> AI Discords for 1/24/2024. We checked **20** guilds, **297** channels, and **3025** messages for you. Estimated reading time saved (at 200wpm): **295 minutes**.

Adeptā€™s turn for a splashy launch:

image.png

The emphasis seems to be UI understanding, which given Adeptā€™s business makes sense as a focus. The demo video shows very good and precise visual QA on 7 screenshots of UIs, but revealed no other part of the Adept product because it was on a gradio interface. Fuyu also uses DPO, which has suddenly become the presumptive winner of the brief DPO vs IPO vs KTO wars. Fuyu-Heavy beats Gemini Pro on the new MMMU benchmark, but itā€™s unclear where GPT4V registers on this (someone run it?)

A couple people called out the side comments on the size of Fuyu-Heavy vs Claude 2 and GPT4-V and Gemini Ultra given those details arenā€™t public, and Adept itself didnā€™t actually even mention their own model size (itā€™s bigger than Fuyu-8B, thatā€™s all we really know). Assuming those frontier models are in the rumored 400B to 1.7T param range, being 10-20x smaller puts Fuyu-Heavy around the 20B-170B lower-upper bounds.

In other news, Mamba was rejected for ICLR as ā€œnot good enoughā€. Lol?

Table of Contents

[TOC]

PART 1: High level Discord summaries

TheBloke Discord Summary

  • Coder Showdown: User Model vs GPT-4: A user named @rombodawg made the claim that their new coding model outperformed GPT-4 in coding challenge tests, specifying that parts of DeepSeek Coder 33B were integrated into their merge.

  • Optimizing LLM Deployment: There was significant discussion around deploying large models like Yi-34B-200K and Goliath-120B with users like @super.deap and @aikitoria seeking strategies for low cost, fast inference, and fitting into 80GB VRAM setups.

  • Emerging from Quantization Quandary: Chatter about quantization revealed mixed feelings; @keyboardking reported that Q8 made models worthless, but @kquant presented a case for their effective use and shared an endorsement for EXL2 quants.

  • Merging Models Mastery: Conversations included insights on model merging tactics, with @alphaatlas1 indicating optimal results when the merged model weights sum up to 1.0 total weight and noting deficiencies when surpassing a 1.4 threshold.

  • Questions on Fine-tuning: Users like @nigelt11 sought clarity on fine-tuning Mistral 7B Instruct v0.2 regarding tags and prompt formatting, and a shared Medium article provided additional guidance on these practices.

Nous Research AI Discord Summary

  • Exploring AI Chatbot Potential: There was a discussion about the possibilities of turning AIs into chatbots capable of interacting with APIs. However, no specific products were identified.

  • Mistral Model Discussions: The utility of longer sequences and larger batch sizes was debated as potentially beneficial for models like Mistral. Clarifications on the differences and applications of fine-tuning versus instruct-tuning for models were sought, with instruct tuning typically following fine-tuning. The correct data format for fine-tuning Mistral was also queried, indicating confusion over the instruction-format associated with instruct-tuning.

  • LLM Training Insights: Using RMS optimization was noted to improve loss results over layernorm in recent work. The anticipation of open-sourcing an implementation if successful was expressed, as well as the intent to evaluate a modelā€™s performance soon.

  • Heterogeneous AI and Model Advancements: A LessWrong post discussed heterogeneous AI architectures combining Transformers and Selective SSM (Mamba). Capabilities of DoraemonGPT in dynamic video understanding, the potential of recurrent LLMs like RWKV for tracking character states, and Contrastive Preference Optimization (CPO) in translation for moderate-sized LLMs were mentioned. Also, Adept Fuyu-Heavy was introduced as a new multimodal model designed for digital agents, which despite its size, outperforms larger models in certain benchmarks as per an Adept.ai blog post.

  • Steering LLMs and Hardware Talk: Interest in activation steering for language models was shown, as well as debates on the efficiencies of different prompt structuring methods using dynamic templates. GPU capability discussions highlighted the AI model running potential on various hardware like the 1080 ti and 3060.

  • Project Obsidian Gears Up for v2: An upgrade for Project Obsidian to v2 was announced, selecting stableLM 2 1.6B as the model of choice. The community responded positively, and a resource for zero-shot generalization in visual document understanding, the InstructDoc dataset, was shared (GitHub - nttmdlab-nlp/InstructDoc).

OpenAI Discord Summary

  • ChatGPT Creativity Call: @abdubs invites users to share distinctive applications of ChatGPT in creative and practical scenarios in a bid to understand the broad benefits the technology provides.

  • GPT-4 Image Prompt Insights & Alternatives: @lugui clarifies that GPT-4 builds prompts based on image descriptions, not generating new images. For image manipulation, @xenowhiz suggests the use of Code Interpreter and its modules.

  • AI Podcast Recommendation: @fran9000 endorses ā€œThe AI Breakdownā€, offering daily news and debates on multiple AI facets.

  • Preferences for Older GPT Versions: @lzgodhook13 inquires about reverting to GPT-3 versions from May to June 2022, citing better performance on straightforward tasks.

  • GPT-4ā€™s Updated Context Window Issues: Users report a decrease in context window after an update, affecting custom model performance. A bug report is created but no direct link is provided.

  • Prompt Engineering Exchange: Frustrations with default list outputs in ChatGPT are discussed with strategies to evade them, including negative prompting, while @brayheart proposes forming a team for a prompt engineering hackathon.

Perplexity AI Discord Summary

  • Pro Perks and Promo Code Pandemonium: Users discussed the advantages of upgrading to Perplexity Pro, highlighting its relevancy checks and suggesting use with GPT-4 for an improved experience. However, some encountered issues with applying discount codes, with solutions ranging from following steps by Redditor u/ArakDemonBlade to reaching out to support at [email protected].

  • Comparing Perplexity AI Companionā€™s Capability: Debate ensued over the Perplexity AI Companionā€™s ability to retain sources for GPT-4 follow-up questions, with contrasting views on its functionality between the extension and the iOS app.

  • Limits and Quirks with Online LLMs and Google Drive: User @mantas_82008 raised a query about increasing the 10 per minute limit for online LLM models, while others shared fixes for accessing large Google Drive documents, including download and conversion suggestions, and a discussion of how Perplexityā€™s prompt box could be strategically used.

  • Diving into Perplexityā€™s Design Insights: A YouTube video featuring Henry Modisett, Head of Design at Perplexity AI, was shared, outlining the nuances of AI design and job acquisition in the field. Additionally, kudos were given for the utility of Perplexityā€™s Copilot feature, complemented with links on how it informs users about global trends.

  • API Insights and Credit Incentives Uncovered: Enthusiasm about Perplexityā€™s API was noted when PPLX 70B was identified as the model equivalent to the ā€œExperimentā€ feature with Copilot, and an explanation about differing responses between browser and API suggested variations in system prompts/sources. A $5.00 credit offer was also mentioned, activated after autoloading at least $2 to oneā€™s account.

LM Studio Discord Summary

  • Switch Hits: From CPU to GPU in AI Ops: Engineers discussed the process of changing from CPU to GPU usage for AI operations, with a recommendation to check the chat page settings. Linux users, particularly those with older processors lacking AVX2 support, sought alternatives to LM Studio, with the suggestion to compile llama.cpp, which supports language model loading.

  • Chasing Tailored Models on HuggingFace: The members expressed confusion regarding the selection of models on HuggingFace, emphasizing the lack of clear Unique Selling Points (USPs) due to minimal documentation. Open LLM Leaderboard was recommended to compare model performance.

  • Gearing Up GPU Layers for Enhanced Model Performance: Discussions in the hardware channel focused on configuring GPU layers to optimize model performance. They evaluated using n_gpu_layers and num_layers settings for improved processing, despite facing issues like underutilized system RAM and compatibility with non-AVX2 instruction support.

  • RAG and Embeddings API in the AI Limelight: There was a focus on using Retrieval-Augmented Generation (RAG) and possible workarounds for not having an NVIDIA GPU for using the OpenAI Embeddings API. A code snippet for reading from PDF using RAG and a relevant GitHub repository were shared. Conversations also extended to exploring RAG through a HuggingFace model and an explanation of RAG from a Databricks glossary entry.

  • Bridging LM Studio and Open Interpreter: An attempt to integrate LM Studio inference with memgpt and Open Interpreter was discussed, with a focus on whether memgptā€™s server can emulate OpenAIā€™s chat and completion call functionalities. This indicates ongoing exploration into interoperable systems within the AI community.

  • Prompting Puzzles and Integration Trials: Members requested ideas for improved prompting without giving a specific context, and shared on-going challenges in integrating LM Studio memGPT with OpenAI, reflecting a broader interest in cross-compatibility and effective prompting strategies in model development.

Eleuther Discord Summary

  • EleutherAI Boosts Open AI Research: EleutherAI has partnered with the National Science Foundation to launch the National AI Research Resource, aiming to provide increased access to AI resources. They have also made strides in AI research with contributions like GPU grants and the development of the GPT-NeoX library, known for its scalability on various high-performance computing platforms.

  • Licensing Landscapes and Legalities for LMs: Within the community, there are discussions indicating ambiguity surrounding the licensing for GitHub repositories as it pertains to model training, with advice ranging from consulting lawyers to exploring local copyright laws. Alongside this, concerns about CoPilot litigation were met with the acknowledgment that legal proceedings can extend over long durations.

  • The Algorithmic Almanac: Debates and examinations thrived around topics like the importance of data quality over size evidenced by references to Wavenet data, potential and pitfalls in newer frameworks like Burn for Rust, and advanced techniques such as MambaByte token-free Language Modeling and Elastic Weight Consolidation in continual updates for models in production.

  • Deciphering Deep Learning Directives: Interpretability discussions highlighted the plotting of decoder weights in Sparse Autoencoder space and connected research updates from the Anthropic team, which focused on discoveries like attention superposition and dictionary learning on MNIST. A noted typo in a key research update reveals the close attention paid to detail within the community.

  • GPT-NeoX Development Dives Deep: Conversations circled around technical aspects such as tensor+expert parallelism in model training with confirmations of similarity to DeepSpeedā€™s implementation. An ongoing DeepSpeed issue related to CUDA initialization is also subject to further investigation by community members.

  • Scaling and Special Channels: A mention of overcoming limitations in the original scaling laws paper alluded to successfully training models at the 1.3B parameter scale, and a pointer was given to discussions regarding 1b parameter models using a specific channel <#1129489948710539334> for in-depth analysis.

OpenAccess AI Collective (axolotl) Discord Summary

  • Axolotl v0.4.0 Ready for Deployment: The OpenAccess AI Collective announced the release of axolotl v0.4.0, which introduces support for new models, numerous bug fixes, and a note of appreciation for the 56 contributors and the A16Z grant.

  • Model Training Mysteries and Maladies: Users discussed challenges and best practices in model training; from ensuring Mamba compatibility with Lora to strategies for efficiently saving model training steps. One user is having trouble uploading to Hugging Face, while another seeks guidance on pretraining CLIP models specifically for domain adaptation.

  • Shifting Shader Discussions: Conversations about GPU Purchasing Decisions became a focal point, where users compared the merits of 8 H100 versus 16 A100 GPUs, considering factors like VRAM for their hardware setups.

  • Medical Dataset Goldmine and Machine Learning Woes: A GitHub repository with multimodal QA datasets was shared, while users grappled with issues from securing funding for compute resources to technicalities of the alpaca format prompt for model inference.

  • Curiosity Chill Caused by Serverless: @dreamgen expressed concerns about the reality of cold-start times in serverless deployments for large models, especially in light of past challenges with providers not caching models or docker images. This highlights a pressing performance issue for practical AI deployment.

Latent Space Discord Summary

  • Lumiereā€™s Space-Time Magic: The Lumiere model by Google was introduced as a space-time diffusion model that can generate video from text. It notably features impressive inpainting capabilities, as outlined in Googleā€™s detailed writeup.

  • Transparency Tussle Over Googleā€™s AI Code: Concerns were voiced about Googleā€™s hesitation to release AI-related code, which makes replicating their research a challenge for the community.

  • Self-Instructing for Smarter AI: An innovative approach called Self-Instruct was shared, aimed at enhancing large language models through self-generated instructions, possibly improving AIā€™s ability to bootstrap knowledge (Self-Instruct paper).

  • Discord Welcomes AI Chatbot Contenders: An invitation was extended to implement a Discord chatbot leveraging large language models, with code available on GitHub.

  • A Stage for AI Scholars: The Latent Space guild is using Discordā€™s new Stage feature to facilitate paper discussions, with a successful turnout of 40 participants for a recent session and plans to discuss the Pythia paper next, along with insight from a related Twitter thread.

  • Never Miss an AI Beat: Members were invited to stay informed of future Latent Space events by signing up here and subscribing to the calendar.

  • RestGPT: LLMs as RESTful Controllers: A spotlight was cast on RestGPT, a project that explores LLM-based autonomous agents controlling real-world applications through RESTful APIs, hosted on GitHub.

Mistral Discord Summary

  • CUDA Role Unrequited: @ziper_rom1 didnā€™t get a reply for a CUDA developer position after a month; community insights imply a likely rejection. The position may already be filled, as implied by a Twitter post announcing a new Nvidia hire.

  • Mistral Speed Bumps: @duck shared Mistral RAG timing benchmarks that showed 17.05 ms for sample times and 175910.47 ms for eval times using llama.cpp, which are considered slow for the intended use case.

  • Mixtralā€™s Mammoth Memory Usage: @l0gr1thm1k encountered CUDA memory errors when deploying Mixtral on NVIDIA T4s, where the 4bit quantized version surpassed the anticipated 24GB memory requirement.

  • Finetuning: Beyond BLEU and ROUGE: @bishwa3819 is finetuning Mistral-7B on a Dolly dataset and queried whether BLEU and ROUGE metrics are adequate for evaluating language model performance on such specific data.

  • Reddit Copilot Bot Powered by Mistral: @hugoduprez created a Reddit copilot bot with Mistral which is notable for its operational speed. A new approach using A-JEPA neural model for audio understanding was shared by @shashank.f1, showcasing a YouTube video on semantic extraction from audio files using the model.

  • Mistral APIā€™s Summarization Challenge: @nico2412_ faced issues with summarizing web content via URL using the Mistral API, a task thatā€™s not directly feasible due to LLMsā€™ lack of internet access. @duck suggested an alternative approached detailed in a GitHub notebook for summarizing articles.

HuggingFace Discord Summary

AI Study Courts VFX Artists: A survey seeking insights from VFX artists and producers is underway as part of an AI study, soliciting valuable industry input.

Greener Alexa Alternatives on Your Own Terms: @mattbcool addresses electronic waste by retrofitting Alexa hardware, detailing efforts to build a local, open-source personal assistant using Raspberry Pis.

CircleCI Powers LLM Automated Testing Course: Deep Learning.ai and CircleCI have teamed up to offer a course on using continuous integration tools to assess LLM applications effectively.

Breathing Life Into Text with 3DTopia: Discovered by @meatfucker, 3DTopiaā€™s GitHub repository promises to transform text into 3D models promptly with downloadable model weights and code.

Python Module Marries Steering Vectors with Hugging Faceā€™s Transformers: @mihai4256 created a Python module that integrates steering vectors with transformers, hinting at more details in a tweet.

LAION Discord Summary

  • GoogleAIā€™s Lumiere Lights Up AI Chat: The introduction of GoogleAIā€™s Lumiere, a robust video diffusion model capable of text-to-video, image-to-video, stylization, and more, sparks discussions among engineers. However, the lack of open sourcing leads to a mixture of excitement and skepticism, with some highlighting the simplicity behind its methods, while others express concerns about realism in AI-generated videos. (Read Paper on LumiĆØre).

  • Data Dominance Drives Debate: Google may have an unmatched data edge for training text-to-video (T2V) models considering their YouTube holdings. Itā€™s suspected that Lumiere leverages YouTubeā€™s extensive video repository, which includes auto-generated captions and a vast quantity of user comments, providing a considerable dataset for training video multimodal language models.

  • Video Model Showdown: Discussions compare Googleā€™s Lumiere with Metaā€™s EMU Video models, emphasizing their capabilities and questioning the naturalness of their AI-generated content. Critiques center on occasional inconsistencies in the generated videos, causing some community members to seek out the best open-source equivalents for video stylization.

  • AI Repository Access Woes: Technical difficulties are evident as users encounter issues downloading LAION-en-aesthetics captions, with Huggingface disabling downloads becoming a point of concern and debate in the community.

  • AI Rivalries Heat Up: A Reddit link is circulated that discusses the performance of RWKV 7B, which may be rivaling Mistral 7B in terms of multilingual support while also being noted for efficient CPU usage and linear runtime. The comparison intrigues enthusiasts who are following the advancements in language model capabilities. (RWKV vs. Mistral Discussion).

Please note that while certain usernames were initially cited, they have been omitted from this summary as their direct relevance to the topics is not clarified to be of importance for an AI Engineer audience.

LlamaIndex Discord Summary

  • Vanna AI Dazzles with RAG for SQL: Vanna AI, a project by @zain_hoda, is gaining attention for its use of Retrieval Augmented Generation (RAG) to improve SQL query generation, capable of indexing DDL/table schemas and text. The project has generated buzz on social media. LlamaIndex Tweet

  • AI Response Optimization Sought: Thereā€™s a community interest in refining the response mechanics of AI bots, specifically in making openaiagentā€™s reply process more iterative like the openai assistant, although a consensus on the method was not reached.

  • Efficiency Hunt in AI Tools: Engineers are seeking efficiency improvements in tools like pandas, weighing options such as a context-aware response synthesizer and pondering the thread-safety of multi-threading the query engine.

  • Host Hunting for LLM Chatbots: The conversation included tips on using open-source models like LLMs without local hosting, with hints about utilizing services like HuggingFace and Replicate for APIs and fine-tuning.

  • Enriching RAGā€™s Performance: Discussions about enhancing RAG focused on the potential of a BGE similarity reranker and the suggestion of reranking after RRF (Reciprocal Rank Fusion) for better outcomes.

  • Conversational Memory for Chatbots: The need for tools to track conversational history, similar to the memory buffer of langchain, was expressed with the possibility of integrating chat memory buffer in chat engines to create conversational bots with memory.

  • Vector Storage Preferences Queried: An individual voiced a request for community opinions on the best vector store companies, prompting a discussion on preferences but no clear favorite emerged.

LangChain AI Discord Summary

X/Twitter Account Unblocked: The X/Twitter account has been recovered, and users previously affected have been unblocked. Those still experiencing issues can seek help by posting in the thread.

Streamline Your Apps with LangChain Streaming API: LangChain reveals a new streaming API to support real-time responsiveness in user applications. Resources include API documentation, specific modules for AgentExecutor and LangGraph (AgentExecutor docs, LangGraph Notebook), and a YouTube tutorial on stream_events. Feedback and discussions on the feature are welcomed on GitHub.

Database Dilemmas & Discussions: Queries range from determining if LangChain is open source to how to best integrate vector embeddings in a Postgres Database schema, alongside a call to share preferred vector storage solutions. Helpful references include the PostgreSQL Schemas documentation.

LangServe Learning Curve: Users in the LangServe channel grapple with utilizing agent_executor and understanding the capabilities of LCELs, some wishing for direct guidance from more experienced members in setting up and expanding tool usage.

Innovations and Connections in Shared Work: The launch of AgentHub, a platform aimed at combining RPA with AI, is announced along with a blog post on potential productivity gains (AI and RPA: The Future of Work). Meanwhile, a user calls for collaboration without providing specific context.

Educate with AI-Oriented Courses: A free 9-part AI series including ā€œBuilding Multimodal AI Applications with LangChain & the OpenAI APIā€ is available at DataCamp, and a new free course on automated testing of AI applications is offered by CircleCI and Deeplearning.ai (Automated Testing with LLMOPS).

DiscoResearch Discord Summary

  • GPT-3.5 Faces Judgment Issues: @calytrix experimented with GPT-3.5 to assess the importance and sentiment in news stories, finding that it fares better in judging sentiment but has difficulty with importance and is biased. To confront these challenges, it was suggested that a specific evaluation system tailored to the modelā€™s abilities might be more effective.

  • Quality Control in AI Training Data: When fine-tuning language models, data quality was a concern raised by @thewindmom; @hammadkhan proposed methods such as eye-balling and heuristic filtering for quality checks. Meanwhile, @bjoernp recommended synthetic data creation as a strategy to diminish the need for extensive evaluation.

  • Reference to Synthetic Data Insights Shared: @_jp1_ mentioned jon durbinsā€™ Airoboros repo as a valuable resource for DiscoResearchā€™s synthetic data generation techniques and linked to jon durbinsā€™ Airoboros GitHub. The repo provides a customizable implementation related to the self-instruct paper.

  • Trials and Tribulations with DPR: @philipmay reported suboptimal outcomes from testing DPR models and investigated question specificity by summation of top result distances. In addition, @sebastian.bodza acknowledged the challenges involved with data referencing in embeddings and indicated that the next phase would involve question generation.

  • DiscoLM German 7B v1 Receives Applause and an Update: User @alex_22398 praised the DiscoLM German 7B v1 for its high-quality language outputs and flagged an issue with extra blank lines. Subsequently, @_chromix_ confirmed a resolution and considered an update to GGUF quantization.

PART 2: Detailed by-Channel summaries and links

TheBloke ā–· #general (1239 messagesšŸ”„šŸ”„šŸ”„):

  • GPT-4 vs Coder Models: Users engaged in a discussion where one participant, @rombodawg, claimed their new coding model is outperforming GPT-4 in tests, providing comparative results of coding challenges. @righthandofdoom asked if the model tested was a new ā€œdeepseek 33b one,ā€ to which @rombodawg replied that the model tested is part of their merge and includes components of DeepSeek Coder 33B.

  • Yi-34B-200K Deployment Queries: @super.deap sought advice on deploying the Yi-34B-200K model optimally, aiming for low cost and fast inference. Various suggestions were posed, including the use of vLLM, reducing bits per word (bpw), and utilizing one A100 GPU for inference of reduced context sizes.

  • Exploring Large LLM Deployment Options: Conversation pivoted around deploying large language models like Goliath-120B, with @aikitoria looking for advice to fit the model into an 80GB VRAM setup. Participants discussed different bit precision weights (bpw) and cache sizes to optimize for VRAM limits.

  • Exchanging Modeling Techniques: The discourse carried on with users exchanging insights on model compression strategies at inference time, with mentions of techniques like LoRA adapters for potentially reducing VRAM requirements.

  • Girlfriend GPT and Its Feasibility: Started by @bubblegum.btc, a discussion highlighted skepticism about how Girlfriend GPT could afford to offer a high volume of messages to its users. Some proposed cost-reducing measures such as caching frequent queries or batching responses.

Links mentioned:

TheBloke ā–· #characters-roleplay-stories (116 messagesšŸ”„šŸ”„):

  • Model Size Concerns Emerge: @keyboardking expressed difficulties in understanding how people utilize models under 10GB, leading to a discussion where @kquant clarified that a 7B model is not under 10GB and provided insights about running models at full precision.
  • Thoughts on Model Quantization: Different opinions surfaced about quantized models with @keyboardking mentioning that Q8 ggufs seemed to render the model worthless, while @kquant explained the actual sizes of q8 models and their effectiveness.
  • Character Creation Techniques Discussed: .justinobserver shared a prompt designed to help users create character cards for roleplay, leading to questions from community members like @givan_002 about how to apply the prompt to multi-round dialogues.
  • Feedback on Model Performance: Users such as @ks_c tested models like bagelmistertour and reported mixed outcomes, indicating a need for more context, and @kquant endorsed using a chat-instruct setup for better stability.
  • Quantization Follow-up by Users: Community members like @kquant suggested LoneStrikerā€™s EXL2 quants for a better experience with models like frankendpo, and @ks_c talked about a positive experience when using higher quantization settings on frankendpo 4x7b, mentioning it was stable without tokenizer issues.

Links mentioned:

TheBloke ā–· #training-and-fine-tuning (67 messagesšŸ”„šŸ”„):

  • Clarifying Fine-tuning Instructions: @nigelt11 asked about an issue where the [INST] tag isnā€™t appearing in the output when fine-tuning Mistral 7B Instruct v0.2, even though their function includes it. @gt9393 and @flail_. clarified that users are not required to type these tags; instead, the tags should be a part of the prompt formatting during fine-tuning.
  • Confusion Over Tokenizer Settings and Dataset Tags: @gt9393 shared that thereā€™s uncertainty and different practices concerning adding add_eos_token/add_bos_token in the tokenizer settings, while also including <s>/</s> in the dataset. @nigelt11 acknowledged possibly missing the add_eos_token detail in their setup.
  • Understanding Instruction Tags and Output: @gt9393 discussed the appropriateness of placing system prompts inside the [INST] tags versus outside in the fine-tuning dataset. The importance of differentiating the instruction template [INST] tags, which should be used for inference, from other system components like eos or bos tokens was a point of dialogue.
  • Adding Identity to Model Chat Behavior: @lordofthegoons inquired about training ā€œidentityā€ into a modelā€™s chat behavior, referencing models like Samantha, without follow-up discussion providing a direct answer.
  • Medium and DataCamp Articles as Guides: @gt9393 shared links to a Medium article (Mistral 7B Fine-Tuning: A Step by Step Guide) and DataCamp tutorial (Introduction to ChatGPT), potentially for further reading on fine-tuning practices. These serve as resources to understand the use of instruction tags and tokenizers in fine-tuning language models.

Links mentioned:

TheBloke ā–· #model-merging (3 messages):

  • Model Merging Optimal at 1.0 Total Weight: @alphaatlas1 mentioned that, although theoretically possible, merges with weights not summing up to 1.0 donā€™t perform as well in reality compared to those that do sum up to 1.0.
  • Clarification on DARE TIES Method: @alphaatlas1 clarified that the original DARE TIES paper suggested using very low densities with total weights above 1.0 for merging, which would combine ā€œoutlierā€ weights from two models without conflict; however, they noted this approach doesnā€™t seem to yield the best results in practice.
  • Merging Models Beyond a Threshold Breaks Down: @sanjiwatsuki observed that the process of merging models tends to break down when the total weight exceeds 1.4.

TheBloke ā–· #coding (4 messages):

  • Inquiry about Hugging Face Pro for Large Models: User @keihakari asked if Hugging Face Pro is suitable for running Deployment Inference with a 70b model.
  • SigLIP as a CLIP Alternative: @jeremy.london mentioned that CLIP can be replaced with SigLIP now.
  • Modifying gguf Fileā€™s Internal Model Info: @lordofthegoons inquired about a method to manually change the data of a gguf fileā€™s internal model info card.
  • Parameter Count Issues with Frankenmerges: @lordofthegoons also noted that frankenmerges tend to result in incorrect parameter counts. ,

Nous Research AI ā–· #off-topic (10 messagesšŸ”„):

  • In Search of API-Savvy Chatbots: @allanyield inquired about companies creating products that use API documentation to turn AIs into chatbots capable of interacting with APIs on behalf of a user. No specific products or companies were mentioned in subsequent chat.
  • Random GIF Interjection: @Error.PDF shared a GIF from Tenor with a language setting notification, which seems off-topic and unrelated to the surrounding technical discussion.
  • Mistralā€™s Competitive Edge with Longer Sequences: @carsonpoole mentioned that using longer sequences and larger batch sizes seems beneficial and expressed that such a model could be compatible with a Mistral implementation.
  • Training Run Curiosities: @everyoneisgross was curious about the duration of training runs using phi 2, suggesting they have a desire to leverage their own 3060 GPU for overnight training.
  • Open Source Intentions Raise Anticipation: In response to @gabriel_symeā€™s query, @carsonpoole indicated an interest in open-sourcing their implementation should it prove successful, generating interest in its potential release.
  • Loss Improvements with RMS: @carsonpoole noted that utilizing RMS instead of layernorm is yielding favorable loss results, suggesting an optimized approach in their current work.
  • Mistral Compatibility Challenges: @carsonpoole acknowledged that direct plug-and-play with a Mistral implementation isnā€™t technically feasible, hinting at complexities in model integration.
  • Evaluating the Modelā€™s Performance: @carsonpoole signaled an intent to conduct evaluations on the model soon, indicating an upcoming phase of performance assessment.

Links mentioned:

Jordan Batter Looksmaxxing GIF - Jordan batter Looksmaxxing No - Discover & Share GIFs: Click to view the GIF

Nous Research AI ā–· #interesting-links (27 messagesšŸ”„):

  • Era of Heterogeneous AI Architectures: @burnytech shared a LessWrong post cross-posted from the AI Alignment Forum, discussing the emergence of heterogeneous AI architectures combining Transformers and Selective SSM (Mamba). @mikahdang remarked on the influential nature of this principle for future AI development.

  • DoraemonGPT for Dynamic Video Tasks: @DoraemonGPT highlighted the capabilities of DoraemonGPT in dynamic video understanding by converting videos into a symbolic memory for spatial-temporal querying, using tools for external knowledge assessment, and a novel LLM-driven planner. This work is detailed in an arXiv paper and aims to handle complex tasks by leveraging LLMs for video scene interpretation.

  • RNNs Showing Promising Potential: @_3sphere commented on the potential of recurrent LLMs like RWKV and Mamba to be better at tracking character states in sequences, spotlighting RWKV 7Bā€™s mission to outpace Mistral with only 1T tokens, as seen in a teaser link shared by @euclaise.

  • Advancements in Moderate-Sized LLMs for Translation: @mister_poodle shared a paper introducing Contrastive Preference Optimization (CPO), an approach improving the translation capabilities of moderate-sized LLMs, presenting potential quality improvements over supervised fine-tuning. The detailed study is available on arXiv.

  • Adept Introduces Fuyu-Heavy Multimodal Model: .benxh presented Adept Fuyu-Heavy, a new multimodal model designed for digital agents, boasting strong multimodal reasoning capabilities, evident in the Adept.ai blog post. Despite its size, Fuyu-Heavy outperforms larger models in certain benchmarks, with further details and examples provided in the announcement.

Links mentioned:

Nous Research AI ā–· #general (231 messagesšŸ”„šŸ”„):

  • Consciousness Conundrum Continues: @nonameusr queried the group on what they believe consciousness is, leading to a discussion with @_3sphere about the ā€œeasyā€ and ā€œhardā€ problems of consciousness and @giftedgummybee jokingly referencing a fictional court case over AI sentience.
  • Towards Steerable AI: Discussing @mihai4256ā€™s work, @teknium expressed interest in the methods behind activation steering for language models. The conversation progressed to @mihai4256 explaining the use of text prompts to influence model behavior, which inspired further inquiry from community members.
  • Prompt Formatting Affects LLMs: Members like @euclaise and @stellaathena debated the efficiency of various methods for structuring prompts in language models, with links to academic papers discussing prompt sensitivity and potential layout issues when using dynamic templates.
  • GPU and AI Hardware Discourse: Chats like @theluckynick and @sirri69 discussed the capabilities and limitations of various GPUs, such as the 1080 ti and the 3060, in the context of running AI models, along with anticipation for future software optimizations.
  • Miscellaneous Tech Banter: @teknium and @carsonpoole exchanged opinions on the merits of different operating systems for running AI models and general computing, while @n8programs shared some technical achievements involving webGL2 and quantization methods for vector representations.

Links mentioned:

Nous Research AI ā–· #ask-about-llms (20 messagesšŸ”„):

  • Clarification on Fine-tuning Versus Instruct-tuning: @moconna inquired about the differences between fine-tuning and instruct-tuning and how to apply them with their own dataset. @besiktas clarified that instruct tuning typically comes after fine-tuning, and fine-tuning would involve causal language modeling on a dataset before potentially applying instruct tuning with high-quality specialized data.

  • Confusion Over Fine-tuning Data Format for Mixtral: @moconna sought clarification on the correct format for fine-tuning Mistral, as tutorials seem to suggest instruction-based formatting, which they associated with instruct-tuning. @besiktas affirmed that the terminologies are vague and the provided format is often used for both fine-tuning and instruct tuning.

  • Direct Approach for Fine-tuning Tasks: When @moconna asked how to fine-tune Mistral for specific tasks in new domains and languages, @besiktas suggested using cleaned data for the new domain and cautioned that success in a completely new language might be uncertain without further research.

  • Continual Model Updates in Production: @kenakafrosty queried the community whether there is consensus on updating fine-tuned models with live usage data. The discussion appeared inconclusive, with varying potential methods mentioned but no definitive strategy.

  • Query and Banter about Optimal Dataset for Mistral: @locutusque sought recommendations for the best dataset to fine-tune Mistral, to which @besiktas humorously suggested MNIST, leading to a playful exchange about visual capabilities of language models like Mistral.

Nous Research AI ā–· #project-obsidian (5 messages):

  • Project Obsidian Upgrade Announced: @qnguyen3 announced plans to upgrade Obsidian to v2.
  • StableLM 2 1.6B Selected for Upgrade: In the upgrade process, @qnguyen3 will be using stableLM 2 1.6B, opting for an even smaller model this time.
  • Community Response to Upgrade: @giftedgummybee simply responded with ā€œNiceā€ to the news of using stableLM 2 1.6B for Obsidianā€™s upgrade.
  • InstructDoc Dataset Shared: @gabriel_syme shared a link to the InstructDoc dataset on GitHub, a resource for zero-shot generalization in visual document understanding (GitHub - nttmdlab-nlp/InstructDoc).

Links mentioned:

GitHub - nttmdlab-nlp/InstructDoc: InstructDoc: A Dataset for Zero-Shot Generalization of Visual Document Understanding with Instructions (AAAI2024): InstructDoc: A Dataset for Zero-Shot Generalization of Visual Document Understanding with Instructions (AAAI2024) - GitHub - nttmdlab-nlp/InstructDoc: InstructDoc: A Dataset for Zero-Shot Generalizā€¦

,

OpenAI ā–· #annnouncements (1 messages):

  • ChatGPT in Action: @abdubs is calling out to @everyone to share their unique and creative uses of ChatGPT in the <#1155775326253756456> channel. They are enthusiastic to learn how the technology benefits usersā€™ lives in various ways, from aiding students to storytelling and enhancing communication.

OpenAI ā–· #ai-discussions (18 messagesšŸ”„):

  • AI Image Prompting Clarification: @lugui explained that GPT-4 models describe an image to build a prompt rather than generating a new image, which leads to very different outcomes.
  • Image Manipulation Alternative Suggested: @xenowhiz responded to a concern about DALL-Eā€™s image recreation capability by suggesting the use of Code Interpreter with its image manipulation modules to achieve desired results.
  • AI Podcast Endorsement: @fran9000 recommended ā€œThe AI Breakdownā€ podcast for daily news and analysis on artificial intelligence, covering a wide range of topics from creativity to ethical considerations.
  • Using Older GPT Versions for Tasks: @lzgodhook13 expressed frustration with newer versions of GPT and inquired about using the version 3 from May to June 2022, citing difficulty with simple tasks such as ordering numbers.
  • Understanding GPT-4 Message Limits: @pope0004 questioned why GPT-4ā€™s usage is limited for premium users, with @jaicraft suggesting API or Copilot Pro for extended access, and @satanhashtag confirming that message restrictions apply to everyone.

Links mentioned:

OpenAI ā–· #gpt-4-discussions (66 messagesšŸ”„šŸ”„):

  • Getting GPT to Spit Out a Novel: @tetsujin2295 expresses frustration that while GPT is willing to produce a lengthy 1500-word summary within a chat, it fails to accomplish the same when directed to summarize in a downloadable document. Suggestions by @loschess include managing the output by breaking down the task into sections due to the token limitation in GPTā€™s responses.

  • Custom GPT Gone AWOL: @sstrader29 encountered their custom GPT model vanishing from search. @loschess advises checking for any emails regarding the incident, implying potential communication from OpenAI about model issues.

  • Granting GPT Email Diplomacy Powers: @greg_nyc is exploring ways to enable GPT to draft email responses through Gmail, with @darthgustav. suggesting the need to create a custom action with Googleā€™s API and to carefully follow Google documentation.

  • File Upload Frustrations in GPT Builder: @alexandre.f.s brings up issues with uploading files for training custom GPTs, having troubles with the GPT Builder. @darthgustav. recommends steps like clearing the browser cache and methodically attaching files one-by-one to prevent corruption.

  • Context Confusion After Update:

    • @kickiniteasy and @ajkuba report a severe reduction in the context window for custom GPTs post-update, drastically impacting the modelsā€™ performance and continuity.
    • @nefas mentions issues with GPT chains in the builder, as they seem to forget previous interactions, a problem that does not occur in the preview window.
    • @cairpli has created a bug-report for the said issues, linking to a Discord channel. (The link appears to be a placeholder ā€™<<>>ā€™ and thus is not included.)

  • GPTā€™s Advertising Ambitions: @9rld is interested in creating a custom GPT that mimics the style of print advertisements, looking to use a web crawler to collect data from ad websites. They are querying whether GPT can extract text from the image-heavy advert content available on these sites.

OpenAI ā–· #prompt-engineering (71 messagesšŸ”„šŸ”„):

  • Avoiding Lists Becomes a Hot Topic: @dbugger expressed frustration with ChatGPT providing lists in responses, regardless of the input. @darthgustav. and others suggested providing explicit instructions on desired format, using examples such as an emotion-based prompt or character-named prompts to discourage list-style output (ā€œEmotionPromptā€).

  • Prompts to Avoid Lists: @eskcanta recommended using ā€˜negative promptingā€™ combined with motivation to avoid the model defaulting to lists, sharing a detailed, character-driven prompt example designed to elicit a more narrative response.

  • Tackling Instagram Caption Challenges: @semir9526 sought advice on crafting prompts for creating Instagram captions in a specific format. Multiple suggestions were offered, including using variable-based structured output and avoiding mentioning character limits which ChatGPT cannot count.

  • Improving Keyword Searches in Documents: @novumclassicum queried how to achieve better results when having GPT scan documents for keywords. @darthgustav. advised understanding stochastic inference and considering task chunking, whereas @eskcanta mentioned possible Python tool integration for string treatment, and both discussed semantic matching for better search accuracy.

  • Prompt Engineering Hackathon Team Formation: @brayheart invited members to team up for a prompt engineering hackathon, opening the door for collaboration, while @eskcanta showed potential interest, asking for specific goals.

OpenAI ā–· #api-discussions (71 messagesšŸ”„šŸ”„):

  • Breaking the List Habit: @dbugger expressed frustration with ChatGPT consistently providing answers in list format, regardless of the prompt. @darthgustav. and @eskcanta proposed various solutions, including providing explicit output templates and style instructions, using EmotionPrompt, and crafting prompts to focus on single items at a time.

  • Marketing Magic Crafting: @semir9526 sought advice for improving an AI-generated marketing prompt for authentic Instagram captions. @darthgustav. advised on prompt structure, using variables, and avoiding specifying Instagramā€™s character limits, instead suggesting to keep captions ā€œbrief and exciting.ā€

  • Search Smarter, Not Harder: @novumclassicum questioned the consistency of GPTā€™s keyword searches within documents. @darthgustav. explained the stochastic nature of the model and suggested using semantic matching for keywords, while also recommending splitting the task into chunks for better results.

  • Prompt Engineering Challenge: @brayheart inquired about interest in a prompt engineering hackathon. @eskcanta showed interest but asked for more details on the hackathonā€™s specific goals. ,

Perplexity AI ā–· #general (160 messagesšŸ”„šŸ”„):

  • Perplexity AI Companion Extension Suggestion: User @mares1317 advocated using the Perplexity AI Companion extension, even suggesting it can retain sources for follow-up questions with GPT-4, despite a contrary point made by @icelavaman who noted that the iOS app cannot keep sources for follow-up queries.
  • Subscribing to Pro Brings Benefits: @thejuicyy subscribed to Pro and was impressed with its relevancy checks. @mares1317 recommended trying Perplexity AI companion with GPT-4 for a significantly different experience compared to the free version.
  • Troubleshooting Discount Code Application: Multiple users discussed issues with applying discount codes for Perplexity Pro. @toothpick4339 shared successful steps from Reddit user u/ArakDemonBlade, @bennsiee struggled to find where to enter a promo code, but @ok.alex and @speedturkey provided assistance and pointed to contacting support at [email protected].
  • Query on Increasing Limit for Online LLMs: User @mantas_82008 inquired about the possibility of increasing the limit beyond 10/minute for online LLM models. @icelavaman replied with a link to a related Discord channel for further information.
  • Challenges with Large Google Drive Documents: @jaybob32 faced issues with accessing large Google Drive documents. Users @gentlefoxssbm, @deicoon, and @me.lk suggested downloading or converting the document, with @mares1317 providing a search link for converters, and the sharing of a solution involving copy-pasting the text directly to Perplexityā€™s prompt box.

Links mentioned:

Perplexity AI ā–· #sharing (4 messages):

  • Perplexity AI Research Queried: User @nocode7 shared a Perplexity AI search link, although the context or content of the research was not specified.
  • Business Chat Recap Mastery: @miamiseipazzesco777 was recognized for effectively recapping some business chats.
  • Insights from Perplexityā€™s Head of Design: @mares1317 shared a YouTube video featuring Henry Modisett, Head of Design at Perplexity AI, discussing the challenges of designing for AI and how to land a job in the field.
  • Praise for Perplexityā€™s Team and Copilot: @zenrobot.eth praised the aforementioned interview with Henry Modisett and also highlighted the utility of Perplexityā€™s Copilot feature, providing a link to a summary of global news trends (Perplexity AI search link) and explaining Copilotā€™s conversational search capability detailed in a blog post.

Links mentioned:

Perplexity AI ā–· #pplx-api (5 messages):

  • Credit Unlocked After Auto Top Off: User @stevemac_90623 mentioned that a $5.00 credit was granted only after completing the auto top off process by entering $2 as the amount.

  • Intrigued by API vs Browser Responses: @obicho expressed curiosity about why responses differ when using the API versus the browser.

  • API Model Equivalent Enquiry: @benhirap inquired about which API model corresponds to the ā€œExperimentā€ feature with Copilot on the chat website.

  • Unraveling the Model Mystery: @noremac258 identified PPLX 70B as the API model equivalent to the ā€œExperimentā€ (with Copilot) from the chat website.

  • Browser vs. API Behavior Explained: @icelavaman explained the differing responses on browser versus API by pointing to the different system prompt/sources that are differently searched. ,

LM Studio ā–· #šŸ’¬-general (71 messagesšŸ”„šŸ”„):

  • GPU Over CPU in AI Operations: @laszlo01 inquired about how to switch from using CPU to GPU in AI and was advised by @heyitsyorkie to search for this setting in the chat pageā€™s settings panel on the right-hand side.
  • Request for Open-Source Chatbot Creation Tools: @jan_naj sought a repository for building customizable GPT-like chatbots with options for local hosting and incorporating memory threads. @fabguy responded with a somewhat facetious link to GitHubā€™s search page, leading to @jan_najā€™s dissatisfaction and @dagbs suggesting a more specific inquiry.
  • Linux Build Lack for Older Processors: @d0mper mentioned using an old computer without AVX2 support and asked about alternatives to LM Studio for loading language models. @heyitsyorkie clarified that the Linux build only supports AVX2 CPUs.
  • Crypto Bot Warning and Ban Requests: @heyitsyorkie alerted users to report any crypto bots spamming Discord invites for prompt banning.
  • Compiling Llama.cpp as LM Studio Alternative: For users like @d0mper seeking an alternative to LM Studio on platforms like Ubuntu, @heyitsyorkie suggested compiling llama.cpp, which supports loading language models.
  • Discussion on Incorporating Stable Diffusion into LMStudio: After some confusion regarding support for StableLM models in LM Studio, @heyitsyorkie mentioned Stable Diffusionā€™s separate C/C++ port and the potential of integrating it into LM Studio. @altryne noted a recent update that made a specific engine compatible.

Links mentioned:

LM Studio ā–· #šŸ¤–-models-discussion-chat (20 messagesšŸ”„):

  • Model Recommendations for a Newcomer: New user @rparada asked for a model recommendation for code conversion and neural network architecture modification. User @fabguy directed them to check the entries in channel #1185646847721742336.

  • Code Snippet for PDF Reading with RAG: @ui.mz shared a code snippet using PyMuPDFLoader to read from a PDF file with RAG and asked for tips since they were encountering a ValidationError related to the OpenAI Embeddings API.

  • Need for an OpenAI Embeddings API Substitute: Following the error discussion, @ui.mz revealed a lack of an NVIDIA GPU, and @fabguy clarified that the intent was to help them figure out their own solution by providing a GitHub repository as a reference.

  • Inquiry About Image/File Reading Models: User @pandora_box_open inquired about a model capable of reading images/files effectively. @heyitsyorkie responded with a Discord link for a model limited to describing images and stated that thereā€™s no RAG system for document reading yet.

  • Explanation of RAG and Potential Integration: User @heyitsyorkie explained the concept of Retrieval-Augmented Generation (RAG) after a prompt from @pandora_box_open, who afterwards expressed hope for its integration. The explanation included a link to Databricksā€™ glossary entry on RAG.

  • Exploring RAG with a HuggingFace Model: @pandora_box_open shared a HuggingFace model link that might showcase an integration resembling RAG.

Links mentioned:

LM Studio ā–· #šŸŽ›-hardware-discussion (60 messagesšŸ”„šŸ”„):

  • GPU Layer Configurations for Various Models: @aswarp and @cloakedman discussed the optimal number of GPU layers for different sizes of models and graphics cards, using values like -1 to offload all layers to GPU or manually adjusting to avoid crashes. @heyitsyorkie suggested playing with layer numbers if -1 causes an error, recommending to inspect model details in LM Studio for guidance.

  • Performance Tied to Hardware Specifications: @smallshinyant sought advice on benchmarking new hardware additions by focusing on metrics like tok/sec, whereas @bobzdar pointed out that extra VRAM allows for running larger or less compressed models, suggesting that performance is not just about speed.

  • Utilizing Maximum Hardware Potential: @aswarp inquired about increasing system RAM usage when it remains largely unused while running models. @.ben.com advised that focusing solely on GPU use is preferable for performance, unless one intentionally wants to use a large model that doesnā€™t fit in the GPU, which would, however, result in slower processing.

  • Software Settings Affect Model Performance: Users like @aswarp and @dylpickle300 encountered issues with model performance and discussed various settings in LM Studio, like n_gpu_layers, num_layers from the modelā€™s GGUF JSON, suggesting adjustments to potentially increase performance despite hitches such as unused system RAM and models stalling.

  • Hardware Support and Model Compatibility: @luthorheim questioned the possibility of running models without AVX2 instruction support, to which @heyitsyorkie provided a link to beta releases that might support older processors. A reminder from @cloakedman highlighted that model files larger than the available VRAM would result in slow performance or the need to downscale quality.

Links mentioned:

LM Studio Beta Releases: no description found

LM Studio ā–· #šŸ§Ŗ-beta-releases-chat (2 messages):

  • Navigating the Sea of Models: @greg0403 expressed confusion about choosing between various models on HuggingFace, questioning their Unique Selling Points (USPs) due to the minimal documentation. They sought guidance for understanding how to differentiate and select one model over another.
  • A Compass for Model Comparison: In response to @greg0403, @kadeshar recommended using leaderboards like the one available at HuggingFace open_llm_leaderboard as a good starting point for comparing model performance.

Links mentioned:

Open LLM Leaderboard - a Hugging Face Space by HuggingFaceH4: no description found

LM Studio ā–· #memgpt (1 messages):

  • Exploring Interoperability Between LM Studio and Open Interpreter: User @222gate is attempting to integrate LM Studio inference with memgpt, and then into Open Interpreter, discussing with @cpacker the similarities between memgpt server and the OpenAI Assistant API. They are investigating if memgptā€™s server can mimic OpenAIā€™s in terms of the chat and completion call functionalities.

LM Studio ā–· #open-interpreter (2 messages):

  • Seeking Prompting Tips: User @222gate asked the community for ideas for improved prompting in general. No specific context or details were provided in the inquiry.
  • Integration Challenge: @222gate mentioned difficulties in integrating LM Studio memGPT and OpenAI and is seeking assistance with the process. The complexities of the integration were not elaborated upon. ,

Eleuther ā–· #announcements (1 messages):

  • EleutherAI Partners with NSF for AI Research: @tastybucketofrice announced EleutherAIā€™s partnership with the National Science Foundation (NSF) to launch the National AI Research Resource (NAIRR), which aims to provide access to critical resources for AI research. The official announcement can be read here.

  • EleutherAIā€™s Commitment to Open AI Research: Starting from the release of Language Models (LMs) in 2020, EleutherAI has advocated for open research and has now contributed GPU grants to enhance academic AI research capabilities. The history of their commitment can be found in their blog post ā€œWhy release a large language model?ā€.

  • Addressing Compute Resources for Researchers: Despite broader access to pretrained models today, @tastybucketofrice points out the persistent issue of limited compute resources as a barrier for researchers. EleutherAI is working to ensure researchers can control how their models behave and the values they encode.

  • Empowering AI Research with GPT-NeoX: To combat HPC challenges, @tastybucketofrice highlights the GPT-NeoX library, which facilitates AI research by running at scale on various platforms, including Oak Ridge National Labā€™s Summit and Frontier, LUMI, AWS, and CoreWeave.

Eleuther ā–· #general (11 messagesšŸ”„):

  • License Confusion in the Code Wild West: @xa9ax inquired about specific licenses on GitHub code repositories that may restrict training models for academic research. @stellaathena noted that virtually no licenses directly address model training, while @avi.ai highlighted that legal outcomes may vary by jurisdiction.

  • Searching for Legal Advice: In response to further queries from @xa9ax, @stellaathena recommended consulting a lawyer for clarification on licensing issues. Meanwhile, @avi.ai suggested conducting initial research into copyright and intellectual property law relevant to oneā€™s own locale.

  • Goodbye to Discord?: @hostiq simply stated, ā€œIt is time to leave this discord,ā€ suggesting they were leaving the community chat.

  • GPU Grant Collaboration Offer: @yikesawjeez reached out to @stellaathena looking for a tie-in with an existing GPU grant, offering a link to a server and a google form for compute access listed in their profile.

  • CoPilot Case Crawl: @clockrelativity2003 inquired about the current status of the GitHub CoPilot litigation. @.undeleted humorously remarked that legal cases take an incredibly long time to resolve, implying the case is still unresolved.

Eleuther ā–· #research (80 messagesšŸ”„šŸ”„):

  • Data Quality vs. Size in ML Training: @catboy_slim_ discussed data quality, remarking that curating diverse, high-quality data is challenging but doesnā€™t require enormous volume. They mentioned memory of Wavenet data being relatively small yet well-curated.

  • GPU/TPU Hardware & Framework Compatibility Discussed: @alofty shared an article about the portability of ML frameworks across different hardware, leading to a debate on the performance of JAX on TPUs and GPUs by .the_alt_man, who advised against PyTorch due to poor XLA support.

  • ā€˜MambaByteā€™ Makes a Splash with Token-Free Language Modeling: An academic paper shared by @pizza_joe introduced MambaByte, a state space model for token-free language modeling, sparking skepticism from _inox until @thatspysaspy noted the involvement of notable researcher Sasha Rush.

  • Burn: A New Rust Deep Learning Framework: @kenakafrosty shared a link to Burn, a Rust-based DL framework, and discussed its potential but noted it must have robust parallelism support to be competitive. @canadagoose1 brought focus to the need for multinode support, while .the_alt_man favored more minimalistic frameworks like JAX for performance.

  • Continual Updates to Fine-Tuned Models in Production Explored: @kenakafrosty inquired about best practices for updating fine-tuned models, leading to a discussion with @fern.bear about methods like Elastic Weight Consolidation (EWC) and the use of loss functions to balance between restricting changes too hard and allowing significant deviations.

Links mentioned:

Eleuther ā–· #scaling-laws (2 messages):

  • Scaling Up to 1.3B Parameters: @stellaathena mentioned that though the original paper had limitations, they provided the compute to train models at the 1.3B parameter scale.
  • Reference to Channel: @random_string_of_character pointed users to discussion results for 1b parameter models in another channel, specifically linked as <#1129489948710539334>.

Eleuther ā–· #interpretability-general (14 messagesšŸ”„):

  • Decoding the Neural Code: User @g_w1 queried about plotting points in space concerning the monosemanticity concept. @woog clarified that theyā€™re plotting decoder weights, which are interpreted as feature directions recovered in the Sparse Autoencoder (SAE) space to neuron direction.

  • Dictionary Elements as Neuron Directions: In further clarification, @woog confirmed to @g_w1 that each dictionary element in the SAE is a direction in neuron space, suggesting a connection between SAE space and neuron space that is visualized through plotting. @g_w1 acknowledged the explanation.

  • An Update on Interpretability Research: User @loganriggs shared a January update from the Anthropic interpretability team outlining a set of preliminary research notes and developments in the field. Themes included attention superposition, dictionary learning on MNIST, and features in multilayer models.

  • Observation on Report Typo: User @ishitatsuyuki pointed out a typo in the shared Anthropic interpretability team update with the title ā€œJoint Superposition Between MLP Neurons and Residual Streamā€ being duplicated twice in the ā€œCounterexamples in Superpositionā€ section.

Links mentioned:

Circuits Updates - January 2024: no description found

Eleuther ā–· #gpt-neox-dev (7 messages):

  • Parallelism Paradigms in Focus: @groggyrhombus inquired if @337128969059172353ā€™s approach was akin to DeepSpeedā€™s tensor+expert parallelism, to which xyzzyrz simply affirmed with ā€œYeahā€.
  • Commendable Progress on Compute: @tastybucketofrice expressed appreciation for @337128969059172353ā€™s efforts and mentioned plans to test the compute over the upcoming week.
  • Deep Dive into CUDA Initialization Issue: @tastybucketofrice linked to an issue in DeepSpeed concerning CUDA initialization before forking and showed interest in investigating this further.
  • Suspicions About Test Errors: @catboy_slim_ suspects recent changes in how CUDA or pytest handles forking, which might be causing test errors, but noted a lack of time to explore the issue.
  • Open to Assistance on Pull Request Alterations: @catboy_slim_ hinted at excluding certain parts from a pull request due to present constraints and welcomed anyone else to take on the task.

Links mentioned:

Issues Ā· microsoft/DeepSpeed): DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective. - Issues Ā· microsoft/DeepSpeed

,

OpenAccess AI Collective (axolotl) ā–· #general (59 messagesšŸ”„šŸ”„):

  • Translator Troubles: @le_mess shared a link to a Tenor GIF, which seems to be language-translated based on browser settings.
  • Seeking for Assistance: @nanobitz recommended that someone (possibly <@525830737627185170>) could assist with an issue, while @dangfutures requested help with replicate configurations and @mistobaan asked for guidance on finetuning phi-2.
  • Explaining ā€˜High Contextā€™: In response to @hamelh asking about the meaning of ā€œhigh context,ā€ @jsancs_ clarified it as an ā€œ8-10k context windowā€.
  • Finetuning Frustrations and Successes: @dangfutures casually noted another day of finetuning, while @nafnlaus00 discussed the differences in learning rates needed when switching from float16 to bfloat16.
  • GPU Purchasing Decisions: Hardware choices were debated; @yamashi pondered between 8 H100 or 16 A100 GPUs, and @casper_ai suggests opting for more VRAM. @dangfutures supports the choice for 16 A100 for increased VRAM, while @c.gato looks forward to Caseusā€™ announcement in the announcements channel.

Links mentioned:

OpenAccess AI Collective (axolotl) ā–· #general-help (13 messagesšŸ”„):

  • Mamba and Lora Compatibility Unclear: @tank02. asked if Mamba works with Lora, but @le_mess responded with uncertainty, stating they have never trained Mamba.
  • Tough Time Uploading Models to HF: @colejhunter is encountering issues with pushing a trained model to Hugging Face (HF); the repo is created but not populated with model files. They provided their config and queried about missing settings beyond hub_model_id.
  • Save Step Strategies in Training: @caseus_ and @noobmaster29 suggest using save_steps or saves_per_epoch with variations like saves_per_epoch: 1 or saves_per_epoch: 10 for controlling model save frequency during training.
  • Saving Models Without Specific Steps: @c.gato mentions leaving save settings blank, which defaults to saving models at the end of each epoch.
  • Seeking Guidance on Pretraining CLIP: @emperor is looking for papers, studies, or blog posts that discuss best practices for further pretraining of CLIP, specifically focused on domain adaptation through a contrastive objective on 50M images.

OpenAccess AI Collective (axolotl) ā–· #datasets (15 messagesšŸ”„):

  • GitHub Treasure Trove Unearthed: @dangfutures shared a valuable GitHub repository containing multimodal question-answering datasets in the medical domain with @nanobitz.
  • Funding Blues: @yamashi lamented about being preoccupied with administrative tasks for securing funding for compute resources, expressing this in response to @dangfuturesā€™s inquiry about their absence.
  • In Search of the Perfect Prompt: @builderx inquired about the correct alpaca format prompt for model inference, leading to a collaborative clarification with @c.gato.
  • Echoes in the Alpaca Pen: @builderx encountered an issue with the alpaca prompt occasionally being repeated in model outputs after training on it through Mistral, prompting a suggestion by @c.gato to seek help.
  • Format Matters for QA Training: In a discussion initiated by @neko.huh on whether raw text should be converted to alpaca QA format for training, @noobmaster29 remarked it might be beneficial if the training focus is on question answering.

Links mentioned:

GitHub - abachaa/Existing-Medical-QA-Datasets: Multimodal Question Answering in the Medical Domain: A summary of Existing Datasets and Systems: Multimodal Question Answering in the Medical Domain: A summary of Existing Datasets and Systems - GitHub - abachaa/Existing-Medical-QA-Datasets: Multimodal Question Answering in the Medical Domain:ā€¦

OpenAccess AI Collective (axolotl) ā–· #announcements (1 messages):

  • Axolotl v0.4.0 Takes Flight: The OpenAccess AI Collective announced the release of axolotl v0.4.0, featuring support for new models, fixes for numerous bugs, and contributions from 56 individuals. @caseus_ extended appreciation to everyone and gave a special shout-out to A16Z for their grant, promising to add discord contributor roles in the following week.

  • Acknowledgment of Community Contributions: @caseus_ thanked individual contributors by tagging them in the announcement and invited those not mentioned to DM with their GitHub and Twitter handles for recognition. Contributors listed include @213644857309134849, @244959984352231425, and many others.

OpenAccess AI Collective (axolotl) ā–· #replicate-help (1 messages):

  • Curiosity About Replicate Serverless Cold-Start: User @dreamgen inquired about current cold-start times on serverless services for large models, noting past issues with providers not caching models or docker images. Thereā€™s a concern that despite advances, load times should be a few seconds, but experiences have varied. ,

Latent Space ā–· #ai-general-chat (49 messagesšŸ”„):

  • Lumiere Leads the Way: @swyxio highlighted the Lumiere model by Google, a space-time diffusion model that generates video from text. They also shared their writeup on the modelā€™s features including its impressive inpainting capabilities.
  • Challenging Googleā€™s Code Conservatism: @guardiang commented on Googleā€™s reluctance to share AI-related code, supported by @shivdinho who agreed and noted the difficulty in replicating Googleā€™s research without code release.
  • Exploring Self-Instruct for AI: @youngphlo provided a link to a paper discussing Self-Instruct, a method to enhance large language models by bootstrapping off their own generations.
  • Discussing Proper AI Language Model Architecture: @bathientran sought advice on collaboration between infra/backend developers and those experienced with language models, prompting a response from @philltornroth expressing a willingness to help bridge communication gaps.
  • Potential Project for Discord LLM Chatbot: @swyxio shared a link to a GitHub repository for a Discord chatbot powered by large language models and asked if anyone was interested in implementing it for the channel.

Links mentioned:

Latent Space ā–· #ai-event-announcements (2 messages):

  • Self-Instruct Paper Walkthrough: User @swyxio announced a session led by <@556359685306056721> to guide through the Self-Instruct paper on the new ai-event-announcements Stage. The community was invited to join and is reminded to sign up here for notifications of future events.

  • Stay Updated with Latent Space Events: The Latent.Space events calendar is available for subscription to stay notified of new events by clicking the RSS logo above the calendar on the right-hand side.

  • Final Frontiers Celebration in SF: @fanahova shared excitement about the upcoming Latent Space demo day anniversary and an event titled Final Frontiers to celebrate it in San Francisco. More details can be found on Twitter.

Links mentioned:

Latent Space (Paper Club & Other Events) Ā· Luma: View and subscribe to events from Latent Space (Paper Club & Other Events) on Luma. Latent.Space events. PLEASE CLICK THE RSS LOGO JUST ABOVE THE CALENDAR ON THE RIGHT TO ADD TO YOUR CAL. ā€œAdā€¦

Latent Space ā–· #llm-paper-club (19 messagesšŸ”„):

  • New Stage Feature for Paper Discussions: @swyxio informed members about utilizing Discordā€™s new Stage feature for the paper discussion with @556359685306056721 and provided a link to the stage.
  • Elevator Music Adds Ambiance: @picocreator joked about the addition of elevator music while waiting for the new Discord Stage feature to start.
  • Emoji Reactions Missed in Discord Stage: @420gunna expressed disappointment over the inability to use emoji reactions during the Discord Stage session.
  • High Attendance at Paper Discussion: @swyxio highlighted that 40 people attended the first paper session after the platform switch, while @youngphlo speculated that more might have tried to join during the initial week of the Luma reset.
  • Prospective Next Paper Teased: @swyxio shared that, pending confirmation, the next paper for discussion could be Pythia, potentially featuring several authors and the possibility of Quentin Anthony joining the latter half of the session. The Twitter thread by @rasbt is also recommended for further insight (Twitter source).

Links mentioned:

Latent Space ā–· #llm-paper-club-chat (1 messages):

  • Autonomous Agents using RESTful APIs: @swyxio shared an interesting older project/paper titled RestGPT, which focuses on an LLM-based autonomous agent that controls real-world applications via RESTful APIs. The project can be found on GitHub here.

Links mentioned:

GitHub - Yifan-Song793/RestGPT: An LLM-based autonomous agent controlling real-world applications via RESTful APIs: An LLM-based autonomous agent controlling real-world applications via RESTful APIs - GitHub - Yifan-Song793/RestGPT: An LLM-based autonomous agent controlling real-world applications via RESTful APIs

,

Mistral ā–· #general (62 messagesšŸ”„šŸ”„):

  • Job Application Woes and Hints: @ziper_rom1 inquired about a CUDA developer position they applied for, expressing uncertainty after not hearing back for a month. Community members including @mrdragonfox and @frosty04212 shared insights, suggesting that no response generally indicates rejection, and that personal connections often influence hiring decisions.
  • CUDA Developer Role Likely Filled: @kim_tech provided an update stating that the CUDA developer position that @ziper_rom1 applied for at Mistral might be filled, citing a Twitter post of a new hire from Nvidia.
  • Running Mistral on CPU-only Setups: @tominix356 asked about running Mistral 7M on a 16GB RAM computer without a GPU, receiving suggestions like trying LM Studio and feedback from @kim_tech about their experiences running similar models on a RAM-heavy laptop.
  • Finding the Right Tool for the Job: A discussion initiated by @xeglion on whatā€™s best for an RTX 3080 evolved into advice on choosing tools like Github Copilot and Google Bard based on oneā€™s specific needs, with @kerunix, @mrdragonfox, and @enerv chiming in about different options and constraints.
  • Local Options for AI Code Assistance: The demand for local AI code completion was discussed, with @enerv mentioning the potential of using Mistral models with local APIs and plugins. The conversation touched on options like Codeium and Sourcegraphā€™s Cody, highlighting the variety of tools available for developers.

Links mentioned:

Cody | AI coding assistant: Cody is the most powerful and accurate AI coding assistant for writing, fixing, and maintaining code.

Mistral ā–· #deployment (1 messages):

  • Mistral RAG Performance Checkpoint: User @duck reported timings for Mistral 8x7b on a 3090 when performing a sort of RAG with Langchain and using llama.cpp for inference. They noted sample times of 17.05 ms for 102 runs and eval times as high as 175910.47 ms for 101 runs, considering these timings to be slow for the use case.

Mistral ā–· #ref-implem (1 messages):

  • Mixtral Memory Mayhem: User @l0gr1thm1k is experiencing CUDA memory errors while trying to load Mixtral into memory. Despite using four NVIDIA T4s with 16GB of memory each, memory usage exceeds the expected 24GB for the 4bit quantized version and results in an error.

Mistral ā–· #finetuning (1 messages):

  • Metric Confusion for Mistral-7B on Dolly Dataset: User @bishwa3819 is attempting to finetune Mistral-7B on the Dolly dataset but expressed confusion about the adequacy of BLEU and ROUGE metrics for evaluating Language Model performance. They questioned if these metrics are sufficient for evaluating a Language Model trained on specific datasets like Dolly.

Mistral ā–· #showcase (2 messages):

  • Speedy Reddit Assistant with a Touch of Mistral: @hugoduprez highlighted the creation of Reddit copilot buddy, a bot made with Mistral which operates so quickly that it appears to be offline.
  • New Insights into Audio Understanding: @shashank.f1 discussed a new approach to audio understanding, featuring a YouTube video that delves into the A-JEPA neural model which can unlock semantic knowledge from .wav or .mp3 audio files.

Links mentioned:

A-JEPA neural model: Unlocking semantic knowledge from .wav / .mp3 audio file or audio spectrograms: šŸŒŸ Unlock the Power of AI Learning from Audio ! šŸ”Š Watch a deep dive discussion on the A-JEPA approach with Oliver, Nevil, Ojasvita, Shashank, Srikanth and Nā€¦

Mistral ā–· #la-plateforme (3 messages):

  • Mistral API Summarization Limitations: User @nico2412_ inquired about using the Mistral API to summarize an article on the web via its URL, expressing difficulties in achieving this task.
  • LLMs Lack Internet Access: @mrdragonfox clarified that large language models (LLMs), like Mistral, do not have direct internet access, which is why they canā€™t call functions using web URLs for summarization.
  • Alternate Solution for Article Summarization: User @duck proposed an alternative method by providing a link to a GitHub notebook that outlines a process for summarizing web content with language models.

Links mentioned:

LLM/llama-cpp-rag - final.ipynb at main Ā· Quad-AI/LLM: Contribute to Quad-AI/LLM development by creating an account on GitHub.

,

HuggingFace ā–· #general (39 messagesšŸ”„):

  • AI Study Survey for VFX Artists: @jordibares shared a survey link looking for insights from VFX artists and producers to be included in an AI study.
  • Quota Reset Request for createSpace: @troymurs requested a createSpace quota reset due to canister crashes by reaching out to <@907238188978950215>. @osanseviero responded advising to send an email to website @ huggingface.co.
  • Fine-tuning Text Generation Models: @sookeyy sought resources for fine-tuning text generation models and was recommended to use remove_unused_columns=False by @robolicious after encountering an error.
  • Interest in Collaborative Projects: Users expressed interest in collaborative projects, with @wondeys looking for partners to create a Texas Holdā€™em Poker AI or an automated trading algorithm, and @dsiegel recruiting for building an Augmented Reality headset from scratch.
  • Organizing a Portuguese Model and Dataset Sprint: @namayra is organizing a sprint/hackathon for Portuguese models and datasets and was directed by @osanseviero to contact Hugging Face at [email protected] after not being able to reach Omar Espejel.

Links mentioned:

HuggingFace ā–· #today-im-learning (1 messages):

  • Alexaā€™s Greener Alternative: @mattbcool is exploring how to create a local, personal assistant using Alexa hardware, aiming to repurpose speakers and mics to minimize waste. They have been researching recent projects with raspberry pis and documented their progress in a personal blog post.

HuggingFace ā–· #cool-finds (2 messages):

  • Deep Learning.ai Launches Automated LLM Testing Course: @manialgie shared a link to a short course in collaboration with CircleCI. The course aims to teach how to use continuous integration tools to evaluate LLM applications more efficiently.

  • Text-to-3D Made Easy with 3DTopia: @meatfucker discovered 3DTopia on GitHub, featuring model weights and inference code that promises Text-to-3D Generation within 5 minutes. They noted that they havenā€™t tried it yet, but it looks promising for easier 3D generation from text.

Links mentioned:

HuggingFace ā–· #i-made-this (3 messages):

  • Tackling Live Transcription Challenges: @apocalypse3917 raised concerns around the problem of background noise in live transcription, questioning whether thereā€™s a client-side solution or an auto-calibration feature to handle this. @ggabe_2 responded with appreciation and explained that their Proof of Concept (PoC) for Whisper didnā€™t specifically address ambient noise, but suggested that Voice Activity Detection (VAD) filters could partly mitigate the issue.

  • New Python Module for Steering Vectors: @mihai4256 announced the creation of a Python module that works with Hugging Faceā€™s transformers to add steering vectors. They shared their accomplishment with a link to a tweet, which likely contains more details about the module: View Tweet.

HuggingFace ā–· #reading-group (3 messages):

  • Vector Search Simplified: @united_dove_38339 shared an informative blog post detailing how to implement vector search using Pinecone Serverless, which is essential for modern applications leveraging LLMs and RAG. Pineconeā€™s serverless solution aims to ease and reduce the cost of vector search implementation.

  • Presentation Prep Update: @chad_in_the_house informed the group of a delay in the presentation preparation, noting the complexity of the papers involved. The presentation is expected to be ready by the end of the week or by next Friday.

  • Unsloth Accelerates Fine-tuning: @zigglewomp introduced an article on Medium discussing the Unsloth technique, which has shown promise in improving memory efficiency and training performance for the Llama2ā€“7b model.

Links mentioned:

HuggingFace ā–· #diffusion-discussions (5 messages):

  • Quest for the Most Challenging Dataset: User @idkman2021 queried about the hardest dataset to work with. However, no specific answers or further discussion followed this question.

  • Video Synthesis Challenge Shared: User @archer_cs sought ideas for a project on video generation using a target audio and a reference video. They shared a GitHub discussion outlining the details of their ambitious project.

  • Improving Stream Structures in GPT-4: User @kiraultra expressed difficulties with the streaming structure while using the GPT-4 turbo API, mentioning the issue of getting no bullet points until the very end of the stream. No specific solutions or follow-up questions appeared in the chat.

  • Discrepancy in Logit Outputs: User @sherlockzoozoo posted a code block showing their implementation of using meta-llama/Llama-2-7b-hf model and tokenizer, and asked why thereā€™s a difference between values in out_toks['scores'] and just_out['logits']. The question stands without a response, leaving the nature of the logit discrepancies unexplained.

Links mentioned:

Video generation using target audio and reference video. Ā· huggingface/diffusers Ā· Discussion #6696: I am working on a personal project which involves : Input a reference video and a target audio, synthesise a target video (lip synced talking head video generation driven by the target audio). I woā€¦

HuggingFace ā–· #computer-vision (4 messages):

  • Enhancing Custom PyTorch Models: User @nielsr_ provided a valuable tip for custom PyTorch models, recommending the use of mixins from the huggingface_hub library to add from_pretrained and push_to_hub functionalities with ease.
  • Seeking Idefics Project Insights: @besiktas inquired about where to ask questions to the team behind the Idefics project; @osanseviero directed them to the projectā€™s discussion tab on the HuggingFace repo.

Links mentioned:

Mixins & serialization methods: no description found

HuggingFace ā–· #NLP (5 messages):

  • Rate Limit Warnings in Azure API: User @kyko6969 is facing issues with rate limit warnings when using embed_with_retry from Azureā€™s OpenAI API in conjunction with langchain. They are looking to catch the warning and implement a time.sleep() function to handle the APIā€™s rate limitation.

  • Training BPE Tokenizer for Pashto: @imranullah inquired about training the BPE tokenizer for low-resource languages like Pashto, as he is encountering garbage text output.

  • Efficient Fine-Tuning with LoRA: @gugaime mentioned that itā€™s possible to perform quantization during fine-tuning by utilizing LoRA, where the base model remains quantized but is frozen.

  • Open Source Text-to-Speech Recommendation: @mattbcool introduced the Coqui AI TTS toolkit as a potential open-source, local Text-to-Speech solution.

  • Mysterious Weights in mBARTforCausalLM: @vikas.p asked about the lm head weight being set as tied in the mbartforcausallm model on the Hugging Faceā€™s transformers repository, seeking clarity on whether it is tied to the input embedding and if some method is hidden that does so.

Links mentioned:

HuggingFace ā–· #diffusion-discussions (5 messages):

  • Seeking Dataset Challenge Opinions: User @idkman2021 inquired about the most challenging dataset but did not provide context for the term ā€œchallengingā€ nor specify the field or application.

  • Innovating Video Generation Techniques: User @archer_cs asked for ideas on a project involving video generation from a reference video and target audio, linking to a GitHub discussion #6696 for more details. The project aims to create a lip-synced talking head video driven by the target audio.

  • Improving Streaming Structure in GPT-4 Turbo API: User @kiraultra brought up an issue with the streaming structure when using the gpt-4 turbo API, mentioning that bullet points and other structural elements only appear at the end of the stream, seeking advice on enhancing this aspect.

  • Understanding Discrepancies in Model Outputs: User @sherlockzoozoo shared a code snippet querying two outputs from the LLama-2-7b model and asked why there are different values in out_toks['scores'] and just_out['logits']. They are seeking clarification on which of these represents the ā€œreal logitsā€.

Links mentioned:

Video generation using target audio and reference video. Ā· huggingface/diffusers Ā· Discussion #6696: I am working on a personal project which involves : Input a reference video and a target audio, synthesise a target video (lip synced talking head video generation driven by the target audio). I woā€¦

,

LAION ā–· #general (41 messagesšŸ”„):

  • Google Unleashes LumiĆØre: @spirit_from_germany shared a tweet by @omerbartal introducing Lumiere, a new video diffusion model by GoogleAI that supports text-to-video, image-to-video, stylization, and more. Thereā€™s a buzz about the modelā€™s capabilities, but @mkaic points out thereā€™s no open sourcing, leading to mixed reactions in the group regarding its potential versus realism (Read Paper on LumiĆØre).

  • LAION Database Access Concerns: Users @_chenwang and @djdhdjjdjdjdj raised issues regarding downloading LAION-en-aesthetics captions, due to Huggingface disabling downloads.

  • Comparison Between LumiĆØre and EMU Video: Thereā€™s an ongoing debate between @mkaic and @thejonasbrothers about whether Googleā€™s LumiĆØre or Metaā€™s EMU video models seem more realistic, with criticisms pointing to occasional inconsistencies and unnatural appearances in the AI-generated content.

  • Enthusiasm Met with Skepticism: @kilgore.trout inquires about the best open-source models for video stylization against the backdrop of Googleā€™s new model LumiĆØre, while others like @.undeleted comment on the still uncanny nature of AI-generated videos.

  • RWKV and Mistral Rivalry Discussed: @SegmentationFault shared a Reddit link discussing RWKV 7Bā€™s performance, possibly reaching the levels of Mistral 7B in multilingual support with additional benefits like linear runtime and efficient CPU usage.

Links mentioned:

LAION ā–· #research (15 messagesšŸ”„):

  • GoogleAI Unveils Lumiere - The Video Diffusion Model: @spirit_from_germany shared a tweet from Omer Bar-Tal announcing GoogleAIā€™s new video diffusion model, Lumiere, which features Text-to-Video, Image-to-Video, Stylized Generation, Inpainting, Cinemagraphs, and @mkaic followed up with the accompanying research paper.

  • GoogleAIā€™s Lumiere Surprises with Simplicity: @mkaic comments on how surprisingly simple the method behind GoogleAIā€™s Lumiere appears upon initial skimming of the research paper, though they hadnā€™t delved into details yet.

  • Lumiereā€™s Potential Training Ground - YouTube: @mkaic speculates that GoogleAIā€™s Lumiere must be training on YouTube, given that itā€™s the largest video repository, and later confirms it by quoting from the paper: ā€œWe train our T2V model on a dataset containing 30M videosā€.

  • Googleā€™s Unrivaled Video Data for T2V Models: @mkaic points out Googleā€™s massive advantage in training text-to-video models due to their ownership of YouTube, which includes auto-generated captions and billions of comments, indicating a substantial dataset for training video multimodal language models.

  • DeepMind Considered a Powerhouse for Project Gemini: @thejonasbrothers underscores DeepMindā€™s notable efforts by reflecting on the significant number of researchers involved in project Gemini, implying their massive resources and capacity in AI research.

Links mentioned:

  • Tweet from Omer Bar Tal (@omerbartal): Introducing Lumiere šŸ“½ļø The new video diffusion model weā€™ve been working on @GoogleAI * Text-to-Video * Image-to-Video * Stylized Generation * Inpainting * Cinemagraphs and more šŸŽØ W/ amazing tā€¦

  • Lumiere: A Space-Time Diffusion Model for Video Generation: We introduce Lumiere ā€” a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion ā€” a pivotal challenge in video synthesis. To this end, we ā€¦

    ,

LlamaIndex ā–· #blog (1 messages):

  • Vanna AIā€™s overnight sensation in SQL generation: The @zain_hoda project Vanna AI is turning heads with its straightforward yet potent interface that leverages RAG (Retrieval Augmented Generation) for enhanced SQL query creation. The bot features abilities to store and index DDL/table schemas and text for its operations. LlamaIndex Tweet

LlamaIndex ā–· #general (49 messagesšŸ”„):

  • Quest for Refined AI Response Mechanics: User @viky6453 inquired if thereā€™s a way for the openaiagent to behave more like the openai assistant, which applies a tool call, message, and tool call repeatedly until the response is deemed good enough, instead of the LlamaIndex openaiagent style where it sends a single message as a response after multiple tool calls. No definitive solution was provided in the discussion.

  • Tech Explorer Seeking Efficiency: In the quest for optimizing pandas query engine with response synthesizer to be context-aware, @techexplorer0 expressed a desire for a chat_engine equivalent, while @pk2594 wondered about threading the query engine to speed things up, questioning its thread-safety.

  • Seeking the Perfect LLM Chatbot Host: User @basil11111 pondered whether itā€™s possible to use open-source models without local hosting and discovered from @nerdaiā€™s response that services like HuggingFace and Replicate can host LLMs, offering APIs and fine-tuning capabilities.

  • Fine-Tuning RAGā€™s Effectiveness: Discussion touched upon the enhancement of RAG applications with @0tarumi exploring the implementation of BGE similarity reranker and @cheesyfishes suggesting that reranking after RRF might yield the best results.

  • Memory Matters: @techexplorer0 sought a tool for tracking conversational history akin to langchainā€™s memory buffer, leading to @cheesyfishes confirming the use of a chat memory buffer in every chat engine/agent, which could be paired with llama-index in langchain for those seeking a conversational chatbot with memory.

Links mentioned:

LlamaIndex ā–· #ai-discussion (1 messages):

rawwerks: šŸ‘‹ community questionā“ what is your favorite vector store company and why? ,

LangChain AI ā–· #announcements (2 messages):

  • X/Twitter Account Recovery: @.bagatur announced that the X/Twitter account has been recovered and will unblock everyone affected. If someone remains blocked, they are instructed to post in the thread for assistance.

  • LangChain Introduces Streaming API: @veryboldbagel shared links to new API documentation for streaming events in LangChain, highlighting the importance for responsive end-user applications. Detailed examples and instructions are provided in the General Docs and for AgentExecutor and LangGraph (AgentExecutor Docs, LangGraph Notebook).

  • Watch and Learn About Streaming Events: A YouTube video titled ā€œStreaming Events: Introducing a new stream_events methodā€ was shared, which explains the significance of streaming in LLM apps.

  • Feedback Request for Streaming Feature: Users are encouraged to provide feedback and report issues regarding the new streaming feature on LangChainā€™s GitHub discussion page found here.

Links mentioned:

LangChain AI ā–· #general (21 messagesšŸ”„):

  • In Search of Open Source Clarity: @pirlog inquired whether the project was open source or free, noting the lack of pricing or explanatory information on the website.
  • Request for Assistance: @irfansyah5572 and @shanumas indicated a need for help with a non-specific issue, showing interest in collaborative problem-solving.
  • Service Downtime and Recovery Noted: @adorable_quokka_56531 mentioned that the services appeared down but later noted they were back up, also suggesting the addition of a status page.
  • Assistance with Database Schemas: @mavrik_55410 sought guidance on storing vector embeddings in a specific schema in a Postgres Database using pgvector and langchain, which led to a clarification discussion with @__ksolo__ about Postgres schemas and configurations.
  • Community Feedback on Vector Storage: @rawwerks opened a discussion on favorite vector storage companies, inviting community opinions.

Links mentioned:

5.9.Ā Schemas: 5.9.&nbsp;Schemas # 5.9.1. Creating a Schema 5.9.2. The Public Schema 5.9.3. The Schema Search Path 5.9.4. Schemas and Privileges 5.9.5. ā€¦

LangChain AI ā–· #langserve (1 messages):

  • Seeking Guidance for LangServe Agent Executors: @hiranga.g faced difficulties getting an agent_executor to run in LangServe and sought assistance from the community. They posted a direct query to @1033432389516546158 regarding the setup process. (Q1)

  • Clarification on LCELs and Tool Selection: @hiranga.g achieved getting a LCEL to work but was uncertain if it is possible to add multiple Tools for the LCEL to utilize. They expressed a belief that LCELs donā€™t allow for creating an ā€œagentā€ and inquired for confirmation or guidance on the matter. (Q2)

LangChain AI ā–· #langchain-templates (1 messages):

sideways1: Has anyone built a Q&A chatbot that interacts with a database of JSON files?

LangChain AI ā–· #share-your-work (4 messages):

  • AgentHub Reveal: @maxbrodeururbas announced the launch of AgentHub, a platform built with a friend, and invites feedback from the community. They added that theyā€™ve written a blog post elaborating on the synergy between Robotic Process Automation (RPA) and AI which can be found here.
  • Call for Collaboration: @truethinker reached out to @939753620423991296 to express interest in connecting, although no context or detail was provided regarding the purpose of the connection.

Links mentioned:

AI and RPA: The Future of Work: The marriage of RPA tooling and AI is going to cause a monumental explosion in productivity in the next few years.

LangChain AI ā–· #tutorials (2 messages):

  • Dive into Multimodal AI with DataCamp and LangChain: @datarhys shared a link to a free 9-part series on AI, including a session on ā€œBuilding Multimodal AI Applications with LangChain & the OpenAI APIā€. This session teaches participants to transcribe YouTube videos using Whisper and then pose questions to GPT about the transcribed content. Check out the entire code-along series and start the specific LangChain code-along with this code-along link.

  • CircleCI and Deeplearning.ai Present AI Testing Course: @manialgie announced the release of a free course in collaboration with Deeplearning.ai on how to test and ship AI-powered applications. The course explores testing LLM-based applications, model-graded evaluations, and automating these processes with CircleCI to enhance application development. Find the course at Automated Testing with LLMOPS.

Links mentioned:

DiscoResearch ā–· #disco_judge (1 messages):

  • GPT-3.5 Struggles with Importance and Sentiment: @calytrix shared that their pipeline using GPT-3.5 to rate news stories is better at judging sentiment than importance but still has strong biases. They noted that adding explanations and descriptive scores did little to improve the systemā€™s discriminative power.
  • Tackling Model Bias and Rating Challenges: @calytrix observed very strong preferences in the numbers the model assigns, suggesting that fine-tuning could mitigate these biases. They emphasized the complexity of the tasks and suggested developing a specific evaluation and scoring system tailored to the modelā€™s capabilities.
  • Complexity in Assessment Recognized: The task of rating importance is particularly difficult for GPT-3.5 due to the implied complexity @calytrix mentioned, something humans find easy.
  • Rating System Recommendations: @calytrix recommended breaking down complex questions, like assessing importance, into more specific ratings that GPT-3.5 can handle more effectively. They also suggested a simplified rating system with limited options, such as ā€œpoor, mixed, good, very goodā€.

DiscoResearch ā–· #general (9 messagesšŸ”„):

  • Gauging Data Quality for Language Model Fine-Tuning: User @thewindmom questioned how to evaluate data quality when fine-tuning language models, emphasizing the avoidance of poor input data. @hammadkhan suggested techniques like eye-balling, deduplication, and heuristic-based filtering for maintaining data quality.

  • DIY Synthetics Reduce the Need for Evaluation: In response to @thewindmom, @bjoernp proposed that creating your own synthetic data can lower the need for external evaluation, hinting at the use of built-in guides and information-rich models to produce such data.

  • Discussing Synthetic Data Generation for Fine-Tuning: @thewindmom asked about the use of synthetic data, particularly referencing NeMo Guardrails, to which @bjoernp mentioned the proactive creation of synthetic data with guardrails that a strong model can utilize effectively for training.

  • Sharing Resources on Synthetic Data and Self-Instruct Methods: @_jp1_ shared that DiscoResearch is still refining their synthetic data generation process and highlighted jon durbinsā€™ Airoboros repo as a key reference, while discussing customized implementations for the self-instruct paper. They provided a GitHub link to Airoboros for further insights and inspirations.

  • Training Dilemmas: Using Instruction+Answer Pairs: In a discussion led by @philipmay, the query raised concerns about using instruction+answer pairs. They inquired if something useful could be done with just pairs of instruction and bad answer for model training, given the absence of a good answer to create triplets for DPO or other training efforts.

Links mentioned:

GitHub - jondurbin/airoboros: Customizable implementation of the self-instruct paper.: Customizable implementation of the self-instruct paper. - GitHub - jondurbin/airoboros: Customizable implementation of the self-instruct paper.

DiscoResearch ā–· #embedding_dev (11 messagesšŸ”„):

  • DPR Models Disappoint: @philipmay expressed that testing with two different DPR models did not yield the expected results, calling the situation ā€œvery strangeā€.
  • Efforts on Enhancing Data Positioning: @sebastian.bodza plans to add a new column showing the position data was found at, while also acknowledging the complexity of the task mentioning, ā€œQuite trickyā€.
  • Summing Distances for Question Specificity: @philipmay discussed a strategy for determining the generic nature of questions by summing distances of the top results, noting that generic and specific questions donā€™t get effectively distinguished this way.
  • Challenges in Discerning Patterns: @sebastian.bodza commented on the difficulty of finding a pattern in the similarity of text and question or between top vectors, specifically highlighting issues related to questions about ā€œPferdemarktā€ due to its multiple instances.
  • Brainstorming Completion and Next Steps: @sebastian.bodza announced the completion of brainstorming with 82k search ideas/questions, emphasizing the next phase will be question generation.

DiscoResearch ā–· #discolm_german (5 messages):

  • Praising DiscoLM German 7B v1: @alex_22398 expressed gratitude for the high-quality language outputs of the DiscoLM German 7B v1, even on an older laptop with the help of TheBlokeā€™s GGUF quantization, while pointing out an issue with the generation of blank lines after output.
  • Blank Line Bug Squashed: @_chromix_ informed about the fix for excess blank lines after output and discussed options for generating an updated GGUF quantization or setting the stop token manually to `ā€

Links mentioned:

DiscoResearch/DiscoLM-70b Ā· Hugging Face: no description found

Alignment Lab AI ā–· #oo (2 messages):

  • Catching up with the team: @teknium reached out to the channel members, expressing a desire to reconnect and encouraging discussion on recent activities amongst colleagues by tagging @748528982034612226. No specific topics or links were mentioned. ,

AI Engineer Foundation ā–· #general (2 messages):

  • Open Inquiry on ML Field Jobs: User @forsaken_ninja reached out to the community with queries regarding job opportunities in the machine learning field, inviting members to engage in the discussion and offer insights. They encouraged others to ping them for a direct conversation. ,,,,