigerx suggested using cloud APIs like those from Hugging Face, together.ai, or other providers to enhance inference speeds, acknowledging that without a GPU, local processing will inevitably be slow.

• Offer to Enhance and Collaborate: @falconsfly offered @wolfsauge help with their project and @wolfsauge expressed eagerness to learn and discuss the ideas after their dinner time.

Links mentioned:

• llama.cpp/convert.py at master · ggerganov/llama.cpp: LLM inference in C/C++. Contribute to ggerganov/llama.cpp development by creating an account on GitHub.
• GitHub - ggerganov/llama.cpp: LLM inference in C/C++: LLM inference in C/C++. Contribute to ggerganov/llama.cpp development by creating an account on GitHub.

Mistral ▷ #models (22 messages🔥):

• Speculating on Mistral’s Model Sizes: @rabdullin expressed that Mistral Medium might be equivalent to 70B params, and Mistral Large could potentially utilize a mixture of experts (MoE), delving into theoretical aspects of model scaling.

• Mistral’s Market Maneuver: @rabdullin highlighted the potential for Mistral AI to generate more revenue after gaining another avenue to offer their models to enterprise customers, particularly in the USA, which could support their efforts in open-source models as well.

• New Larger Model Impressions: @rabdullin applauded Mistral Large for its superior performance as compared to Mistral Medium and outperforming all models from Anthropic, specifically in enterprise and business task benchmarks.

• Discussing Model Tuning and Pricing: @sublimatorniq pointed out the challenges in tuning models without guidance and the significant price difference of new models, despite an observed improvement in model performance.

• Mistral Chatbot Development Issues: Chatbot development using Mistral models exhibited some technical challenges, with @myhaw encountering a specific error message when attempting to initialize conversations with the large model but later resolving the issue while @lerela acknowledged the issue and mentioned a fix that provides clearer error messaging.

Mistral ▷ #deployment (16 messages🔥):

• Mistral’s Open Source Confusion Cleared: There was a brief exchange where @jdwebprogrammer lamented about Mistral seemingly moving to closed source. @mrdragonfox clarified it was never open source, but has two openweight models, and a first release without an openweight model doesn’t imply the end of contributions.

• Acknowledgment of Mistral’s Contributions: The chat participants, specifically @mrdragonfox and @jdwebprogrammer, acknowledged Mistral’s significant contributions to the large language model (LLM) landscape despite the uncertainties around its open source status.

• Vulkan Backend Boosts LLM Performance: @saintvaseline shared their excitement about the new llama.cpp Vulkan backend enabling efficient operation of 7-billion parameter models on average AMD gaming PCs with decent GPUs and expressed an intention to push for even more performance with an 8x7B setup.

• Mixed Reactions to Vulkan Backend: While @saintvaseline reported impressive speeds using the Vulkan backend, @tokenshifter countered with a technical limitation, noting that Vulkan API bypasses tensor accelerators on some GPUs, utilizing the 3D shader engine instead.

• Executing Large Models on Multiple GPUs: @pteromaple inquired about performing inference on large models like Mixtral 8x7B using multiple GPUs, citing a Hugging Face tutorial. @dawn.dusk confirmed that this is indeed the recommended approach.

Links mentioned:

Handling big models for inference: no description found

Mistral ▷ #ref-implem (76 messages🔥🔥):

• Typo Alert in the Learning Notebook: @foxalabs_32486 identified a minor typo in the prompting_capabilities.ipynb example. The phrase “Few-shot learning or in-context learning or is when we give a few examples in the prompts…” should read “Few-shot learning is when we give a few examples in the prompts…” and the error has been acknowledged to be fixed by @sophiamyang.

• The Humanity of Typos: @dawn.dusk humorously remarks that typos are proof of humanity, which prompts a discussion on intentionally incorporating errors to make AI seem more human. @foxalabs_32486 and @mrdragonfox speculate on the ethics of this approach.

• The Ethical Dilemma of Human-Mimicking AI: The chat reveals a reluctance from some developers like @mrdragonfox to create AI that intentionally mimics human errors, citing ethical reasons, even in the face of client requests.

• AI Industry’s Hiring Challenges: @foxalabs_32486 discusses the difficulties in hiring within the AI industry due to a knowledge vacuum and the high demand for those with expertise.

• Market Opportunities in AI: Participants @foxalabs_32486 and @mrdragonfox explore various market potentials for AI, including management consulting and industries beyond corporate focus areas like sports or wellness.

Mistral ▷ #finetuning (33 messages🔥):

• Understanding the Scope of LoRa: @ethux clarified that LoRa is used for fine-tuning behavior, not for adding new information.
• LoRa Fine-Tuning on AWQ Models: @kushagra_67246 inquired whether it’s possible to apply LoRa fine-tuning to an existing AWQ model hosted on Hugging Face, like casperhansen/mistral-7b-instruct-v0.1-awq.
• Resource Requirements for Mistral Finetuning: @kunpengguo was advised by @mr_seeker that full-finetuning Mistral-8x7B requires 1.2Tb of CPU RAM and 96 Gb of VRAM with deepspeed ZeRO3 after experiencing an out-of-memory error.
• Adding Documents to Train Models: @aaronbarreiro discovered through conversation with @mrdragonfox, that while there’s no system akin to OpenAI’s RAG, documents like PDFs need conversion to text for ingestion, but are limited to 32k tokens, and the model has no persistent memory.
• Using a Guide for Mistral Fine-Tuning: @nicklashinsky shared a useful resource (Mistral Fine-tune Guide) for getting started with Mistral fine-tuning; however, no specific standout points were mentioned in the discussion.

Links mentioned:

Brev.dev Console: no description found

Mistral ▷ #showcase (9 messages🔥):

• Mistral Large Deployment Guide Unveiled: User @raoufchebri shared a step-by-step guide for deploying Mistral Large on Azure, integrated with LangChain. They provided a neon.tech blog post detailing the process and asked for feedback from the community.

• Get Syncopated with Mistral: @boles.ai praised the Mistral Large API for creating impressive lyrics for two distinct music styles for his podcast, with the music and vocals provided by Suno.ai.

• Sensei Integrates MistralAI: @deexxcryptz announced that Sensei, a synthetic data generation tool, now supports the MistralAI’s API. More details and a usage guide can be found in their GitHub repository and a tweet linked in the message.

• Mistral Large on YouTube: User @arunprakashai shared a YouTube video titled “Start Using Mistral Large: Powerful and Cheaper than GPT4,” which contains a tutorial on how to utilize the Mistral Large model and integrate it with chat applications.

• Mistral Medical Model Tuning: @cogbuji mentioned a Fine-tuned Mistral / OpenHermes model with medical terminology data, available on Hugging Face. A link to the specific model was given with a brief backstory and a homage to Fela Kuti’s song. Check the model here.

Links mentioned:

• cogbuji/Mr-Grammatology-clinical-problems-Mistral-7B-0.5 · Hugging Face: no description found
• Deploy Mistral Large to Azure and create a conversation with Python and LangChain - Neon: We’re Neon, and we’re redefining the database experience with our cloud-native serverless Postgres solution. If you’ve been looking for a database for your RAG apps that adapts to your application loa…
• Mistral Large: Mistral Large is our new cutting-edge text generation model. It reaches top-tier reasoning capabilities. It can be used for complex multilingual reasoning ta…
• Start Using Mistral Large: Powerful and Cheaper that GPT4: We learn the features of High Performing Mistral Large and do live coding on Chat Completions with Streaming and JSON Mode. The landscape of artificial intel…
• GitHub - migtissera/Sensei: Generate Synthetic Data Using OpenAI or MistralAI: Generate Synthetic Data Using OpenAI or MistralAI. Contribute to migtissera/Sensei development by creating an account on GitHub.

Mistral ▷ #random (136 messages🔥🔥):

• Google’s Gemini and Irony of Resources: @egalitaristen expressed skepticism about the performance of Google’s enterprise tools despite their vast resources. Conversations touched on potential differences between public and enterprise versions, but ultimately @egalitaristen remained unconvinced, hoping for hands-on testing to believe in Google’s capabilities.

• Mistral Discord Casts Doubt on 1.5 PRO’s Abilities: @sublimatorniq and @egalitaristen discussed the capabilities of 1.5 PRO concerning long-context understanding, code generation, and reasoning, with mixed reception. While @sublimatorniq shared a GitHub Gist of a snake game code, @egalitaristen tested the model with both coding prompts and reasoning questions, finding it less than satisfactory, especially compared to other models like Next, Large, and Mixtral.

• Testing AI’s Video Comprehension Skills: @sublimatorniq shared their experience using AI to search and describe video content, indicating that the AI’s performance was poor for certain types of content. Frustration with Google’s lack of community beta testing was also voiced by @egalitaristen, suggesting a disconnect between Google’s internal testing conditions and real-world usage.

• Tokenization Tools and Community Contributions: A useful tool for comparing model tokenization was shared by @daain, providing an online LLM tokenizer. This tool facilitates comparing the token counts of different AI models and aids in debugging prompt templates.

• Link to Prosody Discussion: @kilianbutler provided a link to a blog post discussing how babies learn to speak based on prosody before language, considering it a foundational aspect of communication and a potential area for improving generative speech performance.

Links mentioned:

• LLM Tokenizer: no description found
• Babys talk in baby language in front of fridge: How do humans learn to speak?Prosody is the diverse phenomena of cadence and intonations that humans use to communicate. Babies learn prosody before they learn language. I could rely on academic…
• gist:ace89a8a89cbf8e3f945a3beb287426b: GitHub Gist: instantly share code, notes, and snippets.
• gist:12ea4acb495f6f00e8b6fe53a3eef898: GitHub Gist: instantly share code, notes, and snippets.
• gist:063b582168b0eea79a30a98ad3249731: GitHub Gist: instantly share code, notes, and snippets.
• AVX2 is dimwitted compared to AVX512 · Issue #23 · google/gemma.cpp: On a $10,000 AMD Ryzen 7995WX (znver4 avx512) Gemma 7b instruct sfp is able to solve mathematical riddles. But on a $600 Intel i9-14900K (raptorlake avx2) the same Gemma model gives the fool’s ans…

Mistral ▷ #la-plateforme (122 messages🔥🔥):

• Model Performance Discussions: @sublimatorniq discussed Mistral-Large’s performance, pondering its speed compared to Mistral-Medium and GPT-4’s inconsistent throughput. The lack of supporting data was acknowledged, but the preference for Mistral-Large was still expressed.

• Inconsistencies and Errors with Mistral-Large: Users, including @michaelhunger and @sublimatorniq, reported issues with Mistral-Large on “La Platforme,” such as unauthorized errors, service unavailability (internal_server_error), and read timeouts.

• Challenges with Function Calling on Mistral: @michaelhunger, @liebke, and @sophiamyang engaged in an extended discussion about complexities and inconsistencies when using function calling with Mistral models. Users shared experiences and examples where the model did not behave as expected or required workarounds.

• Integration and Feedback on Mistral’s Function Calling: @alexclubs provided feedback on integrating Mistral Function Calling into the Profound Logic solution, pointing out differences from OpenAI’s implementation and issues with triggering function calls consistently.

• Privacy Policies for “Le Chat” Discussed: A conversation about the privacy implications of using the free Le Chat service led to users sharing the Mistral terms of use and discussing opt-out options.

Links mentioned:

• Technology: Frontier AI in your hands
• Google Colaboratory: no description found
• Legal terms and conditions: Terms and conditions for using Mistral products and services.
• Function Calling | Mistral AI Large Language Models: Function calling allows Mistral models to connect to external tools. By integrating Mistral models with external tools such as user defined functions or APIs, users can easily build applications cater…
• Google Colaboratory: no description found
• AI Assistants are the Future | Profound Logic: With Profound AI, you can enhance your legacy applications with natural language AI assistants in just 3 steps.
• AI Assistants are the Future | Profound Logic.): With Profound AI, you can enhance your legacy applications with natural language AI assistants in just 3 steps.
• GitHub - liebke/mechanician: Daring Mechanician is a Python library for building tools that use AI by building tools that AIs use.: Daring Mechanician is a Python library for building tools that use AI by building tools that AIs use. - liebke/mechanician
• mechanician/packages/mechanician_mistral/src/mechanician_mistral/mistral_ai_connector.py at main · liebke/mechanician: Daring Mechanician is a Python library for building tools that use AI by building tools that AIs use. - liebke/mechanician
• mechanician/examples/notepad/src/notepad/main.py at main · liebke/mechanician: Daring Mechanician is a Python library for building tools that use AI by building tools that AIs use. - liebke/mechanician

Mistral ▷ #le-chat (414 messages🔥🔥🔥):

• UI/UX Suggestions for Mistral: @onuralp. shared feedback on Le Chat’s interface, specifically finding the options for deleting chat confusing and suggesting the addition of a ‘Rename’ option, comparing it unfavorably to the ChatGPT UI.
• Inquiry About Model Support for Function Calling: @catto_chan asked about the capabilities of the Mistral models regarding function calling, to which @mrdragonfox clarified that mistral-small-2402 and mistral-large-2402 support this feature; he also linked to the pricing and features page for further details.
• Concerns About Latex Rendering: Users like @alexeyzaytsev have noted that Latex seems broken in Le Chat’s front-end, considering it a point in need of improvement.
• Groq Hardware Utilization Discussions: @foxalabs_32486 pondered the feasibility of running Mistral models on Groq hardware due to Groq’s on-die memory, sparking a detailed discussion on hardware suitability and economic efficiency with @mrdragonfox.
• Small Details Matter: User @_._pandora_._ pointed out a slight inconsistency in icon sizes within the UI, which they found very distracting. @lerela acknowledged the feedback and promised a fix, which highlights the team’s responsiveness to community feedback.

Links mentioned:

• Pricing and rate limits | Mistral AI Large Language Models: Pay-as-you-go
• Technology.): Frontier AI in your hands
• Technology: Frontier AI in your hands
• Ni No Kuni Ni No Kuni 2 GIF - Ni no kuni Ni no kuni 2 Ni no kuni revenant kingdom - Discover & Share GIFs: Click to view the GIF
• no title found: no description found

LM Studio ▷ #💬-general (511 messages🔥🔥🔥):

• Model Load and Performance Confusions: Users like @sh4d0w66 and @tongnamtuanvu experienced issues when attempting to load large models on LM Studio, reporting errors and seeking clarification on whether their hardware was sufficient. For example, @sh4d0w66 inquired about running a 35GB model with 60GB RAM and 8GB VRAM, and some users advised that it would work but would be slow. @tongnamtuanvu faced errors when trying to load specific models and was unsure how to proceed.

• LM Studio vs. Macs for LLMs: @heyitsyorkie and @johnnyslanteyes discussed hardware configurations, with @johnnyslanteyes explaining that LLM inference on Macs is primarily RAM-dependent, while @heyitsyorkie noted that on Windows, GPU offloading can lead to faster inference.

• Exploring New Ternary Model Research: @garblyx shared excitement over breakthroughs in LLM training reducing model sizes without losing performance. The link to the paper https://arxiv.org/abs/2402.17764 was mentioned as a significant advancement, potentially enabling 120B models to fit into 24GB VRAM GPUs.

• Desire for Updated Fabric Modding Info in LLMs: @surrender asked about how to ensure an LLM uses the most recent information about the Fabric modding API for Minecraft to avoid outdated advice. They pondered whether they should train their own model or use embeddings but were unclear on the steps to achieve this.

• Pine Script AI Request: @andr0. inquired if there’s an AI that can write code in Pine Script. @abbeyy_9021 responded by suggesting to use code llama 70B or directed @andr0. to a custom GPT for Pine Script available at https://chat.openai.com/g/g-VRzMQlMs4-pine-script-pro.

Links mentioned:

• Au Large: Mistral Large is our flagship model, with top-tier reasoning capacities. It is also available on Azure.
• Open LLM Leaderboard - a Hugging Face Space by HuggingFaceH4: no description found
• itsdotscience/Magicoder-S-DS-6.7B-GGUF at main: no description found
• Hugging Face – The AI community building the future.: no description found
• Hugging Face – The AI community building the future.: no description found
• LM Studio Models not behaving? Try this!: The repository for free presets:https://github.com/aj47/lm-studio-presets➤ Twitter - https://twitter.com/techfrenaj➤ Twitch - https://www.twitch.tv/techfren…
• GitHub - havenhq/mamba-chat: Mamba-Chat: A chat LLM based on the state-space model architecture 🐍: Mamba-Chat: A chat LLM based on the state-space model architecture 🐍 - havenhq/mamba-chat
• The Needle In a Haystack Test: Evaluating the performance of RAG systems
• Reddit - Dive into anything: no description found
• Performance of llama.cpp on Apple Silicon M-series · ggerganov/llama.cpp · Discussion #4167: Summary LLaMA 7B BW [GB/s] GPU Cores F16 PP [t/s] F16 TG [t/s] Q8_0 PP [t/s] Q8_0 TG [t/s] Q4_0 PP [t/s] Q4_0 TG [t/s] ✅ M1 1 68 7 108.21 7.92 107.81 14.19 ✅ M1 1 68 8 117.25 7.91 117.96 14.15 ✅ M1…

LM Studio ▷ #🤖-models-discussion-chat (36 messages🔥):

• Quantization Quandaries: @wolfspyre questioned if different quant storage constructs affect how models internalize data, whether these constructs influence the memory or interpretation of the input. A follow-up query was made about a method of forced tokenization by wrapping text in double colons, but @aswarp responded with skepticism, hinting at new developments like Mambabyte that might eclipse current techniques.

• Model Recommendations and Limitations: In response to @ptable’s inquiry, @wilsonkeebs confirmed that LM Studio supports specific quants as they were part of llamacpp updates months ago. However, @ptable mentioned difficulties with senku quant’s compatibility, to which @wilsonkeebs linked a successful example from Hugging Face, showcasing Noromaid with Mixtral-Instruct compatibility.

• PDF Bot Command Precision: @solenya7755 sought advice for improving a PDF chatbot that returns precise commands, with @nink1 recommending more refined prompts and suggesting the AnythingLLM discord and langchain scripts for optimization.

• Speed and Configuration for Mixtral Model: @nullt3r expressed concerns about the speed of the Mixtral 8x7b instruct model on RTX3090 GPUs, sharing a 15t/s output, with @.ben.com reporting better speeds using 2x3090 GPUs. @nullt3r also discovered a significant speed increase using default ollama settings for the Q5 K M version of the model.

Links mentioned:

• RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback: Reinforcement learning from human feedback (RLHF) has proven effective in aligning large language models (LLMs) with human preferences. However, gathering high-quality human preference labels can be a…
• Artefact2/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss-GGUF at main: no description found
• llama : add BERT support · Issue #2872 · ggerganov/llama.cpp: There is a working bert.cpp implementation. We should try to implement this in llama.cpp and update the embedding example to use it. The implementation should follow mostly what we did to integrate…

LM Studio ▷ #🎛-hardware-discussion (109 messages🔥🔥):

• LLM’s for Electric Vehicles: User @mikeypainebrain queried whether to use Large Language Models (LLMs) and prompt engineering for electric vehicles, battery supply chain, or other financial and policy applications on a given hardware configuration. Discussion followed but did not provide specific insights or recommendations.

• Memory Woes with LM Studio: @666siegfried666 experienced crashes and lost partitions while interfacing with LM Studio, speculating it to be related to RAM stability or possibly Windows corruption. Multiple users, including @jedd1, discussed potential hardware issues, suggesting memtests and considering ECC memory.

• TinyBox Preorders and Specs Revealed: @senecalouck shared a link and details about TinyCorp’s TinyBox, designed to commoditize the petaflop. The high-spec hardware, including 6x 7900XTX GPUs and an EPYC 7532 CPU, aims to push limits in both hardware and software for AI processing.

• GPU Compatibility Issues with LLM: Users @warcrow7 and @jans_85817 reported issues loading LLMs onto NVIDIA GPUs with @quickdive. attempting to troubleshoot through questioning and suggestions but admitted limitations due to not having a NVIDIA card for personal testing.

• Potential Windows Corruption Affecting LM Studio: @666siegfried666 continued troubleshooting hardware problems linked to LM Studio’s performance, eliminating CPU and RAM as the cause, leaning toward OS corruption or power supply issues. Advised by @.bambalejo to check for certain settings in Windows that could exacerbate issues.

Links mentioned:

• Join the tinygrad Discord Server!: The place for discussion of tinygrad development and tinygrad usage. | 5204 members
• Tweet from the tiny corp (@tinygrad): A bunch of rambling about the tinybox. I don’t think there’s much value in secrecy. We have the parts to build 12 boxes and a case that’s pretty close to final. Beating back all the PCI-E…
• You Are Banned Ban Him GIF - You are banned Banned Ban - Discover & Share GIFs: Click to view the GIF

LM Studio ▷ #🧪-beta-releases-chat (4 messages):

• Channel Redirection for Queries: @quantumnebula suggested that a question might be more suitable for another channel, but did not specify the topic of the inquiry.

• Adding Images to LM Studio: @heoheo5839 inquired about how to add an image in LM Studio and followed up saying they couldn’t find the ‘Assets’ Bar as instructed by their search.

• Detailed Steps to Include Images: @heyitsyorkie provided a detailed answer to @heoheo5839 about inserting images into LM Studio, indicating that a llava model like PsiPi/liuhaotian_llava-v1.5-13b-GGUF/ must be used and both the mmproj (the vision adapter) and gguf of the model must be downloaded to add images. They also clarified that images could only be described and not generated within LM Studio.

LM Studio ▷ #autogen (9 messages🔥):

• Model Response Time Varies with Token Counts: @thebest6337 explained that each agent processes every other token from the generated text, so an increased number of tokens leads to longer processing times.

• Inquisitive on Setting Seeds: @qlfdengr queried how the seed value was set to 0, questioning whether this function is in the UI of Autogen.

• Gemini vs. Chat GPT in Translation Tasks: @hypocritipus used Gemini and Chat GPT for translating psychological reports into English; Gemini was preferred for the majority but often included unwarranted formatting and inserted its own interpretations.

• Chat GPT Provides Inferior Translations But More Direct: For the final report, @hypocritipus turned to Chat GPT due to Gemini’s noncompliance, noting that Chat GPT’s translation was notably poorer.

• Translation Clarification: @johnnyslanteyes sought clarification on what @hypocritipus meant by translation, which was specified to be from Turkish to English, not relating to medical jargon.

LM Studio ▷ #langchain (1 messages):

.eltechno: yes and it supper fast

LM Studio ▷ #open-interpreter (44 messages🔥):

• WSL Troubles for Local Mode: @nxonxi faced issues with running local mode in WSL (Windows Subsystem for Linux) on Windows 11, unable to establish a connection to an endpoint with an httpcore.ConnectError: [Errno 111] Connection refused error.
• Localhost Conundrum in WSL: The problem stemmed from WSL’s handling of localhost, where @nxonxi discovered using the real local IP network address instead of localhost was necessary.
• Seeking Configuration Guidance: @nxonxi was unsure of the configuration file used to change the URL for connection, and @1sbefore pointed them towards documentation at Open Interpreter’s Docs.
• Solution Close At Hand: @1sbefore provided a code snippet from the documentation which could solve @nxonxi’s issue by setting interpreter.llm.api_base to point at any OpenAI compatible server.
• Triumph Over The Localhost Issue: After considering that @1sbefore provided information on how localhost behaves differently in WSL1 and WSL2 due to networking differences, @nxonxi successfully ran LM Studio and received responses to their requests.

Links mentioned:

• LM Studio - Open Interpreter: no description found
• How to access localhost of linux subsystem from windows: I am using windows 10 and I have ubuntu 16.04 installed as linux subsystem. I am running a rails app on port 4567, which I want to access from windows. I know an approach of using ip address,…

OpenAI ▷ #ai-discussions (83 messages🔥🔥):

• Debating Sora’s Release Format: @g.antoine inquired about the release form of Sora, speculating whether it would be as an integrated part of ChatGPT or a standalone app. In response, @braydie expressed that Sora might initially launch as a separate entity, similar to DALL-E, before potentially integrating with ChatGPT.

• Memory Feature Roll-out Speculations: @.wakamesalad questioned the availability of the memory feature, to which @lugui responded it’s being released gradually to random users.

• Mamba Algorithm Insights and Skepticism: Within a conversation about new algorithms for AI, @lugui explained that Mamba models can handle more context but suffer from forgetting minor details deemed unimportant, which sparked some doubts and discussions.

• Thoughts on Race to AI Excellence: @blckreaper compared Mistral Large to GPT-4, noting it’s just 20% behind in performance. @santhought added that Mistral has teamed up with Microsoft and is available on Azure.

• Feedback on Copilot’s Alleged Biases: @chonkyman777 claimed their evidence of Copilot displaying bias was deleted by the OpenAI bot, eliciting a response from @eskcanta with guidance on how to report such issues directly through Modmail and OpenAI’s feedback form.

Links mentioned:

Chat model feedback: no description found

OpenAI ▷ #gpt-4-discussions (53 messages🔥):

• GPT-4 Troubleshooting: User @howsdajello experienced issues with GPT-4 not responding to input, despite attempting relog fixes. @tartimalin1 also reported GPT-4 giving inaccurate research answers and speculated on language performance differences.
• Customization Confusion: @the.f00l sought clarification on the specifics of uploading ‘Knowledge’ files to custom GPTs, which was resolved by @elektronisade sharing the OpenAI File Uploads FAQ.
• Query on API Performance: @starlss inquired about the response time for API requests with 2-3k tokens, and @rendo1 estimated 15-20 seconds for large requests but noted dependency on other factors.
• API and File Functionality Frustration: @ray_themad_nomad expressed dissatisfaction with GPT-4’s performance even after uploading files and creating custom APIs, with @darthgustav. suggesting constraints due to the size of the text.
• Exploring GPT Visualizations: @chotes shared a link to a conversation with GPT on feature visualizations in neural networks, finding it an enlightening discussion on understanding model responses.

OpenAI ▷ #prompt-engineering (240 messages🔥🔥):

• Meta-Prompting Takes the Stage: A discussion on meta-prompting techniques evolved over multiple posts, beginning with @vlrevolution’s curiosity and leading to a deep dive into producing cohesive, 22-page outputs from a single prompt. Despite initial skepticism, the method sparked interest but descriptions remained vague, shrouded in mystery without definitive shared insights or procedures.

• Safety in Technology Discussion Unravels: In a lengthy exchange about data privacy and safety, users expressed concerns and sought clarification regarding the privacy of their data when using OpenAI’s services. @eskcanta and @madame_architect provided reassurances and context, explaining that despite the large-scale access to data by companies and potential legal implications, it’s unlikely that any one person’s data is being scrutinized without significant cause.

• Prompt Engineering Gurus at Play: The channel’s participants, including @darthgustav and @madame_architect, discussed various aspects of prompt engineering with focus on papers and strategies like MetaPrompting and LongRoPE. @madame_architect has been diligently annotating papers relevant to prompt architecture, now amounting to a curated list of 42 papers, aiming to optimize soft prompts in NLP models for better performance, particularly in few-shot learning scenarios.

• Social Media Content Creation Conundrum: User @tryharder0569 solicited advice for writing prompts to generate authentic, engaging social media content without it sounding outdated or lacking social awareness. @eskcanta responded with suggestions to infuse style and substance into prompts to achieve the desired cool and effortless tone that resonates with modern audiences.

• Conversations on AI Ethics and Output Cohesion: Alongside technical discussions, users like @architect_of_ai and @darthgustav touched on the ethics of AI-generated content, with a focus on whether and how models adhere to ethical frameworks within the self-prompting process. There was also debate over the plausibility of a model autonomously writing cohesive, expansive documents after a so-called base “primer” from fine-tuning.

Links mentioned:

• Meta-Prompting Concept: Asking Chat-GPT for the best prompt for your desired completion, then to revise it before using it: Has anyone employed this approach? I’ve found it helpful when crafting prompts, to literally ask Chat-GPT to help create the prompt for a given goal that I will describe to it while asking what could …
• Usage policies: no description found
• Terms of use: no description found

OpenAI ▷ #api-discussions (240 messages🔥🔥):

• Meta-Prompting Methods Spark Discussion: Users @architect_of_ai and @darthgustav. discuss a complex process of meta-prompting where models teach themselves to generate better prompts. Despite skepticism about the methodology’s transparency, discussions revolve around the self-improvement of language models and their ability to plan and reflect on ethical frameworks.

• In-Depth Prompt Engineering Insights Shared: @madame_architect meticulously annotates the MetaPrompting paper emphasizing its optimization of prompt initialization that could revolutionize prompt designing in NLP applications. Their ongoing effort of compiling and annotating prompt-related research provides a valuable resource for the community.

• Sharing Knowledge With Discretion: @architect_of_ai offers to share links via direct message to bypass the channel’s restrictions, and @.braydie confirms receipt of helpful resources to read about self-discovery in AI.

• Challenges in Code Generation with GPT-4: User @tawsif2781 raises an issue where responses during code generation are incomplete despite setting a max token count, seeking advice from others. @madame_architect and @eskcanta contribute with their own experiences and potential troubleshooting approaches like adjusting complexity.

• Privacy Concerns in AI Usage Addressed: @s_p_e_c shares a response from Support regarding privacy concerns, prompting @eskcanta and @madame_architect to point out the broad lack of privacy in technology and OpenAI’s need for limited access to user data for legal and bug-fix reasons. This invokes a conversation about the necessity and limitations of user privacy in technology platforms.

Links mentioned:

• Meta-Prompting Concept: Asking Chat-GPT for the best prompt for your desired completion, then to revise it before using it: Has anyone employed this approach? I’ve found it helpful when crafting prompts, to literally ask Chat-GPT to help create the prompt for a given goal that I will describe to it while asking what could …
• Usage policies: no description found
• Terms of use: no description found

Perplexity AI ▷ #announcements (1 messages):

• Mistral Large Unleashed for Pro Users: <@ok.alex> announced that Mistral Large is now accessible for all Perplexity Pro users. Pro members can switch to this model in settings or try it out using the Rewrite feature, and it will also be available on mobile apps soon.

Perplexity AI ▷ #general (308 messages🔥🔥):

• Subscription Confusion and Support: User @mithrilman had trouble with a non-clickable “redeem $200 credit” button in a Rabbit R1 promo email and was advised by @icelavaman to contact Rabbit support for a new code. After subscribing without the link, @mithrilman inquired about using the promo and was directed to contact Perplexity support for a refund.
• AI Preferences and Model Strengths: Discussions took place around the strengths and use cases of different AI models. @.claidler found Mistral Large superior for code queries compared to GPT-4, while @jaicraft provided breakdowns of various models, suggesting GPT-4 Turbo as the best overall model.
• Perplexity’s Purpose and Limitations: Users shared thoughts about Perplexity’s suited and less-suited use cases. @brknclock1215 highlighted its strength as an AI answer engine and @cereal joked it shouldn’t be used for filing taxes. It was noted that Perplexity is not optimized for tasks like parsing large files or executing code.
• Perplexity vs. Other Platforms and SEO Issues: @names8619 praised Perplexity for offering better search results than Google due to SEO issues, and there was also comparison between Perplexity and other AI tools, such as Merlin.
• Technical Difficulties and Feedback on Perplexity: Some users experienced technical issues using Perplexity, like @logical__ who was unable to sign into their account and restore Pro access. Feedback on Perplexity’s performance included a comment from @magnusg0500 regarding what appeared to be a nonsensical verbosity in the AI’s response on the website.

Perplexity AI ▷ #sharing (14 messages🔥):

• Exploring Laptop Innovations: @ha.mz4_ shared a link to the Perplexity search about Lenovo’s transparent laptop, indicating curiosity in this new tech development. No further discussion or opinion was provided on the matter.
• Dota Economics Unpacked: @shakif.fahim consulted Perplexity AI’s take on Dota 2’s financial impact. The shared link leads to insights on the game’s economics but doesn’t include personal commentary.
• Gazing into Human Behavior: @t2db linked to a Perplexity search exploring why people stare. The message suggests an interest in understanding the psychological aspects behind this common human action.
• Mistral Shines in Accuracy: @.anuni complimented Mistral large’s performance, especially when compared to GPT-4, sharing a link where Mistral large succeeded in providing accurate information where GPT-4 often fails.
• Crafting Muscle-Building Routines: @commuting5048 provided a detailed prompt for a muscle-building plan and linked a comparative result, noting GPT-4’s thorough approach in specifying the number of sets and reps.

Perplexity AI ▷ #pplx-api (38 messages🔥):

• Switching to Sonar for Better Quality: @clay_ferguson mentioned they switched to using sonar-medium-online for better quality “online” model experiences, indicating satisfaction with its performance over the alternatives.
• Mixed Experiences With Sonar Model: @brknclock1215 reported inconsistent performance with sonar-medium-online, noting good results in some areas but inaccurate weather forecasts and details that seemed outdated or shaky.
• Prompt Design Influences Output: @brknclock1215 confirmed through testing that the system message, or “prompt”, significantly alters the behavior and output of the system, impacting the tone and accuracy of the responses.
• Clarity Sought on Sonar vs. pplx-70b-Online Models: Discussion between @thedigitalcat and @clay_ferguson highlighted a desire for information on the differences between sonar-medium-online and pplx-70b-online, especially in terms of handling recent events and producing concise, factual answers.
• Gibberish Responses Tied to Source Listing Attempts?: Both @thedigitalcat and @clay_ferguson observed that producing gibberish responses by the sonar-medium-online and pplx-70b-online models may be related to their attempts at listing sources, hinting at a potential area for the system to improve.

LAION ▷ #general (232 messages🔥🔥):

• Captcha Conundrum: @mikerhinos initiated a discussion about a captcha with the instruction “click on the object that is different from the others,” underscoring its lack of a target word for computer vision labeling. @nodja responded that such measures are purely anti-bot, without a secondary objective.

• Stable Diffusion 3 Anticipation: @top_walk_town expressed enthusiasm for obtaining Stable Diffusion 3 (SD3), criticizing the limitations of UNet2D and the inability to train on batches with mixed resolutions. A discussion ensued about the possible complexities and anticipated capabilities of SD3.

• Interpreting Probability Flow in Machine Learning: @pseudoterminalx discussed the nuances of how probability flow is influenced by various factors such as the dataset, forward function, and loss function. This was contextualized within a conversation about why diffusion models can still generate new data despite not being perfect learners.

• Ethical and Technical Debates Around AI: Following a Bloomberg article about the US military using AI to target airstrikes in the Middle East, members @thejonasbrothers, @chad_in_the_house, and @pseudoterminalx debated the ethical implications and the effectiveness of replacing human decision-makers with AI.

• Discussion of New AI Models and the Open AI Ecosystem: Various users including @thejonasbrothers, @gothosfolly, and @pseudoterminalx discussed recent developments in the AI community, such as new models being released, the potential uses for T5 within Stable Diffusion, and the evolving policies around open-source models and commercial licenses.

Links mentioned:

• Bloomberg - Are you a robot?: no description found
• Bloomberg - Are you a robot?: no description found
• ChatMusician: no description found
• Release v0.9.1 - DoRA the explorah · bghira/SimpleTuner: This release has some breaking changes for users who: Use RESOLUTION_TYPE=area (resolution_type=area for multidatabackend config) Use crop=false Use crop=true and crop_aspect=preserve as the prec…
• Tweet from Suhail (@Suhail): 1/ We are releasing Playground v2.5, our latest foundation model to create images. We tested our model across 20K+ users in a rigorous benchmark that went beyond anything we’ve seen to date. Th…
• Willys Chocolate Experience Glasgow. Get your Tickets!: INDULGE IN A CHOCOLATE FANTASY LIKE NEVER BEFORE - CAPTURE THE ENCHANTMENT! Tickets to Willys Chocolate Experience are on sale now! at the willys chocolate experience in Glasgow! Tickets to Willys Ch…
• Mixture-of-Experts partial diffusion implementation for base SD 1.x / 2.x pipelines by bghira · Pull Request #4355 · huggingface/diffusers): What does this PR do? This pull request ports our denoising_start code to the text2img pipeline, and the denoising_start and denoising_end code from the img2img pipeline. This brings legacy SD mode…
• no title found: no description found

LAION ▷ #research (45 messages🔥):

• Neural Networks Embrace Fourier: @mkaic discussed their manual implementation of the inverse discrete Fourier transform for neural networks, allowing for synthesis at arbitrary coordinates. They’re exploring memory-efficient solutions and considering refactoring using torch.vmap; the current implementation can be found on their GitHub.

• Potential Efficiency Gains with Non-Uniform FFT: @p.ie.c.e.s provided a link to a non-uniform Fast Fourier Transform implementation in PyTorch, torchkbnufft, which could assist @mkaic in their quest for a more efficient Fourier synthesis method.

• The Efficiency of 1-Bit Large Language Models: The #research channel discussed the implications of BitNet b1.58, a new 1-bit large language model described in a paper found here, potentially heralding cost-effective and high-performance models with new hardware optimization opportunities.

• Exploring Efficient Diffusion in AI: @yoavhacohen sought explanations and example code for Efficient Diffusion Methods (EDM), with other users suggesting resources like the k-diffusion GitHub repository to understand the variations in sampling and training processes that lead to state-of-the-art performance.

• Seeking Citations for Inverted Whisper TTS: @oswald_._ asked for a citation method for an open-source text-to-speech system called WhisperSpeech, found here, for use in an academic research project.

Links mentioned:

• The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits: Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in which every single param…
• Elucidating the Design Space of Diffusion-Based Generative Models: We argue that the theory and practice of diffusion-based generative models are currently unnecessarily convoluted and seek to remedy the situation by presenting a design space that clearly separates t…
• KOALA: Self-Attention Matters in Knowledge Distillation of Latent Diffusion Models for Memory-Efficient and Fast Image Synthesis: SOCIAL MEDIA DESCRIPTION TAG TAG
• Samsung Develops Industry-First 36GB HBM3E 12H DRAM: Samsung’s HBM3E 12H achieves industry’s largest capacity HBM with groundbreaking 12-layer stack, raising both performance and capacity by more than 50% Advanced TC NCF technology enhances vertical de…
• GitHub - mmuckley/torchkbnufft: A high-level, easy-to-deploy non-uniform Fast Fourier Transform in PyTorch.: A high-level, easy-to-deploy non-uniform Fast Fourier Transform in PyTorch. - mmuckley/torchkbnufft
• Reddit - Dive into anything: no description found
• abacus/src/interpolators.py at 28d20a2f3a244d09218e6ddd998db08c7872dc45 · mkaic/abacus): Investigating activation interpolation for sparse neural networks - mkaic/abacus
• GitHub - collabora/WhisperSpeech: An Open Source text-to-speech system built by inverting Whisper.: An Open Source text-to-speech system built by inverting Whisper. - collabora/WhisperSpeech

Eleuther ▷ #general (66 messages🔥🔥):

• Double-Descent Phenomenon Clarification: According to @leegao_, double-descent occurs in validation/test loss and not training loss, which makes its occurrence on training loss particularly interesting.
• Grad Spike Discussions: User @ad8e shared a link to an ArXiv paper discussing gradient estimation and its role in training stability, and @leegao_ acknowledged the issue of gradient spikes, also mentioned by others in the context of large model training. @uwu1468548483828484 reflected on the paper’s insights, noting that gradient spikes might be the result of gradient shifts in early layers.
• The Pitfalls of Rushed LLM Training: @leegao_ recounted a rumor about a failed Google LLM project attributed to a silent data corruption early on in pretraining that went unnoticed, making the point that there is a need for better monitoring during model training.
• Token Troubleshooting: User @transientnative shared a problem related to the addition of tokens to a model, experiencing unexpected “random” output until realizing lm_head.weight differed between the base model, Mistral-Instruct-V0.2, and their modified model.
• LoRA Pretraining Potential: @thatspysaspy mentioned an interesting paper discussing LoRA’s application in model pretraining called “LoRA-the-Explorer”. @alstroemeria313 provided the link to the ArXiv paper.

Links mentioned:

• EleutherAI/pythia-410m-seed6 · Hugging Face: no description found
• Training Neural Networks from Scratch with Parallel Low-Rank Adapters: The scalability of deep learning models is fundamentally limited by computing resources, memory, and communication. Although methods like low-rank adaptation (LoRA) have reduced the cost of model fine…
• Models - Hugging Face: no description found
• A Theory on Adam Instability in Large-Scale Machine Learning: We present a theory for the previously unexplained divergent behavior noticed in the training of large language models. We argue that the phenomenon is an artifact of the dominant optimization algorit…

Eleuther ▷ #research (124 messages🔥🔥):

• Sharing Excitement for an Olympiad-Level Benchmark: A user shared their enthusiasm for the recent release of the #OlympiadBench by @Hothan01 on Twitter, which challenges models with Olympiad-level scientific problems. The benchmark is bilingual and multimodal, presenting a considerable challenge to AI systems with the best-performing model, GPT4V, scoring an average of 17.23%. GitHub Repository | Arxiv Paper

• Discussing Neural Network Mathematics and the Spline View of NNs: Discussions occurred around the “spline view” of Neural Networks (NNs), and algebraic properties of model parameters, with users debating the plausibility and practicality of these concepts. They delved into how affine regions and nonlinear boundaries could be utilized in understanding and potentially enhancing deep neural network behavior.

• Theoretical Exploration of LoRA Gradient Updates and SVD: @thatspysaspy and others engaged in a technical conversation about whether the update for LoRA (Locally Reweighted Approximation) adapters could be equated to a singular value decomposition (SVD) of a gradient for a full weight. They discussed mathematical complications and proposed to conduct experiments to explore this theory further.

• Creative Generative Model Development: User @carsonpoole has been working on a novel type of CycleGAN that incorporates a diffusion model and a discriminator that predicts points between domains rather than just classifying real versus fake. They report subjectively better results early in the training process compared to a traditional CycleGAN.

• Scaling Laws and Training Tokens Discussion: Conversations occurred regarding scaling laws for AI, particularly the relationship between model size and training tokens, with mentions of a recent paper that examined the effects of limited data and data repetition during training. This led to exchanges about the implications of these findings on pretraining strategies for models such as a hypothetical 15 billion parameter model with 4 trillion training tokens.

Links mentioned:

• Deep Networks Always Grok and Here is Why: Grokking, or delayed generalization, is a phenomenon where generalization in a deep neural network (DNN) occurs long after achieving near zero training error. Previous studies have reported the occurr…
• Tweet from Chaoqun He (@Hothan01): 🥳🙌Excited to release 🔥#OlympiadBench🔥, an Olympiad-level bilingual multimodal scientific benchmark. The best-performing model, #GPT4V, attains an average score of 17.23%. Such a challenging benchm…
• Scaling Data-Constrained Language Models: The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the…

Eleuther ▷ #scaling-laws (1 messages):

.the_alt_man: Out of curiosity, how did you make that animation?

Eleuther ▷ #interpretability-general (24 messages🔥):

• Clarifying ‘Energy’ in the Paper: @mrgonao expressed confusion about the term “energy” used in a paper and its equation, stating a lack of intuition for its meaning. @butanium agreed to review the paper for better understanding.
• Redefining ‘Energy’ for Latent Space Analysis: @wendlerc stepped in to clarify that the term “energy” historically refers to the quantification of “information” used in a latent at layer i for decoding/modelling the next-token distribution, but acknowledged that it might not be the best term.
• Unpacking the ‘Energy’ Equation: @wendlerc provided an insightful explanation on how the “energy” expression was refined to be more interpretable, measuring similarity of a latent to an output embedding via normalized squared cosine.
• Confusion on Norms: A conversation emerged around the mathematical notations used in the energy equation, with @mrgonao seeking clarification on the use of various norms, and @nostalgiahurts explaining that 2 represents the Euclidean norm and F stands for Frobenius norm.
• Implementing the Tuned Lens: The discussion continued with @mrgonao and @wendlerc pondering over the proper implementation of the tuned lens, and how to accurately reflect its effects when working with latents and RMSNorm layers for decoding.

Eleuther ▷ #lm-thunderdome (28 messages🔥):

• Model Batch Size Sensitivity: @madison_33844 inquires if batch size variations affect GSM8k results when using Llama-70B, noting discrepancies from the OpenLLM leaderboard. @hailey_schoelkopf replies that differences may occur due to subtle indeterminacies, but significant score discrepancies should not be present.

• LM Eval Harness: Split Selection Queries: @micpie seeks clarification about whether tests evaluate on test or validation splits according to their presence, and the meaning of true and false in loglikelihood output. @baber_ and @hailey_schoelkopf clarify that it signifies whether the target string would be the greedy completion and that one cannot override split selection via the command line, only through YAML file edits.

• Understanding Loglikelihood Outputs: @micpie requires assistance to understand LM eval harness outputs, particularly the loglikelihoods and their true/false values. @hailey_schoelkopf confirms @micpie’s understanding of the evaluation process by indicating the output format includes loglikelihood and whether the target string is the greedy completion.

• Evaluate Multiple Choice on Training Split: @micpie struggles with mismatches between progress bar output and .jsonl line counts in their config. @baber_ clarifies the output is due to two-answer multiple-choice evaluation running each context-option through the model.

• Implementing Multimodal LM Eval: @hailey_schoelkopf follows up on the progress of extending multimodal LM evaluation and whether @paganpegasus needs assistance with instruction/chat formatting or if they should consider fine-tuning their model with already formatted examples.

Eleuther ▷ #gpt-neox-dev (6 messages):

• Seeking Guidance on CoreWeave for GPT-NeoX Training: User @jdranpariya inquired about setting up a multi-node GPT-NeoX training environment on CoreWeave, asking for assistance with using 2 nodes and 4 GPUs with slurm or MPI.
• Navigating CoreWeave and Kubernetes Setup: @jdranpariya questioned if Kubernetes is integral to the setup or if there’s an alternative, expressing uncertainty on connecting virtual servers for their use case.
• Pointers to CoreWeave-specific Inquiry: Responding to the setup queries, @catboy_slim_ suggested that queries specific to CoreWeave infrastructure should be directed to CoreWeave support and indicated that the NeoX documentation provides instructions for launching on slurm.
• Direction for Slurm Cluster Issues: @catboy_slim_ clarified that establishing a slurm cluster falls within CoreWeave’s domain, and @jdranpariya acknowledged the points made.

LlamaIndex ▷ #blog (5 messages):

• LlamaIndex Announces Function Calling Cookbook: The team at LlamaIndex introduced a series of cookbooks for using LlamaIndex with @FireworksAI, highlighting function calling and RAG with FireFunction-v1. The tweet celebrates the compatibility between LlamaIndex and FireworksAI models, sharing its excitement with followers.
• Combining RAG Applications into a Super-RAG Feature: LlamaIndex revealed its latest feature allowing the creation of a distributed super-RAG by connecting RAG applications into a single network, as per their tweet, fans can look forward to creating API services for any RAG application and running queries across this new network (LlamaIndex Tweet).
• Test the Limits of LlamaParse for Complex Documents: An upcoming event by @AIMakerspace titled “Superior RAG for Complex PDFs” will assess the effectiveness of LlamaParse, a proprietary parsing tool designed for complex documents with embedded figures and tables, as announced by LlamaIndex. The free virtual event aims to explore LlamaParse’s capabilities with complex PDFs and will provide code demos and slides to attendees (Event Registration).
• Groq Partners with LlamaIndex for LLM Generation: LlamaIndex integrated @GroqInc’s LPU into its service, which is tailored to support LLM generation with Llama2 and Mixtral models, promising immense speed improvements for application workflows (LlamaIndex and Groq Cookbook).

Links mentioned:

Superior RAG for Complex PDFs with LlamaParse · Luma: The question that continues to be asked in enterprises worldwide is, “How do I deal with complex documents that have figures, tables, and graphs?” The next step in the evolution of dealing…

LlamaIndex ▷ #general (227 messages🔥🔥):

• Querying PDFs and Handling Errors: @vett93 experienced trouble querying PDF files and encountered a connection error. @whitefang_jr suggested checking the setup and deployment of the Ollama model instance and linked to the relevant documentation for assistance.

• Reranking Model Discussions: Users @richard1861 and .sysfor were discussing the comparative effectiveness of reranking models. .sysfor recommends using both FlagEmbeddingReranker and CohereRerank for improved results and mentioned that Cohere seems faster.

• Visualization for ReActAgent: @mrpurple9389 asked if it’s possible to visualize the graph for ReActAgent, to which @cheesyfishes responded that there isn’t actually a graph to visualize for it.

• Golang Integration for Callback Handlers: @sansmoraxz is attempting to transfer existing interfaces to Golang and asked about CallbackHandlers. @cheesyfishes indicated that a refactor to callbacks is being worked on and suggested expected improvements soon.

• Understanding Nodes vs. Documents: @crawftv inquired about the difference between nodes and documents in LlamaIndex and their practical use, showcasing confusion about whether to combine their use within parent-child relationships in the index.

Links mentioned:

• Getting Started With Embeddings: no description found
• Cannot update llamaindex: After llamaindex introduced v0.10 in February 2024, it introduced a lot of breaking changes to imports. I am trying to update llama-index within a conda environment, but I receive the following err…
• Starter Tutorial - LlamaIndex 🦙 v0.10.13: no description found
• Loading Data (Ingestion) - LlamaIndex 🦙 v0.10.13: no description found
• Node Postprocessor Modules - LlamaIndex 🦙 v0.10.13: no description found
• Ollama - Llama 2 7B - LlamaIndex 🦙 v0.10.13: no description found
• New Demo & description - Agents for Amazon Bedrock | Amazon Web Services: With Amazon Bedrock, you can easily build and scale generative AI applications with security, privacy, and responsible AI. This demo shows you how to use Age…
• llama_index/llama-index-integrations/agent/llama-index-agent-openai/llama_index/agent/openai/step.py at b2f0a59c21f651bea1502818ec7f61ab915ca286 · run-llama/llama_index: LlamaIndex is a data framework for your LLM applications - run-llama/llama_index
• Docker Compose | Weaviate - Vector Database: Weaviate supports deployment with Docker. Starting in v1.24.0, there is an image that runs using default values. Alternatively, edit the docker-compose.yml file to customize your instance.
• [Feature Request]: Incorporate AWS Bedrock agents · Issue #11462 · run-llama/llama_index: Feature Description AWS Bedrock agents are automatic orchestration flows for RAG through provided openapi endpoints (which are lambda functions) think of them as tools with corresponding knowledge …
• Real time AI Conversation Co-pilot on your phone, Crazy or Creepy?: I built a conversation AI Co-pilot on iPhone that listen to your conversation & gave real time suggestionFree access to Whisper & Mixtral models on Replicate…
• GitHub - outlines-dev/outlines: Structured Text Generation: Structured Text Generation. Contribute to outlines-dev/outlines development by creating an account on GitHub.

LlamaIndex ▷ #ai-discussion (1 messages):

• Finding the Middle Ground Model: @sysfor is seeking a model to fill the gap between Mistral 7b and Mixtral, as they’ve found Solar to be unsatisfactory. They aim to host Mistral on a 24GB card and have room for around a 10.7b quant 6/8 model for tasks like summarization and log correlation.

HuggingFace ▷ #announcements (1 messages):

• Cosmopedia Unleashed: @lunarflu announced the release of Cosmopedia, a massive 25B token synthetic dataset created by Mixtral, comprising textbooks, blogposts, and stories. The dataset, containing 30M files, can be accessed via LinkedIn.

• huggingface_hub Update 0.21.0: New huggingface_hub library version 0.21.0 released, featuring dataclasses, PyTorchHubMixin enhancements, audio-to-audio support in InferenceClient, and translated documentation, despite some breaking changes. For more details, check the full release notes here.

• Gemma 7B Now Chatting on Hugging Chat: Google’s open LLM Gemma 7B is now available on the Hugging Chat service, as shared by @julien_c on Twitter.

• TTS Arena Unveiled: Announcing TTS Arena, a new project by @reach_vb where users can test, rate, and discover the top open text-to-speech models. This interactive space starts with five models, with more to be included based on community feedback. More information can be found here.

• Data Crowdsourcing Effort Pays Off: The #data-is-better-together initiative released 10k_prompts_ranked, a dataset created in less than two weeks by over 300 community members, aimed to support the development and evaluation of AI prompt ranking systems. The community-building efforts were highlighted in a blog post on HuggingFace.co.

Links mentioned:

• @Wauplin on Hugging Face: ”🚀 Just released version 0.21.0 of the huggingface_hub Python library!…”: no description found
• Tweet from Victor M (@victormustar): 🤯 This @figma plugin lets you push your figma frames directly into a @huggingface dataset!
• Tweet from merve (@mervenoyann): YOLOv9 arrived on @huggingface Hub! 🤩 The model checkpoints: https://huggingface.co/merve/yolov9 Try the demo (@kadirnar_ai): https://huggingface.co/spaces/kadirnar/Yolov9 Find demo for YOLOv9 por…
• Tweet from Xenova (@xenovacom): YOLOv9 just released, and now it’s compatible with 🤗 Transformers.js! That’s right… near real-time object detection running locally in your browser: no server required! 🤯 Try it out yours…
• Tweet from Omar Sanseviero (@osanseviero): Matryoshka Embeddings are here! 🔥 The Sentence Transformers library allows training and running embedding models with embedding sizes that can be shrunk while keeping high quality! Learn about them…
• Tweet from dylan (@dylan_ebert_): LGM Mini 🧊 Image to Interactive 3D in 5 seconds https://huggingface.co/spaces/dylanebert/LGM-mini
• Tweet from Julien Chaumond (@julien_c): BREAKING: ↘️ Quoting Victor M (@victormustar) ✨ Google’s new open LLM Gemma 7B is now available on HuggingChat.
• Tweet from merve (@mervenoyann): 🤗 transformers has a new task guide for mask generation (also known as zero-shot image segmentation) learn how to use the powerful segment-anything models in this guide https://huggingface.co/docs/t…
• Models - Hugging Face: no description found
• DIBT/10k_prompts_ranked · Datasets at Hugging Face: no description found
• @davanstrien on Hugging Face: “The open-source AI community can build impactful datasets collectively!…”: no description found
• Tweet from Lewis Tunstall (@_lewtun): 🪽Introducing OpenHermesPreferences - the largest dataset of ~1 million AI preferences generated by Mixtral and Nous-Hermes-2-Yi-34B 🔥 https://huggingface.co/datasets/argilla/OpenHermesPreferences …
• Tweet from Vaibhav (VB) Srivastav (@reach_vb): Announcing TTS Arena! 🗣️ sound on One place to test, rate and find the champion of current open models. A continually updated space with the greatest and the best of the current TTS landscape! ⚡…
• Introducing the Red-Teaming Resistance Leaderboard: no description found
• AI Watermarking 101: Tools and Techniques: no description found
• Fine-Tuning Gemma Models in Hugging Face: no description found
• Tweet from Bassem Asseh 🤗 (@asseh): .@huggingface worked together with @FetchRewards to take their document #AI solutions to production on @AWS . And guess what ? 👉 “With Yifeng’s guidance, Fetch was able to cut its development t…

HuggingFace ▷ #general (112 messages🔥🔥):

• The Query on Free Inference-API issues: @temperance6095 raised concerns about recurring timeouts (504 errors) when using the free Inference-API for Text-to-Image models, asking for assistance to pinpoint the exact cause. They later noted that the issue was not unique to them and pondered whether rate-limiting was a factor, referencing conversations with HuggingFace Bot for insights.

• Coin Flipping with Mistral Models: @acidgrim inquired about the capability of the Mistral8x7B q8 KM to flip a coin 10 times and report the results, mentioning that their current q5 model only returned “1. Heads 2. Tails”.

• Pushing for Help on Integrating AI: Users like @vishyouluck and @tomato3602 discussed challenges and projects involving integrating AI with tools and APIs, seeking advice on models that support functions and how to incorporate them into websites.

• Chatter on Edge TPU and Technology: Conversations emerged around utilizing Edge TPUs, with @typoilu and @zorian_93363 expressing amazement at the power of tiny yet potent hardware like Google’s Coral accelerators, while @ahmad3794 weighed in on building custom computing frameworks.

• Learning Curves and Ambitions: Amidst various discussions, @sheeshmohit sought guidance on starting in AI and content creation, while @caleb_sol pondered the feasibility of running a tinydolphin LLM on a low-spec Android tablet, signifying the diverse ambitions and learning endeavors within the AI community.

Links mentioned:

• Au Large: Mistral Large is our flagship model, with top-tier reasoning capacities. It is also available on Azure.
• Waiting Waiting Patiently GIF - Waiting Waiting patiently Waiting for you - Discover & Share GIFs: Click to view the GIF
• How to Build a Discord AI Chatbot that Talks Like Your Favorite Character: Would you like to talk to a chatbot that speaks like your favorite character, fictional or non-fictional? Let’s build one! In case you’ve seen my previous tutorial on this topic, stick with m…
• Top HF Users To Follow On X - a Hugging Face Space by mvaloatto: no description found
• Mini PCIe Accelerator | Coral: Integrate the Edge TPU into legacy and new systems using a Mini PCIe interface.
• cahya/gpt2-small-indonesian-522M · Hugging Face: no description found
• betajuned/gpt2-indonesian-unila-guanaco · Hugging Face: no description found
• Real time AI Conversation Co-pilot on your phone, Crazy or Creepy?: I built a conversation AI Co-pilot on iPhone that listen to your conversation & gave real time suggestionFree access to Whisper & Mixtral models on Replicate…
• GitHub - vishalmysore/Tools4AI: How to Use Gemeni with Java , Function Calling, Chaining and validation: How to Use Gemeni with Java , Function Calling, Chaining and validation - vishalmysore/Tools4AI
• MBZUAI (Mohamed Bin Zayed University of Artificial Intelligence): no description found

HuggingFace ▷ #today-im-learning (5 messages):

• Study Group Formation for CS231n: User @shreesha1573 is organizing a study group for the CS231n course on Convolutional Neural Networks for Visual Recognition. They have shared Spring 2023 Assignments along with sections on software setup, Python/Numpy tutorials, image classification, linear classification, and optimization.

• CRM AI Development Quest: @koderfpv is looking for guidance on building an AI and chatbot for their CRM application to predict production time and costs. They have a background in TypeScript, DevOps, and backend development but are new to AI and wish to start a long-term project without using OpenAI APIs.

• Urban Sentiment Analysis Using LLM: @x_5c44a99 shared that they are learning about using LangChain with LLM for sentiment analysis of tweets for urban distribution planning. This could be a step toward addressing urban inequality.

• Uncertainty Over HuggingFace’s Role in Analysis: Following up, @x_5c44a99 is unsure about how HuggingFace could be utilized in analyzing the sentiments from twitter data.

• Exploring DSPy Framework and Gorilla OpenFunctions v2: @n278jm is looking into the DSPy Framework by Stanford NLP and Gorilla’s OpenFunctions v2 believing these could improve their client onboarding process. DSPy aims for programming foundation models, while OpenFunctions v2 offers advancements in function calling for LLMs.

Links mentioned:

• CS231n Convolutional Neural Networks for Visual Recognition: no description found
• GitHub - stanfordnlp/dspy: DSPy: The framework for programming—not prompting—foundation models: DSPy: The framework for programming—not prompting—foundation models - stanfordnlp/dspy
• Introduction to Gorilla LLM: no description found

HuggingFace ▷ #cool-finds (9 messages🔥):

• SUPIR Ascends Above Magnific: @furkangozukara highlighted the impressive performance of SUPIR, an open-source image upscaler and enhancer model, which now operates effectively on 12 GB GPUs such as a single RTX 3060. They mentioned that the model, particularly with Juggernaut-XL-v9 as a base, outperforms more expensive alternatives like Magnific, sharing the evaluation in a YouTube video.

• Speakz Breaks Language Barriers: @teadaniel introduced Speakz AI, which translates media across languages while keeping the original voices and ambient sounds intact. The tool was created to allow enjoying content like YouTube videos in different languages without interruptions for translation.

• An Offer to Share Stories: When @zorian_93363 expressed frustration over being required to create an account to read a full story, @andysingal offered to share a friend link for any story they wished to read.

• Tired of Too Many Accounts: @zorian_93363 lamented the inconvenience of managing too many accounts and remembering passwords but showed interest in a contest mentioned by @andysingal.

• Navigating Paywalls with an Archive Link: In response to @zorian_93363’s comment about needing an account to read a story, @n278jm provided an archive link to access the content without signing up.

Links mentioned:

• Speakz.ai: no description found
• SUPIR: New SOTA Open Source Image Upscaler & Enhancer Model Better Than Magnific & Topaz AI Tutorial: With V8, NOW WORKS on 12 GB GPUs as well with Juggernaut-XL-v9 base model. In this tutorial video, I introduce SUPIR (Scaling-UP Image Restoration), a state-…

HuggingFace ▷ #i-made-this (17 messages🔥):

• Philosophy Gets an AI Twist: @nabereon discusses using Mixtral-8x7B-Instruct-v0.1 to generate question-answer pairs for philosophy students from the AiresPucrs/stanford-encyclopedia-philosophy dataset. They plan to create a larger dataset with IEP entries and Libretexts textbooks, pending consent due to licensing concerns raised by @cakiki.

• Public Contribution Request for AI Policy: .plot shared a blog post inviting comments on the NTIA’s AI Open Model Weights RFC, which discusses the implications of open-weight AI models and the federal policy around them.

• LLMs Benchmarked: @michal.swedrowski. introduces the Performance LLM Board, a resource comparing large language models (LLMs) based on engineering metrics like pricing and response times. Feedback is solicited for improvements and content direction.

• Unveiling Czech LLM Leaderboard: @hynek.kydlicek hosts a Czech-focused LLM leaderboard that evaluates models’ effectiveness in Czech language tasks. The leaderboard aims to present models suited for the Czech language and quantify their capabilities.

• Replicating Imagic Techniques: @chongdashu shares insights from replicating the Imagic paper, detailing text-based image editing with diffusion models. The author expresses enthusiasm for the approach and its applications for anyone with patience and an ear for sound design.

Links mentioned:

• nilq/mistral-1L-tiny · Hugging Face: no description found
• iCloud Photo Sharing: iCloud Photo Sharing lets you share just the photos you want with just the people you choose.
• CZ-EVAL - a Hugging Face Space by hynky: no description found
• Deploying 🤗 Hub models in Vertex AI: no description found
• How to Comment on NTIA AI Open Model Weights RFC: The National Telecommunications and Information Administration (NTIA) is asking for public comments on the implications of open-weight AI models. Here’s how you can participate.
• Papers Decoded — Imagic: Text-Based Real Image Editing with Diffusion Models: In Papers Decoded, we attempt to replicate experiments and results of research papers. It is one of the best ways to get familiar with…
• this is your ableton on musicgen - captains chair 16: i keep saying the season is almost over,but then i have a rifflots of news abound in the patch right now. gonna try our hand at some posts on here to keep ev…
• Cursor Hero demo v0.3.0: https://github.com/TeamDman/Cursor-Hero.githttps://discord.gg/psHtde64FJ#rust #bevy #windows #win32
• GitHub - Rivridis/LLM-Assistant: Locally running LLM with internet access: Locally running LLM with internet access. Contribute to Rivridis/LLM-Assistant development by creating an account on GitHub.

HuggingFace ▷ #reading-group (8 messages🔥):

• Apple’s Image Model Pre-training on the Radar: @johko990 expressed an interest in discussing Apple’s “Scalable Pre-training of Large Autoregressive Image Models” for a future presentation in the reading group.
• Open Slot for Future Presentations: @chad_in_the_house confirmed that the schedule for presentations is open following this week’s session.
• Maximizing YouTube Reach Discussed: @johko990 suggested uploading videos to the official Hugging Face YouTube channel for greater visibility, referencing the increased views on their Community Computer Vision Course content.
• Video Quality and Uploads Under Consideration: @lunarflu agreed with the suggestion to check video quality and consider adding them to the official channel.
• Coming Up: Presentation Scheduled for March 8: @lunarflu indicated a planned presentation for March 8, and @chad_in_the_house shared a link to the related report.

HuggingFace ▷ #diffusion-discussions (11 messages🔥):

• Disappointment in Playground v2.5 Methods: User @pseudoterminalx expressed disappointment with the fact that Playground v2.5 is still using eps prediction and criticized the marginal mention of zsnr, opting instead to use the EDM framework.

• Photo Concept Bucket Announcement: @pseudoterminalx introduced a new dataset with 567,597 captioned images called Photo Concept Bucket, which runs on multiple GPUs and was created using 🤗Transformers and 🤗Accelerate.

• Dataset Featured in Community Highlights: Following up, @lunarflu mentioned the newly shared dataset could be added to the community highlights section, suggesting it’s more fitting there than in the main HF news given its community origin.

• EDM Takes the Spotlight in Newest PR: @keturn pointed out a newly merged PR titled “add DPM scheduler with EDM formulation” in the diffusers repository; however, the PR lacked a proper description (PR #7120).

• Concerns Over PR Handling Practices: @pseudoterminalx voiced frustration about the seemingly preferential treatment given to the Playground team by HF staff, highlighting the rush to merge certain PRs while linking to another PR that lacked attention (PR #4355).

Links mentioned:

• ptx0/photo-concept-bucket · Datasets at Hugging Face: no description found
• add DPM scheduler with EDM formulation by patil-suraj · Pull Request #7120 · huggingface/diffusers: What does this PR do?
• Mixture-of-Experts partial diffusion implementation for base SD 1.x / 2.x pipelines by bghira · Pull Request #4355 · huggingface/diffusers: What does this PR do? This pull request ports our denoising_start code to the text2img pipeline, and the denoising_start and denoising_end code from the img2img pipeline. This brings legacy SD mode…

HuggingFace ▷ #computer-vision (2 messages):

• Inquiry About Local Server Implementation for BLIP Model: @time.e.less shared a link to a HuggingFace model for image-captioning and inquired if it’s possible to run it on a local server similar to how llama.cpp works for LLMs. They are looking for a solution to POST an image and receive a JSON response with a caption without necessarily having to build a Python server.
• Question on Arcface Loss and Embedding Size: @huzuni asked if the embedding size in arcface loss corresponds to the size of the last linear layer. They sought clarification on the technical details of implementing arcface loss.

Links mentioned:

Salesforce/blip-image-captioning-base · Hugging Face: no description found

HuggingFace ▷ #NLP (18 messages🔥):

• Quick Embedding Model Recommendation: @cakiki asked for embedding model advice for a small, non-specialized English dataset, @cubietom recommended BAAI’s bge-small-en-v1.5 from Hugging Face for something quick and fast, and also mentioned the FlagEmbedding project and GTE models.
• Condensing Email Contents for LLMs: User @acidgrim is seeking a library to condense email files that retain essential information for LLM ingestion, mentioned using suma, and is exploring CPU-only, local options.
• Developing a Medical Transformer: @kareem3069 expressed dissatisfaction with the performance of sentence-encoder libraries on medical codes and descriptions, and sought advice for improving model mapping for domain-specific applications.
• Less Verbose CoT Prompting: @djpanda1 shared an approach for reducing token usage by asking LLMs to “think silently” during chain of thought prompting; mixed reactions were received with @vipitis suggesting testing on a larger benchmark.
• Text Generation on CPU-only Systems: @alfred6549 encountered difficulties running a text generation inference repository without a GPU or CUDA. Recommended command options did not resolve the issue, indicating a need for further troubleshooting or alternative recommendations.

Links mentioned:

• Hugging Face: The AI community building the future. Hugging Face has 196 repositories available. Follow their code on GitHub.
• BAAI/bge-small-en-v1.5 · Hugging Face: no description found
• thenlper/gte-small · Hugging Face: no description found

HuggingFace ▷ #diffusion-discussions (11 messages🔥):

• Discontent with Playground v2.5 Methodology: @pseudoterminalx expressed disappointment in HuggingFace’s Playground v2.5 for using “eps prediction” and dismissing zsnr as a mere footnote while opting for the “crappy EDM framework”.
• Unveiling the Photo Concept Bucket: @pseudoterminalx missed out on announcing the Photo Concept Bucket, a 567,597-entry open licensed image dataset, captioned using multi-GPU clusters by volunteers. @lunarflu responded positively, suggesting that it could be added to community highlights.
• Frustration Over Diffusers PR Process: @pseudoterminalx shared frustration about the perceived preferential treatment given by HuggingFace to the Playground team, comparing it to the slow progress of their own pull request. A specific example was signaled by their reference to a Mixture-of-Experts pull request that seems to have stalled.
• Concerns on Arbitrary Method Choices: @keturn contributed to the discussion by pondering over the seemingly arbitrary choice of noise scheduling in Playground v2.5, noting a PR in the diffusers repository that was pushed quickly without much explanation.
• Leveled Up in Levity: @pseudoterminalx humorously noted that even the bot recognized their previous critical comment, informing them of a “level up”.

Links mentioned:

• ptx0/photo-concept-bucket · Datasets at Hugging Face: no description found
• add DPM scheduler with EDM formulation by patil-suraj · Pull Request #7120 · huggingface/diffusers: What does this PR do?
• Mixture-of-Experts partial diffusion implementation for base SD 1.x / 2.x pipelines by bghira · Pull Request #4355 · huggingface/diffusers: What does this PR do? This pull request ports our denoising_start code to the text2img pipeline, and the denoising_start and denoising_end code from the img2img pipeline. This brings legacy SD mode…

LangChain AI ▷ #general (92 messages🔥🔥):

• Image References Pose a Problem: User @deadthray inquired if it’s necessary to pass the image byte string every time while discussing the same image in llava/visoon models, pointing towards a challenge with persisting image references.
• Travel Chatbot Troubles: @ritanshoo shared a challenge with their chatbot for a travel booking website, where it struggles to return relevant answers despite having a large dataset stored in Pinecone.
• LangChain’s Flexibility Debated: @m_gee relayed concerns from Reddit about LangChain’s token consumption and flexibility for production-grade apps. @baytaew defended LangChain’s customizability and introduced LangGraph for better state management and function calling support.
• Python vs. JavaScript in LangChain: @pcube__ asked which programming language—Python, JavaScript, or Go—has the best integration with LangChain for building a webserver with an Azure-hosted LLM API endpoint. @kapa.ai confirmed strong integrations for Python and JavaScript, with no mention of Go.
• Adding Memory to LangChain LCEL: @marknicholas sought guidance on adding memory to a LangChain chain when using a template in Python. While @kapa.ai provided a general approach, they recommended consulting LangChain documentation for precise implementations.

Links mentioned:

• Deployment | 🦜️🔗 Langchain).): In today’s fast-paced technological landscape, the use of Large Language Models (LLMs) is rapidly expanding. As a result, it is crucial for developers to understand how to effectively deploy thes…
• Querying a SQL DB | 🦜️🔗 Langchain: We can replicate our SQLDatabaseChain with Runnables.
• [beta] Structured Output | 🦜️🔗 Langchain: It is often crucial to have LLMs return structured output. This is
• Join the Creepz NFT Alpha Group Discord Server!: Check out the Creepz NFT Alpha Group community on Discord - hang out with 13786 other members and enjoy free voice and text chat.
• Docusaurus | 🦜️🔗 Langchain): Docusaurus is a static-site generator which
• langchainjs/langchain/src/retrievers/score_threshold.ts at e24d2dedbe7ff93db33a5809e604143d60113028 · langchain-ai/langchainjs): 🦜🔗 Build context-aware reasoning applications 🦜🔗. Contribute to langchain-ai/langchainjs development by creating an account on GitHub.
• Quickstart | 🦜️🔗 Langchain): In this quickstart we’ll show you how to:
• Add chat history | 🦜️🔗 Langchain): In many Q&A applications we want to allow the user to have a
• Issues · langchain-ai/langchain)): 🦜🔗 Build context-aware reasoning applications. Contribute to langchain-ai/langchain development by creating an account on GitHub.

LangChain AI ▷ #langserve (1 messages):

• Invalid Discord Link Spammed: User @davisson0429 shared a Discord invite link followed by an extensive series of pipes |||| and a ping to @everyone. The purpose or context of the message was not provided.

Links mentioned:

Join the Creepz NFT Alpha Group Discord Server!: Check out the Creepz NFT Alpha Group community on Discord - hang out with 13786 other members and enjoy free voice and text chat.

LangChain AI ▷ #langchain-templates (1 messages):

• Spam Advisory in LangChain Templates: User @davisson0429 posted a message filled with vertical lines and an @everyone ping, which appears to be spam. The message contained a Discord invite link followed by nonsensical vertical line patterns.

Links mentioned:

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

LangChain AI ▷ #share-your-work (4 messages):

• LangGraph Merges with LangChain: @andysingal shared a blog post about LangGraph, a tool that provides iterative code generation and correction and its integration with Langchain for enhanced code security and integrity.
• “LangChain in your Pocket” Hits Best Books List: @mehulgupta7991 proudly announced that their debut book “LangChain in your Pocket” is now listed on Google under the Best books on LangChain.
• Invitation to Join the Party: @davisson0429 extended an invitation to the community with a Discord join link, encouraging everyone to join their server.
• Survey for Course Interest: @silvermango9927 seeks community input through a Google Form survey for various educational courses such as machine learning, data science, Python for beginners, and web development.

Links mentioned:

• Discord - A New Way to Chat with Friends & Communities: Discord is the easiest way to communicate over voice, video, and text. Chat, hang out, and stay close with your friends and communities.
• Empowering Code Generation: Unlocking Potential with LangGraph: Ankush k Singal
• Product Idea Validation Form: Hi, thank you so much for filling in this form and giving a response. The idea : Creating a lab (course) that teaches in a project-based manner compared to all of the conventional longer video-heavy…

LangChain AI ▷ #tutorials (3 messages):

• Innovative AI Co-pilot for Phones: User @jasonzhou1993 shared a YouTube video titled “Real time AI Conversation Co-pilot on your phone, Crazy or Creepy?” The video showcases a conversation AI Co-pilot on iPhone that listens to conversations and provides real-time suggestions using Whisper & Mixtral models.
• Clarification on Workflow Compilation: @tigermusk inquired about whether workflow.compile() is a runnable object in langgraph. There was no response provided in the message history to clarify this.
• Dubious Link Littering: @davisson0429 spammed the channel with a link to join a Discord server and a large block of vertical bars, which appears to be a disruptive or mischievous act rather than a useful contribution.

Links mentioned:

• Join the Creepz NFT Alpha Group Discord Server!: Check out the Creepz NFT Alpha Group community on Discord - hang out with 13786 other members and enjoy free voice and text chat.
• Real time AI Conversation Co-pilot on your phone, Crazy or Creepy?: I built a conversation AI Co-pilot on iPhone that listen to your conversation & gave real time suggestionFree access to Whisper & Mixtral models on Replicate…

OpenRouter (Alex Atallah) ▷ #announcements (1 messages):

louisgv: Fixed several issues related to message ordering/formatting for Perplexity and Gemma.

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

• OpenRouter Enables Simplicity and Inclusivity: @e__lo highlighted the ease of creating new AI tools with OpenRouter and its ability to integrate models not only from OpenRouter but also from giants like Google Vertex AI, Amazon Bedrock, and Cloudflare AI, ensuring users can request to add any model they wish to use.
• Czech Language LLM Leaderboard Launch: @hynek.kydlicek shared his project – a leaderboard dedicated to evaluating Large Language Models (LLMs) for the Czech language. He pointed out that using OpenRouter is the easiest and most cost-effective option for this extensive task with over 8k samples, providing a link to the project.
• Applause for the LLM Leaderboard Initiative: @alexatallah expressed support and excitement regarding @hynek.kydlicek’s Czech LLM leaderboard, calling the achievement “fantastic!”.
• Beta Testers Wanted for AI Voice Chat App: @beaudjango introduced Pablo, an AI Voice Chat app that facilitates voice interactions without the need for typing and supports multiple LLMs and voices. They’re seeking beta testers and offering free AI credits for services including GPT-4 to those who join, with a TestFlight link provided for those interested in participating.

Links mentioned:

• Join the Pablo - AI Voice Chat beta: Available on iOS
• CZ-EVAL - a Hugging Face Space by hynky: no description found

OpenRouter (Alex Atallah) ▷ #general (73 messages🔥🔥):

• Discrepancies with Chat Templates Identified: @aerk._. highlighted an unexpected response issue when expecting a continuation on the topic of LLMs with Gemma 7B. After some back and forth with @louisgv, a fix was deployed, and @aerk._. confirmed the resolution worked well.
• Template Troubleshooting for Turn-Based Chat: @quentmaker encountered errors with multiple models when attempting to continue conversations beyond 8 user/assistant message pairs. @louisgv and @alexatallah both engaged to offer solutions and acknowledged the need for a fix in OpenRouter’s system.
• Query on OpenRouter’s Revenue Generation: In response to a question from @_lynett about how OpenRouter makes money, @alexatallah mentioned they aren’t optimizing for revenue yet, sharing that potential earnings come from splitting volume discounts with users.
• Rate Limits on OpenRouter Explored: @gunpal5_43100 inquired about rate limits when using ChatGPT with OpenRouter, leading @alexatallah to point towards the documentation on OpenRouter’s website that outlines the current limitations.
• Excitement for Upcoming Models: Discord members, including @wikipediadotnet and @RobinF, discussed their anticipation for the release of Claude 3, while also humorously mentioning the model’s potential aversion to the term “excited”.

Links mentioned:

• no title found): no description found
• google/gemma-2b-it · Hugging Face: no description found
• OpenRouter: Build model-agnostic AI apps

OpenAccess AI Collective (axolotl) ▷ #general (40 messages🔥):

• Consumer Hardware LLM Training Not Feasible: @nafnlaus00 commented on the impracticality of training large language models on consumer hardware, joking about the lack of H100-equipped machines just lying around at home.
• LoRA Training Limitations Discussed: @enka55 sought examples of models on Hugging Face trained with new knowledge using LoRa, while @nruaif and @leoandlibe clarified that LoRA is not the right choice for adding new knowledge, suggesting full fine-tuning instead.
• RunPod Link Verification Request: @nanobitz requested verification for a RunPod direct link found in Issue #1318 on GitHub, where @nruaif responded that it was not working for them.
• 1-Bit LLM Paper Sparks Interest: @_dampf shared a paper on arXiv presenting BitNet b1.58, a 1-bit LLM claiming to match full-precision models in performance, which @nafnlaus00 and @nanobitz discussed as a revolutionary potential paradigm shift for NN hardware designs.
• BitNet Training On Consumer Hardware: @bratao expressed excitement over the potential to train a BitNet model on consumer hardware, given its apparent efficiency compared to full-precision LLMs as per the shared arXiv paper, and @nanobitz speculated about the architecture being different from quantization methods.

Links mentioned:

• The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits: Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in which every single param…
• RunPod Docker Link is broken · Issue #1318 · OpenAccess-AI-Collective/axolotl: Please check that this issue hasn’t been reported before. I searched previous Bug Reports didn’t find any similar reports. Expected Behavior Clicking the link should open RunPod with the templ…
• Seeking Neural Nuggets: Knowledge Transfer in Large Language Models from a Parametric Perspective: Large Language Models (LLMs) inherently encode a wealth of knowledge within their parameters through pre-training on extensive corpora. While prior research has delved into operations on these paramet…

OpenAccess AI Collective (axolotl) ▷ #axolotl-dev (10 messages🔥):

• Seeking Accuracy Before Speed: @dreamgen emphasized the importance of having the AI model perform correctly before focusing on improving its speed.
• Introducing LoRA-the-Explorer (LTE): @caseus_ shared a link to a paper on a novel approach to training neural networks using Parallel Low-Rank Adapters, highlighting the potential of multi-head LoRA even outside of federated learning.
• GitHub Source for Multi-head LoRA: Enhanced by the ongoing discussion, @caseus_ also provided a GitHub link to delve into the specifics of the multi-head LoRA implementation.
• Context Lengths in Fine-tuning Challenges: @xmikemm. inquired about the feasibility of Q-Lora fine-tuning TinyLlama with a 16k context on an Nvidia 4090 GPU, while @caseus_ suggested that it might exceed the VRAM capabilities and offered configuration tips to try.
• Dataset Suggestions for Model Experiments: In response to @xmikemm. looking for relevant datasets before committing to dataset creation, @caseus_ recommended using existing datasets like one found on Hugging Face for conducting experiments with different context lengths.
• Potential Alternative to ReLoRA: The conversation about LoRA-the-Explorer (LTE) led @nruaif to suggest that it may serve as a viable alternative to ReLoRA, possibly indicating a shift in the approach to low-rank adaptations.

Links mentioned:

• LTE: no description found
• casperhansen/longalpaca_1k_test · Datasets at Hugging Face: no description found
• LTE/lte/mhlora/linear.py at main · minyoungg/LTE: Contribute to minyoungg/LTE development by creating an account on GitHub.

OpenAccess AI Collective (axolotl) ▷ #general-help (18 messages🔥):

• Generating Wrong Answers: User @emperor inquired about techniques for producing incorrect answers with plausible explanations using LLMs; @nafnlaus00 suggested asking an LLM to generate responses with believable errors that seem minor but result in a wrong conclusion.
• Runpod vs Vast AI: @stoicbatman sought comparisons between Vast AI and Runpod services; @nanobitz responded indicating that Vast AI may be more cost-effective but with variable machine quality, and lacks abstraction of machine details.
• Axolotl Setup Confusion: @karisna expressed frustration over the confusing documentation for setting up Axolotl, emphasizing the need for clearer instructions, especially for Windows users.
• Benchmarks for Fine-tuned Models: @jovial_lynx_74856 asked about running benchmarks for a fine-tuned model with Axolotl; @nanobitz recommended the external tool lm_eval_harness but acknowledged there isn’t a direct integration with Axolotl for this purpose.
• Conflict in Pydantic with Mistral Config: @ustoll faced an issue with a namespace conflict within Pydantic affecting the Mistral config and was advised by @nanobitz to revert to a prior commit and make a GitHub issue for resolution.

OpenAccess AI Collective (axolotl) ▷ #replicate-help (1 messages):

• Replicate Surprisingly Outmatched: User @dreamgen expressed surprise that despite years of focus, replicate might not be better than expected, challenging its reputation built over time. No further context or specific comparisons provided.

Interconnects (Nathan Lambert) ▷ #news (9 messages🔥):

• Together Compute Teases Innovations: @natolambert shared a tweet from Together Compute to highlight the importance of long context abilities in developing AI.

• AI Ecosystem Entanglement Unravelled: @markkim1 discussed the complex relationship between Together and Cartesia, noting their collaboration and competition concerning Sparse Switching Networks (SSNs), and also mentioned Liquid AI as another entity in the fray.

• Arthur Mensch Sets the Record Straight: @xeophon. linked to a tweet by @arthurmensch stating their ongoing commitment to open-weight models, a reselling agreement with Microsoft, and the independent status of their European company with global ambitions. They are seeing interest for Le Chat and Mistral Large across platforms and plan rapid iterations. Arthur Mensch’s clarification tweet

• Launch of “Starcoder2” and “The Stack v2”: @xeophon. tweeted about BigCodeProject’s introduction of Starcoder2, which offers a 16k token context built on The Stack v2, the largest code dataset with over 900 billion tokens. The data and models are fully open and accessible. Learn more about Starcoder2

• Calls for HuggingFace to Ramp Up Model Training: @natolambert responded to the launch of Starcoder2 suggesting HuggingFace should train more models, acknowledging the progress being made in the code model space.

Links mentioned:

• Tweet from BigCode (@BigCodeProject): Introducing: StarCoder2 and The Stack v2 ⭐️ StarCoder2 is trained with a 16k token context and repo-level information for 4T+ tokens. All built on The Stack v2 - the largest code dataset with 900B+ t…
• Tweet from Arthur Mensch (@arthurmensch): Clarifying a couple of things since we’re reading creative interpretations of our latest announcements: - We’re still committed to leading open-weight models! We ask for a little patience, 1.5k H100s …

Interconnects (Nathan Lambert) ▷ #ml-drama (1 messages):

natolambert: good thread https://twitter.com/mmitchell_ai/status/1761860673989193959

Interconnects (Nathan Lambert) ▷ #random (52 messages🔥):

• Nathan’s Notion for Noteworthy Notes: @natolambert discussed his writing process, mentioning that he collects ideas and links in Notion, takes a pass at combining them into paragraphs, uses Grammarly and ChatGPT for edits, and then copies to Substack. He also commented on trying and leaving Ulysses despite considering a switch back due to increased writing volume.
• Typora Tops Xeophon’s Editor List: User @xeophon. shared their preference for Typora, a markdown editor used for many years, and consideration for Obsidian. Nathan Lambert responded positively to the suggestion of Typora, noting it looked great but also shared his past issues with the complexities of Roam and Obsidian.
• AI News Digest Digests Discord Discussions: User @swyxio shared a link to AI News, a service that aims to summarize discussions from various AI-related Discord servers, saving readers a significant amount of time. The newsletter mentioned Interconnects among newly evaluated discords, with a nod to its admin, Nathan Lambert.
• Demis Hassabis Unpacked on Dwarkesh’s Podcast: @natolambert praised an episode of Dwarkesh Patel’s podcast featuring an interview with Demis Hassabis, CEO of Google DeepMind, and discussed a variety of AI topics including scaling, AlphaZero, and AI governance.
• Family Name Mix-up in the Chat: In a friendly mix-up, users @natolambert and @mike.lambert clarified they are not related despite sharing a last name. Mike Lambert confirmed his affiliation with Anthropic, indicating he’s not there to share sensitive information but simply participating as himself.

Links mentioned:

• Demis Hassabis - Scaling, Superhuman AIs, AlphaZero atop LLMs, Rogue Nations Threat: “scaling is an artform”
• [AINews] Welcome Interconnects and OpenRouter: AI Discords for 2/26/2024. We checked 22 guilds, 349 channels, and 12885 messages for you. Estimated reading time saved (at 200wpm): 1063 minutes. Not much…

CUDA MODE ▷ #general (2 messages):

• Inquiry about CUDA Emulation: @jash403 asked for advice related to creating or running Emulators on CUDA GPUs.
• Emulating a Gameboy on CUDA: @iron_bound shared a GitHub repository, krocki/nvgb, a project that emulates a Gameboy using CUDA. They additionally provided a Towards Data Science article describing how it forms arguably the fastest 8-bit game console cluster in the world.

Links mentioned:

• GitHub - krocki/nvgb: CUDA gameboy: CUDA gameboy. Contribute to krocki/nvgb development by creating an account on GitHub.
• A GAMEBOY supercomputer: At a total of slightly over 1 billion frames per second it is arguably the fastest 8-bit game console cluster in the world.

CUDA MODE ▷ #triton (3 messages):

• Unslothai’s Triton Kernels Impress: @andreaskoepf praised the Triton kernels from unslothai, highlighting their efficiency with 5X faster execution and 60% less memory usage for QLoRA finetuning.
• Integration of Custom Triton Kernels with Torch: @marksaroufim shared a cross-post from another channel discussing how to integrate custom Triton kernels with torch.compile. Details can presumably be found in the referenced Discord post which is not directly accessible.
• Jeremy Meets the Mind Behind the Kernels: @jeremyhoward mentioned that they had a conversation with the author of the unslothai’s Triton kernels, acknowledging the notable work done.

Links mentioned:

GitHub - unslothai/unsloth: 5X faster 60% less memory QLoRA finetuning: 5X faster 60% less memory QLoRA finetuning. Contribute to unslothai/unsloth development by creating an account on GitHub.

CUDA MODE ▷ #cuda (4 messages):

• Understanding L2 Cache Efficiency: @cudawarped discussed memory operations in relation to L2 cache and global memory, noting that bandwidth might be the distinguishing factor since latency is similar. They referenced a Stack Overflow result and a microbenchmarking study to support the argument that L2 cache bandwidth is significantly higher.

• Insights into Nvidia’s H100 Architectural Design: @iron_bound shared their affinity for the detailed architectural breakdown of Nvidia’s H100 GPU found on Chips and Cheese. They highlighted the site’s coverage of the GPU, which targets the compute market, diverging from traditional graphics tasks.

• Momentary Confusion Over Benchmark Availability: @zippika expressed an interest in running benchmarks for the H100 GPU but initially couldn’t locate them, which was followed by a realization that they were indeed available on the same site they liked.

Links mentioned:

• Nvidia’s H100: Funny L2, and Tons of Bandwidth: GPUs started out as devices meant purely for graphics rendering, but their highly parallel nature made them attractive for certain compute tasks too. As the GPU compute scene grew over the past cou…
• Is memory operation for L2 cache significantly faster than global memory for NVIDIA GPU?: Modern GPU architectures have both L1 cache and L2 cache. It is well-known that L1 cache is much faster than global memory. However, the speed of L2 cache is less clear in the CUDA documentation. I
• Nvidia’s H100: Funny L2, and Tons of Bandwidth: GPUs started out as devices meant purely for graphics rendering, but their highly parallel nature made them attractive for certain compute tasks too. As the GPU compute scene grew over the past cou…

CUDA MODE ▷ #torch (12 messages🔥):

• The Story of PyTorch’s Ancestry: @marksaroufim shared a detailed history of PyTorch’s design and origins highlighting its evolution from Torch7, also known as LuaTorch, that began around 2010. This historical walkthrough showcases how the refactoring of LuaTorch’s C backend to be language agnostic led to the PyTorch we know today.

• Custom Triton Kernels and torch.compile: For those working on custom Triton kernels who want them to work with torch.compile, check the PyTorch GitHub example, which supports dynamic shapes, autotuning, and autograd. If issues arise, @marksaroufim advises opening an issue and tagging the expert, @oulgen.

• Compiling Fast Attention with Compiler Challenges: Tri Dao provided insights on why a compiler might struggle to optimize Fast Attention at a mathematical level, with a focus on maintaining numerical stability. The discussion revolved around a work that aimed to improve FlashAttention speed by 2x for training language models and can be found on OpenReview.

• Debating the Potential of a GPU Architecture Solver: The notion of a solver that optimizes GPU architecture sparked debate among users like @iron_bound, @chhillee, and @gogators., discussing the complexity of the task and its comparison to a bin-packing problem, highlighting the inherent difficulty in finding optimal solutions for the deep learning workload distribution.

• Advanced Compiler Technologies for Deep Learning: Users like @w0rlord brought up polyhedral compilation, citing it as an approach to optimal code transformation, with relevance to deep learning problems. @w0rlord shared a link pointing to PolyMage Labs, a company working on such technology, as well as an educational resource on the subject, which garnered interest from @gogators. and can be found here.

Links mentioned:

• PyTorch’s design origins | Soumith Chintala: no description found
• FlashAttention-2: Faster Attention with Better Parallelism and Work…: Scaling Transformers to longer sequence lengths has been a major problem in the last several years, promising to improve performance in language modeling and high-resolution image understanding, as…
• Technology: PolyMage Labs - Compilers for Artificial Intelligence: no description found
• pytorch/test/dynamo/test_triton_kernels.py at 0c8bb6f70c65b0a68fcb282cc1605c79ca5dabce · pytorch/pytorch: Tensors and Dynamic neural networks in Python with strong GPU acceleration - pytorch/pytorch
• Issues · pytorch/pytorch: Tensors and Dynamic neural networks in Python with strong GPU acceleration - Issues · pytorch/pytorch
• oulgen - Overview: I’m a software engineer at Meta where I work on the Hack programming language and PyTorch. - oulgen

CUDA MODE ▷ #algorithms (2 messages):

• Fast InvSqrt() – A Nostalgic Optimization: User @iron_bound reminisced about the fast inverse square root algorithm, famously used in Quake III. A Wikipedia link provided showcases the algorithm which is useful for lighting and reflection computations in games like OpenArena.
• Generic Implementation Challenges: Following the topic of optimization algorithms, @chhillee commented on the complexity of creating a generic version, stating that “unfortunately it’s quite difficult to do it generically.” This reflects the inherent challenge in adapting specialized algorithms for broader applications.

Links mentioned:

Fast inverse square root - Wikipedia: no description found

CUDA MODE ▷ #pmpp-book (1 messages):

watashiwapotato: Has anyone made anki cards for this?

CUDA MODE ▷ #smol-hw (2 messages):

• Clarification on ‘AO’ Abbreviation: @mr.osophy asked, “What does AO stand for 😅” signaling a need for clarification on the acronym.
• Defining ‘AO’: @marksaroufim responded that AO stands for Architecture Optimisation, conceding that it might not be the best name for it.

CUDA MODE ▷ #ring-attention (8 messages🔥):

• Collaborative Effort on Ring Attention: @ericauld shared a work-in-progress notebook illustrating ring attention and flash attention, inviting feedback and collaborative improvements.

• Gratitude for Community Insights: @andreaskoepf expressed gratitude to @325883680419610631 for valuable insights shared within the community.

• Excitement for Team Progress: @marksaroufim expressed excitement about the work and progress of the team, indicating supportive sentiments for the ongoing projects.

• Offer to Assist with Tasks: @andreaskoepf reached out to @831049856851116082 offering help with side tasks or testing, showcasing community readiness to support and collaborate.

• Technical Challenges with Ring Attention: @nshepperd discussed facing difficulties implementing ring attention fwd in jax using jax.Array, specifically with hiding transfer latency through collective-permute and custom calls, and mentioned that automatic partitioning posed challenges in the 0.4.20 version.

Links mentioned:

Google Colaboratory: no description found

Latent Space ▷ #ai-general-chat (27 messages🔥):

• Pika Unveils Lip Sync for Pro Users: @sylviatong shared that Pika has released early access to their Lip Sync feature for Pro users, with the announcement highlighted here. Caught the attention of @swyxio who finds it impressive but still not quite out of the uncanny valley.

• Impressive AI Customer Service Stats Revealed: @swyxio discussed the notable high-scale AI usage findings by Klarna, mentioning that their AI assistant handled 2.3 million customer service chats in the first month, performing the equivalent job of 700 agents. @eugeneyan expressed interest in the valuable data indicating customer satisfaction on par with humans, while @swyxio challenged the rosy outlook by linking a Fast Company article questioning the news integrity.

• Elicit Reaches $1M ARR: @swyxio announced that Elicit, having launched subscriptions just four months ago, has now hit $1 million in annual recurring revenue, celebrating the team’s achievement and hinting at greater things to come.

• Open Call for Interview Questions: @fanahova is set to interview the CEO of Adept and has requested questions from the community. @yikesawjeez humorously asked about open sourcing and wearables in relation to Adept.

• A Technical Hurdle for Running Gemma Locally: @stealthgnome encountered issues running Google’s Gemma on MPS due to complex tensors, sparking a conversation with @swyxio and @yikesawjeez about the compatibility and architectural nuances of the models. Further discussion included linking to the official Gemma PyTorch GitHub and its run script.

• First Blog Post by Noam Shazeer: @swyxio shared the news of Noam Shazeer’s first blog post focused on coding style, specifically about shape suffixes, available for the community to read here.

Links mentioned:

• Tweet from Hamel Husain (@HamelHusain): Something smells really wrong about the Klarna news it’s a bit too much made for TV? https://www.fastcompany.com/91039401/klarna-ai-virtual-assistant-does-the-work-of-700-humans-after-layoffs
• Tweet from Pika (@pika_labs): We know there’s been a lot of talk about AI generated video recently. Well, look who’s talking now! Early Access to Lip Sync is available for Pro users now at http://pika.art.
• Tweet from Sebastian Siemiatkowski (@klarnaseb): This is a breakthrough in practical application of AI! Klarnas AI assistant, powered by @OpenAI, has in its first 4 weeks handled 2.3 m customer service chats and the data and insights are staggering…
• Tweet from Jungwon (@jungofthewon): 4 months after launching subscriptions, we’ve hit $1MM ARR :) Kudos @stuhlmueller @james_elicit @itsmesarahp @k1bird @BenRachbach @Mappletons @VivaLaPanda_ @LukeStebbing @__Charlie_G @OrangJuli…
• Latent Space: The AI Engineer newsletter + Top 10 US Tech podcast. Exploring AI UX, Agents, Devtools, Infra, Open Source Models. See https://latent.space/about for highlights from Chris Lattner, Andrej Karpathy, Ge…
• Tweet from Pika (@pika_labs): We know there’s been a lot of talk about AI generated video recently. Well, look who’s talking now! Early Access to Lip Sync is available for Pro users now at http://pika.art.
• Tweet from Sebastian Siemiatkowski (@klarnaseb): This is a breakthrough in practical application of AI! Klarnas AI assistant, powered by @OpenAI, has in its first 4 weeks handled 2.3 m customer service chats and the data and insights are staggering…
• Tweet from Noam Shazeer (@NoamShazeer): https://medium.com/@NoamShazeer/shape-suffixes-good-coding-style-f836e72e24fd Check out my first blog post.
• gemma_pytorch/scripts/run.py at main · google/gemma_pytorch: The official PyTorch implementation of Google’s Gemma models - google/gemma_pytorch
• GitHub - google/gemma_pytorch: The official PyTorch implementation of Google’s Gemma models: The official PyTorch implementation of Google’s Gemma models - google/gemma_pytorch

Latent Space ▷ #ai-announcements (1 messages):

swyxio: new pod is up! with CEO of Replicate https://twitter.com/swyx/status/1762906839505846418

DiscoResearch ▷ #general (16 messages🔥):

• Inquiry about End-to-End RAG-LLM Optimization: @rasdani raised a question about whether there is research on end-to-end optimization of Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) using gradients. The LESS paper was cited for its method of optimizer-aware data selection, though @rasdani later clarified that it doesn’t backpropagate through data selection.

• Seeking the LESS Paper Link: @maxidl requested a link to the LESS paper, which @rasdani provided, along with clarification that the original technique mentioned does not involve backpropagation through data selection.

• Alternative Models for German Document Extraction: @mab3049 reported issues using Leo Mistral 7B for extracting information from OCR’ed German documents, receiving unrelated results. @bjoernp recommended using DiscoLM_German_7b and advised checking out the demo as well as adopting the correct chat template found on Hugging Face’s documentation.

• Model Recommendations and Proper Templating: In response to @mab3049’s extraction difficulties, @bjoernp suggested using the DiscoLM_German_7b model instead and provided guidance on using the appropriate chat template for better interaction with the model.

• Discussion on Code Chunker and Preference for Goliath Model: @sebastian.bodza complained about past issues with the llamaindex chunker for code, and @philipmay spotlighted the Goliath model on Hugging Face, asking if others consider it the best for German language tasks.

Links mentioned:

• LESS: Selecting Influential Data for Targeted Instruction Tuning: Instruction tuning has unlocked powerful capabilities in large language models (LLMs), effectively using combined datasets to develop generalpurpose chatbots. However, real-world applications often re…
• alpindale/goliath-120b · Hugging Face: no description found
• Tweet from Andreas Köpf (@neurosp1ke): Scrum 🤮
• Templates for Chat Models): no description found
• DiscoLM German 7b Demo: no description found
• Templates for Chat Models: no description found

LLM Perf Enthusiasts AI ▷ #opensource (2 messages):

• Speculating on Llama 3 Release: User @res6969 expressed expectations regarding the release of Llama 3, estimating a possible launch in spring. No specific release date was mentioned or confirmed.

LLM Perf Enthusiasts AI ▷ #speed (6 messages):

• The Pain of Latency: User @res6969 expressed deep disappointment regarding the latency, specifically the seconds it takes for OpenAI APIs to respond.
• Azure Hosting Blues: Following up on the latency issue, @pantsforbirds chimed in to agree, finding the results from Azure hosting to be disappointing.
• Clarifying Latency Queries: Inquiring about the details of the latency problem, @justahvee asked whether the issue pertained to time to the first token or the completion time for a fixed number of tokens.
• Latency Specifics Identified: Clarifying @justahvee’s query, @res6969 specified that the latency concern was regarding the time to the first token.

AI Engineer Foundation ▷ #general (1 messages):

iloveh8: hi any recommendation to prepare for AI engineering interview

AI Engineer Foundation ▷ #events (3 messages):

• Tune In for Live Coding Session on YouTube: @_z shared a YouTube live stream link inviting members to watch and interact as they work on Agent Protocol V2’s Config Options RFC. The stream promises coding insights and engagement with the viewers.
• Don’t Miss the Voice + AI Meetup: @kwindla announced an upcoming Voice + AI meetup hosted by Cloudflare featuring a panel with AI experts such as Jay Jackson from Oracle Cloud and others. The event, complete with demos and pizza, is scheduled at 6:30 pm on Wednesday at Cloudflare, and interested parties can RSVP here.
• Is the Voice + AI Event Streaming?: @yikesawjeez inquired whether the upcoming Voice + AI meetup would be streamed online, expressing a desire to participate as a “reply guy” due to their interest in voice technology. The inquiry hints at remote engagement options, but no specific response has been recorded.

Links mentioned:

• Infrastructure for real-time AI, Wed, Feb 28, 2024, 6:30 PM | Meetup: Please join us on Wednesday Feb 28th at the Cloudflare office (home of the world’s most famous lava lamps) for a panel discussion about infrastructure for real-time AI. Sc
• Coding - Working on Agent Protocol V2 Milestone, Config Options, New RFCs: Hello, I’m Ziggy!I’m an Open Source Developer, gamer, and tech enthusiast. You can find me on GitHub at https://github.com/jzanecook Interested in contributi…

Datasette - LLM (@SimonW) ▷ #ai (1 messages):

• Chatty Claude Ignores JSON Formatting Requests: @derekpwillis expressed frustration with Claude as it often ignores instructions to strictly produce JSON objects and instead adds a prefatory statement like “Here’s a JSON object extracted from the text”, even when explicitly directed to start with { and end with }. This unnecessary narrative is super annoying for users seeking clean JSON outputs.