12/22/2023: Anyscale's Benchmark Criticisms

Criticisms:

• Need to expose cost per token, throughput, and windowed versions not just burst
• Don't compare public LLM endpoints because batching/load/timing really matter
• straight up contested by Vipul of Together
• in ML history it hasn't worked to overoptimize for one thing.

[TOC]

OpenAI Discord Summary

• Performance and Usability of AI Models: Discussion under #ai-discussions regarding user experience with different AI models. Issues reported with Bard and a preference expressed for MSFT Copilot, particularly in storytelling tasks. "@muyfashionista shared their testing of multiple AI models and mentioned issues with Bard". Also, the distinction between Bing Chat and Microsoft Copilot was clarified.
• GPT-4 and Its Expected Value: Questions raised about the worth of upgrading to GPT-4 and if the pricing for AI models was justified. According to discussions, many in the community found the productivity boost from models like ChatGPT and Copilot worth the cost for coding work.
• AI-related Challenges and Bugs: Across various channels, a range of issues and potential bugs were discussed. From api-discussions, performance issues with GPT and OpenAI API were noted. User @bmaxhacks noted API responses taking more than 2.5 seconds. Problems with models not responding well to custom formats, GPTs disappearing, and a potential bug allowing 80 messages per hour on GPT-4 were all pointed out. Exchanges explored AI model performance changes with different programming languages.
• Future AI Outlook and Discussion: On #openai-chatter, users speculated about future AI developments, like possible GPT-6 release and potential impacts on the economy. Also, voiced concerns about OpenAI's transparency, profitability, and service investments.
• Prompt Engineering for Image Creation: Conversation in #prompt-engineering around guiding AI's visual outputs and controlling what appears in generated images. User @eskcanta shared advice on maximizing output quality, emphasizing the value of clear, detailed prompts and avoiding negative instructions. User @suptrox desired negative prompts to guide the AI model.

OpenAI Channel Summaries

▷ #ai-discussions (50 messages🔥):

• Performance and Usability of AI Models: @muyfashionista shared their testing of multiple AI models and mentioned issues with Bard, including making mistakes and either going on indefinitely or not returning responses. They found MSFT Copilot a better choice for storytelling and found fewer hallucinations compared to Bard.
• Difference Between Microsoft Bing and Microsoft Copilot: In light of @eljajasoriginal's query, @muyfashionista clarified that Microsoft has rebranded Bing Chat to Microsoft Copilot. They also pointed out a difference between the MSFT Copilot for Microsoft 365 version and the one found in Bing Chat.
• Upgrade from GPT 3.5 to GPT 4: In response to @6nueve's query, @elektronisade and @lugui suggested that upgrading to GPT4 could be useful, especially for tasks like coding. They cautioned, however, that the productivity of the AI model also heavily depends on the language used, the project type, and the quality of the prompt.
• Value of Paid AI Models for Work: @6nueve wondered about the worth of paying for AI models, which led to a discussion with @lugui, @afterst0rm, and others. The general view was that the productivity boost from using AI models like ChatGPT and Copilot justifies the cost, provided the user does coding for work. They found it enhanced their productivity and made work more enjoyable.
• AI Model Performance with Different Programming Languages: @afterst0rm and @lugui discussed that the AI models' performance changes with programming languages. They found models to be better at mainstream languages like Python, Java, and JS, while newer languages like Rust gave less satisfactory results. The AI models could even generate boilerplate code in Python but struggled with newer syntax and patterns.

▷ #openai-chatter (165 messages🔥🔥):

• Discussion on GPT-V Performance: User @pudochu expressed considerable frustration with GPT-V, particularly its speed and vision capabilities, asserting that it is not fit for purpose in its current state. User @captainsonic, however, suggested that successful use of GPT-V may require more prompt engineering and customization for specific use cases.
• GPT-4-Turbo Usage: There were several discussions around GPT-4-Turbo's performance and cost effectiveness. Users such as @beowulfbr and @feltsteam0 analyzed the cost per token of GPT-4-Turbo and its alternatives, noting that the price varies depending on use case and the volume of tokens processed.
• Concerns about OpenAI Transparency and Profit Utilization: @mischievouscow and @feltsteam0 had a back-and-forth conversation about how OpenAI might be using its profits. There was speculation about investing in NVIDIA GPUs and an assertion that the costs of training and inference would take up a significant portion of revenues.
• Speculation on Future AI Development: The conversation includes speculations about when GPT-6 will be available, with comedic references to the awaited release of GTA 6. There were also discussions on the potential economic outputs resulting from AI advancements, with @samaltman suggesting that AI might increase economic output by 20-30x.
• Platform and Feature Issues: Several users reported experiencing issues with the OpenAI platform. @kingofcringe reported missing the stop generation button while @cozy_artist had issues generating a file to download. @jaicraft, however, discovered a feature where text from a response can be selected and replied to.

▷ #openai-questions (57 messages🔥🔥):

• Issues with GPT and Custom Formats: User @ritalin60mg raised a concern about GPT-3.5 not responding well when asked to reply in a custom format compared to GPT-4. They are seeking a standard way to instruct it that minimizes character usage.
• Managing Threads and Deletion: @yummisery was unclear about the deletion of threads and whether they were automatically deleted after inactivity. Responding to this, @brokearsebillionaire clarified that while runs time out, threads do not and it's the user's responsibility to clean them up.
• ChatGPT Behavior and Troubleshooting: @princesslunarcat reported issues with ChatGPT, shifting from GPT 4 to GPT 3.5 after one message. @solbus offered various troubleshooting suggestions, but the issue persisted. The problem was then reported to the OpenAI support.
• Performance Issues with OpenAI API: @bmaxhacks raised a concern about the OpenAI API taking more than 2.5 seconds for most responses, despite a small amount of data being sent.
• Incorporation of External APIs and Iteration Issue: @yummisery asked whether assistants' code interpreter tool could call external API endpoints. They further inquired how to handle iteration involving multiple API calls in the context of a function.
• Repetition Control for User Inputs: @solononforever sought advice on pseudocode for tracking conversation history to prevent user repetition using prompts or langchain, to which @snowmobile responded with a python code suggestion.
• Function Calling in LangChain: @bluehipp0 asked why there are no open source LLMs that provide function calling and sought examples of using LangChain to simulate function calling.

▷ #gpt-4-discussions (50 messages🔥):

• GPTs Disappearing Glitch: There were numerous reports of GPTs and chats disappearing, often returning later. @csharp69, @redash999, and others experienced this issue. Specifically, @redash999 mentioned a recently created GPT called "Water Engineer", which did not reappear.

• Confusion over GPT4.5 Turbo Pricing: @killymbapps expressed confusion over how pricing works for GPT4.5 Turbo for GPT assistants, noting a lack of clear information.

• Stock Data Analysis GPT: @emperor_azir shared a link to a GPT that provides live financial data and technical analysis.

• Potential Bug: @raidedcluster discovered a potential bug that allows them to get 80 messages per hour with GPT-4, instead of the limit.

• Potential Artistic Applications for GPTs: @fortun8te, @jamiecropley, and others discussed the potential for GPTs to analyze and understand art styles based on image inputs. They expressed hope for future improvements in this area. @elektronisade stated current GPTs cannot grasp styles beyond rough categories.

• Restrictive GPT Names: @wndapnda02 reported an issue where GPTs with verified domain names were restricted for public sharing, suggesting possible trademark violations.

• Problem with Knowledge Base Analysis: @bacaxnot complained about custom GPTs now preferentially using Code Interpreter over Retrieval for analyzing their knowledge base, leading to slower and less accurate responses.

• ChatGpt Rumor: @chemo2493 stated that GPT 4.5 Turbo was debunked as a rumor.

▷ #prompt-engineering (7 messages):

• Creating Specific AI Visualizations: User @suptrox expressed a wish to prompt a DALL-E style model to visual an expansive garden and nothing else. They noted having trouble with unwanted elements appearing in the image and expressed a desire for 'negative prompts' to guide the model.
• Guiding AI Visual Output: Responding to @suptrox, @eskcanta shared an approach to guiding AI visual outputs. They emphasized focusing on positive descriptions of what should be in the image and avoiding negative instructions. They also reiterated the importance of precise and detailed prompts.
• Image Creation Example: @eskcanta provided a concrete example, using a prompt to create four images of a detailed, unspoiled, and endless garden. It garnered positive reactions from other users.
• Feedback on Approach: @catking335 reacted positively to the images produced, asking @eskcanta about the creation process. @eskcanta reiterated the value of clear, detailed, and positive prompts in guiding ChatGPT-4.
• Appreciation of Technique: @bambooshoots also appreciated the technique demonstrated by @eskcanta, noting it sparked some ideas. They expressed excited anticipation for exploring it further.

▷ #api-discussions (7 messages):

• Image Generation with DALLE: User @suptrox wanted to generate an image that only showcases a garden landscape, and wondered if DALLE supports negative prompts to suppress unwanted elements.
• @eskcanta responded by advising that AI generally performs better with positive rather than negative instructions. They demonstrated this by generating a series of four images representing an "endless natural, unspoiled, pristine garden" using the ChatGPT-4 model. Their advice for getting the desired output was to accurately describe what one wants, review the output, and iteratively refine the instructions based on the observed discrepancies.
• Several users, such as @catking335 and @bambooshoots, expressed admiration for the generated image. @catking335 asked about the creation process, to which @eskcanta detailed the prompt they used and recommended techniques to enhance the model's output, such as having a more detailed conversation with the model to fine-tune the desired output.

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Mistral Discord Summary

• In-depth discussion on AI model competition, with focus shifting towards small multimodal models, small language models, distributed learning models and cheap fine-tuning models, as suggested by @red_code.
• Extensive exchange regarding Open Source Front End for Mistral AI akin to OpenAI's playground, the use of litellm with various models, how to measure token count in Mistral output, and training Mistral with unique datasets. A link to litellm config.yaml and vLLM issue on Autogen was shared. User @rrross also followed up with a blog post on how to count Mistral tokens.
• Detailed subject matter on model capabilities, comparison and use-cases. This includes selection of a suitable model for English to Norwegian translations, application of Mistral in the realm of coding and retrieving Linux commands, and the performance of a 7B model with a 3090 GPU.
• Showcased the versatility of Mistral AI with examples including hosting Mistral Models on own Servers, a bot compatible with Mistral API - llmcord, a new third-party package using Mistral.AI's Web API here, and a Twitter post being shared.
• Exchanged miscellaneous discussions on the usage of r/MistralAI subreddit, possibility of using Mistral on Android, running AI models on phones, with a GitHub link being shared about getting models to run on iPhones.
• Delved into issues faced by users on the Mistral platform, including the erratic responses from mistral-small, payment issues for users in India, query on the existence of a bug bounty program, lack of clarity on the Embeddings endpoint request limit and how to obtain an API access invite.

Mistral Channel Summaries

▷ #general (70 messages🔥🔥):

• Future Competition of Models: User @red_code predicts that soon the competition would focus on small multimodal models or small language models, distributed learning models, and cheap fine-tuning models.
• Open Source Front End: User @dv8s inquired about an open-source or publicly available front end for Mistral AI, akin to OpenAI's playground.
• Use of Litellm with Models: A discussion between @cyb3rward0g and @brokearsebillionaire regarding the use of litellm with various models, specifically how it interacts with instruct fine-tuned Mistral models. They shared links to litellm config.yaml and vLLM issue on Autogen.
• Token Count in Mistral Output: @rrross queried how to measure the token count in a Mistral output. @sublimatorniq suggested using a library similar to OpenAI's. @rrross followed up with a blog post on how it can be done. @daain also suggested an online tokenizer tool.
• Training Mistral with Unique Datasets: @novemberfalls mentioned an interest in training the Mistral model with a unique dataset. @antononcube asked for clarification on the dataset structure and intended use, with potential recommendations depending on the dataset attributes.

▷ #models (78 messages🔥🔥):

• GPU Support for Larger Models: @novemberfalls asked if a 3090 GPU would support the 7B model, to which @mrobino confirmed that it would, given Quantized Yes.
• Queries on Model Capabilities: @christiansieb asked for more examples in the documentation as they've faced issues with context relevance in both Mistral and Mixtral in comparison to ChatGPT.
• Discussion on Translation Models: @ved_ikke asked for model recommendations for English to Norwegian translation work. Various models like gpt3.5, Helsinki NLP, Mixtral, Yi, and GPT4 were discussed by users, including their experiences and preferences. @laoji_lcg expressed satisfaction with GPT3.5 for translating English into Chinese.
• Mistral for Programming: Several users, including @ved_ikke and @laoji_lcg, had a discussion on Mixtral's utility in the realm of coding. They agreed it was excellent in understanding and debugging code, but @laoji_lcg believed its output tended to be complex compared to streamlined code from GPT4.
• Chatbot for Linux Command Retrieval: @giblaz asked for model recommendations for retrieving Linux commands. @dutchellie suggested trying Mixtral.

▷ #deployment (106 messages🔥🔥):

• Discussion about the performance of different models on different hardware: @dutchellie and @sublimatorniq discussed the performance of the llama.cpp backend, which has been built into the exllamav2 model, and noted that it gives considerable gains in performance. Dutchellie shared their experiences of using exllamav2 on an AMD GPU, and its improvement from 6t/s with llama.cpp to 50t/s (source).
• Difficulties with Mac and AMD hardware: They noted the absence of Mac support in popular projects and mentioned that users with these systems are always chasing after the tech, though they agreed that Macs perform significantly well for large models.
• Discussion around exllama and ollama: The discussion skewed towards the comparison between exllama and ollama, with dutchellie sharing that exllama appeared to be outperforming ollama in their personal tests. @sublimatorniq raised a known issue with "mixtral on mac" that has been reported for long delays and slowness (source).
• LLM models and fine-tuning: There was also a mention of the recent release of Dolphin 2.6 Mixtral by Eric Hartford, and a jesting mention of the addition of 'samantha-based empathy data' in the new release.
• Download speed issues with Huggingface: Lastly, they expressed frustration with the download speeds from Huggingface, with sublimatorniq noting particular difficulty connecting to Huggingface from Cambodia.

▷ #showcase (9 messages🔥):

• Hosting Mistral Models on own Servers: @ds_gamer reported that they have hosted Mistral models on their own servers including other OS AI models and offer an OpenAI-compatible API access for free. However, they mentioned that except for Mixtral model which is currently a paid-only service, due to high computation costs associated with it.

• Introduction to Open Source Bot llmcord: @jakobdylanc shared an open-source bot named llmcord which is compatible with the Mistral API and allows multiplayer chat with Large Language Models (LLMs).

• Use of gpt-4-vision-preview in llmcord: @jakobdylanc confirmed to @antononcube that gpt-4-vision-preview can be used with the bot llmcord. This bot supports both OpenAI and Mistral API.

• Third Party Package using Mistral AI's Web API: @antononcube shared a link to a new third-party package using Mistral.AI's Web API which can be accessed here.

• Tweet Link: @mrobino shared a link to a tweet without any additional context.

▷ #random (12 messages🔥):

• Usage of r/MistralAI subreddit: User @anonboxis questioned whether anyone in the channel uses the r/MistralAI subreddit.
• Use of Mistral on Android: User @scottsilverstein asked if it's possible to use Mistral on an Android phone. @akshay_1 responded that it cannot run locally yet.
• Running AI Models on Phones: @sublimatorniq shared a GitHub link about getting some models to run on iPhones. Further, @rtyax mentioned that Mistral can run on a phone with 12GB memory, citing that koboldcpp and llama.cpp work with Termux. However, they also noted that it'd be slow on a phone with 8GB memory.
• Autopoietic Cognitive Architecture Paper: User @poltronsuperstar shared that they drafted a paper on autopoietic cognitive architecture, with an amusing note on the acronym nearing "CACA", meaning poop in French. They noted it as an attempt at AGI.

▷ #la-plateforme (42 messages🔥):

• Issues with Mistral Endpoint Responses: User @sublimatorniq raised an issue about receiving chopped and overly short responses, sometimes stopping unexpectedly, from the mistral-small model, even after setting max_tokens: 1024. The finish_reason reported is 'stop'. This issue occurs regardless of context length and seems to arise frequently when the model is early into its response. @lerela acknowledged the issue and promised an investigation.
• Payment Issues from India: @tafferboy is experiencing payment method failures for Mistral from India due to restrictions against recurring charges. A manual payment or wallet top-up feature was suggested as potential workarounds. @lerela acknowledged the issue and stated they are looking into options.
• Bug Bounty Program Query: @poltronsuperstar asked about the existence of a bug bounty program and whether finding a vulnerability on the platform would aid in securing a developer role. There was no explicit response to this query.
• Embeddings Endpoint Request Limit: @lautaro.p asked about the request limit for the Embeddings endpoint but didn't receive a response.
• API Access: @aurelien6964 inquired how one could get an invite for the API but did not receive a response.

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Nous Research AI Discord Summary

• @nonameusr introduced LUNA-SOLARkrautLM-Instruct on Hugging Face: Hugging Face Link.
• .beowulfbr shared an academic paper on code translation with LLMs: arXiv Paper.
• Training Mixtral and other model variants was a focus of conversation, with @wesh3104 and @ldj discussing the fine-tuning of OpenHermes-Mixtral-8x7B.
• @fullstack6209 initiated the talk about the best performing models in the 7b-13b, 4-8bit range. @teknium suggested either the Hermes or Openchat models.

Interesting links were shared and discussed within the guild:

• @asada.shinon mentioned concerns about Opera, citing user data selling and other dubious practices, sharing a detailed report: Rentry Report.
• @emrgnt_cmplxty shared a Twitter link of a project named AgentSearch, aiming to enhance knowledge accessibility for LLM agents: Project on Twitter.
• Talk on the cost implications of utilizing various models, with .beowulfbr discussing the expense of using gpt-4-turbo for handling 1,000,000 tokens.
• Feedback from @night_w0lf on Gemini Pro/Bard's performance in Python coding, acknowledging Gemini Pro's superior performance over GPT4 and mentioning an upcoming version of ChatbotUI.
• Discussion on the creative nomenclature of some models, specifically, Meow and Sauerkraut SOLAR, initiated by @fullstack6209, @gabriel_syme, and @beowulfbr.

Nous Research AI Channel Summaries

▷ #interesting-links (9 messages🔥):

• @nonameusr shared a tweet from Charlie B. Holtz: Twitter Link
• @asada.shinon raised concerns on Opera (GX/One/Crypto Browser) due to its practices, citing that the company is known for selling user-data, censorship, backdoored software, and involvement in predatory loans and anti-competitive behavior. Contact details along with a link for more details were shared: @0VERIMAGINE Twitter and Rentry Report
• @metaldragon01 shared a tweet from _akhaliq: Twitter Link
• @.beowulfbr discussed the cost difference between gpt-4-turbo and other models such as CursorAI and ChatGPT. Notably, they found gpt-4-turbo more expensive for handling 1,000,000 tokens.
• @nonameusr introduced LUNA-SOLARkrautLM-Instruct – a UNA-Sauerkraut variant of the powerful Upstage, shared through a Hugging Face link: Hugging Face Link
• @night_w0lf provided feedback on Gemini Pro/Bard's performance in coding, particularly Python. They expressed satisfaction with Gemini Pro's performance over GPT4, and shared news about a forthcoming version of ChatbotUI supporting many API providers and local models via Ollama.

▷ #general (133 messages🔥🔥):

• Discussion on Training Mixtral and Other Models: There were substantial discussions about the training of Mixtral and different models. @wesh3104 mentioned their preference for Medium as an alternative to the paid use of their LLM (programming assistant).
• OpenHermes-Mixtral-8x7B Fine-tune: @wesh3104 and @ldj discussed the OpenHermes-Mixtral-8x7B fine-tune posted on Hugging Face. @ldj clarified that you could train on a single node.
• Litmus Tests for LLMs: @n8programs detailed a litmus test for sentence generation, which is apparently challenging for all but a few high-level language models. They had a discussion with @nonameusr on this topic.
• Code Translation with LLMs: .beowulfbr shared an interesting paper about how large language models (LLMs) perform when translating code from one programming language to another.
• AgentSearch, an Open-Core Effort: @emrgnt_cmplxty shared a link to a project on Twitter called AgentSearch which works towards making humanity's knowledge accessible for LLM agents. Multiple users provided feedback on it, discussing its merits and areas for improvement.

▷ #ask-about-llms (6 messages):

• Discussion about Best Models in the 7b-13b Range: User @fullstack6209 inquired about the best performing models in the 7b-13b, 4-8bit range. @teknium suggested either the Hermes or Openchat models, or any of their merges.
• Nomenclature of Models: Users @fullstack6209, @gabriel_syme, and @beowulfbr engaged in a discussion about the eccentric names of models, particularly Meow and Sauerkraut SOLAR.
• Validation of Openchat's Performance: User @beowulfbr confirmed the good performance of the Openchat model.

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HuggingFace Discord Discord Summary

• HuggingFace model issues and applications: Community addressed varying drops in connections when using HuggingFace, potential usage of models like T5ForConditionalGeneration and Seq2SeqTrainer for various tasks, utilizing HuggingFace resources for learning, and issues encountered when uploading models via Git Bash. Users shared advice on running LLM on lower-spec PCs, generating multiple-choice questions using AutoTrain with Seq2Seq, and the cost comparison between GPT-4 and fine-tuning an open-source model. A query was also raised about the housing of data on HuggingFace.
• Tools and resources: Links were shared to coding chatbots like Bing Chat, the Huggingface NLP Course, recent projects Tweak Mpl Chat and Cheshire-Cat, live competitive programming contest Fall Challenge 2023, and an explanation of Virtual Sensors by IEC.
• User-created projects: Includes a model for TOEIC reading part 5 generation, a ppo agent that plays Huggy trained using the Unity ML-Agents Library, a rapid model named Mixtral-8x7B-Instruct-Demo, a blog post on generating music as MIDI, a tutorial on evaluation metrics for regression models, and a presentation on Ensemble Learning recorded on YouTube. A new test model was also mentioned but without additional details.
• Discussion on computer vision and NLP applications: Debates included the possible use of diffusion models for separating superimposed images and models for converting images into 3D objects. A query was made about combining prompt tuning and LORA in one shot. The use of LayoutLM with the RVL-CDIP dataset and the challenges of applying LLAMA-2 to a height comparison task were discussed. Users also sought help for integrating GPT models with Gradio UI in Google Colab.

HuggingFace Discord Channel Summaries

▷ #general (37 messages🔥):

• Issues with HuggingFace Models and Dropbox: @gokiburigeemu queried about if HuggingFace uses Dropbox for housing data and if there is a limit to connections. They mentioned an issue of too many connections with HuggingFace models.
• Re-generation Commands in mistral orca 7b for Q/A generation: @enka55 is seeking a way to continue generating Q/A after the model stops, despite setting the max_new_tokens to 16K. Outlining the similar case with ooba textgen, they stated it generates a few Q/A and then stops unless generating is manually continued.
• Modifying T5ForConditionalGeneration and Seq2SeqTrainer for Training: @ianbestfellow is looking for guidance on how to modify the forward part of T5ForConditionalGeneration for training and Seq2SeqTrainer.
• Quantizing and Upload Model to Hugging Face Issues: @sumit.sumit is encountering an AttributeError when trying to quantize a model in Collab and upload it to Hugging Face, both with the facebook/opt-350m model and vilsonrodrigues/falcon-7b-instruct-sharded model.
• Running LLM models on Low-End PCs: @melvjn_03 pondered if LLM models can run on low-end PCs, to which @lawls.net clarified they can, especially with quantized GGUF files. They also mentioned that it's possible to run the model equivalent of ChatGPT 3.5-turbo on a local gaming PC.
• Creating Multiple Choice Questions with AutoTrain and Seq2Seq: @robolicious is considering using AutoTrain with Seq2Seq for a specific use-case geared towards creating multiple choice questions based on some examples and a specific level of difficulty. @doctorpangloss suggested trying 30 shot generation before opting for fine tuning or training in gpt4.
• Bing Chat and Jan as Coding Chatbots: @melvjn_03 expressed interest in trying out coding chatbots, to which @lawls.net suggested Bing Chat and shared a link to Jan, an open-source alternative to ChatGPT that runs offline.
• GPT-4 vs Fine Tuning Cost Comparison: @robolicious requested a source that compares the cost of using GPT-4 vs fine tuning an open-source model.

▷ #today-im-learning (9 messages🔥):

• Huggingface NLP Course: @llmsherpa shared a link to the Huggingface NLP Course here.
• Issue with Amazon-Reviews-Multi Dataset: @vatsal2161 pointed out an issue with the amazon-reviews-multi dataset from the Huggingface NLP course, stating that the dataset is now defunct.
• Learning Methods for New NLP Topics: @regi6135 seeks guidance on how to learn about recently emerging NLP topics, requesting for pointers or links for a better understanding.
• Progress Update by Neuralink: @neuralink shared an update of their learning progress, mentioning their work on DoReMi, end-to-end FP8 training in 3D Parallleism, and other relevant subjects.
• Issues Uploading to Huggingface via Git Bash: @deadsg reported a problem uploading to Huggingface from Git Bash and requested help.
• Discussion on Note-Taking Format: @onceabeginner mentioned being inspired by @neuralink's note-taking format and discussed wanting a deeper understanding of specific topics.

▷ #cool-finds (6 messages):

• Panel Tweak Chat: User @andysingal shared a link to a project called Tweak Mpl Chat, also mentioning that it has been duplicated from ahuang11/panel-fleet.
• Modern NLP Repos: @andysingal also mentioned about adding Panel Tweak Chat project to a list of modern NLP repositories on Github.
• Cheshire-Cat: @nickprock shared a project called Cheshire-Cat, a framework to develop AI assistants which supports Hugging Face models.
• Fall Challenge 2023: @lustforserotonin posted a link to a live competition on CodinGame named Fall Challenge 2023.
• Virtual Sensors: @grojas123 introduced to the concept of Virtual Sensors, a technology based on machine learning. Shared a link from IEC which provides a detailed explanation about it.

▷ #i-made-this (6 messages):

• PPO Agent Playing Huggy: @cloudhu shared their model of a ppo agent that was trained to play Huggy. The agent was trained using the Unity ML-Agents Library.
• Mixtral-8x7B-Instruct-Demo at Lightning Speed: @myg5702 shared a Hugging Face space, featuring a model named Mixtral-8x7B-Instruct-Demo that is running at a lightning speed.
• Generating Music as MIDI: @alexsushidog posted a blog about generating music as MIDI by training a transformer from scratch in JAX.
• Evaluation Metrics for Regression Models: @torres8552 created a Kaggle notebook that explains the math behind commonly used evaluation metrics in regression tasks. The notebook provides insight into how to interpret these metrics and how to create custom functions to compute them using Python. The notebook is available on Kaggle.
• Ensemble Learning Presentation: @johko990 delivered a presentation at a Python Pizza conference on Ensemble Learning, which is available via YouTube. The talk reinterprets the fairy tale of Snow White in the context of Ensemble Learning.
• New Test Model: @bread browser mentioned creating a new test model, but didn't provide any further details or links.

▷ #diffusion-discussions (2 messages):

• Superimposition of Images: @cachalote123 asked whether it's possible to use diffusion models or any other technology to separate two superimposed images from old films.

• Conversion of Images to 3D Objects: @drishya1 was seeking advice on which model would be the best choice for converting images to 3D objects after successfully using the control net to convert scribbles to images.

▷ #computer-vision (1 messages):

• Integrating GPT Models with Gradio UI in Google Colab: User @designfailure is seeking help for compiling two GPT use cases using Gradio UI in a Google Colab environment. The desired functionalities are:
  1. GPT-4 Vision or LlaVA to capture images in response to a prompt query.
  2. A GPT chatbot that parses and retrieves image captions, using them to complete chat responses.

▷ #NLP (17 messages🔥):

• Running Prompt Tuning and LORA: @opencuiguy asked if it's possible to combine prompt tuning and LORA in one shot and desired to address the issue where prompt tuning does not expose the new token for flexibility.
• Fine-Tuning Sent-Transformer: @nickprock inquired about the number of examples required to fine-tune a sentence transformer using msmarco with tripletloss, as he could only find information related to TSDAE requiring between 60-100k sentences.
• Modifying T5ForConditionalGeneration & Seq2SeqTrainer: @ianbestfellow sought guidance on how to modify the forward part of T5ForConditionalGeneration and the Trainer for his research.
• LayoutLM and RVL-CDIP: @gh6608 asked if anyone has had success using LayoutLM with the RVL-CDIP dataset.
• Transliteration Model Training: There was a discussion between @asterix3651 and @vipitis about creating a transliteration model; @asterix3651 needs a model for converting words from one language to their romanized form even for out-of-vocabulary words.
• LLAMA-2 Issue: @notooth described how the LLAMA-2 model was having difficulty with a height comparison task and was seeking guidance on how to improve its performance.
• Loss Measurement in Text Generation: @exponentialxp was curious about the parts of the text for which the loss is measured during text generation - whether it's confined to the Assistant/Response or includes all parts.

▷ #diffusion-discussions (2 messages):

• Separation of Superimposed Images: @cachalote123 asked about the possibility of using diffusion models or any other technology to separate superimposed images originating from old films.
• Converting Images to 3D Objects using Diffusers: @drishya1 is trying to convert images into 3D objects using diffusers. They mentioned having used Control Net to convert scribbles into images and are now seeking advice for the best model to convert these images into 3D.

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OpenAccess AI Collective (axolotl) Discord Summary

• Discourses on how to create embeddings with a regular LLM, with @marijnfs asking about the possibility of averaging the activations of a higher-up layer or using the last token's activation vector.
• Queries and thoughts on GPT4 fine-tuning, presented by @dangfutures.
• An exploration of open source alternatives to Weights & Biases for generating loss/evaluation graphs, with @rtyax seeking suggestions and @noobmaster29 proposing Tensorboard.
• Encompassing interchanges about the merge of a pull request #787 after rebase, discussions about changes in permissions, and the news of a merged PR #2232 from @nanobitz, @caseus_, and @dreamgen.
• Brief dialogue on the features and benefits of torch SDP attention as an alternative to flash attention by @caseus_, sharing the torch documentation link for additional information, courtesy of @nanobitz.
• Conception of Mistral class code's sliding window feature mentioned by @nanobitz.
• Suggestions from @le_mess and @caseus_ on utilizing Axolotl for fine-tuning models and starting with tinyllama example.
• Insights into datasets compatible with Axolotl by @dreamgen, @touristc, @le_mess, and @visuallyadequate, discussing resources like ShareGPT, OpenAssistant/oasst1, OpenOrca, and datasets found in Hugging Face, such as Guanaco-Unchained-Refined and Wizard-Vicuna-Refined.
• Discourse on using toxic-dpo and other fine-tuning datasets, the anticipation for Dolphin 3.0, paucity of open source options for RAG datasets, and the relevance of Chatbot Arena Conversations dataset from @dreamgen, @faldore, and @nruaif.

OpenAccess AI Collective (axolotl) Channel Summaries

▷ #general (6 messages):

• Creating embeddings with a regular LLM: @marijnfs inquired about the possibility and methods of creating embeddings with a regular LLM. They asked if it's an option to take the activations of a higher-up layer and average them, or take the last token's activation vector.
• Fine-tuning GPT4: User @dangfutures asked if anyone has had any success fine-tuning GPT4.
• Open Source Alternatives to W&B: User @rtyax sought suggestions for open source alternatives to Weights & Biases for generating loss/evaluation graphs. @noobmaster29 suggested Tensorboard as one such open source option.

▷ #axolotl-dev (22 messages🔥):

• Discussion on Merging PR: User @nanobitz discusssed about merging a pull request #787 after rebase. User @caseus_ confirmed that it should be fine and made necessary changes in permissions to allow the merge.
• Permissions Adjustment: There was further discussion about adjustments for permissions led by @caseus_ in response to an accidental push to the main, trying to limit future issues.
• Merge of huggingface/accelerate PR: In another conversation, user @caseus_ shared the news of a merged PR #2232 related to FP8 support integration by huggingface/accelerate.
• torch SDP Attention: User @nanobitz and @caseus_ engaged in a brief discussion about the benefits and functionality of torch SDP attention, with a link shared to the torch documentation for further details. It was pointed out as an alternative to flash attention by @caseus_.

▷ #general-help (10 messages🔥):

• Mistral Class Code: @nanobitz mentioned that the Mistral class code has a sliding window feature.
• Token Initialization: @dreamgen expressed a desire for the ability to initialize the embedding of a new token from some existing token id.
• Fine Tuning Queries: @thetonsil. asked two questions about fine-tuning models:
  • They enquired whether fine tuning can be done using only CPU resources. @le_mess responded, stating that while it is theoretically possible, it would likely be very time-consuming and suggested renting a cloud GPU instead.
  • They also sought guidance on using Axolotl. As per the advice of @le_mess, they were directed to the examples in the examples folder, specifically the mistral and llama models.
• Running the Examples: @le_mess also provided instructions on how to run these examples using the accelerate launch -m axolotl.cli.traion <example_path> command after setting up the environment.
• Starting with TinyLlama: @caseus_ suggested trying out a tinyllama example as a potential starting point.

▷ #datasets (8 messages🔥):

• ShareGPT and OASST Datasets: In a discussion on datasets compatible with Axolotl, @dreamgen mentioned ShareGPT as an original resource with some paper analyzing and breaking down chats based on user intent, while @touristc faced difficulties working with popular datasets like RyokoAI/ShareGPT52K and OpenAssistant/oasst1.
• OpenOrca Dataset: @le_mess suggested OpenOrca as a functional dataset with Axolotl. Confirming the correct dataset type, @touristc asked if it should be set as alpaca_w_system.load_open_orca.
• Multiple-Round Conversational Datasets: @touristc expressed interest in identifying multiple-round conversational datasets that work well with Axolotl, pointing out the limitation of the OpenOrca dataset being one round QA. @nanobitz mentioned that the current discussion could be deemed as a single round.
• Guanaco-Unchained-Refined and Wizard-Vicuna-Refined Datasets: @visuallyadequate shared the links to two datasets on Hugging Face named Guanaco-Unchained-Refined and Wizard-Vicuna-Refined and highlighted their main focus was giving lists and code blocks consistent formatting.

▷ #rlhf (13 messages🔥):

• Using Toxic-DPO Dataset in Fine-Tuning: User @dreamgen mentioned that they have been using the toxic-dpo dataset in recent fine-tuning exercises, but raised a concern about mixing signals from unalignment and quality data. They suggested filtering big DPO datasets like Intel/orca_dpo_pairs for safety rejects in the "chosen" answer.
• Waiting for Dolphin 3.0: User @faldore indicated that they are waiting for Dolphin 3.0 before further fine-tuning their system, with the aim of having a solid system prompt, instruct, conversation, RAG, and Agent dataset in place. However, they noted that Dolphin is already quite uncensored.
• Lack of Open RAG Datasets: In response to @dreamgen's inquiry about RAG datasets, @faldore mentioned that there are not many great open source options and development in this area is needed.
• Use of Chatbot Arena Conversations Dataset: @dreamgen raised a question about why many models are not using the Chatbot Arena Conversations dataset for DPO/RL. In response, @nruaif explained that the dataset might be too large, yet they have observed people using smaller subsets.
• Publication of Dataset Subsets: Upon @dreamgen's query about whether the subsets of the Chatbot Arena Conversations dataset are published, @nruaif did not provide a concrete answer. @dreamgen speculated that a subset where gpt-4 wins could be relatively safe.

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LangChain AI Discord Summary

• Active discussions on various AI models and solutions, with particular focus on the NexusRaven model; skeptics and advocates alike shared their perspectives, and the relevant Hugging Face link was provided.
• Announcement by @glenn_sjobs about an upcoming AI Hackathon involving the use of LangChain in the developer environment; the official link was shared for additional information.
• Opinions and queries about the functionalities of various platforms were discussed including S3 Storage, Streamlit, ContextualCompressor, and VSCode Auto Import; a considerable repository of Python course material was also referenced in the Google Drive link.
• Introduction of Cumuli, a new Chrome extension for AWS optimized with GPT-4 Turbo with vision; the GitHub link for the extension was provided.
• Sharing of a new book on LangChain, discussing how to effectively use the LangChain framework and work around its inherent weaknesses; the authors shared links for purchasing on Amazon.
• Users expressed questions related to controlling the length of chat history while using session-level memory, with the functionalities of RunnableWithMessageHistory, charact_prompt | llm and RedisChatMessageHistory(session_id, url=REDIS_URL) being discussed.

LangChain AI Channel Summaries

▷ #general (27 messages🔥):

• S3 Storage Advice: @lhc1921 suggested considering S3 cloud or on-premises storage.
• Python Problems Resource: @b1llygamidesuyo shared a link to a Google Drive folder containing approximately 5 terabytes of Python course material.
• Streamlit Button Inquiry: @infinityexists. inquired about how to add a button (having an image or PNG) in Streamlit.
• ContextualCompressor Prompt: @legendary_pony_33278 asked for help about the prompt for using ContextualCompressor in a non-English language.
• Discussion on NexusRaven: Users @_egeres and @lhc1921 discussed the NexusRaven model. _egeres found out that this model can replicate the behavior of OpenAI's function calling API, while @lhc1921 expressed skepticism over the claim that it surpasses GPT-4.
• Hackathon Announcement: @glenn_sjobs, a senior software/AI engineer for the US Secretary of Defense, informed the community about an upcoming AI Hackathon that would include LangChain in the developer environment. He shared the official link for more information and the application process.
• VSCode Auto Import Issue: @solononforever asked for assistance with VSCode's auto import feature, which wasn't working properly for them.

▷ #langserve (1 messages):

• Session-level Memory Control: @cryptossssun inquired about controlling the length of chat history while using session-level memory in the RunnableWithMessageHistory function, particularly when using charact_prompt | llm and RedisChatMessageHistory(session_id, url=REDIS_URL).

▷ #share-your-work (2 messages):

• New Book on LangChain: User @tusharg shared a link to a new book on LangChain available on Amazon. The book provides insights on how to leverage LLMs' capabilities and explores their fundamentals, ethical dimensions, and application challenges.
• Cumuli, a new Chrome extension for AWS: @petrgazarov introduced Cumuli, a Chrome extension that adds a LLM chat panel to all AWS pages. It allows users to add screenshots of the console to their queries for context-aware responses. The extension uses GPT-4 Turbo with vision. The extension can be accessed on GitHub.

▷ #tutorials (1 messages):

• Latest Book on LangChain: @tusharg shared a link to a new book on Amazon which covers LangChain framework to develop applications like agents and personal assistants, web searches integration, and code execution. The book also focuses on leveraging LLMs' capabilities and working around their inherent weaknesses.

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Alignment Lab AI Discord Summary

• Progress in chunking documents for embedding to generate a multi-billion synthetic token dataset; demonstrated approach employs recursive segmentation into 512 length pieces with the title appended back before embedding, detailed by @emrgnt_cmplxty.
• Discussion on applying hierarchical search to the chunked documents, using the natural hierarchy of the web; an initial vector search indexes the leading chunk and title for each webpage, followed by a fetch of the full chunks for the top N matches, which are then re-ranked using the most similar chunk from each. The method amounts to an approximate 30 times reduction in the embeddings needed for the initial search stage.
• Mention of document summary preparation by @gabriel_syme, corroborating the preparatory processes shared by @emrgnt_cmplxty.
• Introduction to the Mergekit toolkit on GitHub for merging pretrained large language models, illustrated with a user model case: MetaMath-neural-chat-7b-v3-2-Slerp.
• Inquiry from @cryptossssun on the development of long context field abilities, target context recognition and extraction in AI models.
• Query on the fine-tuning process in comparison with a 7-billion parameter model, leading to a remark by @caseus_ on the model "qlora" under discussion, denoting its current state as less than satisfactory.

Alignment Lab AI Channel Summaries

▷ #general-chat (9 messages🔥):

• Chunking documents for embedding: @emrgnt_cmplxty discussed their approach to preparing documents for embedding, mentioning that they are chunking the documents recursively into 512 length chunks, and then appending the title back on before embedding.
• Generating a multi-billion synthetic token dataset: @emrgnt_cmplxty stated the approach of preparing the documents for embedding will help generate a multi-billion synthetic token dataset without worries about quality.
• Hierarchical Search: @emrgnt_cmplxty clarified that by hierarchical search, they mean they use the natural hierarchy of the web to perform the search. They index the leading chunk + title for each webpage and use that as the first vector search. They then fetch the full chunks for the top N matches and use the most similar chunk from each webpage to re-rank, which results in approximately a factor of 30 reduction in the embeddings needed to search over at the first stage.
• Preparation for document summaries: @gabriel_syme mentioned that they are also preparing for a similar process with document summaries.

▷ #oo (3 messages):

• Mergekit Toolkit for Merging Language Models: @lightningralf shared a link to the Mergekit toolkit on GitHub, which offers tools for merging pretrained large language models.
• Use of Mergekit in MetaMath-neural-chat Model: Further elaborating on the usability of Mergekit, @lightningralf provided an example of MetaMath-neural-chat-7b-v3-2-Slerp, a model by @Weyaxi, which used Mergekit for model merging.
• Inquiry about Long Context Field: @cryptossssun raised a query on whether anyone is focusing on aspects such as the long context ability and target context recognition and extraction.

▷ #open-orca-community-chat (2 messages):

• Fine-tuning Inquiry: @emrgnt_cmplxty asked about the fine-tuning process and the comparison of a model to the 7-billion parameter model.
• Model Quality: In response, @caseus_ clarified that the model being discussed is a qlora and commented that it's not that good.

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Latent Space Discord Summary

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

• QA Agents: User binary6818 shared information about a project called camelQA which offers AI services for testing mobile apps, and asked if anyone knows similar projects.
• Converting Scanned Books to Chat: User .kareemk asked for recommendations on open-source options for an OCR PDF to RAG pipeline to create a "chat with my scanned books". User coffeebean6887 suggested that most OCR libraries should work fine with printed and scanned text but more complex documents like hand-written notes may require advanced OCR models.
• Anyscale LLM Performance Benchmark Criticism: User guardiang mentioned that Anyscale is receiving criticism for the LLM performance benchmark they published yesterday, sharing this link to the topic.

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Skunkworks AI Discord Summary

• User @tomsegura in the general channel expressed optimism about the Skunkworks AI community and its potential for significant advancements, stating "I do truly believe that someone like you guys are the future and are going to be what brings us real advancements that real people (us) can use."
• A discussion initiated by @cyborgdream in the datasets channel about the potential applications of a State Of The Art synthetic-generated dataset, comprising of 1-2 billion tokens, intended to benefit the Open source AI community.
• The off-topic channel featured a conversation about a tweet linked by `@shreepandey’. The tweet highlighted a significant decrease in latency within a real-time voice chat tutor developed by Smarterchild.chat. This sparked interest about the methods employed to achieve this improved performance.

Skunkworks AI Channel Summaries

▷ #general (2 messages):

• User @tomsegura expressed support and belief in the potential of the Skunkworks AI community, stating "I do truly believe that someone like you guys are the future and are going to be what brings us real advancements that real people (us) can use."

▷ #datasets (2 messages):

• Potential Synthetic Generated Dataset: User @cyborgdream enquired about the desirable area of interest for a State Of The Art synthetic dataset of about 1-2B tokens that could be beneficial for the Open-source software AI community.

▷ #off-topic (2 messages):

• Decrease in Latency of Speech Feature: @shreepandey shared a link to a tweet from @8teAPi. The tweet discusses the real-time voice chat tutor developed by Smarterchild.chat that has significantly reduced latency. @shreepandey and @skaios were interested in knowing how this reduction in latency was achieved.

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DiscoResearch Discord Summary

• Users inquired about the performance of qLoRA, with @tcapelle asking how it's determined that qLoRA doesn't perform well.
• @tcapelle further suggested model fine-tuning via freezing the top layers of the model, rather than using LoRA or qLoRA.
• A new guild member, @philipmay was introduced and expressed gratitude for the invitation.
• A productive dialog was triggered by @jiha, who shared a YouTube video titled "OpenDalle - Like running Dall-e Local", showcasing OpenDalle’s capabilities.
• @datarevised was recognized for creating the OpenDalle video shared in the chat and @jiha appreciated the quality of the video, expressing that OpenDalle seems cool based on the video content.

DiscoResearch Channel Summaries

▷ #mixtral_implementation (2 messages):

• Query on QLoRA Performance: @tcapelle asked for information about how it's determined that qLoRA doesn't perform well.
• Suggestion for Model Fine-tuning: @tcapelle proposed that rather than using LoRA or qLoRA, it might be beneficial to try freezing the top layers of the model.

▷ #general (4 messages):

• Introduction of new member: @philipmay joined the chat and expressed gratitude for the invitation.
• Discussion on OpenDalle Video: @jiha shared a YouTube link to a video titled "OpenDalle - Like running Dall-e Local", which appears to showcase the capabilities of OpenDalle.
• Creator of the OpenDalle Video: @datarevised confirmed they created the OpenDalle video shared in the chat.
• Appreciation for OpenDalle Video: @jiha complimented the quality of the video and expressed that OpenDalle seems cool based on the video content.

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LLM Perf Enthusiasts AI Discord Summary

• User @emfastic has developed an internal tool employing decorator syntax and expressed future intentions for it to go open-source.
• Minimal usage of embeddings by @emfastic, else there would be a consideration of using llamaindex.
• An inquiry from swyxio surrounding the slowness issues facing some users, asking "how slow are you seeing it".

LLM Perf Enthusiasts AI Channel Summaries

▷ #general (2 messages):

• User @emfastic mentioned that they crafted an internal tool using decorator syntax and are planning to open source it soon.
• @emfastic also stated that they are not making extensive use of embeddings, else they would consider using llamaindex.

▷ #speed (1 messages):

swyxio: how slow are you seeing it

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MLOps @Chipro Discord Summary

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

• Issue with XGBoost on Apple M Pro Series: User @leg4l reported an issue with running XGBoost on a Macbook Pro with Apple M Pro Series, where the application utilized the efficiency cores but exerted minimal pressure on the performance core. A possible GitHub discussion related to this problem was shared: Incomplete CPU utilization with M1 Pro, M1 Max, and M1 Ultra architectures - Issue #8320