[AINews] AI Discords Newsletter 11/15/2023
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 🔜
Guild: Latent Space
Latent Space Guild Summary
- @slono's creation of a RTK/Redux agent based on a condensed version of the RTK documentation.
- Query by @chef regarding the most effective small models for instruction finetuning, with a preference for models that can be finetuned on a single A100 and are accessible on the Hugging Face hub. @eugeneyan's recommendation of the 7B classes for mpt-instruct, falcon-instruct, and mistral in combination with Lora on a 16GB GPU RAM.
- @last_ride_1707's question on suitable metrics for a pre-PMF freemium code generation tool, followed by @coffeebean6887's inquiry for more information on the type of metrics sought: evaluative metrics for testing code generation or growth metrics for measuring adoption.
- The request by @Phill for suggestions on the simplest method to launch Slack bots as a frontend UX for OpenAI Assistants.
- Announcement by @swyxio regarding the discussion on the Huggingface DPO paper, with an invite to all here.
- Information from @chef about Hugging Face's released codebase that replicates Zephyr utilizing DPO instead of PPO for instruction fine-tuning, found here.
- Direct communication between @eugeneyan and @swyxio about the impending discussion on DPO, directing to this link for the discussion.
- Sharing of the Code Diffusion Model paper and version 1 pdf by @picocreator, available at these links respectively here and here.
- @swyxio's announcement of next week's discussion topics featuring @296887155819675650 as the lead, to check the topics click here and here.
Latent Space Channel Summaries
### Channel: [ai-general-chat](https://discord.com/channels/822583790773862470/1075282825051385876) Summary (3 messages): Discussion on AI Models and Metrics in ai-general-chat:- RTK/Redux Agent Update: @slono mentioned that they created a RTK/Redux agent that could be potentially useful, based on a condensed version of the RTK documentation.
- Small Models for Instruction Finetuning: @chef inquired about the most effective "small" models currently available for instruction finetuning. They expressed a preference for models that can be finetuned on a single A100 and are accessible on the Hugging Face hub. @eugeneyan recommended the 7B classes for mpt-instruct, falcon-instruct, and mistral, which can work efficiently with Lora on a 16GB GPU RAM.
- Metrics for Code Generation Tool: @last_ride_1707 asked for advice on appropriate metrics to track for a code generation tool operated on a freemium model and is pre-Product-Market Fit (PMF). @coffeebean6887 asked for clarification whether the interest was in evaluative metrics for testing code generation or growth metrics measuring adoption.
- Slack Bots as Frontend UX for OpenAI Assistants: @Phill sought suggestions on the simplest method to launch Slack bots as a frontend User Experience (UX) for OpenAI Assistants.
- @swyxio announced a discussion on the Huggingface DPO paper with user @451508585147400209, inviting everyone to join the event.
- Links:
- Discord link to the discussion.
- Replica Zephyr using DPO: @chef pointed out that Hugging Face has released a codebase to replicate Zephyr using DPO instead of PPO for instruction finetuning.
- DPO Discussion Schedule: @eugeneyan and @swyxio mentioned that the discussion on DPO was imminent, with @eugeneyan providing a Discord link for the discussion.
- Code Diffusion Model: @picocreator shared links to the Code Diffusion Model paper and version 1 pdf.
- Future Discussion Topics: @swyxio announced the topics for the following week's discussion, with @296887155819675650 as the lead.
---
## Guild: [LangChain AI](https://discord.com/channels/1038097195422978059)
### LangChain AI Guild Summary
- @beffjezos2700 encountered a problem in **upserting embeddings into Pinecone** and requested help while offering a reward.
- Certain undefined **prompts caused timeouts**, as reported by @fishyccy.
- @vudumagic made an unaddressed inquiry regarding the potential **integration of OpenAI Vision API into LangChain**.
- @vj19 asked for advice on tools to **scrape date-specific, keyword-based data** from websites.
- @coreyb42 shared an in-depth analysis of the potential of GPTs in **data analysis.**
- @itscabral posed a question about the possibility of creating a **'RecordManager' using local files** instead of SQLite.
- A discussion of **GPTs and retrieval augmentation systems** like Rag took place, with @.cannaboss highlighting an inherent issue related to the weights in GPT-4.
- @.cannaboss proposed the implementation of multiple, task-specific **"knowledge planes" with trained weight tensors** as a solution to the aforementioned issue.
- @minecraftjuicer presented the idea of chatbots generating **perfect prompts** based on user input, but raised concerns over response time.
- @dennisyurk was interested in exploring **advanced projects or chains** that interact with data warehouses, and was dissatisfied with the examples provided.
- @.trouble_ offered to **schedule pair programming sessions** with other members for LangChain projects.
- @veryboldbagel explained how **Runnables can be configurable, changing logic based on the user ID with runtime modifications**. They also provided examples from LangServe and OpenGPTS repositories with relevant links:
- [LangServe Server.py Parameter](https://github.com/langchain-ai/langserve/blob/main/langserve/server.py#L433)
- [Examples in OpenGPTS](https://github.com/langchain-ai/opengpts/blob/main/backend/app/server.py#L32)
- [Configurable Runnables](https://github.com/langchain-ai/langserve/blob/main/examples/configurable_chain/server.py#L76)
- @alex_35579 discussed the concerns with how the **semi-structured RAG template** handles data in LangChain, especially the loading of the **docstore** from a text array and the absence of proper search functionality.
- @arcypojeb shared their project on developing a **hierarchical cooperative multi-agent framework** utilizing websockets for LLM<->LLM communication. Links to the project files were shared:
- [ServerMain.py on Github](https://github.com/CognitiveCodes/NeuralGPT/blob/main/Chat-center/ServerMain.py)
- [ChainlitCli.py on Github](https://github.com/CognitiveCodes/NeuralGPT/blob/main/Chat-center/ChainlitCli.py)
- [ServerNeural on HuggingFace](https://huggingface.co/spaces/Arcypojeb/ServerNeural)
- [QA-Docs-Chainlit-Langchain on HuggingFace](https://huggingface.co/spaces/Arcypojeb/QA-Docs-Chainlit-Langchain)
- [Multiagent Authoritarian example on Github](https://github.com/langchain-ai/langchain/blob/master/cookbook/multiagent_authoritarian.ipynb)
LangChain AI Channel Summaries
### Channel: [general](https://discord.com/channels/1038097195422978059/1038097196224086148) Summary (5 messages🔥): General Discussions on LangChain AI Discord Chatbot:- Embeddings Upsert Issue with Pinecone: @beffjezos2700 sought help with a problem related to upserting embeddings into Pinecone, and expressed willingness to pay for the assistance.
- Prompts Timing Out: @fishyccy brought up issues with certain prompts timing out. The specific reasons for these timeouts aren't provided.
- LangChain and OpenAI Vision API: @vudumagic queried if the new OpenAI Vision API is a part of LangChain yet. The query is left unanswered.
- Data Scraping Tools: @vj19 asked for recommendations on tools or integrations that can scrape data from websites based on a date range and search keywords.
- Role of GPTs in Data Analysis: @coreyb42 presented detailed perspectives on how GPTs could be used in data analysis compared to heavy application frameworks like LangChain. They mentioned that GPTs might be a good fit for those who produce output similar to what’s generated by Tableau.
- RecordManager using local files: @itscabral posed a question about the possibility of creating a `RecordManager` using local files instead of SQLite.
- Issues with the new OpenAI Assistant API: @.cannaboss cautioned about a failure with GPT-4 when using the new OpenAI Assistant API for direct retrieval. According to them, this is due to a fundamental issue with retrieval augmentation systems like Rag, where external knowledge is not truly integrated into the model's learned weights.
- Solution to GPT Retrieval Issue: @.cannaboss suggested switching in multiple, task-specific "knowledge planes" with trained weight tensors as a potential solution to the problem.
- Generation of Perfect Prompts: @minecraftjuicer discussed the possibility of a chatbot generating perfect prompts based on user input to get the best possible answers. However, concerns about potential delays in response time were also raised.
- Interaction with Date Warehouses: @dennisyurk sought examples of more advanced projects or chains that interact with date warehouses. They found the basic examples provided in langchain and llama index insufficient.
- Offer for Pair Programming Sessions: @.trouble_ offered to schedule zoom calls to collaborate with other members on projects using LangChain.
- @veryboldbagel provided information on how runnables are configurable and shared links to examples of this usage within the LangServe and OpenGPTS repos. They further stated that one could change the logic at runtime based on the user ID.
- Links:
- @alex_35579 brought up the issue of how the semi-structured RAG template handles data in LangChain. Specific points of concern are how the docstore is loaded from a text array and that in a real-life application, this data needs to be stored persistently. They mentioned that the template currently loads the entire file into the context without search, which isn't viable for production. They sought advice on how to store the data to properly integrate with langchain retrievers and enable the correct searches.
- Project Description: @arcypojeb shared their non-commercial project that utilizes websockets for LLM<->LLM communication. The project is a multi-purpose AI assistance platform with a focus on integrating existing AI-powered apps and tools.
- Progress Update: Arcypojeb mentioned that they've managed to establish websocket connectivity between different AI platforms/frameworks including Gradio, Chainlit, Tkinkter, and PySimpleGUI. They have also created multiple interfaces for clients and servers, and established a basic mechanism for an AI-driven question-answering logic.
- Future Plans: The intention is to integrate this with a case-specific version of such agent and use it as the main logic driving the server<->client connectivity.
- Links:
---
## Guild: [Nous Research AI](https://discord.com/channels/1053877538025386074)
### Nous Research AI Guild Summary
- Discussion around the **efficient handling of multimodal datasets**, particularly those in parquet format. Strategy proposed by @tsunemoto involved storing images as blobs with a shared python script to aid in querying and conversion of blobs back to images.
- **Estimation** of dataset creation costs, and speculation on potential **dataset origins** (leak, scraping, etc.), particularly focused on Dall-e 3 API utilized dataset.
- The possibility of **reworking dataset prompts** with an AI system to **generate images** using natural language instead of tags, brought forward by @tsunemoto for potential multimodal benchmarking advantages.
- Link shared by tsunemoto: [Initial Message from tsunemoto](https://discord.com/channels/1053877538025386074/1132352574750728192/1174118466970718269)
- **Collaborative dataset processing** initiative, with users dividing up the work on different parquets to avoid duplication in the face of impending Discord CDN policy changes.
- Resource referred: [Midjourney Dataset](https://huggingface.co/datasets/vivym/midjourney-messages?row=0)
- Sharing and discussion of several **AI research papers, blog posts, and relevant media sources** largely covering areas of AI benchmarks, model performance, dataset usage, and AI speech synthesis solutions.
- Sources shared ranged from arXiv.org papers, Hugging Face resources, Twitter discussions, and YouTube videos on the topic.
- Debate on **model benchmarks and their commercial applications**, with focus on perfect scores from models like Goliath-120B and Capybara Yi-34b. Specific comparison of Capybara Yi-34b to GPT-4 noted.
- Links: [Capybara Yi-34b vs. GPT-4](https://www.reddit.com/r/LocalLLaMA/comments/17vcr9d/llm_comparisontest_2x_34b_yi_dolphin_nous/?rdt=55967&onetap_auto=true), [Goliath-120B Readme](https://huggingface.co/alpindale/goliath-120b?text=Hey+my+name+is+Julien%21+How+are+you%3F#merge-process)
- Usage of **Archive.org** proposed for **potential pretraining datasets**, with some initial ideas brought up.
- Detailed inquiries on tokenizing and inferencing with Capybara 34b, with guidance provided by @teknium and links shared to external snippets.
- Noted **greeting exchanges** in the welcome channel but little else of import.
- Detailed **model-specific queries** in the "ask-about-llms" channel, majority of the discussion revolving around **fine-tuning Local Language Models (LLMs)**, exploration on the merging of adapter weights post-fine-tuning, faster inferencing possibilities with OpenHermes and VLLM, and potential uses of the AWQ model.
- Notable Link shared by yorth_night: [PEFT script for merging adapters](https://github.com/geronimi73/qlora-minimal/blob/main/merge_qlora.py)
- @teknium's **meme** of the quote **"Do things that won't scale"** added in the "memes" channel with an accompanying [link](http://paulgraham.com/ds.html) for context.
Nous Research AI Channel Summaries
### Channel: [off-topic](https://discord.com/channels/1053877538025386074/1109649177689980928) Summary (6 messages🔥): Summary of "Off-Topic" Discussion:- Multimodal Dataset Optimization: @tsunemoto shared a more efficient method for working with a dataset in parquet format, involving storing all images as blobs. A python script was shared for browsing and querying the parquet, including automatic conversion of blobs back to images.
- Dataset Creation and API costs: @tsunemoto and @yorth_night speculated on the origin of an extensive dataset, concluding that its creation via the Dall-e 3 API would have cost around $800,000. They wondered if the data could have been leaked or scraped.
- Potential Application of Language Models: A discussion ensued about reworking the prompts in the dataset using a Language Model, so that images can be generated with natural language rather than tags. @tsunemoto showed interest, seeing potential benefits for multimodal benchmarking.
- Division of Dataset Processing: @tsunemoto and @yorth_night decided to work on different parquets to avoid duplications, with tsunemoto taking 000002.parquet and yorth_night taking 000001.parquet. The channel users are encouraged to join in the processing effort to beat the clock on Discord's CDN.
- Links:
- Paper and Articles shared: Several research papers and blog posts were shared and discussed:
- @euclaise shared an AI research paper from arxiv.org
- @euclaise recommended another AI paper from arxiv.org
- @euclaise also pointed out a Hugging Face blog post on attention sinks.
- @metaldragon01 posted a link to Reddit discussion about LLM comparison tests.
- @yorth_night alerted everyone about a potentially useful dataset on Hugging Face that might be affected by Discord's policy change.
- @max_paperclips introduced a Twitter thread discussing AI benchmarks.
- @tsunemoto also shared a tweet about AI speech synthesis.
- @yorth_night referred to an interesting tweet on transformer performance.
- @joey00072 recommended a YouTube video for further insights on the subject.
- @yorth_night brought up a Twitter post discussing a unique approach to AI modeling.
- Preserving Dataset and Script Sharing: @yorth_night and @tsunemoto discussed the importance of preserving the dataset from Discord's policy change and how to speed up the process using specific scripts. @tsunemoto confirmed having downloaded the dataset from Hugging Face.
- Discussion on AI Benchmarks and Commercial Application: There was discussion about the reliability and specifics of certain AI benchmarks involving @teknium, @ldj and @makya. They also discussed the potential commercial uses and limitations of certain AI models, as well as the likelihood of new model releases, particularly referencing @gabriel_syme, @tokenbender, and @_automagic's remarks.
- Capybara Yi-34b comparison with GPT-4: @teknium shared a link to a benchmark test on Reddit. The user @ldj opined that 70B models have previously accomplished the test with slight mistakes, but it's the first time a model has received a perfect score in both versions. However, @euclaise expressed skepticism about a perfect score from a merge model like Goliath-120B.
- Discussion about Goliath: @euclaise mentioned that Goliath mixed up some details when given a detailed writing prompt. Later, @alpindale, who is apparently connected to the model's development, clarified that he took slices from different layer ranges from each model and then stacked those slices on top of each other, describing this process in the Goliath-120B Readme.
- Potential use of Archive.org for pretraining datasets: @euclaise proposed potentially creating datasets from Archive.org and some initial ideas including Usenet, Open textbooks, Children's books, and Manuals.
- Tokenizing and inferencing Capybara 34b: Yorth_night inquired about starting the inference for Capybara 34b with some technical issues and errors. He was given guidance by @teknium who also provided code for inference for the Open Hermes 2.5 model from his/her own repo.
- Links:
- Capybara Yi-34b vs. GPT-4
- Goliath-120B Readme
- Guidance AI on GitHub (mentioned by @mihai4256)
- Price drop announcement on OpenRouter (mentioned by @alexatallah)
- @_akhaliq's tweet about 120B models (mentioned by @gabriel_syme)
- @SpazeCraft shared a greeting: "Blessings".
- @teknium responded with a greeting using a waveyboy emoji.
- Oscar- and Multilingual Models: @mtybadger noted Oscar- was chosen for its multilingual capabilities despite being smaller but slightly higher quality than RPJ.
- Merging Adapter Weights after Fine Tuning: @lightninglemons queried on how to merge adapter weights after fine-tuning to the base model to get the final finetuned model. @yorth_night suggested using PEFT, sharing a sample script.
- OpenHermes and Fast Inference with VLLM: @ac1dbyte wondered if OpenHermes would allow faster inference with VLLM. @teknium affirmed this idea.
- AWQ Model in VLLM: @gabriel_syme mentioned experiencing a slowdown when loading the AWQ model in VLLM, although both models have the same configuration.
- Fine-Tuning for Semi-Structured JSON Outputs: @ac1dbyte expressed the need for fine-tuned OpenHermes to generate semi-structured JSON objects for post-processing. @teknium suggested starting with prompting before moving on to fine-tuning.
- Fine-Tuning LLMs to Speak Low-Resourced Languages: @4biddden asked about the possibility of fine-tuning models on low-resourced languages. @crainmaker suggested that the feasibility would depend on the complexity of the language and the extent of the dataset coverage.
- Links:
- @teknium shared a quote: "Do things that won't scale" and posted a link related to it.
---
## Guild: [Alignment Lab AI](https://discord.com/channels/1087862276448595968)
### Alignment Lab AI Guild Summary
- **Dataset Creation and Cost-Efficiency**: User @igoforth discussed the importance of datasets in creating high-quality finetunes, questioning the cost-effectiveness of alternative methods such as using ChatGPT vs. GPU time consumption.
- **Experience with Federated Learning and Adapter Methods**: @erogol enquired about experiences with federated learning and adapter methods, discussing its potential for situations where data privacy is sensitive but model updates can be shared. Relevant resources were shared:
- [NVIDIA Developer Blogpost on Federated Learning](https://developer.nvidia.com/blog/adapting-llms-to-downstream-tasks-using-federated-learning-on-distributed-datasets/)
- [BigScience Workshop GitHub code](https://github.com/bigscience-workshop/petals)
- **Collaboration Request for the Techstars Startup Weekend San Francisco AI Event**: User 5811g, a Machine Learning Engineer, is seeking team members for an AI project at the Techstars Startup Weekend San Francisco AI. The project involves Large Language Models (LLMs) in automating e-commerce operations. Event details were provided via [EventBrite](https://www.eventbrite.com/e/techstars-startup-weekend-san-frisco-ai-tickets-752164623637?aff=ebdssbdestsearch).
- **Discussion on Lora Model Merging**: @teknium brought up a discussion on merging fine-tuned models, mentioning a project where swapping lora adapters was the primary feature. They noted that fine-tuned models should provide different responses, but also disclosed their lack of personal experiments with attaching a lora model.
- **AI Learning and Prompt Conditioning**: Members discussed various aspects of AI learning, referencing an [interview](https://www.abc.net.au/news/science/2023-11-15/jeremy-howard-taught-ai-to-the-world-and-helped-invent-chatgpt/103092474) with Jeremy Howard on AI topics. @turgutluk sought clarification on the CRLFT mechanism in the openchat codebase, with @imonenext confirming it as prompt conditioning + weighted CE. Advice was given for training using a split dataset and implementing prompt conditioning strategies in multi-turn tasks.
Alignment Lab AI Channel Summaries
### Channel: [ai-and-ml-discussion](https://discord.com/channels/1087862276448595968/1087876677603958804) Summary (4 messages): AI and ML Discussion Topics- Dataset Creation and Cost-Efficiency: @igoforth discussed about the importance of dataset in creating high quality finetunes. They questioned about the cost-effective alternative to a GPT-4 generated dataset, and if using chatGPT is more economical than spending on GPU time.
- Experience with Federated Learning and Adapter Methods: @erogol asked about experiences with federated learning, particularly with recent adapter methods. They see potential in it for scenarios where data can't be shared, but model updates can, and for scaling model training to the crowd.
- Links:
- User **5811g**, a Machine Learning Engineer (MLE), is looking to form a team to participate in an upcoming AI event. The project idea involves the use of Large Language Models (LLMs) in automating e-commerce business operations.
- Details of the Event: The event is the Techstars Startup Weekend San Francisco AI. Tickets can be procured via the provided link: Techstars Startup Weekend
- @teknium expressed that after fine-tuning two models A and B separately, they should provide different responses when tested with the same prompt. However, @teknium also stated that they have not personally experimented with attaching a lora model, usually opting for a merge instead.
- @teknium mentioned a project where the primary feature was swapping lora adapters. However, they recommended seeking advice from @563068096747798529, @1117586410774470818, or @317006433797537792 for more insights on this topic.
- Jeremy Howard's Interview: @jeremyhoward shared an article highlighting an interview he did in which he discussed AI topics.
- Clarifications on CRLFT: @turgutluk asked for clarification on the CRLFT mechanism used in the openchat codebase, to which @imonenext confirmed it's indeed prompt conditioning + weighted CE.
- Model Training Advice: For training using a dataset split between gpt3.5 (85%) and gpt4 (15%), @imonenext suggested to start with gpt 3.5 weight 0.1 and gpt 4 weight 1.
- Prompt Conditioning Strategy: @imonenext advised that in multi-turn tasks, it's helpful to remind the AI of the condition in every turn, as models tend to forget them otherwise.
---
## Guild: [Skunkworks AI](https://discord.com/channels/1131084849432768614)
### Skunkworks AI Guild Summary
- Discussion on **AI Paper Discovery and Curation**, with talks on seeking a blog post on **Multimodal LLMs**, tracking trending **AI papers** and potential solutions such as AI paper forwarding and exclusive Discord channels. Key references:
- Blog post potentially describing multimodal LLMs: https://arxiv.org/abs/2307.10802
- AI paper recommender bot: [https://x.com/fly51fly?t=mALtFOokv0NqKZ9z56wsVQ&s=09](https://x.com/fly51fly?t=mALtFOokv0NqKZ9z56wsVQ&s=09)
- Conversation about **S-LoRa Library**, a Stata package for survey data, along with a link to a new research paper:
- S-LoRa library: https://github.com/S-LoRA/S-LoRA
- Research Paper: [https://arxiv.org/abs/2311.03285](https://arxiv.org/abs/2311.03285)
Skunkworks AI Channel Summaries
### Channel: [general](https://discord.com/channels/1131084849432768614/1131084849906716735) Summary (2 messages): Discussion on AI Paper Discovery and Curation:- Seeking a Blog Post on Multimodal LLMs: @le_mess asked for help in finding a blog post that describes multimodal Language-Limited Models (LLMs), where different modalities like audio and images are projected into the LLM using a neural network. A potential match was suggested by @.mrfoo, who referred to the blog post https://arxiv.org/abs/2307.10802.
- Tracking Trending AI Papers: Users @interstellarninja and @tokenbender discussed the difficulty of keeping up with trending AI papers on Twitter due to the increasing volume of publications. @tokenbender suggested using an AI paper recommender bot found at https://x.com/fly51fly.
- AI Paper Forwarding Service: @interstellarninja suggested the idea of an AI paper forwarding service or using Bard to filter emails with relevant papers. @tokenbender suggested potential automation to post papers on Discord channels, despite potential challenges with the Twitter API.
- Features for an Ideal AI Paper Tracker: A potential solution discussed was a Discord channel exclusively dedicated to AI papers, with a bot that ranks and summarizes papers.
- @digthatdata mentioned the S-LoRa library and shared a link to it. S-LoRA is a Stata package that performs Local Regression Adjustment (LoRA) for survey data with complex designs.
- Additionally, @digthatdata shared a link to a research paper on arxiv.org.
- Links:
---
## Guild: [MLOps @Chipro](https://discord.com/channels/814557108065534033)
### MLOps @Chipro Guild Summary
- Announcement of a new webinar titled **"Building Active Learning Pipelines & Improving Computer Vision Models"**, shared by @stephenoladele_mldev. The webinar is set to cover several key topics:
- A practical demonstration on **identifying and correcting label errors** as well as constructing active learning pipelines.
- Insights into **rectifying data quality issues**.
- Recommended **strategies for building data pipelines**.
- A Q&A section focusing on **developing high-quality image datasets**.
- [Register for Webinar](https://encord.com/lp/from-data-to-diamonds-active-learning/)
- Discussion by @5811g on the potential use of **Language Model Learners (LLMs)** to automate tasks in e-commerce business operations.
MLOps @Chipro Channel Summaries
### Channel: [events](https://discord.com/channels/814557108065534033/869270934773727272) Summary (1 messages): New Webinar: Building Active Learning Pipelines & Improving Computer Vision Models:- Announcement by @stephenoladele_mldev about a new webinar that focuses on implementing active learning pipelines and improving computer vision models, with topics to be covered including:
- A hands-on demo on finding and fixing label errors and creating active learning pipelines
- Insights into fixing data quality issues
- Best practices for building data pipelines
- Q&A on curating high-quality image datasets
- @5811g mentions leveraging Language Model Learners (LLMs) to help automate e-commerce business operations.
- Links:
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This guild has no new messages. If this guild has been quiet for too long, let us know and we will remove it.
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This guild has no new messages. If this guild has been quiet for too long, let us know and we will remove it.
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This guild has no new messages. If this guild has been quiet for too long, let us know and we will remove it.
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## Guild: [YAIG (a16z Infra)](https://discord.com/channels/958905134119784489)
### YAIG (a16z Infra) Guild Summary
- Announcement on the integration of **Mistral and Stable Diffusion** into Cloudflare Workers AI, as shared by user stevekamman, including a [Discord link](https://discord.com/channels/595317990191398933/1154819662161395742/1173496868811059200) to the announcement.
- Discussion on an **Open Source Infrastructure** project for "ingesting large volumes of data for semantic search" by user .danme, who is seeking consultation from individuals dealing with large-scale knowledge graphs. The project, named VectorFlow, is available on this [Github Repository](https://github.com/dgarnitz/vectorflow).
YAIG (a16z Infra) Channel Summaries
### Channel: [ai-ml](https://discord.com/channels/958905134119784489/1013536071709118565) Summary (1 messages): AI and Machine Learning Discussions:- Mistral and Stable Diffusion on Cloudflare Workers AI: @stevekamman has shared that Mistral and Stable Diffusion are coming to Cloudflare Workers AI and has shared a Discord link to the announcement.
- Open Source Infrastructure for Ingesting Large Volumes of Data: @.danme discussed their current project, an open-source infrastructure designed to ingest large volumes of data for semantic search. They're seeking individuals working with knowledge graphs at a large scale for a conversation regarding their project. They shared the Github Repository for their project called VectorFlow.
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