December 4, 2023

[AINews] AI Discords Newsletter 12/4/2023

This is AI News! an MVP of a service that goes thru all AI discords/Twitters/reddits and summarizes what people are talking about, so that you can keep up without the fatigue. Signing up here opts you in to the real thing when we launch it 🔜

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OpenAI Discord Summary

• Intense debates and personal perspectives on the pace of AI development, human evolution intertwined with AI, ethical considerations of halting human evolution, and individual aspirations for eternal life unfolded throughout various exchanges.
• Several technical issues were identified with ChatGPT and GPT-4, encompassing performance problems, 'something gone wrong' errors, and browser lag. Widespread uncertainties about the availability and pricing of new subscriptions to ChatGPT Plus and GPT-4 surfaced.
• Users aired security concerns and expressed a critical need for robust bug reporting mechanisms leading to discussions about OpenAI's bug bounty program.
• Enthusiastic conversations about GPT-4 applications colorfully outlined individual use cases, personal experiences, and demonstrated a variety of ways to get pleasure and value from interacting with the AI.
• Responsible application of OpenAI's tools and services became a focal point as users explored concerns about misuse of ChatGPT, sharing of accounts, and potential legal repercussions associated with these actions.
• Users reported a range of challenges with accessing or using GPT-4 and ChatGPT, whether because of site performance, difficulty accessing paid Plus accounts, or issues reaching ChatGPT's waiting list. This processor's technical issues extended to working with specific software, VPNs, and even file-reading errors.
• The advent of an ambiguity tester prompt designed to fine-tune prompts provoked lively discussion and sharing of experiences. Users shared their attempts to leverage such prompts in role-playing scenarios and their struggles when GPT seemingly disregards clear guideline prompts.
• ChatGPT's potential integration with non-AI contexts, such as music production system Reaper, was discussed, along with the benefits of incorporating the AI chatbot into role-playing scenarios.
• A minor section of AI enthusiasts exhibited interest in domain-specific GPTs for fields such as chemistry, indicative of the growing leap of AI applications into various professional and academic disciplines.

OpenAI Channel Summaries

▷ #ai-discussions (80 messages):

• Advocacy for Speedier AI Development: @this_was_a_disaster expressed a desire for rapid development of AI, expressing hope for its rapid contribution to technological advancement even at the risk of potential dangers. He envisioned AI improving itself exponentially and advancing technology to science fiction realms.

• Debate over Human Evolution with AI: @.dooz and @.pythagoras debated about the potential for AI to either eradicate or evolve humanity. .dooz expressed concern over loss of human existence, comparing it to the extinction of human ancestors. .pythagoras, on the other hand, contended that integration with AI represents a form of evolution, not a disappearance.

• Discussion on Ethical Aspects of Human Evolutionary Path: @bambooshoots argued that it might be egotistical to expect humans to last forever. They raised the conundrum of whether we should stop evolution if we could and addressed the risk of irreversible damage to the biosphere due to uncontrolled human behavior.

• Personal Views on Eternal Life: @.pythagoras voiced a personal aspiration to live forever, questioning the value of death and supporting ambitions to live on other planets.

• Quick Interruption about AI Outputs: @lugui advised against pasting AI outputs in the conversation channels, leading to a short discussion about the frustration of AI-mimicked responses disrupting genuine human interactions.

▷ #openai-chatter (284 messages🔥):

• Issues and Concerns with ChatGPT and GPT-4: Users like @shawn_17007, @bredator., and @smokzz reported various issues with ChatGPT, including "something gone wrong" errors, slow response time, and browser lag. @frayvarth identified these as common problems.
• Pricing and Availability of ChatGPT Plus and GPT-4: There were multiple inquiries about buying access to ChatGPT Plus and GPT-4, with users such as @11coop33, @basel42, and @norvzi asking about the availability of subscriptions. @eskcanta, @satanhashtag, and @lugui clarified that new subscriptions are currently disabled without any announced ETA.
• Bug Reporting and Security Concerns: Users @bughub and @otaldohenrique discussed encountering errors and vulnerabilities, expressing the need to report these issues. @lugui and @eskcanta suggested reporting to OpenAI's bug bounty program on Bugcrowd.
• Applications and User Experiences of GPT-4: Interesting use cases of GPT-4 and user experiences came up, such as in the discussions of @merpnderp and @srittik. @merpnderp shared personal experiences with integrating OpenAI's APIs into work flows and their cost concerns, while @srittik shared their enjoyment talking with ChatGPT in various languages.
• Terms of Service and Misuse of ChatGPT: A discussion led by @otaldohenrique, @dabuscusman and @eskcanta revolved around concerns and regulations about misusing ChatGPT, sharing accounts, and possible legal actions. The conversation highlighted the importance of ethical usage and potential risks of account sharing.

▷ #openai-questions (110 messages):

• Chatbot Availability and Performance Issues: Several users including @kryptonordic, @antikythera_mechanism, @sebanunezf, @mustard1978, @imranbsheik, and @Razontex reported experiencing problems with accessing, subscribing to, or using ChatGPT and GPT-4. These issues varied from site speed and performance concerns to difficulties joining the waiting list for ChatGPT 4 Plus due to its current pause on new subscriptions.

• Use of VPN with ChatGPT: Users including @tale95_05727 and @mike_19897 mentioned encountering issues while having specific software or VPNs installed. In some cases, disabling certain functionalities or using a VPN improved performance and solved issues.

• Account Access and Billing Concerns: Users like @signate mentioned being unable to access their paid Plus account, as well certain billing issues.

• Usage Cap for GPT-4: @durbanpoisonpew reported reaching a usage cap after sending few messages on GPT-4, despite being a paying customer.

• Technical Issues: Various users, including @nachos.ai, @blaster_34658, @silver_stone_wro, and @dotails, reported technical glitches and difficulties while using ChatGPT. Some of the issues involve problems with reading files or dealing with error messages. In many cases, the exact cause of these problems was not identified, making troubleshooting a challenge.

▷ #gpt-4-discussions (48 messages):

• Custom GPT for Writing Emails: User @jungle_jo asked for recommendations on a custom GPT for writing emails. @smilebeda highlighted some constraints and potential privacy issues in creating a custom GPT and questioned its practicality. They also mentioned the existence of a message-per-hour cap on GPTs.
• Building GPTs with PDFs: @sabastianblood shared a concern about memory issues and poor accuracy when trying to build a GPT using numerous PDFs. @pietman outlined the current document limit as of their knowledge to be 10 documents with a maximum of 512MB each and also noted difficulties regarding PDFs with images.
• ChatGPT Plus Waiting List: @dainjamouth inquired about the clearing of the ChatGPT Plus waiting list. Several users including @pietman and @satanhashtag chimed in explaining that no clear timeframe existed. The discussion then shifted towards understanding the financial implications of increased subscriptions.
• Generating Structured Data with GPT-4-Vision: @.janxy expressed a wish to generate structured data, preferably JSON, with GPT-4-Vision. @pietman suggested asking the GPT to convert the image into JSON, especially if the image is in a tabular format.
• Limitations with Knowledge Files in GPT: @woblit sought help regarding errors encountered when uploading numerous documents as a custom GPT’s knowledge data. @solbus pointed out the limit of ten 512MB files for a GPT's knowledge files, also highlighting a separate 2M token limit per file, and possible issues with the TXT file format.

▷ #prompt-engineering (77 messages):

• Making of Ambiguity Tester Prompt: User @ex_hort. made a detailed explanation of an Ambiguity Tester Prompt which is designed to help fine-tune user prompts. He also explained the use of it and acknowledged that Eskcanta was the original designer. Link to chat
• Reinforcement Learning Project - Chain of Density for RAG: User @merpnderp asked about the feasibility of using Chain of Density for RAG in reinforcement learning, specifically in the context of improving the accuracy of embedding retrieval.
• Designing Role-Playing Based Prompts: User @shokkunn asked about strategies to design prompts suitable for a role-playing-intensive scenario involving chatbots.
• Guideline Ignoring Issue: User @gingerjoe8791 reported that ChatGPT sometimes ignored clear guidelines provided in a simple article generation prompt and asked for help identifying potential issues.
• AMBI Tester v3: @ex_hort. later shared the initial design and plan for a newer version of the Ambiguity tester, dubbed as the "AMBI tester v3" and sought help from @eskcanta who was instrumental in contributing to the initial AMBI tester design.
• Use of ChatGPT in Music Production: User @ferropop, a music producer, queried about the feasibility of integrating ChatGPT into a music production system called Reaper. He asked specifically about the reliability of using CSV datasets generated by ChatGPT to create curves inside Reaper.
• ChatGPT for Chemistry: User @exilze asked the community for suggestions on a good GPT model suitable for chemistry.

▷ #api-discussions (77 messages):

• Ambiguity Tester Prompt Discussion: @ex_hort. introduced an updated version of the ambiguity tester prompt, a tool originally developed by @eskcanta to help fine-tune prompts by identifying areas of ambiguity, conflict, and confusion. @ex_hort. shared an example of how to use it and discussions ensued over its functionalities, usability, and potential improvements.
• Creating Character for Role-play with GPT: @shokkunn asked for guidance on how to prompt for a role-play heavy chat AI. @ex_hort. recommended defining the desired character and behaviors very clearly and even suggested using an ambiguity tester for fine-tuning the prompts.
• Adherence to GPT Prompt Instructions: @gingerjoe8791 expressed frustration that GPT sometimes ignores clear guidelines in the provided prompts. @ex_hort. and @eskcanta suggested the possibility of inadequate phrasing of the guidelines or conflict in the instructions, hence the need for clarity and refinement of the instructions.
• Music Integration with ChatGPT: @ferropop wanted to integrate ChatGPT into Reaper, a digital audio workstation, by creating curves based on CSV datasets generated by ChatGPT. @eskcanta suggested a stepwise approach of extracting data from a given file, transforming it into an appropriate format, and then writing a CSV file according to specifications.
• Chemistry GPT Inquiry: @exilze asked if anyone knew a good GPT for chemistry, implying some interest in a domain-specific GPT. No responses were provided during the monitored period.

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Nous Research AI Discord Summary

• Discussion about the conceptual understanding of knowledge graphs as state machines where time is a conditional variable, alongside the inquiry for AI resources beyond OpenAI.
• Notable shared links from users: a research paper on Foundation Models and the Transformer architecture, a Github repository named 'mamba' open for contributions, and a Google Colab notebook with less context provided.
• SD models and Clip training: User interaction on responses for how many epochs to use for a 4.5k image dataset, whether Clip truncates prompts, and if different resolutions are effective while training.
• Exchange of thoughts on benchmarks as proxy for human value, with an argument that dynamic evolution of benchmarks may suffice for AGI approximation.
• Inquiry and assistance over creating an API endpoint for custom GPT's responses on a flask frontend.
• Shared resources featuring a pull request on Github adding QuIP# support, a tool for LLM visualization, a 3D matmul explainer and an arXiv paper discussing the inefficiency of Transformer architecture on long sequences.
• Interactive discussions with bots: humorous questions about conspiracy theories on the moon being flat, extending incomplete patterns, and even a request for a space elevator design.
• Extensive conversation regarding evaluation benchmarks for multimodal vision models, with suggestions including referencing to repository llava.
• Issues and dialogues on Hermes Vision's identification flaws, retrieval issues in GPT models (notably Gizmo), and best practices for fine-tuning models with raw and structured data.
• Shared valuable resources and tutorials like the OpenML Guide and a tutorial for running CUDA code on Google Colab.
• Diverse inquiries and discussions concerning forward-looking decoder models, implementing open-source models, project ideas involving language learning model (LLM) study buddy, fine-tuning practice with Hermes 2.5, and a terminal LLM tool.
• Humorous exchange concerning a Nous Research caught on camera meme, with users jokingly agreeing on the depicted scenario.

Nous Research AI Channel Summaries

▷ #ctx-length-research (8 messages):

• Discussion on Knowledge Graph Representation: @natefyi_30842 and @maxwellandrews engaged in a conversation about the structure of knowledge graphs. A suggestion was made to perceive a knowledge graph as a state machine where every condition is maximally true given the available information and that time is a conditional variable applying to every element of the graph.
• Search for Resources: @spaceman777 showed interest in finding good resources on AI, hinting at a desire for suggestions other than OpenAI.
• Paper on Foundation Models and Attention Mechanisms: @if_a shared a research paper centered around foundation models and the Transformer architecture. The paper explores subquadratic-time architectures like linear attention, gated convolution and recurrent models, and structured state space models (SSMs).
• Mamba: A Github Repository: @random_string_of_character shared a Github repository named 'mamba' asking for contributions to its development.
• Shared Google Colab Notebook: @maxwellandrews posted a link to a Google Colab notebook, although without providing context about its content.

▷ #off-topic (24 messages):

• Training SD Models and Clip Queries: @yorth_night asked several questions about training SD models and use of Clip in training, including whether Clip truncates prompts, whether a mix of resolutions work and how many epochs to use with a 4.5k image dataset. @tarian was of the opinion that Clip does have a token limit and a mix of resolutions probably would not work well due to batching issues. The number of epochs suggested was to train for a large number of epochs and monitor the metric/loss.
• Colab Workflow Sharing: @yorth_night also wanted to know if it was possible to share the colab workflow to check if the token limit for Clip could be changed. @tarian offered to check the workflow.
• Benchmark and Human Value: @sciumo voiced an opinion that benchmarks act as a proxy for human value. According to them, users base risk/valuation on benchmarks relative to their domain of interest, and therefore, they believe a dynamic evolution of benchmarks is sufficient for AGI approximation.
• Creating API Endpoint for GPTs Response on Flask Frontend: @a.asif inquired about the possibility of creating an API endpoint for a GPT's response, essentially to show the response on a flask front end. @tarian suggested using the OpenAI API but @a.asif clarified that they specifically referred to custom GPTs and wondered about making an API endpoint for those custom GPTs.

▷ #interesting-links (6 messages):

• Addition of QuIP# Support: @metaldragon01 shared a pull request on Github adding support for QuIP# in a text-generation UI. QuIP# is described as a novel 2-bit quantization method with superior performance.
• LLM Visualization Tools: @jimmy6dof posted a tool for visualizing how LLMs work, alongside a similar 3D matmul explainer and an accompanying HN Discussion.
• Efficient Transformer Architectures:@metaldragon01 linked to a paper on arXiv discussing foundation models based on the Transformer architecture and their computational inefficiency on long sequences.
• Impact of Transformative Technologies: @Fynn questioned whether the discussed innovations significantly impact day-to-day operations or if they're equivalent to the perceived minimal impact of RWKV.

▷ #bots (10 messages):

• Bot Count Inquiry: @cyborg_1552 asked about the number of bots, with @teknium responding with three bots <@1127644854307012718>, <@1128158104245248101>, and <@1103403003513417769>.
• Moon and NASA Query: @giftedgummybee directed a humorous question at bot <@1128158104245248101>, asking why the moon is flat, implying a NASA conspiracy, and joking about who might be eating the moon.
• Language Model Task: @crainmaker asked bot <@1128158104245248101> to extend a pattern of reverse-ordered words, intending it to complete the task like a language model.
• Space Elevator Design Request: @.interstellarninja asked bot <@1128158104245248101> for an elaborate plan with design specs for a space elevator to the moon.

▷ #general (102 messages):

• Evaluation Benchmarks for Multimodal Vision Models: User @interstellarninja sparked a conversation about the need for evaluation benchmarks for multimodal vision models. @vatsadev suggested comparing with llava while @boplicity12 mentioned that the llava base repo has evaluation scripts. @gabriel_syme shared a relevant link in the obsidian channel.
• Discussion About Idiosyncrasies of Hermes Vision: Several users noted that Hermes Vision consistently identifies many objects as hamburgers and has trouble identifying other objects. This led to a humorous exchange involving mathematics and hamburgers.
• Issues with Retrieval in GPT Models (Gizmo): @j109 raised a point that Gizmo wasn't retrieving information even though the network response showed retrieval was enabled. No specific resolution was proposed during the discussion.
• Fine-tuning Practice with Raw and Structured Data: @zakkor opened up a discussion on best practices for fine-tuning models with raw and structured data. Various insights were shared, with @yorth_night suggesting that finetuning on raw data can add context-specific knowledge and that training twice usually increases performance. Further inquiry was made about applying this in reverse with the QLORA approach (training first on raw data, merging, then training on instruct).
• Resources and Tutorials: @lily_33846 shared a link to OpenML Guide on GitHub, an extensive library of AI resources. @_evelynm also shared a link to her tutorial on GitHub for developing and running CUDA code on Google Colab.

▷ #ask-about-llms (23 messages):

• Discussion on Forward-Looking Decoder Models: User @kissgogoat initiated a conversation asking if decoder-only models include forward-looking attention, with @coffeebean6887 comprehensively explaining why they do not. The discussion then turned to the attention mechanisms in various models, such as BERT, before @main.ai voiced skepticism about whether unmasked attention would significantly improve language learning models (LLMs).
• Query About 8-bit Version of autoawq: User @jdnuva raised a query asking if there's an 8-bit version of autoawq, though no answer was given.
• Implementing Open Source Models: @akhxl shared a query on how to utilize a GitHub repository about 'Simple and efficient pytorch-native transformer text generation' to benefit open-source models like Nous or Mistral. They requested guidance as they were unsure about the application in an open-source context.
• Seeking Advice for an Instruction-Based Project: @ayinde_adek sought advice for a project involving an LLM study buddy capable of explaining machine learning concepts and good software engineering practices, considering the use of code LLMs like DeepSeek-Instruct 6.7. However, there were concerns about compromising coding abilities whilst improving language abilities.
• Hermes 2.5 Fine Tuning: @gerred enquired about the context length on which Hermes 2.5 was fine-tuned, and @teknium confirmed it to be 4k.
• Terminal LLM Tool: @raddka shared a GitHub link to a simple terminal LLM conversation tool they developed.
• Discussion on Transformer Rewind and Layering (HF TRL): @xela_akwa raised the topic of HF TRL, although the discussion was not further expanded.
• Insights on Replit AI's Approaches: @coffeebean6887 explained Replit AI's approach in enhancing performance: emphasis on data refinement, model efficiency, and tokenizer customization.
• Practical Code Applications and Overfitting: @gabriel_syme expressed the thought that overfitting could be beneficial in practical, narrow code applications, particularly emphasizing the significance of the tokenizer.

▷ #memes (3 messages):

• Nous Research Caught on Camera Meme: User @Error.PDF posted a humorous meme implying that Nous Research was caught on camera. User @zohad_sikder questioned the reality of the meme, to which @nonameusr responds affirmatively, playing along with the joke.

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LangChain AI Discord Summary

• Discussion on Running OpenAIAssistantRunnable with OpenAI's Code Interpreter, raised by @dachsteinhustler in general channel. No further details or follow-up discussion were provided.
• @refik0727 sought recommendation for a ChatGPT that can work with real-time datasets and generate visualization like bar charts. Relevant tutorial requests were also made but no suggestions were offered.
• @eshnil illustrated a proposal for improving Web Frameworks such as NextJS, VueJS, SvelteKit, etc., by introducing a standard way(A document.getTools() function was suggested) for AI to understand their capabilities in the general channel.
• @shikcodework_18270 inquired about the inclusion of metadata from fetched documents into the context parameter of the prompt in LangChain. Shared relevant codes in the general channel for better context.
• @skumar2022 encountered a connecting issue with SAP SQL ANYWHERE 17 DB via LangChain and requested support. Error details and related code on Github were shared in the general channel.
• In share-your-work channel, @tusharg asked for an update on a previous request from @746449061938462721 and @703607660599181377, without giving more details about the original request.
• @appstormer_25583 shared AI created rock band artwork link, developed using GPT in the share-your-work channel. The artwork has psychedelic themes.
• A brief mention of OpenML guide embeddings by @lightningralf in the share-your-work channel, with no additional context given.
• Announcement in the announcements channel about the future plan and updates in the LangChain project by hwchase17, with a link shared to the relevant github discussion. The upcoming changes involve splitting out integrations into langchain-community.

LangChain AI Channel Summaries

▷ #announcements (1 messages):

hwchase17: posted some updates thoughts and plan on how we'll be splitting out integrations: https://github.com/langchain-ai/langchain/discussions/14243 <<<{"image":"https://opengraph.githubassets.com/e27fb250ed5cf0d6f2266a7386a9de0ba066ba22f421900ed94fb32230ce10c6/langchain-ai/langchain/discussions/14243","title":"LangChain Integrations · langchain-ai/langchain · Discussion #14243","description":"LangChain OSS Announcement TLDR: We’re moving all integrations to langchain-community over the next week. This will be fully backwards compatible. Target date 12/08/23. Then, we’ll work on splittin...","detectedType":"GitHub"}>>>

▷ #general (18 messages):

• Running OpenAIAssistantRunnable with OpenAI's Code Interpreter: @dachsteinhustler questioned if it's possible to run an OpenAIAssistantRunnable using code_interpreter tool of openai. Further discussion or clarification was not provided.
• Recommendation Request for a ChatGPT: @refik0727 asked for recommendations on a ChatGPT that can utilize real-time datasets and potentially create bar charts and other reports. He also requested relevant tutorials, but no suggestions were made in the provided messages.
• Improving Web Frameworks for AI Use: @eshnil posed a suggestion for enhancing Web Frameworks like NextJS, VueJS, SvelteKit, etc., where a standard way could be defined for AI to discover their capabilities, potentially through a document.getTools() function.
• Metadata Inclusion in LangChain: @shikcodework_18270 sought advice on how to include metadata from retrieved documents into the context parameter of the prompt in LangChain. He shared relevant code snippets for better clarification.
• Connecting to SAP SQL ANYWHERE 17 DB Using LangChain: @skumar2022 encountered an error while trying to connect to SAP SQL ANYWHERE 17 DB using LangChain and shared a link to his code on Github for assistance. The error was related to NoSuchModuleError concerning the sqlanywhere.pyodbc plugin.

▷ #share-your-work (3 messages):

• Inquiry on a previous request: User @tusharg asked @746449061938462721 and @703607660599181377 if they had any updates or responses regarding a past request (unspecified).
• AI-created Rock Band Artwork: @appstormer_25583 shared a link to an AI created rock band artwork with psychedelic themes. The artwork was created using GPT and can be viewed at this link.
• OpenML Guide Embeddings Mention: @lightningralf made a brief, standalone mention of OpenML guide embeddings without additional context or details.

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Alignment Lab AI Discord Summary

• Concerns regarding unexpected alterations to model parameters and outputs raised during the initialization of peft_model.
• Queries pertaining to the establishment of an API endpoint for GPT responses that can interface with a Flask frontend.
• A suggestion for the introduction of monetization measures such as launching a token was put forward.
• Discussions relating to the lack of standard API middleware for text generation, particularly in the multi-modal context, contrasted with the existence of such standards for image generation.
• GPT internals visualizer, a novel tool for providing intricate visualizations of GPT model operations, was shared.
• A proposition for employing a payment model akin to Together AI backed up with fund resources and networks.
• An important technical limitation was highlighted: the incompatibility between Torch gradient checkpointing and JIT.

Alignment Lab AI Channel Summaries

▷ #ai-and-ml-discussion (1 messages):

spirit_from_germany: https://fxtwitter.com/_albertgu/status/1731727672286294400 <<<{"detectedType":"Twitter"}>>>

▷ #general-chat (7 messages):

• Concern about Model Modification: @tural.sadik raised an issue about a discrepancy in behavior of the model objects. When initializing peft_model, the outputs and total parameters of the original model seem to change significantly, although they were expecting them to remain distinct: "... I end up both giving the same/similar output which should not be the case. Even the number of total parameters of the initial model object change."
• API Endpoint for GPTs Responses: @a.asif enquired about the possibility of creating an API endpoint for GPTs' response that could connect to a Flask frontend.
• Token Launching Suggestion: @zolandinho suggested considering the launch of a token for monetizing hard work.
• API Middleware for Image Generation: @rusch discussed that there are pretty standard API middleware for image generation, but text generation doesn't have a clear standard yet, especially as multimodal methods are growing in popularity.
• GPT Internals Visualizer: @entropi shared a link to a GPT internals visualizer that provides an in-depth visualization of GPT model operations.

▷ #oo (1 messages):

cryptossssun: I have some fund resources and networks. What if it's a payment model similar to Together AI

▷ #oo2 (2 messages):

• Torch Gradient Checkpointing and JIT Compatibility: @imonenext noted that Torch gradient checkpointing and JIT are not compatible with each other.

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LLM Perf Enthusiasts AI Discord Summary

• Discussions on integrating AI in Github surfaced, with predictions on the technology being implemented for PRs and code reviews by the end of next year.
• Debates about the vLLM / ExLlama equivalent for Stable Diffusion that would offer a high performance backend with intelligent batching. The topic was broached in both the #opensource and #speed channels.
• The inception of DiscoResearch, an AI research initiative focusing on non-English models and innovative evaluation methods. The community launched two AI models: DiscoLM 120b and DiscoLM 70b. The community has issued an open invitation to join their Disco(rd) channel.
• Conversation around the development of an assistant for manuscript assembly, functioning like a co-pilot for writing a paper, managing citations, framing data, etc.
• Queries about techniques for handling unknown prompts in LLMs, particularly how to ensure that the system responds with "I don't know" when faced with unknown or unfamiliar prompts.

LLM Perf Enthusiasts AI Channel Summaries

▷ #general (1 messages):

pantsforbirds: How long until Github allows (paying) users to use AI in PRs and code reviews? I'm betting by end of next year

▷ #opensource (2 messages):

• LLM / ExLlama Equivalent for Stable Diffusion: @the.palmik queried about the high performance backend with intelligent batching equivalent in vLLM / ExLlama for Stable Diffusion.
• DiscoResearch: AI Research Initiative: @_jp1_ introduced DiscoResearch, an AI research initiative focusing on non-English models and innovative evaluation methods.
• DiscoLm Models Launch: Two models were launched: - DiscoLM 120b is a 120b model merging Llama2-70b's functionalities. - DiscoLM 70b is a 70b model derived from LeoLM 70b with added multilingual abilities.
• DiscoResearch Community: _jp1_ encouraged everyone to join the DiscoResearch community in their Disco(rd).
• Future Plans: _jp1_ mentioned ongoing work on the successor of the EM German model family. The upcoming Disco Judge models will offer customizable benchmarking.

▷ #resources (1 messages):

thebaghdaddy: has anyone used (or made!) a good assistant that can be used for manuscript assembly? Like a copilot for writing a paper (citation manager, framing data etc)

▷ #speed (1 messages):

the.palmik: Any idea what's the vLLM / ExLlama equivalent for Stable Diffusion? Meaning a high performance backend with intelligent batching, etc.

▷ #reliability (2 messages):

• Handling Unknown Prompts in LLMs: @vishjain initiated a discussion on techniques to ensure that LLMs respond correctly with "I don't know" to an unknown or unfamiliar prompt.

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Latent Space Discord Summary

Only 1 channel had activity, so no need to summarize...

• GPT Uses: @danimp discussed various applications of GPT, including summaries, translations, rephrasing, providing a "better search" functionality, aiding in trip planning, and assisting with legal, insurance, and finance questions.
• Security Concerns: @guardiang pointed out the common, insecure practice of adding post-it notes with passwords on monitors.
• Use of AI in Sports: @guardiang described how they used AI as a caddy in a disc golf game. They suggested this could potentially be applied to other situations as well.

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MLOps @Chipro Discord Summary

• Announcement of a live session on the use of Retrieval-Augmented Generation (RAG) with Langchain and Llama 2, geared towards ML Engineers, Data Scientists, Data Engineers, and Software Engineering Managers. The session promises to deliver insights into overcoming static knowledge using RAG, expanding knowledge horizons, and understanding how RAG leverages external databases. The session, taking place on December 6th, 2023, 11:30 AM EST, will be led by Hudson Buzby. The link to register: https://www.qwak.com/academy/how-to-use-rag-with-langchain-and-llama-2?utm_source=Discord&utm_medium=Chip_hyuen&utm_campaign=Rag_LLM_Dec23

Note: No other relevant discussions or topics were mentioned in the given channels.

MLOps @Chipro Channel Summaries

▷ #events (1 messages):

amitqwak: 📺 Live Session: How to use RAG with Langchain and Llama 2 - Delivering context-aware interactions 📆 Date: December 6th 2023 11:30 AM EST Who is it for: ML Engineers, Data Scientists, Data engineers, Software engineering managers, MLOps

📣 Why is it relevant: ‍Learn how RAG enhances LLMs by breaking free from static knowledge, sourcing real-time data, and providing more contextually relevant responses​​.‍ Understand how RAG leverages external databases, minimizing the need for exhaustive retraining and keeping AI systems up-to-date​​.‍ Discover how RAG draws from specialized databases to provide detailed, accurate answers, balancing breadth and depth in information retrieval​​. Gain insights into the architecture of RAG, including its data ingestion pipeline, retrieval mechanism, and generation component​​.

Price: Free 🔗 Registration link: https://www.qwak.com/academy/how-to-use-rag-with-langchain-and-llama-2?utm_source=Discord&utm_medium=Chip_hyuen&utm_campaign=Rag_LLM_Dec23 <<<Join Hudson Buzby, December 6th at 11:30 AM EST to explore the groundbreaking advancements in AI language models with Retrieval-Augmented Generation (RAG) and Large Language Models (LLM). 

This session includes a live demo and will delve into:

Overcoming Static Knowledge

‍Learn how RAG enhances LLMs by breaking free from static knowledge, sourcing real-time data, and providing more contextually relevant responses​​.‍

Expanding Knowledge Horizons ------>>>

▷ #general-ml (2 messages):

• The user @_vitaminc posted twice in the general-ml channel. Both messages were the same and stated: "Wrong channel". Other discussion points, links or blogposts of interest were not provided.

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AI Engineer Foundation Discord Summary

• Weekly Meeting Announcement: A reminder by @hackgoofer about a weekly meeting on Discord was noted, with an event link provided for interested members.
• Resource Sharing: @juanreds agreed to share a set of unspecified, potentially important links requested by @hackgoofer in the future, after they were organized.

AI Engineer Foundation Channel Summaries

▷ #general (2 messages):

• @hackgoofer requested for relevant links on an unspecified topic from @juanreds.
• @juanreds agreed to share the links after organizing them post work.

▷ #events (1 messages):

hackgoofer: Weekly Meeting tomorrow! https://discord.gg/4tJyBwYd?event=1181318557842296832 <<>>

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Skunkworks AI Discord Summary

Only 1 channel had activity, so no need to summarize...

pradeep1148: https://www.youtube.com/watch?v=MFBA_cmqWQE <<<{"title":"Generate gif images with AnimateDiff in Google Colab","image":"https://i.ytimg.com/vi/MFBA_cmqWQE/maxresdefault.jpg","description":"I tried "AnimateDiff" with "Google Colab", so I summarized it. The diffusers version was not stable, so I am using the official repository version.https://no...","detectedType":"YouTube"}>>>

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