> AI Discords for 2/19/2024. We checked **20** guilds, **313** channels, and **3952** messages for you. Estimated reading time saved (at 200wpm): **346 minutes**.

As mentioned in yesterday’s recap, Andrej shipped his Tokenization tutorial with accompanying github repo (tweet):

Play

It is sobering how this 2hr tutorial is necessary to fully understand tokenization up to the RegEx patterns used in GPT4’s tokenizer, but, as Andrej notes, even then it is far from complete to get up to Llama 2 tokenization with SentencePiece, and yet tokenization was at the core of many LLM failure modes at least from GPT2-GPT4.

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Table of Contents

[TOC]

PART 0: SuperSummary

  • Model Optimization and Efficiency
    • Quantization and Compatibility: Discussions highlighted efforts in quantizing models like Mistral 7B and Zephyr-7B, focusing on reducing memory requirements for better compatibility with consumer hardware, notably for running on 8 GB VRAM CUDA GPUs. Intel’s exploration into a new weight-only quantization algorithm for LLMs, despite lacking comprehensive documentation, sparked interest for its potential to enhance model efficiency without sacrificing performance
    • Efficiency Improvements: A significant focus was placed on improving computational efficiency and model robustness. Techniques include selective augmentation for classifiers, reducing computational costs by an average of 57.76%, and discussions on the efficient use of memory tokens versus traditional methods like kNN for Transformers
    • Challenges in Model Implementation and Fine-Tuning Technical Troubleshooting: Communities shared challenges ranging from hardware compatibility issues (e.g., AVX2 support, multiple GPU configurations) to software-specific problems like VSCode not recognizing certain modules. There’s a shared struggle in implementing and fine-tuning AI models, particularly noted in the difficulty of loading quantized versions of models for specific tasks like RAG
    • Fine-Tuning and Model Merging: The nuances of fine-tuning LLMs, including the use of LoRA configurations and the complexities of merging models fine-tuned on different datasets, were frequently discussed. These discussions highlight the technical depth and experimentation within the community to optimize model performance and output consistency
  • Advancements and Applications of LLMs
    • Innovative Uses of LLMs: From enhancing RAG applications to exploring multi-model learning strategies, the AI communities are actively exploring ways to extend the capabilities and applications of large language models. The potential of LLMs to act as AI assistants or leverage meta-reasoning capabilities for improved reasoning structures represents the cutting edge of AI research and development
    • Dataset and Model Accessibility: The creation and sharing of encoded datasets for machine learning applications, as well as the open-source release of advanced models and codecs, indicate a strong community drive towards democratizing access to AI resources. This includes discussions on SDXL VAE encoded datasets and Audiogen AI’s open-source audio codecs
  • Ethical Considerations and Community Engagement
    • Censorship and Ethical AI Use: Conversations around implementing censorship in chat models and constructing model guardrails reflect ongoing concerns regarding ethical AI usage. The community explores various approaches, including discriminator models and prompt tuning, to ensure responsible model behavior
    • Community Resources and Collaboration: The AI communities actively collaborate on compiling resources, sharing knowledge, and troubleshooting. This includes the sharing of comprehensive documents to centralize AI resources, discussion on benchmarking models for efficiency and ethical considerations, and addressing technical challenges together.

PART 1: High level Discord summaries

TheBloke Discord Summary

  • Buzzing to a Halt with Linux Mint: @kalomaze encountered frustrating faint buzzing sounds from speakers while using Linux Mint, exacerbated by system crashes affecting functionality like printscreen.

  • Colab’s A100 somewhat Alpaca-norous: @karisna faced memory issues while fine-tuning Mixtral 8x7B on a Colab A100, despite adjustments in settings and batch size reduction, with recommendations involving gguf management.

  • Intel’s Mysterious Quantization Dance: Intel’s “auto-round” GitHub repo sparked conversations on a new weight-only quantization algorithm for LLMs, with community debates around potential benefits given incomplete documentation.

  • Bewildered Over Basic Code for Mistral 7B: @vivek2722 sought assistance for loading the quantized version of Mistral 7B for RAG, yet no immediate solutions surfaced in the messages available.

  • Censorship Conundrums: Various approaches to implement censorship in chat models were discussed, including using a discriminator model, prompt tuning, and exploration of NVIDIA NeMo-Guardrails and reinforcement learning, as highlighted by @jeremy.london with a reference to the leaked GPT-4 prompt in a YouTube video.

  • Template Turmoil in Model Merging: @givan_002 articulated concerns regarding which template to use after merging two different models, NeverSleep/X-NoroChronos-13B and elinas/chronos-13b-v2 that were fine-tuned on Vicuna and Alpaca respectively, mindful of the inconsistency in inference outputs.

  • Quantum Leap in Dataset Handling: Echoing community empathy, @ikaridev faced a dataset leak ordeal and addressed the nuances of balancing datasets, which could include sensitive content, in the context of roleplay and function-calling.

  • Guardrails on the Guard: Drawing upon collective wisdom, strategies for constructing model guardrails were debated, suggesting the use of a more refined and nuanced discriminator model and the injection of a specific token sequence to steer towards desired model behavior.

Eleuther Discord Summary

  • LLMs Augment Robustness and Efficiency: A recent preprint highlighted by @millander points to the use of LLMs (Large Language Models) to improve classifier robustness by rewriting inputs to emulate in-distribution examples. Selective augmentation, through entropy-based evaluation, can also cut computational costs by an average of 57.76%, offering a more efficient approach.

  • Creative Encoding and Model Portability Discussions: SDXL VAE encoded datasets, such as ArtBench and FFHQ, are accessible on Hugging Face, and a script for the SDXL VAE encoding of ImageNet-1k was shared. For applying large-scale AI models in consumer hardware, a quantized Mistral-7B was recommended for its compatibility with an 8 GB VRAM CUDA GPU.

  • Memory Tokens and Model Structuring Insights: Complications of liquid neural networks were debated, and distinctions between RMT-T memory tokens and kNN for Transformers were discussed. The impact of conversational context on LLM performance was questioned, suggesting decontextualized inputs might improve response quality. Language model training intricacies such as semantics and syntax are recognized as intertwined elements critical for prediction accuracy.

  • Synthetic Prompt Structuring for GPQA: A new optional subfield in fewshot_config was proposed for GPQA to introduce structured fewshot prompts like those used in minerva_math or gsm8k_cot, as seen in a GitHub example.

  • Codec Advancements and GPT-NeoX’s Development Path: Audiogen AI announced open-source audio codecs with a discrete model providing a total bit rate of 13.2kbps, and GPT-NeoX was noted for incorporating design elements from Megatron and focusing on correct development priorities.

OpenAI Discord Summary

  • Pricing Perplexities and AI Alternatives: The potential cost of a new OpenAI release and the ChatGPT Plus’s message cap agitated users, leading to comparisons with Google’s premium model and alternatives like Gemini for creative writing. Meanwhile, discussions surfaced about educators exploring AI tools like Claude and Microsoft Co-Pilot for classroom use, emphasizing the sector’s eagerness to integrate AI without restrictive barriers like phone verification.

  • GPT-4 Browser Bafflement: Issues with GPT-4’s responsiveness in web browsers versus mobile for @iamryuzaki led to discussions on peer-to-peer AI, difficulties with custom knowledge bank retrieval, localization challenges for AI assistants, and optimizing Custom GPTs for voice interactions, particularly for job interview practice.

  • Email Categorization Hustles: @ben.30 strives to improve an email categorization process with a success rate of 75% and a threshold for the ‘unknown’ category, whereas prompt debugging techniques were shared to identify model inconsistencies and potential issues in performance.

  • Prompt Engineering Puzzles: Intricacies in prompt constructions affecting AI outputs were highlighted, with @eskcanta advising on self-evaluating prompts using meta-prompts. @drcapyahhbara confronted challenges in using GPT for writing novels, with each sentence resembling an introduction, an issue open for guidance in prompt refinement.

LM Studio Discord Summary

  • LM Studio’s GGUF Model Limitation: LM Studio has been clarified to support only GGUF models, and there’s been no mention of BNF grammar compatibility.

  • Recommended Models for Academic Writing: Mistral 7B was suggested for tasks like academic writing, with discussions touching upon hardware requirements for running such models effectively.

  • Integration and API Concerns with LM Studio: Questions were raised about integration capabilities of LM Studio, specifically regarding API calls and its compatibility with Azure AI.

  • Hardware Compatibility and Model Installation: Installation of models contingent upon AVX2 support was a hot topic, with solutions provided for libcblast errors and discussions on the potential use of multiple GPUs to handle more powerful models.

  • Model Capabilities and Quantization Methods: A new llama version of miniCPM supporting fp16/int4 ggufs is available, while discussions included quantization efficiencies and comparison of the Qwen series in LM Studio.

  • GPU Selection Dilemma for AI: The NVIDIA RTX 3090 was recommended over the 4070 Super due to its larger VRAM capacity which is beneficial for running larger model sizes.

  • Attempted Mod for 3090 VRAM Expansion: A mod to expand the RTX 3090’s VRAM to 48GB was discussed, highlighting BIOS restrictions and referencing attempts shown on Bilibili and YouTube.

  • RAM Upgrade Goes Unrecognized in LM Studio: Even though LM Studio didn’t recognize an upgrade from 16GB to 64GB RAM, the issue was identified as a display bug that doesn’t affect model functionality.

  • Configuring LM Studio for Multiple GPUs: There was a request for guidance on assigning LMStudio to a specific GPU when multiple models are available, with advice pointing to a useful instructional thread.

  • AMD GPUs and AI Workloads: Users discussed the drawbacks of using AMD GPUs for AI workloads, emphasizing Nvidia’s optimized hardware support as a preferable choice.

  • VSCode Ignoring ‘crewai’ Module: There’s a reported problem with Visual Studio Code not recognizing the ‘crewai’ module, although it appears in piplist, indicating a potential IDE-related issue.

Mistral Discord Summary

  • LLMs Can Be Stage Actors: @i_am_dom elucidated that LLMs can be finetuned to act as AI assistants, emphasizing the flexibility in behavior shaping during the fine-tuning stage. @jamshed1900, @mrdragonfox, and @drnicefellow concurred Mistral-next eclipses its antecedents in reasoning capabilities.

  • Innovation in Multi-Model Learning: @mehdi_guel revealed plans for an exploratory venture blending in-context learning with chain-of-thought strategies. Meanwhile, @mrdragonfox educated that Mixtral’s MoE structure does not entertain the extraction of standalone experts, as the expertise is diffusely embedded within the model.

  • The Varied Mileage of VLLM: @ethux noted inconsistent performance with VLLM in a sharded environment, in contrast to seamless operation with TGI. The deployment efficacy of Mixtral on g5.48xlarge instances remained unanswered.

  • The Finer Points of Fine-tuning: @timuryun entered the fine-tuning fray with a question met by keen assistance, mostly from @mrdragonfox. Discussion threads touched on using Q&A approaches for model education and the nuances of LoRA configurations for fine-tuning a 7B Mistral model, advocating for a better grasp of parameters and tuning techniques.

  • Curating a Collective AI Knowledgebase: User @_red.j shared an AI master document to centralize resources for AI aficionados, following a conversation with ML experts on Twitter. The community was invited to expand the document with their top AI sources.

LlamaIndex Discord Summary

  • Upcoming LlamaIndex Webinar Lights Up the RAG Stage: LlamaIndex has announced a webinar for Thursday at 9am PT, showcasing innovative uses of Retrieval-Augmented Generation (RAG) by the recent hackathon winners. The webinar reveals advanced knowledge synthesis and reasoning applications like ADU Planner and Counselor Copilot, providing a glimpse into RAG’s potential beyond baseline chatbots.

  • Meta-Reasoning and RAG Reranking Touted in LLM Discussions: A new paper titled Self-Discover posits the integration of meta-reasoning capabilities in LLMs, which @peizNLP highlighted could enhance traditional AI reasoning structures. Furthermore, Florian June’s blog celebrated by LlamaIndex details improving RAG systems with reranking techniques, hinting at smarter data retrieval.

  • Tech Troubles and Tips Tackled in General Chat: @wrapdepollo and @whitefang_jr assist users with broken links and updates to text nodes, referencing the Document Management guide for support. Meanwhile, @david1542 and @cheesyfishes delve into clustering support in VectorDBs, recommending usearch for including algorithms like K-Means and DBSCAN.

  • Agonizing Over Agent Behavior: The agents’ integration with tools sparks a conversation led by @mst2205, expressing challenges in getting agents to understand and combine results from different query engines. The discourse includes tips such as including the current date in prompts and references to AgentBench GitHub for evaluating agents.

  • To Customize RAG or Not? A Discussion on Self-Hosting LLMs: In a debate over creating a custom RAG system versus using RAG-as-a-service, @skiboyec and @desk_and_chair muse on the benefits of customization against service convenience. The discussion touches on self-hosting, scalability, API costs, and usage intent, reflecting on the specific needs and capabilities required by users.

HuggingFace Discord Summary

  • LayoutLMv3 Errors Persist: truedescription ran into issues using LayoutLMv3 from Hugging Face; despite setting truncation and padding to true, an error prevailed without a clear solution.

  • Creating Latin American RL Course Channel: sebaskja showed interest in establishing a channel specifically for Latin American Reinforcement Learning course members, looking for guidance.

  • Sora Videos Spark Amusement: A Twitter link shared by chalm3rs. displayed the latest Sora videos from the OpenAI team, causing a stir of interest and humor among users.

  • Diving into CI/CD for ML Ops: kingabzpro introduced a guide for CI/CD in Machine Learning, aimed at aiding the automated deployment and testing process.

  • Generative AI’s Rise to Daily Relevance: An article discussed how generative AI has woven itself into the fabric of everyday life in the UK and Australia, with the emergence of “prompt whisperers” shaping the utilization of the technology.

  • Advancing AI Intelligence Measurement: An older paper proposed the necessity of a different feedback signal to gauge AI and human intelligence more accurately, contesting current benchmarking methods.

  • Quantized Zephyr-7B Tailored for Customer Support: The Zephyr-7B model received fine-tuning attention — employing quantization and the AutoGPTQ library — for a customer support chatbot application.

  • Exploring Banner Ads on HuggingFace Spaces: myg5702 initiated a discussion about the feasibility of incorporating banner ads on HuggingFace spaces, learning they may be acceptable for community-driven content.

  • Visualizing Multilingual Text with Aya Dataset: cakiki highlighted the diversity of languages supported by CohereForAI’s Aya, sharing a visualization of the Aya dataset.

  • Annotated Mamba Project Completes: The Annotated Mamba, a project by Sasha Rush, was made available at Annotated Mamba, earning praise and discussion for its detailed explanation.

  • Melding Fashion and AI: In the #diffusion-discussions channel, mohdfaiez sought assistance to create an AI tool for changing clothes on images, using an example from the Pincel app.

  • QLoRA Finetuning Roadblocks: kingpoki struggled with errors during the QLoRA finetuning process, seeking community advice on a NotImplementedError encountered with AutoPeftModelForCausalLM.from_pretrained.

  • Inquiries on Advanced NLP Models: There were queries regarding code walkthroughs for RA-DIT and REPLUG instruction tuning, and challenges with Whisper large v3 language transcription—which erroneously interpreted Spanish as Chinese—prompting requests for support.

OpenAccess AI Collective (axolotl) Discord Summary

  • Checkpoints for Keyboard Grace: @seungduk queried the integration of checkpointing on a keyboard interrupt (ctrl+c) within the system, and @nanobitz confirmed that this feature may have been implemented previously but is uncertain of its current state. Further inspection of the code is initiated with a prompt to review at OpenAccess AI Collective GitHub.

  • Yolo Learns the Hard Way: @le_mess took a risk by running computations on an A100 for 10 days without setting checkpoints, prompting a blend of sympathy and humor from other members.,@yamashi tagged along with a “yolo” spirit, while @noobmaster29 reacted with a facepalm emoji.

  • VRAM Diet for a 7B Giant: @noobmaster29 shared experiences related to the VRAM requirements for quantizing a 7B model, including a helpful script from TheBlokeAI on GitHub.

  • Benchmarking Blues with BioMistral: An accusation by @yamashi towards BioMistral for purportedly misreporting benchmarks sparked a critical exchange on the accuracy and ethics of model benchmark evaluations, referencing a relevant tweet.

  • RunPod Runaround: Issues with RunPod setups were addressed with @m4ttfl0 sharing a potential workaround for a directory clobbering problem, evidently a known issue #813 on GitHub. Additionally, @noobmaster29 lamented over the lengthy and sometimes failing setup process, including an inquiry about an error code that might suggest a system memory shortfall.

LAION Discord Summary

  • Juggernaut Unleashed Without UI: Members discussed how to operate the Juggernaut XL model sans UI, including a helpful checkpoint available at Hugging Face, accompanied by practical code for implementation.

  • Multipurpose Datasets and LoRA Implications: Conversations touched on SDXL VAE preprocessed image/text datasets with a dataset example at Hugging Face, and debated LoRA’s effect on model realism versus trained aesthetic preferences following a Reddit discussion.

  • Alpha-Prompt LORA Invites Scrutiny: An Alpha-Prompt LORA model introduced by @qwerty_qwer and TwoAbove promises more detailed SD prompts, with testing welcomed at Hugging Face.

  • AI Resource Aggregation for the Hungry Minds: A comprehensive document titled The AI Info Diet ™️ was shared, featuring a curated list of AI tools, news, and resources, aimed at keeping engineers updated, and is open to community contributions (Google Doc).

  • CAD Systems Await AI Revolution: Inquests about AI-integration into CAD programs surfaced, pointing out the current complications due to a lack of datasets and standards for parametric 3D shapes, while a claim was made about Mistral’s new ‘Next’ AI potentially surpassing capabilities seen in GPT-4 according to early testers (Reddit source).

Latent Space Discord Summary

  • Groq Chips Away Competition: Community members, including @swyxio and @shivdinho, engaged in discussions on Groq’s performance claims, questioning its cost-efficiency and speculating on its real-time application potential. The unique no-DRAM and horizontally scalable architecture, loaded with SRAM chips, is of particular interest for its potential in real-time LLM instances.

  • Retrieval Redirection: @fanahova pointed out deficiencies in vector-based retrieval systems, referencing an article on vector retrieval limitations and noting the industry’s overlooking of advancements in graph-based models and methods like HNSW.

  • Chatbot RAG Rethought: A conversation was sparked regarding the need for retrieval-augmented generation (RAG) in chatbots, where the use of LLMs for deducing user intent and implementing function calls was a central theme.

  • Benchmarking Brilliance with HELM: The guild discussed the introduction of Prof. Percy Liang’s HELM benchmark, a new framework for evaluating LLMs, recognized for its instructional approach and absolute ratings which promise a more nuanced assessment of models.

  • Miscellaneous Mentions: Various resources were shared, including an article on MoonBit’s AI-native toolchain design, a talk by Google’s Jeff Dean on trends in ML, and the open-source AI wearable project ADeus on GitHub, noting advancements and innovation in the AI space.

CUDA MODE Discord Summary

  • CUDA Cores Unwrapped: @nshepperd sought clarification on whether “cuda core” refers to the fp32 and int32 arithmetic units. @_t_vi_ detailed that each unit executes a warp’s instruction, with efficient register-based switching. This conversation can aid in better understanding of underlying CUDA execution mechanics.

  • PyTorch and GitHub Convergence: @p0.tato pointed to TensorListMetadata and multi_tensor_apply contributions, while @ardywibowo shared a blog post on LLM acceleration using PyTorch. They also highlighted the existence of llama.cpp, vLLM, and MLC-LLM for generative model optimization.

  • NVIDIA Secrets and Surprises: @apaz discovered varying graphics, memory, video clock speeds via nvidia-smi. @stefangliga shared the interesting behavior of boost clocks on NVIDIA GPUs, depending on the temperature, hinting at performance tuning based on environmental conditions.

  • Ring Attention Deep Dive: @ericauld critically assessed flash-attention, questioning the backward implementation and sparking a broader discussion on cache mechanics and possible enhancements for ring attention led by @iron_bound and @andreaskoepf. An issue was opened at ring-attention for development of a naive version that also manages partial kv-blocks processing.

  • Flash Attention in JAX Spotlight: @nshepperd embarked on integrating flash attention bindings into JAX to explore SPMD pattern, and discussed the hurdles like removing Torch dependencies from Tri Dao’s flash attention repo, illuminating JAX as an easier platform for this work.

Perplexity AI Discord Summary

  • Gemini Pro Houdini Act: Users report that Gemini Pro access disappeared from Perplexity Pro, sparking speculations on potential updates that might introduce Ultra or Pro 1.5 access.
  • Timeline Tease for Updates: A user’s query about the update timeline was met with a cryptic reply and a Discord link by @ok.alex, suggesting more information might be available through the link.
  • Playground Limits Are Subscription-Free: In a clarification, @icelavaman stated that text length limits in the Perplexity Playground are not tied to subscription levels, and pointed to the API documentation for details on context sizes.
  • Merch March Madness?: A humorous exchange occurred when @lord.wex inquired about Perplexity merchandise, which led to the sharing of a speculative “merch by March” tweet by @AravSrinivas.
  • GPT-4 Turbo: Under Lock or Non-Existent?: Debate ensues on whether Perplexity Pro is using a GPT-4 turbo, with confirmations that it’s only the standard version, coupled with uncertainty about the availability of a turbo version.
  • Peeking Under Perplexity’s Hood: Articles on the mechanics and designer behind Perplexity AI shared by @soabonen and @sjohri respectively could provide deep dives for the interested: How does Perplexity work? and Who designed Perplexity?.
  • Fine-Tuning Not an Option: @retonq’s question about fine-tuning the pplx-online model was shot down with a clear No from @icelavaman.

LangChain AI Discord Summary

  • Visualizing LangChain’s Internal Workings: Users discussed the need for visualization tools for LangChain’s chains or calls, with some referencing the lack of current documentation on the feature. The changelog and LCEL documentation were shared to help users understand the updates and replacements of deprecated Chain Classes.

  • Addressing ChatVertexAI’s Validation Errors: There was a query regarding NEGLIGIBLE level Response Validation errors in ChatVertexAI, with no consensus reached on how to adjust the safety configurations or turn off the response validation.

  • Enhancing Chroma’s Retrieval Efficiency: An idea was floated about improving Chroma’s retrieval performance by transforming questions into a list of keywords, intended to produce better results than the current method.

  • Demystifying LLM Parameters: A brief explanation provided clarity on the parameters in large language models (LLMs), indicating they are weightings applied to tokens from a prompt to generate responses in the backend.

  • Learning Through LangChain Tutorials: A comprehensive LangChain tutorial playlist with over 60 videos was shared to educate users on LangChain use cases, offering a rich resource for developing applications with generative AI. The “LangGraph Retrieval Agent” video specifically details the use of Retrieval Agents within this context.

DiscoResearch Discord Summary

  • Multilingual Expert LLM Development Discussions: Discussions focused on creating LLMs with expertise in multiple languages and domains, with suggestions like using LLaMA-Factory for pretraining LLMs in new languages and leveraging large-scale clusters utilizing frameworks such as GPT-Neo-X, Megatron-LM, and Axolotl.

  • Clarifying the Cost of Benchmarking: Users discussed budget-friendly benchmarks, with fasteval mentioned as a quick, albeit not free, evaluation tool. It was noted that fasteval still costs over 5€ in OpenAI credits per model.

  • Temporary Service Interruption Resolved: The DiscoLM German 7b Demo server experienced downtime as the GPUs were in use for evaluations, later confirmed to be back up and running.

Alignment Lab AI Discord Summary

  • Crowdsourced AI Resource Compilation: User @_red.j shared a collaborative The AI Info Diet ™ Google Document with resources for keeping up with the latest in AI. The document welcomes contributions and includes the Alignment Lab AI Discord server as a resource.

PART 2: Detailed by-Channel summaries and links

TheBloke ▷ #general (1195 messages🔥🔥🔥):

  • Audio Buzzing Troubles on Linux Mint: @kalomaze is experiencing random faint buzzing sounds from speakers on Linux Mint and lamenting the ongoing issues with the operating system, including a crash that resulted in the loss of printscreen functionality (source).
  • Model Training Challenges on Colab A100: @karisna is attempting to fine-tune Mixtral 8x7B on a Colab A100 but runs into memory issues despite trying an array of settings and reducing the batch size significantly. Suggestions involve managing batch sizes and incorporating gguf for the process (source).
  • Intel’s Intriguing Quantization Method: Intel’s GitHub repo “auto-round” hints at a new weight-only quantization algorithm for LLMs that may offer better performance at lower precision, causing discussion about potential benefits and skepticism given the lack of full documentation (source).
  • Chatbot Integration with Intel’s New Quant: @tibbnak noticed Intel uploaded some quants to Hugging Face, claiming to achieve good benchmark scores on quantization, suggesting it might be as effective as higher precision but at significantly reduced size (source).
  • Concerns Over Model Merging Techniques: @givan_002 expressed concerns regarding model merging where different templates are used for base models (such as Vicuna and Alpaca), wondering which template should be used after merging to avoid inappropriate inference outputs (source).

Links mentioned:

TheBloke ▷ #characters-roleplay-stories (90 messages🔥🔥):

  • Seeking Clarity on Roleplay and Function-Calling: User @gman5723 inquired about a model adept at both roleplay and function calling. @mrdragonfox clarified that function calling essentially involves JSON formatting and requires manual execution.

  • Dataset Woes and Words of Support: @ikaridev shared a link to a dataset marked for sensitive content, then later mentioned the unfortunate leak of their datasets to @c.gato, evoking sympathy and support from the community.

  • Concern Over Deterministic Model Responses: @_dampf observed that the bagelmisterytour model tends to repeat phrases, especially at the start, even when changing sampler settings. @ycros acknowledged this might be related to sampler settings or long contexts influencing determinism.

  • The Challenge of Using Deep Learning Models: Discussion about using deep learning models included challenges like managing batch sizes, as @kaltcit noted a constraint to batch size 1 with 10420 sequence length and the problematic nature of a loss registering as 0.0.

  • Learning Rate Recommendations for DPO: Amidst insights on Deep Partial Optimization (DPO), @c.gato suggested much lower learning rates could be necessary, potentially in connection with LoRA adapters and double merges as a non-optimal but practiced method.

Links mentioned:

MinervaAI/Aesir-Preview · Datasets at Hugging Face: no description found

TheBloke ▷ #training-and-fine-tuning (13 messages🔥):

  • Tackling Censorship in Chat Models: @octopus_ raised a question about how to implement censorship for a model, specifically avoiding discussing pricing without context. The strategy should be akin to how ChatGPT manages adult content censorship.

  • Strategies for Implementing Chat Model Guardrails: @maldevide suggested using a discriminator model to serve as a guardrail or fine-tuning the model according to the desired responses. Further, @maldevide proposed prompt tuning, which uses a specific token sequence to guide the model to the intended latent space.

  • Challenges with Discriminator Models: @octopus_ tried using a discriminator model for censorship but encountered many false positives. @maldevide advised increasing the examples in the n-shot to reduce false positives and adding a chain of thought workflow for better reasoning.

  • Insights on Model Censorship and Guardrails: @jeremy.london referenced a relevant paper and the NVIDIA NeMo-Guardrails project that discusses censorship and guardrails in models. This approach involves confirming whether generated content meets specific rules and then logging and flagging non-compliant outputs.

  • Guardrail Complexity and the Need for Reinforcement Learning: @jeremy.london noted that reinforcement learning is needed to refine guardrails for practical use, and shared a YouTube video on the leaked GPT-4 system prompt. Despite efforts, there’s always a gap in model censorship which usually begins with the dataset.

Links mentioned:

The LEAKED GPT-4 system prompt is Insane!: 🚨BUY or GIFT Beginners course of Generative AI (with 34% Discount) - https://bit.ly/3HQXsQd (Coupon: LETSGO) 🎉🔗 Links 🔗ChatGPT History - https://chat.op…

TheBloke ▷ #model-merging (1 messages):

  • Confusion Over Model Merging and Inference Templates: User @givan_002 expressed confusion on which template should be used after merging two models, NeverSleep/X-NoroChronos-13B which comes from Xwin-LM/Xwin-LM-13B-V0.2 and elinas/chronos-13b-v2. They are concerned about the potential for inappropriate token outputs since each base model was fine-tuned on different templates, Vicuna and Alpaca respectively.

TheBloke ▷ #coding (1 messages):

  • Mistral 7B Loading Code Request: @vivek2722 asked for the basic code or any useful link to load the quantised version of Mistral 7B for Retrieval-Augmented Generation (RAG), mentioning that they were facing issues with the process. No solutions or links were provided in the available message history.

Eleuther ▷ #announcements (1 messages):

  • LLM Augmentation Enhances Classifier Robustness: @millander presented a new preprint showing how to improve robustness of classifiers by augmenting inputs using an LLM (Large Language Model). Input rewriting by the LLM makes out-of-distribution inputs resemble in-distribution examples, often outperforming paraphrasing techniques. Read the full preprint on Arxiv.

  • Reducing LLM Augmentation Costs with Selective Application: The same preprint by @millander also details how entropy-based selective augmentation can reduce computational expense by focusing on uncertain model predictions, cutting the augmentation rate by an average of 57.76%.

  • Discussion on Black Box Classifier Improvement Techniques: @millander invites discussion on their work regarding black-box classifiers in <#747850033994662000> channel or through direct messaging for those interested in deeper engagement. For a summarized version, check out the Twitter thread.

Links mentioned:

Tweet from Kyle O’Brien (@KyleDevinOBrien)): How can we make classifiers more robust when we can’t modify the weights or assume its architecture — effectively making it a black box? In our preprint, we demonstrate that we can improve robust…

Eleuther ▷ #general (160 messages🔥🔥):

  • SDXL VAE Encoded Datasets Availability: User @top_walk_town inquired about image/text datasets preprocessed with the SDXL VAE. @.mahouko shared links to ArtBench and FFHQ on Hugging Face, and mentioned that ImageNet-1k was also processed but not publicly uploaded.

  • ImageNet-1k SDXL VAE Encoding Provisions: @.mahouko offered a converter script to @top_walk_town for encoding ImageNet-1k using the SDXL VAE, claiming it’s likely faster than an existing dask script used by @top_walk_town.

  • nanoT5 and Positional Embedding Challenges: During a discussion on T5 training, @.mahouko noted that nanoT5’s experimentation with ALiBi embeddings was less stable, citing their GitHub, and suggested that alternative weight initializations from Google’s t5x might be nuanced, involving attention scale factor and choice of optimizer.

  • RLHF/RLAIF/Synthetic Data Hackathon Announcement: User @canadagoose1 mentioned an RLAIF hackathon on Saturday and referred to the same location as a past Eleuther meetup. The details of what occurs at such a hackathon were inquired about by user @.the_alt_man.

  • Large-Scale AI Model Portability for Consumer Hardware Concerns: @eyeamansh sought benchmarks for AI model portability on typical consumer setups to develop open-source applications with models from Hugging Face. Users @rallio. and @_3sphere suggested looking into setups used by NSFW RP communities and koboldai, while @philpax recommended a quantized Mistral-7B for fitting on an 8 GB VRAM CUDA GPU.

Links mentioned:

Eleuther ▷ #research (173 messages🔥🔥):

  • Liquid Networks Critiqued for Complexity: .the_alt_man expressed skepticism regarding liquid neural networks, suggesting they complicate training without justifiable benefits. In a follow-up, they asserted that the introduction of pseudo-neuroscientific elements creates more challenges than enhancements in the models.

  • Differences in Memory Token Model Granularity Discussed: micpie detailed the distinctions between RMT-T memory tokens and the kNN approach of memorizing Transformers, highlighting their unique granularities and retrieval methods.

  • Exploring Data Efficiency with Liquid Nets & CNN+Capsules: jckwind spent significant time delving into liquid net structures and data-efficient modeling using a MNIST-based proof-of-concept combining CNNs, capsules, and liquid networks. They also found interest in a recent paper that combines capsules with multi-headed attention.

  • Insight into Model Training Dataset Influence: _lm pondered the negative impact of conversational context on the performance of LLMs like ChatGPT-4, referencing a behavior where decontextualized questions sometimes yield better responses. catboy_slim_ and synquid discussed related work, with synquid sharing a related paper on the critical role of causal reasoning in intelligence development.

  • Revisiting Coarse PoS and Semantics in Model Training: In a discussion initiated by jstephencorey about language model training stages, rybchuk pointed out that the coarse part of speech might actually pertain to semantics, which is pivotal for next-token prediction. miaumiks and rybchuk exchanged views on how semantics, grammar, and syntax are all integral components of LLM training.

Links mentioned:

Eleuther ▷ #lm-thunderdome (1 messages):

  • Discussing Fewshot Config for GPQA: @hailey_schoelkopf suggested that an optional subfield could be added to fewshot_config for GPQA, referencing a structured fewshot prompt like the one found on GitHub. They mentioned the possibility of incorporating a hardcoded prompt similar to those used in minerva_math or gsm8k_cot.

Links mentioned:

gpqa/prompts/chain_of_thought.txt at main · idavidrein/gpqa: Baselines and analysis for the Google-proof Q&A (GPQA) dataset - idavidrein/gpqa

Eleuther ▷ #multimodal-general (5 messages):

  • Open Source Audio Codecs by Audiogen: @elyxlz shared a tweet by Audiogen AI announcing their open-source audio codecs. No further discussion provided on this tweet.
  • Audiogen Codec on GitHub: @elyxlz provided a link to Audiogen Codec’s GitHub repository, showing the source for Audiogen’s audio codec efforts.
  • Audiogen Codec Bit Rate Details: @nostalgiahurts highlighted the discrete model’s codebook size of 2048, which results in a total bit rate of 13.2kbps as detailed in the codec’s Hugging Face configuration.
  • EMA Proves Beneficial for GAN Vocoder: @nostalgiahurts remarked that using Exponential Moving Average (EMA) has proven beneficial, noting it’s a newer application in the field of GAN vocoders though it’s been seen before in BigGAN.
  • Low Hanging Fruit in GAN Vocoder Improvement: Responding to the comment about EMA, @elyxlz acknowledged that employing EMA was quite a straightforward enhancement for their GAN vocoder.

Links mentioned:

GitHub - AudiogenAI/agc: Audiogen Codec: Audiogen Codec. Contribute to AudiogenAI/agc development by creating an account on GitHub.

Eleuther ▷ #gpt-neox-dev (2 messages):

  • GPT-NeoX Gets Inspiration from Megatron: User @jdranpariya acknowledged that elements of GPT-NeoX are derived from Megatron.
  • Affirmation of Prioritization: In a succinct follow-up, @jdranpariya seemed to affirm that the development team has the right priorities.

OpenAI ▷ #ai-discussions (103 messages🔥🔥):

  • GPT Pricing Speculation and Employment Enthusiasm: Users debated the potential cost of a new OpenAI release, with @theultimateprompter suggesting a pricing strategy based on multiplying frames by minutes and the DALL·E pricing model. @solbus chimed in, emphasizing the uncertainties around pricing and services but confirmed that no official announcements have been made yet.

  • The Cap Debate: @sevenero expressed frustration with ChatGPT Plus’s message cap, leading to a cancelled subscription, and compared it unfavorably to Google’s premium model without a message cap, which sparked a discussion of supply and demand in relation to OpenAI’s capacity.

  • Message Limit Frustrations and Google’s AI Comparison: User @blckreaper discussed using Google’s Gemini for tasks due to ChatGPT’s message limits and claimed Gemini’s creative writing style bypasses AI detection successfully. The conversation continued with @droggerhd highlighting the superior accuracy of GPT-4 and the anticipation for model 1.5 with a large context window.

  • Exploring AI Alternatives for Education: @smitha inquired about how teachers could use ChatGPT without phone verification for students, sparking a discussion about using alternative AI tools like Claude in classroom settings and Microsoft Co-Pilot.

  • Predictability in AI Joke Generation: @sugarsniper observed a pattern in ChatGPT’s responses when requesting “groaner jokes”, leading to a further exploration with @eskcanta regarding how AI’s structured training influences the diversity and creativity of its outputs.

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.

OpenAI ▷ #gpt-4-discussions (115 messages🔥🔥):

  • GPT-4 Browser Troubles for @iamryuzaki: @iamryuzaki is facing issues with GPT-4 not responding on any browser despite having a subscription and trying multiple computers and browsers. The bot works on mobile but remains unresponsive in web browsers.

  • Discussing Peer-to-Peer AI Philosophy: @jay_low666 mused about the concept of an AI with peer-to-peer protocol, akin to Napster, utilizing the power of PCs worldwide. In response, @darthgustav humorously remarked on the potential inefficiency and insecurity, jesting about unwanted gaming hacks infiltrating AI.

  • The Ups and Downs of Custom Knowledge Banks: @jaredquek reported problems with Custom GPT knowledge bank retrieval; despite proper instructions, the bot favors online searches or pulls entire texts. @darthgustav engaged actively, suggesting troubleshooting steps like disconnecting and reconnecting knowledge sources.

  • Global vs. Local Markets Chatbot Dilemma: @ricardop20 deliberated whether to target his AI assistant to the global market or focus on local needs in Portugal. @darthgustav advised on the versatility of localization which allows for catering to both markets effectively.

  • Voice Chat Optimizations for Interview Practice: @718moe inquired about Custom GPTs optimized for voice chat, specifically for job interview practice. @eskcanta guided through an approach using basic ChatGPT to craft and refine instructions, suggesting an iterative process to develop a concise yet helpful bot.

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.

OpenAI ▷ #prompt-engineering (44 messages🔥):

  • Seeking Prompt Optimization for Email Categorization: @ben.30 is looking to improve the accuracy of an email categorization system using Power Automate and GPT-3.5; success rate currently is at 75%, with the system designed to attribute a confidence level to its assessments and to default to ‘unknown’ if below a certain threshold.
  • Complex Prompts and Model Response: @darthgustav suggests that if previous API calls aren’t linked, reviewing commonalities in failure cases could illuminate the root cause. They also mention the relative retrieval rate (51%) when GPT-3.5’s context is about half full.
  • Evaluating and Refining AI Prompts: @eskcanta offers advice to @d1scobo1 on how to evaluate and rewrite complex AI prompts to reduce restrictive conditions and enhance performance; they share examples of how prompts affect AI responses.
  • Troubleshooting Knowledge Base From Uploaded Files: @eskcanta raises awareness about a known bug affecting AI performance related to knowledge from uploaded files which could be impacting @d1scobo1’s assistant behavior.
  • Novel Writing Prompt Challenge: @drcapyahhbara encounters difficulties with GPT creating narrative content where every sentence is treated as an introduction, resulting in unnatural transitions; they are seeking guidance in the prompt-engineering channel.

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.

OpenAI ▷ #api-discussions (44 messages🔥):

  • Optimizing Email Categorization with GPT: @ben.30 seeks to improve the success rate of an email categorization process that utilizes Power Automate with a GPT function. Despite a current 75% success rate and a 5% threshold for uncategorized emails, he is looking for further improvements and input on prompt structure.
  • In-Depth Prompt Structure Shared: In a dialog with @darthgustav, @ben.30 explains his detailed prompt structure which includes context, service descriptions, and keywords, and seeks an external review of his current prompt design for possible improvements.
  • Prompt Review and Debugging Advice: @eskcanta offers an approach to reviewing and debugging prompts by submitting them to ChatGPT with a meta-prompt, to self-evaluate inconsistencies or potential issues which could be affecting model performance.
  • Streamlining Complex Instructions for Better AI Performance: @d1scobo1 embeds the AI on a website to answer client questions based on a provided file about software engineering career details. Upon feedback, they are optimizing the directives to allow more natural responses and address a potential bug shared by @eskcanta.
  • Dialogue on Enhancing Novel Writing Prompts: @drcapyahhbara reports an issue regarding the AI’s tendency to treat every sentence as an introduction to a novel, leading to unnatural transitions. @eskcanta offers support on prompt engineering in the #api-discussions channel.

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.

LM Studio ▷ #💬-general (141 messages🔥🔥):

  • LM Studio Format Confusion: User @heyitsyorkie explained that LM Studio only runs GGUF models, not other formats like @suisse7 inquired about BNF grammars.
  • Model Recommendations and Usage Discussions: @egalitaristen shared a GitHub link and engaged in a discussion on what model might be best for tasks like academic writing, suggesting Mistral 7B. Conversation revolved around hardware capabilities for different context sizes.
  • Integration and API Queries: Multiple users like @akiratoya13, @kvrmd, and @i.apol0 inquired about various integration capabilities with LM Studio, such as sending system messages via API calls and connecting LM Studio to Azure AI.
  • Model Installation and Hardware Compatibility Issues: Users like @digit18 and @krypt_lynx discussed challenges and solutions around installing models and the necessity of AVX2 instruction support, with @heyitsyorkie providing a workaround for libcblast related errors.
  • Exploring Advanced Use Cases for LM: @krypt_lynx and @jedd1 talked about the potential for using multiple GPUs for LLMs, with the former user considering an unplanned system upgrade to accommodate more powerful models.

Links mentioned:

LM Studio ▷ #🤖-models-discussion-chat (23 messages🔥):

  • miniCPM Struggles and Anticipations: @dootmate expressed frustrations as miniCPM still doesn’t work, looking forward to a fix. The discussion pointed towards an issue on GitHub mentioning lacks support in llama.cpp, with hopes that LMStudio’s update to the latest build might resolve this.
  • miniCPM Now Supports llama.cpp: @dootmate mentioned the release of a llama version of miniCPM, openbmb/MiniCPM-2B-dpo-bf16-llama-format, and further noted that it now has support for fp16/int4 ggufs.
  • Potency of Qwen Models in LMStudio: In a comparative query, @borisrusev questioned the capabilities of q2_k versus q8 in the context of Qwen1.5-72b-chat, to which @heyitsyorkie humorously likened Q8 to the smart set and Q2 to the less capable counterpart.
  • Best LLM for Coding and Vision: For coding LLMs, @heyitsyorkie recommended Deepseek Coder 33b, and @r3vs_ inquired about llava-1.6-mistral-7b for vision, to which heyitsyorkie admitted a lack of experience with vision models.
  • Hardware Considerations for Running Large Models: @old_skooler shared excitement about running Mixtral 8x7b Dolphin with expected new memory, and @jedd1 provided practical information on model operation speeds, VRAM usage, and performance drops when exceeding VRAM capacity.

Links mentioned:

LM Studio ▷ #🎛-hardware-discussion (63 messages🔥🔥):

  • The Great GPU Debate: User @j.o.k.e.r.7 sought advice on whether to choose a 3090 or 4070 Super, both available at the same price, sparking a discussion on performance and VRAM. @heyitsyorkie recommended the 3090 for its 24GB of VRAM and superior performance in tasks like gaming, stable diffusion, and running up to 70b Q4 models, with @nink1 suggesting looking for second-hand deals due to miners offloading cards.

  • Modding the 3090 for Extra VRAM: @.bambalejo shared their interest in a VRAM upgrade mod for the RTX 3090 to reach 48GB, noting limitations due to VBIOS and sharing links to Bilibili and a Russian YouTube video detailing attempts at the modification.

  • Big RAM, No Recognition in LM Studio: After @ethanboyle upgraded their RAM from 16GB to 64GB, LM Studio failed to recognize the new capacity. @heyitsyorkie explained this as a known bug but assured that the models would still work despite the inconsistency, advising that clearing specific cache locations might resolve the issue.

  • Choosing GPUs for LM Studio: @dyter07 inquired about how to assign LMStudio to use a specific GPU when multiple are installed, prompting @jedd1 to reference a helpful thread that gives instructions on setting GPU preferences.

  • Steer Clear of AMD GPUs for AI?: As @seicross explored how to utilize their AMD Rx 5500 xt for language models, @exio4 commented on AMD’s lack of optimized support for AI workloads, suggesting users might find better performance and value with Nvidia’s hardware instead.

Links mentioned:

LM Studio ▷ #crew-ai (3 messages):

  • VSCode unable to recognize ‘crewai’ module: @circulustreme is experiencing an issue where Visual Studio Code (VSC) is not acknowledging the installed crewai module despite efforts to install, upgrade, and manage packages through various terminals and conda. The module appears in piplist but VSC doesn’t seem to recognize it.

Mistral ▷ #general (104 messages🔥🔥):

  • Debunking LLM’s Acting Skills: @i_am_dom clarifies a common misconception about LLMs, explaining that their behavior is shaped during the fine-tuning stage to act as AI assistants. The fact that you can make an LLM “act any way you want” at this stage was emphasized to indicate the ability to shape its responses.
  • Mistral-next versus Llama Performance Inquiry: Users @jamshed1900, @mrdragonfox, and @drnicefellow discuss the performance of Mistral-next. Although performance comparisons are limited, there’s a consensus that Mistral-next shows better reasoning than its predecessors.
  • Finetuning Finesse Request: @timuryun seeks assistance with finetuning, getting directed to someone experienced - <@266127174426165249>, by @drnicefellow.
  • Open Source or Not?: Questions about whether models like Mistral-next are open source and available for download arose in conversation with @timuryun, @drnicefellow, and @mrdragonfox. It was clarified that Mistral-next is currently a prototype test on lmsys and not openly available.
  • Discussing AI Model Capabilities and Investments: A discourse led by @i_am_dom, @mrdragonfox, and others suggests that the infrastructure, funding, and expertise behind Mistral are comparable to those at OpenAI, though decisions on training much larger models like a 100B from scratch are still under wraps.

Links mentioned:

Chat with Open Large Language Models: no description found

Mistral ▷ #models (22 messages🔥):

  • Experimenting with Multi-Model Approaches: @mehdi_guel plans to experiment by combining in-context learning with chain-of-thought in a multi-model approach and will update the group on the results.

  • Expert Extraction Enigma: @redbrain inquired about extracting individual experts from Mixtral for use as standalone dense Mistral 7b models for experimental purposes, recognizing the impracticality but expressing interest in the conceptual possibility.

  • Understanding MoE’s Nature: Multiple posts by @mrdragonfox clarified that a Mixture of Experts (MoE) model like Mixtral cannot have its experts extracted as standalone models because the expertise is not isolated; it’s distributed across the model, and the routing happens at the token level.

  • Deconstructing MoE Could Be Futile: In response to @redbrain’s continued interest, @mrdragonfox explained that attempting to deconstruct Mixtral would likely not produce coherent outputs, and emphasized that even if the process is feasible, the result wouldn’t surpass a standard 7b Instruct 0.2 model.

  • Performance Puzzle with GPU vs. CPU: @mikifireblue observed a slow token generation rate when using a GPU (NVIDIA GTX 1660 TI) compared to using only CPU, while testing with the model “mistral-7b-instruct-v0.1.Q4_K_M.gguf” and llama-cpp, leading @ginterhauser to suggest trying an AWQ format as it is better suited for CPU usage.

Mistral ▷ #deployment (4 messages):

  • Shard Struggles with VLLM: @ethux mentioned that VLLM does not work well with sharding and expressed disappointment with performance.
  • Trouble Taming VLLM; TGI Triumphs: In a separate message, @ethux confirmed having the same sharding issue with VLLM but reported no problems using TGI.
  • Inquiring About Mistral on a Giant: User @espadrine questioned if Mixtral was successfully deployed on a g5.48xlarge instance, but no follow-up information was provided.

Mistral ▷ #finetuning (22 messages🔥):

  • Faint Presence of @timuryun Acknowledged: The user @timuryun signified their presence and readiness to discuss fine-tuning.

  • Anticipation Builds as Question Looms: @mrdragonfox is primed to assist, urging @timuryun to pose their fine-tuning query.

  • Elusive Fine-tuning Inquiry Emerges: @timuryun queries @266127174426165249’s expertise in fine-tuning, though responses suggest more details are needed.

  • Dissatisfaction Acknowledged, Assistance Offered: Despite @timuryun providing little detail, the community remains responsive, with @mrdragonfox encouraging a post of “stuff” for potential aid.

  • Contemplating the Depths of Fine-tuning Strategies: @sven_72358 opens a dialogue on the efficacy of using Q&A pairs for model education, referencing both a tryhellix ai article and personal attempts with a 7B model, which lead to an interjection from @tom_lrd discussing general skepticism around imparting knowledge through fine-tuning and a GitHub project.

  • LoRA Configurations Debated in the Finetuning Frontier: @iamcoming5084 reaches out for configuration advice on fine-tuning the 7B Mistral model, which spurs @mrdragonfox to differentiate between LoRA and full fine-tuning methodologies, emphasizing the need for careful parameter selection and methodology understanding.

Links mentioned:

base_model: mistralai/Mistral-7B-v0.1model_type: MistralForCausalLMtokenizer - Pastebin.com: Pastebin.com is the number one paste tool since 2002. Pastebin is a website where you can store text online for a set period of time.

Mistral ▷ #showcase (2 messages):

  • The AI Info Diet - A Master Doc for AI Enthusiasts: User @_red.j shared a master document aimed at helping people stay updated with the latest tools, news, and information in AI, created during a Twitter space conversation with ML experts.
  • Open Invitation to Contribute: _red.j encouraged everyone to add their favorite AI news and information sources to the document, mentioning they’re adding the server to it as well.

Links mentioned:

The AI Info Diet ™️: no description found

LlamaIndex ▷ #announcements (1 messages):

  • LlamaIndex Webinar Announcement: @jerryjliu0 announced a webinar for Thursday at 9am PT, focusing on innovative community demos that won the recent LlamaIndex hackathon. The session will explore RAG use cases beyond basic chatbots, and viewers can register here.
  • Showcasing Hackathon Winners: The LlamaIndex webinar will feature four projects that creatively use RAG for advanced knowledge synthesis and reasoning:
    • ADU Planner: Streamlining accessory dwelling unit planning, view project.
    • Counselor Copilot: Aiding counselors with AI, view project.
    • neThing.xyz: Enhancing learning and knowledge, view project.
    • Home.AI: Innovating home management, view project.

Links mentioned:

LlamaIndex Webinar: RAG Beyond Basic Chatbots · Zoom · Luma: RAG is one of the main use cases for LLMs, but many developers are using RAG to build basic Q&A chatbots over simple, static datasets. What are use cases for RAG beyond basic chatbots? We’re…

LlamaIndex ▷ #blog (3 messages):

  • Meta-Reasoning for LLMs in the Works: @peizNLP introduced a new paper titled Self-Discover, which focuses on enhancing LLMs with meta-reasoning capabilities by having them autonomously select appropriate reasoning modules. This advancement could revolutionize the traditional fixed reasoning structures seen in AI. See the tweet.

  • Exploring RAG’s Potential Beyond Q&A: LlamaIndex announced a webinar discussing the diverse applications of Retrieval-Augmented Generation (RAG) beyond simple Q&A chatbots, hinting at innovative community use cases yet to be revealed. Mark your calendars with this link.

  • Enhancing RAG with Smart Reranking: Florian June’s blog post garnered praise from LlamaIndex for its accessible guide on implementing reranking techniques in RAG systems, including the usage of a BGE-based reranker and LLM-powered alternatives. Deep dive into reranking.

LlamaIndex ▷ #general (118 messages🔥🔥):

  • Broken Notebook Link Alert: @wrapdepollo noted that the example notebook linked on the Document Management page has an inaccessible link, but provided an alternative URL that works. They emphasized this in case the issue was unintentional or if others needed access to the notebook.
  • Discord User Requests Node Update Guidance: @yashshukla9279 sought advice on updating text in a node and ensuring metadata alignment within LlamaIndex. @whitefang_jr directed them to the document management guide for CRUD operations on nodes.
  • AzureOpenAI vs OpenAI Reliability Concerns: User @theoxd shared their experience of AzureOpenAI being less reliable than the standard OpenAI interface, with tools ceasing to function every week or so. There’s no follow-up response to this concern within the provided message history.
  • Querying VectorDB Clustering Support: @david1542 asked if anyone knew of a VectorDB that supports clustering algorithms like K-Means and DBSCAN. @cheesyfishes replied mentioning usearch’s capabilities with a link to its GitHub repository.
  • Thorny Agent Interactions with Tools: @mst2205 described a difficulty in getting a ReActAgent to comprehend and combine the results of a date tool and an obsidian query engine for handling queries like “Which note did I write today?” @cheesyfishes suggested including the current date in the prompt and reflected on the general challenges of agent behavior with open-source LLMs.

Links mentioned:

LlamaIndex ▷ #ai-discussion (3 messages):

  • RAG Customization vs. Service: @skiboyec queried about the advantages of building a custom RAG system over using a RAG-as-a-service provider, questioning if a custom system can offer better retrieval performance without concerns for self-hosting, scalability, or API costs.
  • Purpose Defines Building or Subscribing: @desk_and_chair responded speculating that the decision might depend on the goal—whether to leverage RAG for personal use or to offer RAG as a service to others. They likened the situation to preferring a good burger without necessarily wanting to grill it themselves.

HuggingFace ▷ #general (63 messages🔥🔥):

  • LayoutLMv3 Troubles: `truedescription` faced an error when using LayoutLMv3 from Hugging Face with the processor and model, even after setting truncation and padding to true. An explicit suggestion to resolve the issue wasn't provided in the discussion.
  • Craving for Creation: `sebaskja` expressed interest in creating a channel for Latin American RL course members and sought guidance on how to set it up.
  • Video Mayhem: `chalm3rs.` shared a link from Twitter showcasing the latest Sora videos by the OpenAI team, stirring interest and amusement amongst the users.
  • Quest for Knowledge on APIs: `dipto7613` sought assistance on making an API for illusion but faced challenges due to a profusion of endpoints and an expressed need for more information.
  • Conversational AI Performance Evaluation: `rwamit` asked for the best methods to evaluate a fine-tuned NER model, hinting at an interest in IOB tagging but the conversation ended without a clear resolution.

Links mentioned:

Tweet from Borriss (@Borriss): The Sora videos posted by the OpenAI team are getting wilder.. (Part 2) 7 new ones:

HuggingFace ▷ #cool-finds (4 messages):

  • Generative AI Takes Over: User @erksu. shared an article discussing how generative AI became integrated into the daily lives of the majority, with a specific focus on its use among teenagers in the UK and employees in Australia. The article highlights the shift from curiosity to practical applications such as studying, advice, and creating content, along with a mention of “prompt whisperers.”

  • Reflecting on the Nature of Intelligence: @sebaskja linked to an older paper suggesting that benchmarking AI simply based on skill at specific tasks isn’t enough to measure true intelligence. The paper’s abstract argues for the need for a better feedback signal to evaluate AI and human intelligence Download PDF.

  • Fine-Tuning Zephyr-7B: @not_lain found an insightful blog detailing how Zephyr-7B was fine-tuned using quantization, PEFT, and SFTTrainer for a customer support chatbot. It also discusses the integration of the AutoGPTQ library by Huggingface to enable low-precision operations on models.

  • Vibrant GIF Cheers Up the Chat: @moonmhmed posted a humorous and lively GIF with the message “Why Should Your Mouth Have All The Fun”, originally from Saturday Night Live. The GIF serves to inject a bit of fun into the conversation, featuring Cecily Strong’s swing dance.

Links mentioned:

HuggingFace ▷ #i-made-this (8 messages🔥):

  • Banner Ads Inquiry on HuggingFace Spaces: User @myg5702 inquired about the possibility of having banner ads on huggingface.co spaces, prompting a response from @lunarflu, who specified that ads might be allowed for community-incentivizing content like Patreon links but not random ads that could monetize HuggingFace’s resources.

  • Launching a CI/CD Machine Learning Guide: @kingabzpro announced a comprehensive CI/CD for Machine Learning guide that covers everything from GitHub repository setup to automating model testing and deployment with GitHub Actions, aimed at simplifying the journey into ML Ops.

  • Visualization of the Aya Dataset: @cakiki shared a visualization of the Aya dataset, showcasing the languages supported by CohereForAI’s Aya.

  • Discussion on Server Specs for Fast Image Generation: @amirgame197 brought up the swift image generation performance of @myg5702’s server, to which the latter revealed the use of a powerful Nvidia A40 large instance on a cloud server.

Links mentioned:

HuggingFace ▷ #reading-group (6 messages):

  • Annotated Mamba Completed: @tea3200 shared a completed resource on the annotated mamba, a project by Sasha Rush, available at Annotated Mamba.
  • Praise for Annotated Mamba: @lunarflu expressed admiration for the annotated mamba, hinting at its potential to become a legendary blog post.
  • Clarification on Authorship: @tea3200 clarified that the annotated mamba was written by Sasha Rush, and the idea of having it posted on HuggingFace’s platform was mentioned.
  • Vision Transformers on the Horizon: @tea3200 announced an intention to create a similar annotated resource, but for vision transformers.
  • Encouragement for New Project: @lunarflu showed support for @tea3200’s upcoming project on vision transformers, encouraging them to proceed.

Links mentioned:

Mamba: The Hard Way: no description found

HuggingFace ▷ #diffusion-discussions (3 messages):

  • Seeking Guidance on AI Clothing Tool: User @mohdfaiez inquired about starting an AI tool to enable users to change clothes according to their needs.
  • Clarifying the Use Case: In response to @m.0861’s query if the requirement was for a 3D model or image generation, @mohdfaiez shared a blog post about Pincel, an app that uses AI to change clothes on photos, indicating a similar image generation application is the goal.

Links mentioned:

Change Clothes on Photo Using AI - Pincel: Change clothes on a photo effortlessly with Pincel AI, the best online app for fast and easy outfit changes using instant AI magic.

HuggingFace ▷ #NLP (4 messages):

  • QLoRA Finetuning Frustration: User @kingpoki encountered an error while using AutoPeftModelForCausalLM.from_pretrained for QLoRA finetuning, which occurs during the merging process with the model. They posted their code snippet and a NotImplementedError stack trace, seeking insight into the issue.

  • Seeking Guidance on RA-DIT and REPLUG: @austintb. inquired about any available code walkthroughs or demos on RA-DIT or REPLUG instruction tuning for RALMs (Realm Adaptive Language Models).

  • Whisper Misinterpretation Mystery: @pantera4738 is struggling with hugging face API transcription using whisper large v3; the API outputs the transcription in Chinese instead of Spanish for the provided audio file. They shared their Python code to seek help with the language discrepancy in transcription.

HuggingFace ▷ #diffusion-discussions (3 messages):

  • Quest for AI-powered Wardrobe Changes: User @mohdfaiez inquired about building an AI tool that would enable users to change clothes on images. They are seeking guidance on where to start this endeavor.
  • Clarifying Concept Visualization: @m.0861 asked for clarification on whether @mohdfaiez aims to change clothes on a 3D model or through image generation.
  • Revelation of AI Fashion Tech: @mohdfaiez shared an example with the Pincel app, a photo editor that uses AI to change outfits in photos, inviting inspiration for their project. The app allows users to upload a photo, mark areas with a brush, and then swap clothes using AI, as highlighted in their infusion of creativity and technology.

Links mentioned:

Change Clothes on Photo Using AI - Pincel: Change clothes on a photo effortlessly with Pincel AI, the best online app for fast and easy outfit changes using instant AI magic.

OpenAccess AI Collective (axolotl) ▷ #general (71 messages🔥🔥):

  • Yolo Computational Risks: @le_mess gambles with 10 days of A100 computing without checkpoints, prompting empathy and a humorous “yolo” acknowledgment from @yamashi and facepalm emoji from @noobmaster29.
  • Quantizing a Giant: @noobmaster29 discusses the VRAM requirements to quantize a 7B model, sharing experiences and resources such as a script from TheBlokeAI’s GitHub.
  • BioMistral Called Out: @yamashi criticizes BioMistral for allegedly misreporting benchmarks, sparking a discussion about the accuracy and fairness of model benchmarking practices.
  • Training Time Teasers: @le_mess jests about the extreme range of time it could take to fine-tune a 1.3B model, from “5 seconds to 109 years,” and then provides a serious estimate of 6 days for @qwerty_qwer’s 2.4 million training pairs on a 4090 GPU.
  • Model Training Observations: @c.gato contemplates how sample packing might affect training due to the higher effective learning rate for longer context samples and wonders if it should be a concern.

Links mentioned:

OpenAccess AI Collective (axolotl) ▷ #axolotl-dev (5 messages):

  • Considering Checkpoint on Keyboard Interrupt: @seungduk inquired about the possibility of the system saving a checkpoint when a keyboard interruption (ctrl+c) occurs, suggesting it could be configurable.
  • Past Feature of Checkpointing Revisited: @nanobitz acknowledged that checkpointing during a keyboard interruption was previously implemented but expressed uncertainty about its proper functionality.
  • Check Underway for Implementation Integrity: Following the discussion, @seungduk mentioned they would investigate the matter further.
  • Code Inspection by the Collective: @caseus_ provided a GitHub link to the specific code segment related to training and potentially checkpointing, inviting members to review the implementation.

Links mentioned:

axolotl/src/axolotl/train.py at main · OpenAccess-AI-Collective/axolotl: Go ahead and axolotl questions. Contribute to OpenAccess-AI-Collective/axolotl development by creating an account on GitHub.

OpenAccess AI Collective (axolotl) ▷ #runpod-help (6 messages):

  • Directory Clobbering Workaround Shared: User @m4ttfl0 provided a solution for the directory clobbering issue by suggesting the use of a custom template with a different persistent volume mountpoint, sharing the issue link for reference: RunPod template not working with network volumes.

  • Inquiry About RunPod Setup Time: User @noobmaster29 asked how long it should take for RunPod to set up, noting that their setup seemed to be taking an unusually long time.

  • Frustration with Stuck Setups: @noobmaster29 expressed frustration over encountering several non-responsive (“dead”) pods during the setup process.

  • Seeking Clarification for Error Code: @noobmaster29 asked if the error code -9 indicated an out-of-system memory issue.

Links mentioned:

RunPod template not working with network volumes, /workspace/axolotl empty · Issue #813 · 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 Other users also encountered this: #467 According t…

LAION ▷ #general (67 messages🔥🔥):

  • Juggernaut XL Checkpoint Discussed: @spirit_from_germany inquired about generating images using the Juggernaut XL model without UI, leading to a conversation that included a checkpoint on Hugging Face and code snippets for implementation.
  • Image/Text Datasets with Preprocessed Embeddings: @top_walk_town queried about datasets with images preprocessed by SDXL VAE, and @pseudoterminalx mentioned the existence of such datasets, though with certain quality limitations, providing a Hugging Face link for reference.
  • Reddit and LoRA Approaches Open for Debate: @segmentationfault8268 shared a Reddit post discussing LoRA approaches, which sparked a conversation around model realism and trained aesthetic preferences.
  • Alpha-Prompt LORA Shared for Testing: @qwerty_qwer extended an invitation to test an Alpha-Prompt LORA model that was co-developed with TwoAbove, designed to generate detailed SD prompts from descriptions, and available on Hugging Face.
  • AI Tools, News, and Resources Compilation: @_red.j introduced a master Google Doc titled The AI Info Diet ™️, compiled during a Twitter space with ML experts, meant to keep up with the latest in AI, and open for additional contributions (link to document).

Links mentioned:

LAION ▷ #research (5 messages):

  • Goody2 Model Card Introduced: User helium__ shared a link to the Goody2 model card, perhaps suggesting it as a resource for interested parties.
  • Search for AI-Enhanced CAD Programs: User glasscow inquired about any CAD programs that incorporate AI for real-time model designing, aiming to simplify the creation of 2D or 3D models for use in the Unity engine.
  • Challenges in AI-Powered CAD Development: unjay. highlighted difficulties in developing AI-powered CAD software, citing the lack of standardization in parametric 3D shapes and the absence of suitable datasets.
  • AI Precision vs Human Consistency: User atlasunified made a point about precision in AI, contrasting it with humans’ ability for consistent repetitive precision.
  • Mistral’s ‘Next’ AI Might Outdo GPT-4: vrus0188 shared a Reddit link stating that early testers believe Mistral’s latest open-source ‘Next’ AI could surpass GPT-4.

Links mentioned:

Reddit - Dive into anything: no description found

Latent Space ▷ #ai-general-chat (70 messages🔥🔥):

  • Groq’s Competitive Speed and Cost: @swyxio and others discussed the performance claimed by Groq, including speculation on how it achieves such speed and cost-efficiency. @slono mentioned applying for access hoping it transforms their work, while @shivdinho pondered the real-time applications it may enable.

  • Deep Dive into Groq’s Technology: @coffeebean6887 spent time understanding Groq’s approach, sharing their realization of its unique no-DRAM, horizontally scalable architecture, featuring a significant number of SRAM chips for real-time LLM instances. Economical viability, considering the high cost of Groq’s infrastructure, is questioned by the community.

  • Limitations of Vector-Based Retrieval Highlighted: In relation to an article on vector-based vs graph-based retrieval, @fanahova comments on the misleading marketing that disregards the modern advancements of methods like HNSW since their early beginnings.

  • Discussions on Chatbot RAG Implementation: Users discussed determining the necessity of retrieval-augmented generation (RAG) for user messages in chatbots. Ideas included LLMs to deduce user intent, user controls, asynchronous multi-level responses, and the power of function calls by @ashpreetbedi.

  • New HELM Benchmark Announced by Percy Liang: @swyxio shared the launch of Stanford Professor Percy Liang’s new HELM benchmark, an instructional evaluation framework complete with absolute ratings for more comprehensive LLM assessment.

Links mentioned:

CUDA MODE ▷ #general (6 messages):

  • Sketchy Feelings on Discord: @cropinky expressed discomfort over an unspecified subject, saying, “This feels illegal to do”, a sentiment echoed by @apaz with, “That does look really illegal”.
  • Contentious Link Shared: A questionable link was shared by @euclaise, which only shows a string followed by a long encrypted image data without further explanation.
  • Reassurance from joseph_en: Addressing @cropinky’s concern, @joseph_en provided reassurance about needing to demonstrate llama7B and 13B on a single system and explained it as a necessary workaround for a technical challenge.
  • Tips for Using NVIDIA Tools: @gogators. advised that all NVIDIA tools are compatible with Python scripts for CUDA files. They recommend using breakpoints with cuda-gdb and cite the efficiency of debugging standalone .cu files over Python processes, with the assistance of automation features like GPT-4 providing mock driver functions.

Links mentioned:

Mamba: The Hard Way: no description found

CUDA MODE ▷ #triton (3 messages):

  • Sasha Rush’s Latest Contribution: User @mortezism shared a link to Sasha Rush’s annotated Mamba model, which can be found at Annotated Mamba. The message also contained an unprocessed image code.

  • Inquiry about Running FP8 Operations in Triton: @neuralink asked if non-matrix multiplication operations like element-wise addition or square root could be run in FP8 using Tensor Cores in Triton.

  • Understanding Tensor Core Capabilities: @iron_bound responded to @neuralink, explaining that Tensor Cores are specifically designed to perform matrix multiplication and accumulation. They did not provide further information on FP8 operations in Triton.

Links mentioned:

Mamba: The Hard Way: no description found

CUDA MODE ▷ #torch (1 messages):

  • Pure, Native PyTorch Pushes the Limits: @ardywibowo shared a blog post discussing the acceleration of generative AI models using pure, native PyTorch that focuses on LLM optimization. The post cites performance improvements over 8x for Segment Anything and spotlights open-source projects like llama.cpp, vLLM, and MLC-LLM.

  • Compiled Kernels vs. Traditional Libraries: @ardywibowo is skeptical about the blog’s claim that compiled kernels can outperform CuBLAS & FlashAttention2 and questions whether this is legitimate.

  • Seeking Benchmarks for Performance Comparison: @ardywibowo inquires if there are comprehensive benchmarks available that compare PyTorch features with other solutions like FasterTransformer, TensorRT, etc.

  • When to Choose torch.compile Over CUDA?: @ardywibowo seeks insights from the community on deciding when to opt for torch.compile as opposed to diving into CUDA mode for optimizations.

Links mentioned:

Accelerating Generative AI with PyTorch II: GPT, Fast: This post is the second part of a multi-series blog focused on how to accelerate generative AI models with pure, native PyTorch. We are excited to share a breadth of newly released PyTorch performance…

CUDA MODE ▷ #algorithms (6 messages):

  • Parallel Algorithms Meet Hardware Efficiency: @ericauld shared a Twitter post highlighting how state space models like Mamba could benefit from hardware-aware parallel scans.

  • Deep Dive into Automatic Differentiation: @ericauld expressed an interest in studying automatic differentiation more deeply and cited it in relation to FlashAttention’s recomputation technique. They provided a link to a book on Amazon titled Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, highlighting its potential usefulness.

  • Group Study on Gradient Checkpointing Proposed: @ericauld mentioned the relevance of gradient checkpointing, referenced in a specific book, and showed interest in chapters 1-4. They invited others to read and discuss together.

  • A Call for Cost-Effective Learning: @msthil2 responded positively to the idea of group study and also lamented the high cost of academic books. They joked about being “noob tier” but willing to engage with the material.

  • Cost-Friendly Alternative Suggestion: @iron_bound suggested exploring online libraries as a solution to access expensive academic content without the steep price tag.

Links mentioned:

Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation: Griewank, Andreas, Walther, Andrea: 9780898716597: Amazon.com: Books: no description found

CUDA MODE ▷ #beginner (10 messages🔥):

  • Presentation Mix-Up Averted: @cs_os_05101 mistakenly attributed a presentation to @euclaise which led to a small confusion, but was quickly rectified by @apaz redirecting the credit to the correct individual, Jane (<@354465570030092290>).
  • GitHub Gems for PyTorch Enthusiasts: @p0.tato clarified that their presentation was on OSS code from the PyTorch repository, specifically highlighting TensorListMetadata and multi_tensor_apply in MultiTensorApply.cuh and a related PR that was reverted.
  • Discovering NVIDIA’s Triple Clock Speeds: @apaz learned about the three different clock speeds (graphics, memory, video) that can be queried using nvidia-smi, adding a new layer of insight into GPU performance monitoring.
  • NVIDIA’s Boost Clock Secrets Revealed: @stefangliga shared a fun fact that the advertised boost clock for NVIDIA GPUs is not absolute, and the actual clocks can vary depending on environmental conditions, with a rough estimate of gaining 50MHz for every 1°C reduction in temperature.

Links mentioned:

CUDA MODE ▷ #pmpp-book (4 messages):

  • CUDA Core Clarification Request: @nshepperd inquired whether the term “cuda core” specifically refers to the fp32 and int32 arithmetic units.
  • Understanding CUDA Core Processing: @nshepperd speculated that there could be interleaved processing or pipelining when there are more threads than arithmetic units.
  • Insight on CUDA Execution Mechanics: @_t_vi_ explained that each of the four units within a CUDA core executes a warp’s or subwarp’s instruction at a given time, highlighting the efficient switching mechanism due to static registers within the register file.
  • Acknowledging the Explanation: @lucaslingle expressed his understanding and gratitude for the clarification provided by @_t_vi_.

CUDA MODE ▷ #jax (10 messages🔥):

  • Exploration into XLA and SPMD: @nshepperd mentioned working on flash attention bindings for JAX, gaining insights into how SPMD (Single Program, Multiple Data) works within XLA through this process.
  • Binding Projects Could Aid Ring Attention Development: @nshepperd brought attention to the potential relevance of flash attention repo bindings for the Ring Attention project, suggesting a connection to the extensive Jax implementation mentioned by @ericauld in the Ring Attention channel.
  • JAX as a Preferred Environment for Flash Attention: @nshepperd noted the existence of multiple flash attention projects coded in pure JAX, perhaps because it’s perceived easier than using CUDA.
  • Torch Dependency in Tri Dao’s Repo an Obstacle: @nshepperd also addressed the challenge of removing Torch dependencies from Tri Dao’s flash attention repo, implying it’s a non-trivial task.

CUDA MODE ▷ #ring-attention (28 messages🔥):

  • Diving into flash-attention Mechanics: @ericauld initiated a focused examination of the flash-attention GitHub repository, highlighting key areas of interest such as the compute_attn_1rowblock and compute_dq_dk_dv_1colblock methods. They underscored basic questions about the backward method’s integration with PyTorch and the data structures involved.
  • Backtracking the Backward Pass: @ericauld and @mickgardner exchanged insights on the complex backward implementation in the flash-attention repo, with @mickgardner acknowledging its daunting nature and @ericauld identifying the potential use of flash::copy for gradient communication.
  • TPU Architecture and Cache Mechanics Explored: @iron_bound discussed aspects related to caching on Google’s TPUs, sharing a link to related code in the jax GitHub repository, while @nshepperd provided additional context on TPU architecture including references to scratch space and CMEM.
  • Ring Attention vs. Flash Attention Reimplementation: @ericauld questioned the rationale behind rewriting the flash-attention logic in the ring_attention.py file, sparking a discussion about reusability and customization for ring attention, particularly from @andreaskoepf.
  • Contributor Coordination on Ring Attention: @andreaskoepf proposed potential improvements to the flash-attention codebase to support ring attention via a PR and outlined plans to scrutinize the Jax implementation. An issue was created for developing a naive educational version that processes partial kv-blocks.

Links mentioned:

Perplexity AI ▷ #general (34 messages🔥):

  • Gemini Pro Access Vanishes: Users @harlon0389, .themantis, and @jaicraft discuss the disappearance of Gemini pro access from Perplexity Pro, leading to speculations about updates potentially adding Ultra or Pro 1.5 access.
  • No Set Timeline for Updates: @sandeepmuthangi inquires about a timeline for updates, to which @ok.alex responds by providing a vague reply and a Discord redirect link, potentially alluding to information related to the query.
  • Perplexity Chat Length Limits Clarification: @icelavaman explains to @roy_royce that text length limits in the Perplexity Playground are independent of subscriptions. The API documentation outlines context sizes, indicating potential future increases; this info is independent of a Pro subscription.
  • Merchandise Inquiry Leads to Humor: @lord.wex asks about Perplexity merchandise which prompts @mares1317 to share a link to a speculative tweet by @AravSrinivas about “merch by March,” causing amusement among users like @jaicraft.
  • Users Discuss GPT-4 Availability and Speed: @abiggenius ponders whether Perplexity Pro uses GPT-4 turbo, but @icelavaman confirms it’s the standard version. Further discussion by @gooddawg10 suggests availability is still uncertain, while @brknclock1215 shares an unrelated link discussing Perplexity’s market strategy.

Links mentioned:

Perplexity AI ▷ #sharing (2 messages):

  • Discovering Perplexity’s Mechanics: @soabonen shared a link exploring the inner workings of Perplexity: How does Perplexity work?
  • Unveiling the Designer of Perplexity: @sjohri provided a link to find out who was behind the design of Perplexity: Who designed Perplexity?

Perplexity AI ▷ #pplx-api (2 messages):

  • No Fine-tuning for pplx-online Model: User @retonq inquired about the possibility of fine-tuning a pplx-online model. However, @icelavaman responded with a definitive No, accompanied by an emoji.

LangChain AI ▷ #general (18 messages🔥):

  • Seeking Visualization of LangChain’s Chains: @andreu.codina asked about visualizing chains or calls in LangChain, noticing the absence of such information in the current documentation, referring perhaps to a feature observed a month ago.

  • LangChain Chain Classes Update: @rajvir3 provided a detailed list of deprecated Chain Classes linking to the changelog, and questioned how to implement SimpleSequentialChain and Sequential Chain now. @theepic.dev clarified that these are being replaced by LCEL, demonstrating with code examples and referring to the LCEL documentation.

  • Troubleshooting ChatVertexAI Configurations: User @molnarbalazs sought assistance with an issue regarding NEGLIGIBLE level Response Validation errors when using ChatVertexAI, looking for a way to turn off this response validation or adjust safety configurations.

  • Chroma Retrieval Issues: @theepic.dev experienced performance issues with invoking Chroma’s retriever and theorized that transforming questions into a list of keywords might yield better results.

  • Understanding LLM Parameters: @nrs9044 inquired about the meaning of parameters in large language models (LLMs), with @anthology_ explaining that parameters are weightings, and tokens from a prompt use these parameters in the backend to formulate responses.

Links mentioned:

LangChain AI ▷ #share-your-work (1 messages):

  • LangChain Tutorials Galore: User @mehulgupta7991 shared a comprehensive LangChain tutorial playlist with over 60 videos. These tutorials are aimed at educating users on various use cases of LangChain, a framework for developing applications with generative AI.

Links mentioned:

Langchain: This playlist includes all tutorials around LangChain, a framework for building generative AI applications using LLMs

LangChain AI ▷ #tutorials (2 messages):

  • Exploring LangGraph Retrieval Agent: @pradeep1148 shared a YouTube video titled “LangGraph Retrieval Agent,” explaining the use of Retrieval Agents in deciding when to retrieve from an index, and demonstrating how to implement one by providing specific instructions.

  • Diving into LangChain with a Tutorial Series: @mehulgupta7991 highlighted a comprehensive playlist with over 60 tutorials focused on different LangChain use cases. The tutorials serve as a resource for learning how to build generative AI applications using large language models (LLMs).

Links mentioned:

  • Langchain: This playlist includes all tutorials around LangChain, a framework for building generative AI applications using LLMs
  • LangGraph Retrieval Agent: Retrieval Agents are useful when we want to make decisions about whether to retrieve from an index.To implement a retrieval agent, we simple need to give an …

DiscoResearch ▷ #general (5 messages):

  • Mixing Language and Expertise in LLMs: @johannhartmann expressed interest in an LLM with multiple experts for different languages and domains, pondering how to prompt accordingly to guide languages to the right models.
  • LLM Pretraining Resources & Language Model Discussions: @johannhartmann mentioned LLaMA-Factory, a pretraining framework for various models like LLaMA and BLOOM, suggesting it could be used for pretraining LLMs in new languages and seeking experiences from those who pretrained top German models.
  • Expert Recommendations for LLM Training on Large-Scale Clusters: @bjoernp recommended using GPT-Neo-X, Megatron-LM, or variants such as epfl and deepspeed for large-scale cluster pretraining (exceeding 128 GPUs), also mentioning Axolotl, which supports sample packing and could approach the efficiency of more complex methods.
  • Acknowledgment of Large-Scale Pretraining Advice: @remek1972 confirmed they are training on a large-scale cluster and thanked @bjoernp for the helpful advice.
  • A Cry for Help?: @phantine shared a distressing message stating they are “Trapped in mental hospital”, with no further context provided.

Links mentioned:

GitHub - hiyouga/LLaMA-Factory: Easy-to-use LLM fine-tuning framework (LLaMA, BLOOM, Mistral, Baichuan, Qwen, ChatGLM): Easy-to-use LLM fine-tuning framework (LLaMA, BLOOM, Mistral, Baichuan, Qwen, ChatGLM) - hiyouga/LLaMA-Factory

DiscoResearch ▷ #benchmark_dev (3 messages):

  • Seeking Budget-Friendly Benchmarks: User @yobibyte inquired about free benchmarks that might be similar to the openllm leaderboard for those with limited GPU resources.
  • Fasteval as an Alternative: Despite the request for free options, @johannhartmann mentioned using fasteval with mt-bench(-de) for quick and less resource-intensive model evaluations.
  • Real Costs of Fasteval Clarified: @bjoernp pointed out that even with the suggested solution, fasteval incurs costs of more than 5€ in OpenAI credits per model, challenging the notion of it as a free benchmarking tool.

DiscoResearch ▷ #discolm_german (4 messages):

  • DiscoResearch Demo Downtime Explained: @maxmaier_ asked whether the demo server on https://demo.discoresearch.org was down just for them or for everyone. @_jp1_ confirmed that it was down because the GPUs were used for evaluations and promised to bring it back up as soon as possible.
  • Server Back in Business: Following up, @_jp1_ notified that the demo server should be operational again, indicating the downtime was temporary and the issue resolved. @maxmaier_ expressed their gratitude for the quick fix.

Links mentioned:

DiscoLM German 7b Demo: no description found

Alignment Lab AI ▷ #general-chat (1 messages):

  • AI Enthusiasts Create Collaborative Master Doc: User @_red.j shared a Google Document titled The AI Info Diet ™ created during a Twitter space meeting with ML experts, intended as a resource for people to keep up with the latest tools, news, & information in AI. The document is available for anyone to contribute their favorite sources and _red.j has also added the Alignment Lab AI Discord server to the list.

Links mentioned:

The AI Info Diet ™️: no description found

Skunkworks AI ▷ #off-topic (1 messages):

pradeep1148: https://www.youtube.com/watch?v=DFT0tMBwh04

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

jeffreyw128: how do you access it? i can’t for the life of me figure it out in the console lol