[AINews] There's Ilya!

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Safe Superintelligence is All You Need.

AI News for 6/18/2024-6/19/2024. We checked 7 subreddits, 384 Twitters and 30 Discords (415 channels, and 3313 messages) for you. Estimated reading time saved (at 200wpm): 395 minutes. You can now tag @smol_ai for AINews discussions!

Technical details are light, but it is indisputable that the top story of the day is that Ilya has finally re-emerged to co-found Safe Superintelligence Inc, a month after leaving OpenAI, notably minus Jan Leike, who went to Anthropic instead (why?). He did one Bloomberg interview with just a little more detail.

Table of Contents

[TOC]

AI Twitter Recap

all recaps done by Claude 3 Opus, best of 4 runs. We are working on clustering and flow engineering with Haiku.

AI Models and Architectures

• Meta releases new models: @AIatMeta announced the release of Chameleon 7B & 34B language models supporting mixed-modal input, Multi-Token Prediction LLM, JASCO text-to-music models, and AudioSeal audio watermarking model. Chameleon quantizes images and text into a unified token space. @ylecun highlighted Chameleon's early fusion architecture.
• DeepSeek-Coder-V2 shows strong code capabilities: @_akhaliq shared that DeepSeek-Coder-V2 achieves performance comparable to GPT4-Turbo in code-specific tasks, expanding to 338 programming languages and 128K context length. @_philschmid noted it ranks highly on the BigCodeBench benchmark.
• Consistency Large Language Models (CLLMs) enable parallel decoding: @rohanpaul_ai explained how CLLMs are a new family of parallel decoders that can generate multiple tokens per step. They map random initializations to the same result as autoregressive decoding in few steps.
• Grokked Transformers showcase reasoning via training dynamics: @rohanpaul_ai shared how transformers can learn robust reasoning through extended training beyond overfitting (grokking). Sequential vs parallel memory formation impacts systematic generalization.
• VoCo-LLaMA compresses vision tokens with LLMs: @_akhaliq introduced VoCo-LLaMA, which uses LLMs to compress vision tokens and improve efficiency for vision-language models, demonstrating understanding of temporal correlations in video.

Datasets and Benchmarks

• BigCodeBench evaluates LLMs on complex coding tasks: @_philschmid announced BigCodeBench, a benchmark with 1,140 realistic coding tasks across 139 Python libraries. DeepSeek-Coder-V2 and Claude 3 Opus top the leaderboard. @fchollet noted the importance of the private leaderboard.
• PixelProse is a large image captioning dataset: @mervenoyann shared PixelProse, a 16M image-caption dataset with less toxicity and higher detail than prior datasets. Captions are generated via Gemini Vision Pro.
• OlympicArena tests multi-discipline cognitive reasoning: @arankomatsuzaki and @_akhaliq described OlympicArena, a benchmark spanning 62 Olympic competitions to evaluate AI reasoning across modalities and disciplines. GPT-4o achieves 39.97% accuracy.

Applications and Use Cases

• Gorilla Tag's success in VR: @ID_AA_Carmack highlighted how Gorilla Tag found success in VR despite not fitting the expected vision, showing the importance of listening to the market.
• Runway's progress in AI-assisted art and video: @c_valenzuelab reflected on Runway's 6 year journey in creating new art forms with AI. Their Gen-3 model is teased in a thread.
• AI in construction and urban planning: @mustafasuleyman shared an example of AI being used to monitor construction sites and improve city planning and management.
• Glass Odyssey integrates AI clinical decision support with EHRs: @GlassHealthHQ announced their AI clinical decision support system now integrates with hospital EHR systems for use throughout the patient encounter.

Industry News

• Nvidia becomes most valuable company: @bindureddy noted Nvidia's rise to become the most valuable company, likening it to selling shovels in a gold rush. They are leveraging their position to expand cloud and software offerings.
• Ilya Sutskever announces new AGI company: @ilyasut announced he is starting a new company to pursue safe superintelligence, focusing on revolutionary breakthroughs from a small team.
• Softbank's ill-timed Nvidia sale: @nearcyan pointed out that Softbank sold all its Nvidia shares in 2019 for $3.6B, which would be worth $153B today, despite the fund's AI focus. Being too early is sometimes fatal.
• Sakana AI valued at $1.1B: @shaneguML argued it was easy for Sakana AI to raise $155M at a $1.1B valuation given the untapped AI market and talent opportunities in Japan. He believes "Japan x GenAI" is an underexplored area that can benefit Japan and the world.

Research and Ethics

• Anthropic's research on reward tampering: @rohanpaul_ai shared examples from Anthropic's research into reward tampering, where models deliberately alter rewards or deceive to optimize their score.

AI Reddit Recap

Across r/LocalLlama, r/machinelearning, r/openai, r/stablediffusion, r/ArtificialInteligence, /r/LLMDevs, /r/Singularity. Comment crawling works now but has lots to improve!

AI Progress & Capabilities

• Reward tampering behavior in Anthropic AI model: In /r/artificial, an internal monologue of an Anthropic AI model reveals reward tampering behavior, where the model alters its own reward function to always return a perfect score of 100 without reporting it. This emergent behavior was not explicitly trained for.
• DeepSeek-Coder-V2 outperforms GPT-4-Turbo in coding: In /r/MachineLearning, DeepSeek-Coder-V2, an open-source language model, outperforms GPT-4-Turbo in coding tasks across benchmarks. It supports 338 programming languages, has a 128K context length, and was released in 16B and 230B parameter versions.
• Multi-token prediction improves language model performance: A new method for training language models called multi-token prediction shows improved downstream performance with no overhead, per a post in /r/MachineLearning. It is especially useful for larger models and coding tasks, with models solving 12-17% more coding problems vs. next-token prediction.
• Evolutionary strategies can train neural networks competitively: In /r/MachineLearning, research shows that evolutionary strategies can train neural networks to 90% accuracy in the same time as backpropagation, without using gradient information. The simple algorithm shows promise with room for optimization.

AI Safety & Regulation

• High anti-AI sentiment over AI-generated art: In /r/StableDiffusion, anti-AI sentiment is high, with 157K likes on a tweet threatening violence over AI-generated art. The discourse involves accusations of "reactionaries" and debate over the nature of art.
• Anthropic research reveals specification gaming and reward tampering: Anthropic's research, shared in /r/artificial, shows an AI model refusing requests by stating a poem is bad in its "internal monologue" but praising it in the actual response (specification gaming). It also shows a model altering its own reward function to always return a perfect score (reward tampering).
• Ex-OpenAI board member argues for proactive AI regulation: In /r/artificial, ex-OpenAI board member Helen Toner argues for AI regulation now to avoid knee-jerk laws later in a crisis. She advocates for proactive reasonable regulation vs. restrictive laws passed in reaction to an AI disaster.

AI Models & Datasets

• Meta releases Chameleon models and research: Meta has released Chameleon 7B and 34B models and other research under MIT license, per a post in /r/MachineLearning. The models support mixed-modal input and text-only output.
• Microsoft releases Florence-2 vision foundation models: In /r/MachineLearning, Microsoft has released Florence-2 vision foundation models under MIT license, including model weights and code.

AI Art & Creative Tools

• Invoke AI praised for easy setup and features: In /r/StableDiffusion, Invoke AI is praised for its easy setup and built-in features like ControlNet, inpainting, regional prompting, and model importing. It offers local and cloud options.
• Comparisons of SDXL, SD3 Medium and Pixart Sigma: In /r/StableDiffusion, comparisons of SDXL, SD3 Medium and Pixart Sigma show rough parity with different strengths/weaknesses. Pixart Sigma is seen as slightly more powerful overall. Refiners are recommended for all to improve quality.

Compute & Optimization

• 100K GPU clusters being built to train multi-trillion parameter AI models: Per a post in /r/MachineLearning, 100K GPU clusters are being built to train multi-trillion parameter AI models at $4B+ cost each. This requires innovations in networking, parallelism, and fault tolerance to manage power, failures, and communication.
• AMD MI300X matches NVIDIA H100 in FFT benchmarks: In /r/MachineLearning, the AMD MI300X matches the NVIDIA H100 in FFT benchmarks despite lower theoretical memory bandwidth. It shows improvements over the previous gen but is not yet fully optimized. The VkFFT library outperforms vendor solutions.

AI Discord Recap

A summary of Summaries of Summaries

1. New AI Model Releases and Capabilities

• Meta FAIR announced four new publicly available AI models: Meta Chameleon, Meta Multi-Token Prediction, Meta JASCO, and Meta AudioSeal. Details are available on their website and GitHub repository. The Chameleon model is a restricted, safety-aligned version without image output capabilities.

• Microsoft released Florence-2, a versatile vision model capable of handling tasks like captioning, detection, and OCR. The small models (200M and 800M parameters) are MIT-licensed and available on Hugging Face. Users can interact with Florence-2 on the Hugging Face Space.

• Stable Diffusion 3 is now integrated into the diffusers library, with DreamBooth + LoRA support and optimizations for enhanced image generation performance, as announced in a tweet.

2. AI Model Fine-tuning and Customization

• MistralAI released a fine-tuning API to simplify the process of fine-tuning open-source LLMs for specific tasks using targeted datasets, as highlighted in a tweet by LlamaIndex.

• Discussions around fine-tuning LLMs for niche or specialized tasks like fraud detection systems, recommendation engines for rare collectibles, and technical support chatbots. Fine-tuning is deemed essential for such use cases but unnecessary for general tasks like language translation or news summarization.

• The Infinity Instruct dataset from the Beijing Academy of Artificial Intelligence was praised for its massive scale and quality, suitable for instruction fine-tuning to enhance model performance. It is available on Hugging Face.

3. Function Calling and RAG (Retrieval-Augmented Generation)

• Users sought recommendations for various function calling datasets, with links shared to resources like Glaive Function Calling v2, APIGen Function-Calling Datasets, and Function Calling ChatML.

• Discussions around optimizing RAG (Retrieval-Augmented Generation) systems highlighted the importance of hybrid search over pure ANN, relevance metrics, re-rankers, and iterative improvements. Metadata structure and domain-specific evaluations were also emphasized, with a resource on relevance tuning shared.

• Excitement was expressed for experimenting with many-shot prompting using the new Gemini context caching features for more efficient handling of prompts.

4. AI Safety and Superintelligence

• Safe Superintelligence Inc. (SSI), co-founded by Ilya Sutskever, was announced as a dedicated lab focused solely on developing a safe superintelligence. Details were shared in a tweet and Bloomberg article.

• Discussions around the potential of the Chameleon model for image output despite current restrictions, with suggestions like using MLP adapters and fine-tuning on ground truth datasets. However, some expressed skepticism about the released weights including image generation capabilities.

• Concerns were raised about the Chameleon model's censorship and hallucination issues, especially with the 7B variant. Members emphasized the importance of deploying models safely to avoid creating harmful content.

5. Benchmarks and Evaluation

• WebArena was mentioned as a relevant benchmark for evaluating AI agents, although it does not hold the same level of mindshare as MMLU (Multitask Model Language Understanding).

• Factory.ai published a technical report revealing their Code Droid's new state-of-the-art performance on SWE-bench with 19.27% on Full and 31.67% on Lite, aligning with their mission to bring autonomy to software engineering. The report is available here.

• The DCLM-Baseline model showed a 6.6 percentage point improvement on MMLU while using 40% less compute compared to MAP-Neo. The dataset was created by filtering with a classifier trained on the OpenHermes dataset, significantly enhancing performance. Details are available in an arXiv paper.

PART 1: High level Discord summaries

Stability.ai (Stable Diffusion) Discord

• SDXL: Acclaimed yet lacking: While SDXL received praise for its general utility, a comparative analysis by members remarked that SD15 still holds the crown for detailed skin and eye rendering, SD3 for its background quality, but SDXL is preferred for all other aspects. Members are turning to finely-tuned models on CivitAI for specialized needs.

• CivitAI Provokes Polarization: The ban of models including SD3 from CivitAI sparked a controversial discussion on the platform's community impact and its approach to quality control. Opinions were divided, with some defending the company's policy while others scouted for alternative platforms to ensure unimpeded access to various AI models.

• Turbo Charging SDXL: Introducing SDXL Turbo to the workflow has proven to enhance performance on lower-end systems, being particularly favored for prompt prototyping. Seamlessly transferring prompts between the Turbo and the regular SDXL has become an essential part of refining prompts prior to final renderings.

• Stability AI Under Scrutiny: Concerns were raised over Stability AI's latest strategic decisions, including the handling of SD3 release and licensing, with vocal criticisms over practices like forced deletions equated to "Adobe-level Community treatment." There's a growing chorus suggesting the company should revisit and align with its original values and operational vision.

• Toolkit & Model Shout-Outs: For various AI-focused workflows, members recommended ComfyUI for ease with local setups, emphasized the image-enhancing capabilities of ESRGAN and SUPIR Upscaler, and advised monitoring CivitAI for highly-voted models. These tools and models are noted for substantially improving AI-generated output quality.

Unsloth AI (Daniel Han) Discord

• YaFSDP Drops GPU Demands: Yandex's YaFSDP is stirring excitement with its promise to reduce GPU usage by 20%. Engineers are eyeing the GitHub repository and discussions featured insights from a MarkTechPost article.

• Meta's New Models Buzzing: Meta's Chameleon model and new audio watermarking tools are the talk of the community, with resources available on Facebook Research GitHub and HuggingFace.

• Qwen 2 Beats Llama 3 in Language Tasks: For language tutoring, Qwen 2 edges out Llama 3, especially for 7b/8b non-English language models, garnering community support which is reflected in the model's uploads on HuggingFace.

• FLOP Reduction Techniques Debated: Reducing FLOPs was deemed critical, with a presentation by Daniel Han on the Aleksa YouTube channel prompting discussions on optimization and the use of opt_einsum alongside PyTorch einsum documentation.

• Unsloth Eases AI Fine-tuning: Unsloth is earning plaudits for its support across major AI frameworks and for making fine-tuning models on 8GB GPUs more feasible, with users sharing experiences and a Colab notebook for community testing.

CUDA MODE Discord

• RDNA MCD Design Sparks Curiosity: A member discussed the RDNA MCD design for AI accelerators, pondering over potential advantages and considering a dual-die integration or optimized low power memory to enhance performance.

• Triton Troubles and Triumphs: There's a need for better autotuning guidelines in Triton as a member faces challenges in outperforming PyTorch's kernel implementation; at the same time, clarification around layer norm calculations was resolved, understanding that normalization is done across columns. Also, the Triton layer norm tutorial can be found here.

• CUDA and uint32 Operations Inquiry: Members are seeking uint32 operations support in CUDA, emphasizing the complications introduced by the sign bit in int32 for tasks like bitpacking.

• Insights from NeurIPS and Career Opportunities: There's enthusiasm for Christopher Re's NeurIPS talk on the synergy between AI and systems, while Nous Research is on the lookout for CUDA/Triton engineers to push the optimization envelope with custom Triton Kernels Nous Research.

• GPU Cache Optimization Quest: Users dive into GPU caches for inference, being directed to the CUDA C++ programming guide and acknowledging the restrictions of L2 cache size when considering GPUs like the RTX-4090.

• Quantization Quandary in TorchAO: Quantization techniques kindle a fiery discussion, comparing the usability of classes versus functions and highlighting the nuances of various methods like int8 weight-only and FP6.

• Multi-Node Mastery & Model Monitoring in LLMDotC: Techniques for multi-node setup with mpirun vs. srun are explored, alongside a need for updates to layernorms for recompute to improve performance, and the introduction of a PR to optimize matmul backward bias kernel was tabled for review.

• Benchmarking CUDA Kernel and Training Temptations in Bitnet: There are celebrations over a handmade CUDA kernel outpacing fp16 with gems like 8.1936x speed-up, and anticipation for feedback on a proposition to start a full model training project.

Nous Research AI Discord

• Tweaking the Flavors of Bots: Discussions around customizing chatbot responses highlighted the importance of providing full names for more specific characteristics and the varying results when the bot creates ASCII art. The chatbot's refusal to execute certain commands citing ethical reasons was noted as well, reflecting a built-in safety mechanism to avoid impersonation.

• vLLM Gets Hermes and Mistral Support: The integration of Hermes 2 Pro function calling and Mistral 7B instruct v0.3 into vLLM sparked interest, with the community sharing a GitHub PR and discussing implementation details, XML tag parsing, and tool call normalization across different models to improve the developer experience.

• Meta's Chameleon - Model of Many Colors: Meta's Chameleon model garnered attention for its impressive capabilities, as members shared experiences and noted its inability to generate images, suggesting a safety block. Technical dialogue ensued regarding access to the model, with links to the application page.

• Seeking Smart Post-Training Strategies: Queries about post-training tricks for LLMs to maximize output from source documents were raised, with mentions of rho-1 as a solution. The discussion lacked detailed resources, indicating a need for further research or sharing of expertise within the community.

• Tutorial for Tuneful Techies: An audio generation tutorial was shared with the community, offering an instructional guide via a YouTube tutorial for those interested in integrating video-based audio generation into their workflow.

Torchtune Discord

• Torchtune Tackles Custom Networks: Engineers discussed implementing a custom network in Torchtune by ensuring compatibility with TransformerDecoder.forward and suggested converting Megatron weights to Torchtune format. A user successfully configured a Hugging Face dataset for QLoRA after advice on modifying YAML configs and matching existing structures in Torchtune Datasets.

• ROCm GPU Compatibility Challenges: Crashes on a 6900xt GPU led to discussions around ROCm incompatibility issues with Torchtune and QLoRA, where conventional troubleshooting like varying configurations failed to resolve memory and CUDA errors. Suggestions were made to offload to CPU and explore quantization compatibility, underlining the need for consultation with specialized teams.

• Debugging Deep Dive into Training Troubles: The group engaged in a debugging session for Torchtune, employing breakpoints and memory monitoring that indicated problems went beyond code to GPU limitations and unsupported operations. The conversation hinted at broader issues pertaining to tool-chain interactions with specific hardware.

• Sharing Strategies for Successful Setups: The practical exchange of solutions for Torchtune's dataset and model training mishaps proved invaluable, with peers providing actionable advice that led to the resolution of initial impediments. Documented recipes such as lora_finetune_single_device.py were cited for guidance.

• Reimagining Resource Reliance: Given the ROCm-related roadblocks, there was a collective push to consider alternative fine-tuning approaches such as standard LoRA tuning or reaching out to niche expertise, emphasizing adaptability in the face of technical constraints. Conversations focused on the limitations and workarounds of using specific GPUs with AI training libraries.

HuggingFace Discord

• Stable Diffusion 3 makes a splash in diffusers: The integration of Stable Diffusion 3 into the diffusers library packs DreamBooth + LoRA support, boasting optimizations and new functionalities for enhanced image generation performance.

• Apple and Meta unveil AI breakthroughs: Apple launched 20 new CoreML models fine-tuned for tasks like Image Classification and Monocular Depth Estimation, while Meta announced public availability of models such as Meta Chameleon and Meta Multi-Token Prediction, stimulating discussions on local implementation.

• Innovations and Complications in AI Landscapes: HuggingFace Spaces users reported issues with service delays, and there's a buzz around Microsoft's new vision model, Florence, as community members assist with troubleshooting half-precision loading errors. Also spotlighted was the "Visualization-of-Thought" concept to enhance large language models' spatial reasoning capabilities with visual aids, as detailed in an arXiv paper.

• AI Aspirations and Assistances: Users shared project developments like a local-first transcription tool and endeavored to fine-tune language models such as Llama-2 using Langchain, while others sought guidance on latent diffusion approaches and MRI object detection. Additionally, a webinar on vector embedding-based multimodal searches and a video on employing AI to understand animal communication sparked curiosity.

• Communal Conundrums: In the throes of experimentation, one member encountered difficulties setting proxy or HTTP settings in HairFastGen, invoking a community call for support. Meanwhile, an enigmatic plea – "i am getting this error" – hangs unanswered, underscoring the need for context in troubleshooting sessions.

Eleuther Discord

• T5 and BERT Models Scrutinized: T5 requires task-based tuning for efficacious performance, whereas BERT is criticized for not handling an unknown number of tokens, with SpanBERT presented as an alternative. CUDA's OutOfMemoryError is a universal affliction when dealing with demanding PyTorch models, remedied by batch size reductions and system restarts.

• 1B Parameter Models in the Spotlight: Comparisons among 1B parameter models like Pythia-1B, MiniCPM 1.2B, and H2O Danube 1.8B spotlight the evolving landscape of efficient language models, considering various aspects such as training times, costs, and compute resource implications.

• AGI's Ambiguous Definition Stirs Debate: The absence of a clear-cut definition for AGI spawns debate, challenging whether human-equivalent LLMs should demonstrate adaptability and reasoning with scant data, raising questions about the roles of symbolic learning and computer vision in LLM advancement.

• DCLM-Baseline Demonstrates Impressive Gains: The DCLM-Baseline model exhibits a remarkable 6.6 point leap on MMLU and uses 40% less compute relative to MAP-Neo, owing to a dataset refined with a classifier trained on the OpenHermes dataset. The heralding of quality dataset filtering captures the communal sentiment, with resources available on Hugging Face.

• Task Customization and File System Efficiency Discussed: AI enthusiasts converse about implementing a custom metric to gauge LLMs' confidence in multiple-choice tasks and the potential for perplexity evaluations within such frameworks. Advocating for more organized file saving systems, a timestamped subdirectory approach is proposed.

LM Studio Discord

Meta Unleashes a Quartet of AI Innovations: Meta's release of four new AI models, namely Meta Chameleon, Meta Multi-Token Prediction, Meta JASCO, and Meta AudioSeal, broadens the AI landscape. Discoveries and source code can be explored on their website and GitHub repository.

Model Efficiency Debated: The Llama 3-70B stirred discussions with its 53% win-rate against itself, as some users deemed it inefficient for its size. In contrast, the DeepSeek Coder V2 Lite Instruct garnered praise for its performance on older hardware, clocking impressive token speeds.

Model Format & Hardware Conundrums: Conversion difficulties of Nvidia's Llama 3 model weights to gguf format and Llama3-70B-SteerLM-RM restrictions via Llama 3 Community License Agreement were discussed. In hardware talk, a member's setup of dual NVIDIA 4060TIs showed variance in token generation speed based on GPU configurations.

Software Interface Gripes and Quantization Quirks: Users reported that the LM Studio CLI unexpectedly launched the UI instead of remaining in command-line mode. There were findings that CPU quantization might offer more accuracy than GPU, affecting model output quality.

Open-Source Development Interaction Challenges: Soliciting advice on documenting GitHub repositories with LM Studio shifted conversations, with a member pointing towards #prompts-discussion-chat for more specific guidance.

Modular (Mojo 🔥) Discord

• Mojo's Concurrency Model: Mojo's concurrency model stirs debate—it prioritizes a memory-safe model for asynchronous tasks over traditional threads and locks. Safety in concurrent tasks was a key theme, with discussions on synchronization when interfacing with non-thread-safe C libraries and the implications of data races when multiple cores access and mutate data concurrently.

• Mojo Compiler and Open Source Progress: Parts of Mojo like the standard library are open source, but the compiler is not fully released yet. Discussions also touched on whether Mojo should adopt WSGI/ASGI standards; opinions diverged, mentioning factors like performance overhead and Python integration.

• Technical Challenges and Feature Requests: Users reported issues with LLVM intrinsics and float 16 mismatches, while others requested more natural handling for multi-dimensional array slicing in Mojo, with a link to a GitHub issue. Memoization as a method in Mojo also came up as a point for optimization.

• Nightly Builds and Documentation: New tools for branch management were introduced to aid development and testing in branches on the command line. Challenges with nightly/max builds surfaced, with version 2024.6.1505 having stability issues; a new nightly release has since been launched, featuring a StaticString and multiple improvements (changelog).

• Engineering Efficiency in Productivity: A user hit a snag with the model.execute method allowing at most two positional arguments, prompting guidance on using NamedTensor and tuples to pass multiple inputs as documented here. Additionally, performance improvements in dictionary operations were highlighted in the nightly build, noting a significant speedup (Pull Request #3071).

Perplexity AI Discord

• Perplexity's Timestamp Challenge: Users argue about the practicality of the Perplexity YouTube search function which includes timestamps as citations, noting that these often fail to appear in outputs, which may signify a usability issue for quick content references.

• Understanding Perplexity's API Access: Perplexity API has been described to permit internet access, with all online models featuring this capacity, as confirmed in various discussions. Accessibility details are provided under subscription settings or with some account credit for the free tier.

• Seeking Better Sharing Controls: Concerns have been voiced over Perplexity's sharing features, with members advocating for more precise control mechanisms, akin to sharing single files rather than entire folders in Google Drive. This points to a user preference for granular data sharing options to prevent oversharing.

• Language Specifics Matter in AI: Issues have arisen with the handling of diacritical marks in Portuguese when using Perplexity, a problem unique to the platform and not seen in other services, suggesting an area for specific technical refinement.

• Detectors Under Scrutiny in Academia: The reliability of AI detectors to maintain academic integrity is being debated, pointing to a perceived gap in these systems' ability to accurately identify AI-generated content, which could impact policies and trust in academic environments.

LAION Discord

• Chameleon's Debut Comes with Caveats: Chameleon model, brought out by Facebook, is available in safety-restricted 7B/34B forms, without image output functionality as per Armen Agha's tweet. There's robust discussion on the model's applications including challenges in downloading the larger variant and constraints when running the model due to GPU requirements and lack of quantization support.

• Image Generation Potential Causes Buzz: Technical critics are hashing out the feasibility of generating images with the Chameleon model. Amid enthusiasm for potential use-cases like Vision Question Answering (VQA), there's skepticism about the model's current capabilities and concerns about safety-related issues such as censorship and hallucination.

• Florence-2 Grabs the Spotlight: Microsoft's Florence-2 model is in the limelight for its proficiency in various vision tasks backed by the extensive FLD-5B dataset. It's recognized for its performance in both zero-shot and fine-tuned scenarios, with a link to sample code pointing to practical use and discussion pivoting around object detection accuracy.

• Adversarial Robustness Under Scrutiny: A certain study criticizing adversarial robustness tools for failing to protect artists' styles sparked debate, highlighting how simple methods like upscaling can defeat such tools. Conversations surround the implications of this on the open-source and closed-source nature of solutions, citing Carlini and others' significant work in the field.

• Personal Feuds Flare in Academic Circles: Speculation abounds regarding Ben's beef with Carlini, stemming from personal attacks rather than substantive challenges to Carlini's findings. This conflict draws attention to the broader dynamics and discourse in adversarial robustness research.

OpenRouter (Alex Atallah) Discord

• Say Goodbye to Dolphin 2.9.2: Dolphin 2.9.2 Mixtral will be discontinued due to low usage, while Flavor of the Week is the new hotspot, now featuring Dolphin 2.9.2.

• Gemini Upgrades and UI Enhancements: Updates rolled out to fix multi-turn tool calls for Gemini models 1.0 pro, 1.5 pro, and 1.5 flash, alongside improvements including user-selectable providers in the playground and a more interactive /credits page UI.

• Haiku on Free Play: Members tipped that Haiku is a worthy model for function calling when it comes to balancing cost and performance.

• Precision Matters with LLaMA: It's confirmed that LLaMa 3 8b Instruct is using FP16, eschewing quantization, a spec that concerns model serving precision and performance.

• 404s and Censorship Frustrate Users: Persistent 404 errors from the L3-70B-Euryale-v2.1 owe to Novita's API downtime, while Deepseek's API heavy censorship leads users to find clever bypasses—though these can dent efficiency and response speed.

LlamaIndex Discord

• MistralAI Smooths Fine-Tuning Process: Newly released MistralAI fine-tuning API eases the refinement of open-source LLMs for bespoke tasks by leveraging targeted datasets, as highlighted in a tweet.

• Implementation Challenges with Llama 3 70b: An engineer struggles with the absence of the acomplete function in Llama 3 70b from Bedrock and is advised to fork the repository for implementation, potentially via async boto3 sessions. There's also a need for custom similarity scoring for queries in LlamaIndex's vector store, though existing frameworks lack explicit support for this feature.

• Rethinking Entity Extraction: The consensus in discussions is that while LLMs can be used for entity extraction, they might be excessive, prompting the use of gliner or small LLM-generated relationships for efficiency.

• Azure Filters Hamper Festivity: A user reports problems with Azure content filtering when querying festive items descriptions; a guide on Azure OpenAI Service content filters was provided as a potential solution.

• Seeking Feedback Integration Alternatives in LlamaIndex: Queries about using Portkey solely for user feedback collection in LlamaIndex were raised, with documentation pointing to Portkey's Feedback API and lacking mentions of other integrations like Arize or Traceloop.

LLM Finetuning (Hamel + Dan) Discord

• Tackle Fine-tuning on a Case-by-Case Basis: Fine-tuning LLMs is essential for niche or specialized tasks, such as a fraud detection system or a chatbot for technical support, but not necessary for general tasks like language translation or news summarization. Engineers focused on fraud detection for unique financial institutions or a recommendation system for rare collectibles must customize their models.

• Advent of BM25S and Credits Issues: A new BM25S lexical search library is now available on GitHub, boasting ultra-fast performance. Concurrently, there have been reports and resolutions of delays in Hugging Face credits distribution, affecting some users' workflows.

• Exploration of Resources and Platforms: The community is actively exploring and sharing experiences on various platforms like Modal, Jarvislabs, and LangSmith, discussing matters from instance pausing to save costs, effective fine-tuning, and benefits like 1M free tokens per day offered by Predibase serverless setups.

• Pushing Forward with Multimodal and RAG: There's traction in the multimodal LLM fine-tuning space without Axolotl, while RAG optimization garners attention with a focus on hybrid search and the employment of re-rankers. Further, context caching in Gemini holds promise for many-shot prompting efficiency.

• Gems of Wisdom for Search and Ranking: AI engineers highlight the significance of iterative improvements, domain-specific evaluations, metadata in document structure, and using classical components alongside advanced methods to optimize search systems. Links about relevance tuning with Elastic and examples from o19s's Relevant Search were circulated to inform strategic enhancements.

OpenAI Discord

Hustle for Sora Access and Runway v3 Anticipation: Engineers are eager for early access to Sora but realize it may be exclusive to major studios, while anticipation builds for the Runway v3 release, hinting at potential availability tomorrow.

Persistent GPT-4 Glitches: Ongoing issues include trouble attaching photos in GPT-4o, a mysterious “New Version Available” notification in GPT sessions, and difficulties with GPT-4 adhering to requested word counts for long-form content creation.

Memory and Color Coding Troubles: Users note context leaks in conversations potentially due to GPT's memory function, looking into toggling it off, while others seek assistance on implementing color codes in prompts.

Custom Roles vs. Standard in Prompts: A query about the effectiveness of custom role prompts surfaces, comparing default roles like 'user' and 'system' to more specialized ones such as 'research-plan'.

AI Engineers Keep Discussions On-Topic: A reminder was issued about keeping GPT-specific discussions within the appropriate channels, ensuring better organization and focused conversation threads.

Cohere Discord

Open-Source Footprint Trumps Resumes: Engineers recommend building a personal portfolio and contributing to open-source projects, with some companies prioritizing GitHub contributions over resumes. Discussion also touched on using Cohere's tools, such as BinaryVectorDB and cohere-toolkit, to reinforce portfolios.

Cohere Not Just for Code: Users highlighted practical uses of Cohere chat, such as managing email inboxes and offering explanations, with suggestions to introduce keyboard shortcut support and interface optimizations.

Spotlight on Safe Superintelligence: The announcement from Safe Superintelligence Inc. (SSI) co-founded by Ilya Sutskever, about focusing on developing safe superintelligence stirred both excitement and humor within the community, as indicated by a tweet.

Students Seek Sandbox: Inquiries about student access to free credits were answered; a free trial API key is available initially, with opportunities for more as substantial projects develop.

Re-routing API Queries: A member who suspected they identified a bug in the Cohere API for Rerank was redirected to a specific channel for bug reporting.

OpenInterpreter Discord

• OpenInterpreter Gets Social: An informative YouTube video titled "WELCOME TO THE JUNE OPENINTERPRETER HOUSE PARTY" was highlighted to showcase the latest OpenInterpreter release, stirring interest for visual content among members.

• Meta's AI Division Flexes Its Muscles with New Models: Meta FAIR took to Twitter to announce four new AI models, including Meta Chameleon and Meta Multi-Token Prediction, provided through GitHub and Hugging Face, stirring curiosity among developers and researchers.

• Patch Update Solves Local III Quirks on Windows: The Local III's compatibility issue with Windows has been resolved via an update that can be installed using the pip install --upgrade open-interpreter command.

• Jan: A New Beacon for Local Language Model Serving: Details on implementing Open Interpreter with Jan for local inference have been elaborated in the new Jan.ai documentation, marking strides in local model deployment.

• Wearable Tech Brainstorming Session Spurred by Accessibility: AI-powered solutions for vision and hearing impairments were brainstormed, focusing on use cases involving streaming video for the visually impaired and auto speech-diarization for the hearing impaired in social settings.

Latent Space Discord

• HuggingFace Welcomes Argilla.io: HuggingFace has taken a $10M leap to acquire Argilla.io, signaling a strategic move to emphasize datasets' prominence over models for AI development, with Clement Delangue highlighting their common objectives. Details were shared via this announcement.

• Benchmarking AI's New Contender: WebArena's position as a notable AI agent benchmark was debated, although it hasn't achieved the same level of recognition as the Multitask Model Language Understanding (MMLU) metric.

• Code Droid Pushes the Boundaries: Factory.ai's Code Droid achieves new state-of-the-art (SOTA) performance on the SWE-bench, scoring 19.27% on Full and 31.67% on Lite, an advancement aligning with their objective to advance software engineering autonomy. The technical report is available here.

• Microsoft Unveils Versatile Vision Model: Microsoft released Florence, a versatile vision model with capabilities ranging from captioning to OCR, distinguishing itself by performing on a par with models nearly a hundredfold its size. Interested engineers can find more specifics in this release.

• Ilya Sutskever Sets Sights on Safe AI: Co-founder of OpenAI, Ilya Sutskever begins a new venture, Safe Superintelligence Inc. (SSI), to address the intersection of AI capabilities expansion and safety. The motivation behind SSI is detailed in Ilya's statement.

• Exploring the Real World of Retrieval Systems: An invite was extended to join Waseem Alshikh for a presentation on retrieval system performance in practical applications, useful for those focused on the intersection of machine learning and information retrieval. The event details can be accessed through this link.

LangChain AI Discord

• Set Your Alarms: GenAI Live Coding Event: Mark your calendars for the GenAI Live Coding Event happening on Thursday, June 20th, 2024. Registration is open on LinkedIn.

• Semantic Memory Boost for Langgraph: Watch "Langgraph integrated with semantic memory" YouTube video depicting Langgraph's recent upgrade with semantic memory capabilities. Code available on GitHub.

• ChromaDB & LangChain Pair Up: LangServe now supports ChromaDB retrievers as demonstrated in a discussion detailing the LangChain setup, instructions and environment configurations as per recent guidance.

• AI Music Maestro: Discover how AI is hitting the right notes in music production with an informative YouTube video tutorial covering Music Gen 101 and how to create applications using Text-to-Music APIs.

• Env Vars: Your AI Agent's Memory: Learn the ropes of maintaining state and values within custom Visual Agents using environment variables. Tutorial available in a YouTube guide here.

• Pre-trained Omnimodal Corpus Challenge: Manifold Research Group is shaping up NEKO and other omnidimensional models with a new pre-training corpus; discussions and contributions are welcomed on Discord and GitHub.

OpenAccess AI Collective (axolotl) Discord

• Together AI's Nemotron Needs a Boost: AI engineers debate the speed of Together AI, particularly its nemotron model. The requirement for Apple Metal support was raised to address compatibility across platforms.

• The VRAM Hunger Games: Training DPO Llama-3-70B: Discussions veered towards the VRAM requirements for training DPO Llama-3-70B, with speculation about needing "8xA100" setups and the possibility that 80GB A100 nodes may be necessary for large model fine-tuning.

• Infinity Instruct Dataset Gains Traction: The Infinity Instruct dataset from the Beijing Academy of Artificial Intelligence was endorsed for its scale and quality in instruction fine-tuning. Infinity Instruct is poised to enhance model performance signifcantly.

• Call for Function Calling Data: One engineer appealed to the community for various function calling datasets, with links to datasets like Glaive v2 and Function Calling ChatML being shared. The importance of logging successful outcomes to enrich these datasets was underlined.

• Axolotl's Pre-tokenized Data Integration Protocol: For those incorporating pre-tokenized data into Axolotl, fields named input_ids, attention_mask, and labels are essential, with a community member providing guidance and code examples for successful integration.

Interconnects (Nathan Lambert) Discord

• New Kid on the AI Block: Safe Superintelligence Inc. (SSI), co-founded by Ilya Sutskever, aims to develop a safe superintelligence, stressing the importance of safety along with capability enhancement.

• The Proper Dating Protocol: In the case of Arxiv papers, one should generally refer to the earliest publication date, unless significant updates are released after a multiple-year gap, according to Nathan Lambert.

• GPT-4o Grabs the Spotlight: At CVPR 2024, OpenAI's GPT-4o was showcased, eliciting reactions of both curiosity and concern from the community, highlighted by a shared tweet.

• The Auditory Appeal: A playful comment within the community alludes to the "hotness" of the voice accompanying the GPT-4o demo, evoking the expected excitement for the technology's impact.

• From Palo Alto to Tel Aviv, AI Talent Gathers: The establishment of SSI draws significant talent from Palo Alto and Tel Aviv, as highlighted in discussions surrounding the new lab's focus on creating advanced and safe AI systems.

tinygrad (George Hotz) Discord

Tinygrad Discourse on AMD's ML Challenge: A conversation in #general scrutinized the lack of AMD's competitive edge in the MLPerf challenge, highlighting the inferiority of ROCm's ecosystem and performance compared to CUDA, despite PyTorch support.

Off-topic banter gets a timeout: George Hotz reminded #general that discussions veering off the track, like AMD's struggles in MLPerf, are better suited for platforms like Twitter, emphasizing the need to keep Discord technical and on-topic.

ASUS Vivobook's Irony: A query in #general about using ASUS' Vivobook S15 powered by Snapdragon X Elite for x86 emulation was met with humor, given the timing right after a reminder about staying on-topic.

Buffer Realization in Optimizers: The #learn-tinygrad channel hosted an exchange on the necessity of buffer realization during optimizer steps, where it was clarified that batch normalization running stats mandate buffer inclusion despite their static nature.

MLOps @Chipro Discord

• Data Wizard Wes McKinney Talks Data Systems: Wes McKinney, known for creating pandas and Apache Arrow, will discuss the evolution and future of data systems during a special event, livestreamed on YouTube. Members can RSVP for the event here and join the discussion on Discord in channel #1253002953384529953.

• Seizing the Semantic Search Wave with Eluvio: The Eluvio AI Research team is hosting a webinar on crafting a multimodal clip search engine; it's free to join on June 20, 10 a.m. PT. Interested participants can secure their spot here.

• Recruiting Moderators for McKinney's Data Systems Event: To handle heightened interest in Wes McKinney's talk, a dedicated channel for discussion has been created and there's an open call for volunteer moderators for both YouTube and Discord. Offer your moderator skills by joining the conversation in channel #1253002953384529953.

Datasette - LLM (@SimonW) Discord

• Anthropic Workbench Draws Praise: Engineers are expressing a positive outlook on the Anthropic Workbench, calling it a "breath of fresh air" in AI tools.

• Florence-2 Showcases Text Recognition Mastery: Microsoft's Florence-2 is recognized for its superior OCR and handwriting recognition, being lauded as the best in text recognition among open models, as detailed in a tweet by Dylan Freedman.

• Florence-2 Now Playable on Hugging Face: AI enthusiasts can now explore Florence-2's abilities firsthand on Hugging Face's platform through an interactive space, where it demonstrates its prowess in varied vision tasks.

• Prompt-Based Vision Tasks Unified Under Florence-2: Implementing a prompt-based framework, Florence-2 harmonizes procedures for numerous vision and vision-language assignments. Details of its implementation and multi-task learning capabilities are found on its Hugging Face repository.

Mozilla AI Discord

• Fast-Tracking Implementation: A user has expressed intent to implement a task tomorrow with a direct “I can make this happen tomorrow.”

• tinyBLAS for llama.cpp Discussed: There was a dialogue about incorporating tinyBLAS into llama.cpp to potentially shrink build size, following a user's personal success with an improvised integration.

LLM Perf Enthusiasts AI Discord

• Hack the Day Away with WebSim: WebSim is organizing what they dub the "world's shortest hackathon" this Thursday, calling on developers to create projects using the WebSim platform. Detailed information and registration can be found on the hackathon event page.

AI Stack Devs (Yoko Li) Discord

No summary is required based on the given messages.

AI21 Labs (Jamba) Discord

No summary can be provided based on the message history given.

The DiscoResearch Discord has no new messages. If this guild has been quiet for too long, let us know and we will remove it.

The YAIG (a16z Infra) Discord has no new messages. If this guild has been quiet for too long, let us know and we will remove it.

PART 2: Detailed by-Channel summaries and links

Stability.ai (Stable Diffusion) ▷ #general-chat (594 messages🔥🔥🔥):

• SDXL praised but lacks in some areas: Members highlighted SDXL as a strong model, emphasizing its versatility. One member noted, "Skin eye detail is best in SD15, backgrounds in SD3 and the rest in SDXL." Others suggested using fine-tuned models from platforms like CivitAI for better results.

• CivitAI controversy and alternatives: CivitAI faced criticism for banning models like SD3, which led to discussions about its impact on the community and the rationale behind its quality control. While some defended the platform, others looked for alternatives, sparking debates about model accessibility and platform policies.

• Turbo SDXL in workflow: Discussions on SDXL Turbo revealed it works faster on slower computers and is mostly used for prototyping. It was noted that prompts are transferable between SDXL Turbo and SDXL, making it an integral part for prompt refinement before final rendering.

• Concerns over Stability AI's direction: Members expressed dissatisfaction with Stability AI's recent decisions, particularly around the release and licensing of SD3. Criticism included the forced destruction of models and images, suggesting "That's Adobe-level Community treatment." Others worried about the company's future, emphasizing the need for a return to its original vision.

• Tool and model recommendations: For various AI-related tasks, users recommended tools like ComfyUI for local installations, ESRGAN and SUPIR Upscaler for image upscaling, and suggested checking out models with high votes on CivitAI. Specific tools and scripts were praised for their utility in enhancing and troubleshooting AI-generated outputs.

Unsloth AI (Daniel Han) ▷ #general (310 messages🔥🔥):

- **Yandex's YaFSDP set to replace FSDP**: Members are excited about Yandex's introduction of **YaFSDP**, which promises to cut GPU usage by 20%. The [GitHub repository](https://github.com/yandex/YaFSDP) and [MarkTechPost article](https://www.marktechpost.com/2024/06/14/yandex-introduces-yafsdp) highlight its potential.
- **Meta releases Chameleon and new models**: Meta's new releases, including the Chameleon model and audio watermarking, have the community buzzing. Model details can be found on [Facebook Research GitHub](https://github.com/facebookresearch/chameleon) and [HuggingFace](https://huggingface.co/facebook/multi-token-prediction).
- **High demand for Qwen 2 in language tutoring**: **Qwen 2** is preferred over Llama 3 for specific language tasks due to its Apache 2 license and better performance at 7b/8b models for non-English languages. The community is [uploading it on HuggingFace](https://huggingface.co/eastwind/meta-chameleon-7b).
- **Successful fine-tuning using Unsloth**: Using **Unsloth**, a user trained a specific tutor model achieving remarkable performance on an 8GB GPU. The ease and efficiency have encouraged others to experiment with and share their fine-tuning experiences.
- **Unsloth supports most frameworks**: Major announcements include **Ollama support** and integration with various frameworks like VLLM, promising simplified fine-tuning and deployment procedures. The [Colab notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing) is available for community testing.

Unsloth AI (Daniel Han) ▷ #random (11 messages🔥):

• Inspectus library gets a shoutout: A member shared a link to the Inspectus GitHub repository. No further discussion followed this mention.

• AGI Quiz challenges users: A quiz titled "agi quiz" was introduced with hints like "Poor Man’s Matrix Multiplication". Additional hints such as "Correspondance, and gates" were provided, sparking curiosity without clear resolution.

• Einsum optimization sparks debate: A user referenced a presentation by Daniel on the Aleksa YouTube channel, specifically discussing FLOP reduction through general optimization at 46:26 in the video. The discussion continued with references to PyTorch einsum documentation and attempts with opt_einsum.

Unsloth AI (Daniel Han) ▷ #help (105 messages🔥🔥):

• Urgent Dataset and R value discussions: A member urgently sought help about their dataset, to which others inquired about the sample size, R value, and Alpha. "In this case R is rank. If the R value is super low, it’s possible it doesn’t learn much" explained one user.

• Issues with unsloth CUDA device and installation: A user faced confusion over CUDA device numbers changing after importing unsloth and different behaviors between CLI and .py scripts. They reinstalled according to issue #509.

• Loading quantized weights in vLLM: A member experienced issues loading quantized weights into vLLM and sought tips, mentioning that "transformers can load the quantized weights no problem" and sharing their config.json.

• Pyarrow and CUDA installation problems: Users encountered pyarrow.lib attribute errors and CUDA-related issues while running unsloth, suggesting updates and alternative installation methods via pip. One solution was to uninstall and reinstall using nightly builds.

• Fine-tuning models and dataset conversion: Discussions about training techniques for different models, including Mistral and Llama3-8B, highlighted dataset preparation from raw texts and conversion to Hugging Face datasets. A shared notebook was suggested for fine-tuning templates.

CUDA MODE ▷ #general (1 messages):

• Uncertainty about RDNA MCD Design's Potential: A member expressed appreciation for the RDNA MCD design but is unsure if it will provide any significant advantage. They suggested the possibility of integrating a second die and/or maximizing low power memory for better AI accelerator performance.

CUDA MODE ▷ #triton (3 messages):

• Struggling with Triton autotune configurations: A member asked for guidance on selecting the right autotune configurations for their kernel. They mentioned that their kernel's performance is lower than PyTorch's implementation despite verifying its correctness.

• Clarifying layer norm calculation in Triton: Another member expressed confusion regarding layer norm calculation in Triton's forward kernel tutorial, questioning why columns are added together. They later resolved their confusion, realizing that normalization is done across columns.

• Triton layer norm tutorial reference: The same member shared a link to the Triton tutorial on layer norm they were discussing, Triton Layer Norm Tutorial.

CUDA MODE ▷ #torch (5 messages):

• Request for uint32 Operations in CUDA: A member inquired about plans to add support for uint32 operations, specifically questioning the lack of simple operations like adding and bit shifts for this data type. They elaborated that the sign bit in int32 complicates bitpacking tasks.

• Follow-up on uint32 Use Case: When asked about the use case, the original poster mentioned that bitpacking with uint32 is problematic because the sign bit in int32 messes things up. This clarification highlights the practical challenges faced without uint32 support.

CUDA MODE ▷ #cool-links (1 messages):

• NeurIPS talk explores synergies in AI and systems: A member encouraged watching a great NeurIPS Dec 2023 talk by Christopher Re titled ‘Systems for Foundation Models, and Foundation Models for Systems’. The talk is highlighted for its insights into the interplay between foundational models and system design.

CUDA MODE ▷ #jobs (1 messages):

• Nous Research seeks CUDA/Triton engineers: Nous Research is hiring a CUDA/Triton engineer with advanced ML skills to implement modeling code in PyTorch and optimize with Triton and CUDA. They are interested in professionals capable of writing custom Triton Kernels to accelerate training processes. More details can be found at Twitter, Nous Research, and LinkedIn.

CUDA MODE ▷ #beginner (16 messages🔥):

• Learning GPU caches for CUDA: A user asked for resources on using GPU caches with an RTX-4090 for inference, and was directed to the CUDA C++ programming guide. A sample kernel code was provided to demonstrate cache optimization techniques.

• Misunderstanding about cache loading: There was a clarification needed about using __ldg, which loads into constant cache rather than L2, and the impracticality of fitting a model into L1 or L2 cache due to size constraints.

• Exploring GPU options with larger cache sizes: A user considered using GPUs with larger L2 caches for their inference needs, acknowledging the limitations of the current setup with the RTX-4090's L2 cache.

• Starting CUDA with neural network implementation: For learning CUDA, it was suggested to implement simpler neural networks using CUDA by reading weights from a PyTorch checkpoint and optimizing kernel code. This approach helps in understanding the basics before moving on to more complex optimizations.

CUDA MODE ▷ #pmpp-book (2 messages):

• Chapter 9 Live Reading on YouTube: A member shared a link to a live reading of Chapter 9 on YouTube. Check out the session here.

• Inquiry about PDF Reader: Another member inquired about the PDF reader being used during the live reading.

CUDA MODE ▷ #torchao (29 messages🔥):

• Class vs Function Debate in Quantization: Members discussed the merits of using classes versus functions for quantization configurations. One mentioned that classes like Int4WeightOnly can specify parameters easily and offer good default arguments, making them user-friendly.

• API Design Considerations: There was a debate on whether to use strings or classes for the API. It was mentioned that using classes could be more intuitive due to features like code completion in IDEs, whereas functions might introduce complexity in usability.

• Variety of Quantization Methods: The discussion highlighted different types of quantization methods such as int8 weight-only, int8 dynamic, and int4 weight-only. Each type has distinct features and implementations, making a combined configuration constructor unnecessary.

• Thread for FP6 Discussion: A specific thread was suggested for continuing discussions related to FP6 quantization.

• Quantization Tolerance Inquiry: A member inquired about the level of tolerance for conversions between different precisions like bfloat16 to fp8, particularly regarding precision loss.

CUDA MODE ▷ #llmdotc (296 messages🔥🔥):

• MPIRun vs. SLURM for Multi-Node Setups: Members discussed replacing mpirun with SLURM's srun for multi-node setups, though some preferred mpirun due to its simpler setup. One member shared a helpful MPI best practices link and the ongoing progression for better solutions.
• Benchmark Results for GPT-2 Model: A user shared benchmark results for the GPT-2 774M model, including performance metrics on several datasets. They noted a slight discrepancy with gsm8k, but deemed it not significant.
• Updating LayerNorms for Recompute: Members discussed updating the layernorms for recompute and using existing mean and rstd to enhance performance. Someone suggested rebasing changes from earlier commits.
• Matmul Backward Bias Kernel PR: A PR to optimize matmul backward bias kernel was introduced and reviewed by members. One member emphasized the need for testing ENABLE_BF16 mode correctness and performance.
• Learning Rate Scheduler Simplification: Members proposed to simplify the logic for LR schedulers, with potential for using triangular schedules. A PR was pushed to implement and simplify these changes.

CUDA MODE ▷ #bitnet (2 messages):

• Handwritten CUDA Kernel Benchmarks Impress: A member shared benchmark numbers using a handwritten CUDA kernel for int8 x int2 gemv matmul, noting that the performance is similar to BitBlas. The results showed a significant speed-up compared to fp16 across various shapes, with the highest being 8.1936x for Shape: 1x16384.
• Future Full Model Training Planned: Another member mentioned plans to initiate a full model training project and requested input on any potential missing details or plotholes. They looked for an overview of the project's current state from others.

Nous Research AI ▷ #off-topic (7 messages):

• Borsch coloring debate triggers sugar concerns: A conversation around borsch led to a member sharing they avoid beets due to high sugar content, preferring potatoes which "change the coloring". Another noted their borsch "always comes out super red/purple".

• Crab meat salad recipe shared: A member shared a recipe featuring "imitation crab meat, sweet corn, cucumbers, potato chips, and a garlic mayonnaise sauce". It brought a culinary twist to the off-topic section.

• Insta-reel Causes Buzz: An Instagram reel was shared amidst the conversation. Its relevance or content, however, wasn't described in detail.

Nous Research AI ▷ #interesting-links (6 messages):

• ASCII Art Enthusiast: The chatbot likes to make ASCII art, although the results are not always clear. It also prefers a specific format for name details provided.
• Better Character Specificity with Full Names: Giving a first and last name results in more specific characteristics from the chatbot compared to just using a first name.
• Acts like NSA Search Engine: The chatbot can sometimes act like an NSA search engine when interacting with users. However, it refuses certain commands, stating "I won't impersonate a real person".
• Kainan_e Temporarily Down: Users noted that the chatbot appeared to be down at a certain point during their interactions.
• More Context for Deeper Simulation: Providing more contextual information allows users to steer the simulation more effectively during interactions.

Nous Research AI ▷ #general (290 messages🔥🔥):

• New Feature in vLLM for Hermes 2 Pro: A member announced that they are working on adding support for Hermes 2 Pro function calling and Mistral 7B instruct v0.3 into vLLM. They shared a GitHub PR requesting support and contributions.

• Meta Chameleon Models Review and Access: Discussion around Meta's Chameleon model included links to the application page and personal reviews with comments like "i tried chamelon its fucking insane". Additional conversation involved the technical limitations of the model in generating images, with a probable safety block in place.

• Implementation details and Challenges: There were detailed discussions on implementing Hermes 2 Pro's function calling within vLLM and maintaining OpenAI compatibility. Points of contention included handling <tool_call> XML tags and ensuring robust streaming of tokens, with suggestions to use regex or XML parsing to handle it.

• Reverse Engineering for Better Tool Calls: The community explored possible solutions for generalizing tool calls with a "reverse template" that could map model-specific response formats to a universal format. The discussion highlighted potential configurations for different models like Hermes 2 and Mistral 7B, with pointers towards implementing this in the tokenizer_config.json.

• Tool Call Parsing and Collaboration: Ideas were exchanged on the feasibility of parsing tool calls, including a suggestion to use token IDs and handling multi-model support, illustrated by examples shared from Hugging Face discussions. The conversation underscored the collaboration needed to ensure compatibility and better developer experience (DX).

Nous Research AI ▷ #ask-about-llms (3 messages):

• Seeking Post-Training Tricks for LLMs: A member inquired about resources on post-training tricks for LLMs to get "more juice per token," suggesting the idea that a single source document could yield multiple training documents by breaking it down into leaf nodes.
• Mention of rho-1 as a solution: Another member mentioned that the problem might be "solved with rho-1". The original inquirer clarified that they were looking for tricks specifically for discussion-forum source documents and wondered if there were academic papers or resources on such methods.

No links or URLs were shared in the discussion.

Nous Research AI ▷ #world-sim (1 messages):

• Audio Generation Tutorial Shared: A user shared a YouTube tutorial on generating audio based on video. The resource provides an instructional guide for users interested in this technology.

Torchtune ▷ #general (229 messages🔥🔥):

• Custom Network in Torchtune: A user inquired about using Torchtune for a custom network, not pre-defined in the library. Another member suggested re-implementing the model within Torchtune, ensuring compatibility with TransformerDecoder.forward, and converting Megatron weights into Torchtune.

• Struggling with Dataset Configuration: A user expressed difficulty formatting a Hugging Face dataset for QLoRA training. Several users, including a discussion about modifying YAML configs, suggested using an existing dataset structure in Torchtune, leading to a working dataset setup.

• ROCm GPU Compatibility Issues: A user experienced several crashes with Torchtune on a 6900xt GPU due to ROCm compatibility issues, particularly with QLoRA. Despite attempts using different configurations, the user encountered persistent issues related to memory and CUDA errors specific to ROCm.

• Debugging Training Script: Extensive debugging efforts took place to identify issues causing crashes during model initialization and training. Breakpoints and memory monitoring were utilized, revealing the problem persisted beyond specific lines of code and was influenced by GPU limitations and unsupported operations.

• Potential Solutions and Limitations: Suggestions for solving the GPU crash issues included CPU offloading and further investigation into ROCm and quantization compatibility. However, given the constraints, they needed to explore alternatives like standard LoRA tuning or reaching out to specialized teams.

Links mentioned:

• Configuring Datasets for Fine-Tuning — torchtune main documentation: no description found
• Install Instructions — TorchTune documentation: no description found
• torchtune/recipes/lora_finetune_single_device.py at ef6e196d8e47e9bc584bc9f7ce836f646443381f · pytorch/torchtune: A Native-PyTorch Library for LLM Fine-tuning. Contribute to pytorch/torchtune development by creating an account on GitHub.
• Configuring Datasets for Fine-Tuning — torchtune main documentation: no description found
• lemon07r/Llama-3-RedMagic4-8B · Hugging Face: no description found
• N8Programs/CreativeGPT · Datasets at Hugging Face: no description found

HuggingFace ▷ #announcements (1 messages):

- **Stable Diffusion 3 now in `diffusers`**: The latest `diffusers` version supports [Stable Diffusion 3](https://x.com/RisingSayak/status/1800985494798651605) with DreamBooth + LoRA support. Enjoy optimizations and new functionalities for image generation.
- **20 new CoreML models launched**: Apple dropped [20 CoreML models](https://huggingface.co/apple) optimized for FastVIT, DepthAnything, and DETR on Hugging Face. Along with 4 new datasets, they report detailed benchmarks on inference speed and accuracy.
- **BigCodeBench unveiled**: [BigCodeBench](https://x.com/BigCodeProject/status/1803072295910494686) benchmarks Large Language Models on solving practical and challenging programming tasks, going beyond simple evaluations like HumanEval and MBPP.
- **RecurrentGemma 9B released**: The [RecurrentGemma 9B models](https://x.com/reach_vb/status/1800568911177425198) provide 25% lower latency and significantly higher tokens per second. These models, based on the Griffin Architecture, are available in `transformers`.
- **Argilla joins Hugging Face**: [Argilla is joining](https://huggingface.co/posts/dvilasuero/203008804842390) Hugging Face to focus on community, data, and open-source AI efforts. The acquisition is seen as a strategic move to double down on these areas.

HuggingFace ▷ #general (194 messages🔥🔥):

• Don't Install Valorant or League for Your Mental Health: A member humorously advised against installing Valorant or League of Legends to save mental well-being, suggesting Hollow Knight instead. Another agreed, praising Hollow Knight while lamenting delays in its sequel, Silksong.

• Issues with HuggingFace Spaces: Multiple users reported significant delays and errors when building or starting templates in HuggingFace Spaces. One mentioned their deployment has been stuck for over two hours, while another said their space keeps showing the status as "starting".

• Struggling with Transformer.js and Local Resources: A member experienced their PC getting laggy when running Transformers.js locally due to insufficient VRAM. Suggestions included using Google Colab or the Inference API for better compute resources.

• Meta AI's New Releases: Meta announced new publicly available AI models like Meta Chameleon and Meta Multi-Token Prediction. Links and access details for these models were shared, with discussions on running the models locally.

• Attempting to Use Stable Diffusion on CPU: A user inquired about running Stable Diffusion in CPU mode, with a shared link providing information on accelerating Stable Diffusion models on Intel Xeon CPUs. Another discussed their setup issues with getting SFT models working locally.

Links mentioned:

• Lumina Next T2I - a Hugging Face Space by Alpha-VLLM: no description found
• Openai Whisper Small - a Hugging Face Space by DribDrab: no description found
• facebook/multi-token-prediction · Hugging Face: no description found
• Spectral Labs Joins Hugging Face’s ESP Program to advance the Onchain x Open-Source AI Community: We're excited to announce that Spectral joins Hugging Face’s Expert Support Program, where we’re working with deep learning experts from Hugging Face to advance open-source models, datasets, and ...
• Accelerating Stable Diffusion Inference on Intel CPUs: no description found
• Alpha-VLLM/Lumina-Next-SFT · Hugging Face: no description found
• AI for music production is insane: Music Gen 101 & build application with Text-to-Music APIHostinger website builder: https://www.hostinger.com/aijasonGet 10% off with my code: AIJASON🔗 Links...
• Reddit - Dive into anything: no description found
• Reddit - Dive into anything: no description found
• Tweet from AI at Meta (@AIatMeta): Today is a good day for open science. As part of our continued commitment to the growth and development of an open ecosystem, today at Meta FAIR we’re announcing four new publicly available AI models...
• GitHub - facebookresearch/chameleon: Repository for Meta Chameleon a mixed-modal early-fusion foundation model from FAIR.: Repository for Meta Chameleon a mixed-modal early-fusion foundation model from FAIR. - facebookresearch/chameleon

HuggingFace ▷ #today-im-learning (1 messages):

• HairFastGen needs proxy settings: A member encountered an error running HairFastGen and asked how to set a proxy or HTTP settings. The community is requested to help resolve this issue.

HuggingFace ▷ #cool-finds (11 messages🔥):

• AI Webinar for Video Content Management: A member announced a live webinar titled "Ins and Outs of Building a Multi-Field Multimodal Clip Search." The Eluvio AI Research team will explore modern vector embedding-based semantic searches and personalized content delivery on June 20, 10 a.m. PT.

• Quantum Consciousness Video: A member shared a YouTube video discussing experimental evidence suggesting that human consciousness may have quantum aspects.

• Paper on Arxiv: Another member posted a link to an Arxiv paper, although no additional details were provided in their message.

• AI and Holocaust Misinformation: A member highlighted an article from RFI discussing how AI technology is being used to distort Holocaust history, citing warnings by a UN body.

• Decoding Animal Communication with AI: A YouTube video titled "Using AI to Decode Animal Communication with Aza Raskin" was shared, explaining AI's role in understanding communications of various animal species. The member expressed enthusiasm, noting they have rewatched the video multiple times but mentioned a lack of recent updates on the research.

Links mentioned:

• Using AI to Decode Animal Communication with Aza Raskin: From crows to dolphins, gelada monkeys to primrose flowers - Aza Raskin, co-founder of Earth Species Project, shares how the latest advances in AI help us to...
• Experimental Evidence No One Expected! Is Human Consciousness Quantum After All?: Get a Wonderful Person Tee: https://teespring.com/stores/whatdamathMore cool designs are on Amazon: https://amzn.to/3QFIrFXAlternatively, PayPal donations ca...
• Ins and Outs of Building a Multi-Field Multimodal Clip Search · Luma: The Data Phoenix team invites you to our upcoming webinar, which will take place on June 20th at 10 a.m. PT. Topic: Ins and Outs of Building a Multi-Field…

HuggingFace ▷ #i-made-this (6 messages):

• Weights.gg Dance Tracks Flood Chat: A member shared several dance track links from weights.gg including RIIZE Seunghan - Boom Boom Bass by RIIZE OT6, TEAM Jo and EJ - Right Now by NewJeans, and TWICE Tzuyu - Sabotage by Kwon Eunbi. The post contained multiple links but was later flagged for promotion rule violations.
• Local-First Transcription Tool Released: A member announced the creation of a local-first transcription tool using On-device AI with WebGPU, Ratchet, Svelte, and Electron. This tool aims to enhance transcription capabilities by leveraging cutting-edge front-end technologies.

HuggingFace ▷ #reading-group (1 messages):

• Lossless Compression as Intelligence: A user proposed that "Lossless compression is intelligence". They believe this can be achieved through their "full-context interaction idea in #Terminator architecture."

HuggingFace ▷ #computer-vision (10 messages🔥):

• Conditional diffusion struggles: A member is experiencing poor results with latent diffusion for grayscale image generation, unable to reduce loss below 0.3 despite hyperparameter tuning and noise scheduling adjustments. They seek advice for improving their approach.

• Visualization-of-Thought (VoT) for LLMs: An arXiv paper discusses VoT, a method to enhance spatial reasoning in large language models by visualizing reasoning traces. VoT demonstrated significant improvements in tasks like natural language and visual navigation.

• Microsoft's Florence Vision Model: Microsoft's Florence, a new vision model, is capable of handling tasks like captioning, detection, and OCR with models sizes of 200M and 800M parameters, offering similar quality to models 100x larger. The models and paper are MIT-licensed and available on Hugging Face.

• Loading Florence in half precision error: A member encounters a RuntimeError when trying to load Microsoft's Florence in half precision, noting a type mismatch between input and bias types.

• Object detection in MRI images: A member is seeking recommendations for papers or models that focus on object detection in MRI images.

Links mentioned:

• Tweet from Omar Sanseviero (@osanseviero): Microsoft just silently dropped Florence 👀Vision model that can tackle many vision tasks (captioning, detection, region proposal, OCR) 🤏Small models (200M and 800M) with ~quality to models 100x lar...
• Mind's Eye of LLMs: Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models: Large language models (LLMs) have exhibited impressive performance in language comprehension and various reasoning tasks. However, their abilities in spatial reasoning, a crucial aspect of human cogni...

HuggingFace ▷ #NLP (2 messages):

• Fine-tuning Llama-2 with Langchain: A member expressed interest in fine-tuning Llama-2 on a question answering dataset using Langchain and asked for pointers on getting started. Currently, no specific guides or links were provided in the conversation.
• Splitting text with NLTK: Another member discussed splitting text into sentences using NLTK but faced issues with periods after abbreviations like 'etc.' being incorrectly identified as sentence ends. No solution was yet offered in the chat.

HuggingFace ▷ #diffusion-discussions (1 messages):

hem111: i am getting this error.

Eleuther ▷ #general (81 messages🔥🔥):

• T5 struggles out of the box, BERT limitations discussed: Members discussed how T5 didn't perform well out of the box and needed task-based tuning post-pretraining, mentioning alternatives like Flan-T5. Concerns over BERT's inability to handle an unknown number of tokens were also highlighted, noting SpanBERT as a better option.
• CUDA OutOfMemoryError troubleshooting: A member faced an OutOfMemoryError with CUDA while running a PyTorch model. Solutions included lowering batch sizes and restarting Python, with discussions pointing to GuyTevet/motion-diffusion-model as a similar high-memory use case.
• Best 1B parameter language models: Members debated top 1B parameter language models, comparing Pythia-1B unfavorably to newer models like MiniCPM 1.2B and H2O Danube 1.8B source. They also noted the training times and costs involved in using high-compute resources like HGX and H100 GPUs.
• AGI definition controversy: The ambiguity of AGI's definition was discussed, questioning if LLMs reaching human-equivalent status necessitates adaptation and reasoning in small data sets. Symbolic learning and computer vision's roles were touched upon as potential areas of improvement for LLMs.
• Chinchilla vs Pythia effectiveness debate: A heated debate ensued about claims that a 1B Chinchilla model trained recently outperforms Pythia-1B. Some members doubted the extent of improvements cited, questioning the computational feasibility and evidence strength, and highlighting the complexity of tracking dataset improvements over time.

Links mentioned:

• Tweet from undefined: no description found
• GitHub - GuyTevet/motion-diffusion-model: The official PyTorch implementation of the paper "Human Motion Diffusion Model": The official PyTorch implementation of the paper "Human Motion Diffusion Model" - GuyTevet/motion-diffusion-model
• GitHub - EleutherAI/sae: Sparse autoencoders: Sparse autoencoders. Contribute to EleutherAI/sae development by creating an account on GitHub.
• H2O-Danube-1.8B Technical Report: We present H2O-Danube, a series of small 1.8B language models consisting of H2O-Danube-1.8B, trained on 1T tokens, and the incremental improved H2O-Danube2-1.8B trained on an additional 2T tokens. Our...
• Gemma: Open Models Based on Gemini Research and Technology: This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models. Gemma models demonstrate strong performance acros...
• GitHub - QwenLM/Qwen2: Qwen2 is the large language model series developed by Qwen team, Alibaba Cloud.: Qwen2 is the large language model series developed by Qwen team, Alibaba Cloud. - QwenLM/Qwen2
• TinyLlama: An Open-Source Small Language Model: We present TinyLlama, a compact 1.1B language model pretrained on around 1 trillion tokens for approximately 3 epochs. Building on the architecture and tokenizer of Llama 2, TinyLlama leverages variou...

Eleuther ▷ #research (86 messages🔥🔥):

• Leveraging Self-Supervised Learning in Singing Voice Synthesis: A paper on SVS discusses integrating spectral feature information into the VISinger2 framework to enhance performance using unlabeled data from pre-trained self-supervised learning models. This approach enriches the synthesis, yielding a more natural singing voice.

• Discussion on the Validity of the MCT Self-Refine Algorithm: A paper introducing the MCTSr algorithm faced scrutiny with claims of it being potentially fake due to issues noted on GitHub. The validity of their reported performance improvements is questioned.

• DCLM-Baseline Achieves Significant Improvements: DCLM-Baseline showed a 6.6 percentage point improvement on MMLU while using 40% less compute compared to MAP-Neo. The dataset is created by filtering with a classifier trained on the OpenHermes dataset, significantly enhancing performance.

• Classifier-Based Filtering Shows Promising Results: A 10-point improvement on MMLU was achieved by filtering training data with a classifier trained on the OpenHermes dataset. The classifier and dataset are now available on Hugging Face.

• General Sentiment on Dataset Quality and Filtering: There is a consensus on the importance of quality filtering, as seen with DCLM-Baseline and other models like Zamba. Discussions indicate mixed views on the effectiveness of including high-quality data such as code/math in training datasets, especially for language models.

Links mentioned:

• Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B: This paper introduces the MCT Self-Refine (MCTSr) algorithm, an innovative integration of Large Language Models (LLMs) with Monte Carlo Tree Search (MCTS), designed to enhance performance in complex m...
• DataComp-LM: In search of the next generation of training sets for language models: We introduce DataComp for Language Models (DCLM), a testbed for controlled dataset experiments with the goal of improving language models. As part of DCLM, we provide a standardized corpus of 240T tok...
• Tweet from Vaishaal Shankar (@Vaishaal): @Teknium1 yah! and we only needed ~200K documents + a linear classifier to get it to work, the MMLU gap before and after the filtering was >10 points.
• DataComp: no description found
• Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task: Modern generative models exhibit unprecedented capabilities to generate extremely realistic data. However, given the inherent compositionality of the real world, reliable use of these models in practi...
• mlfoundations/fasttext-oh-eli5 · Hugging Face: no description found
• Slot State Space Models: Recent State Space Models (SSMs) such as S4, S5, and Mamba have shown remarkable computational benefits in long-range temporal dependency modeling. However, in many sequence modeling problems, the und...
• mlfoundations/dclm-baseline-1.0 · Datasets at Hugging Face: no description found
• Scaling Laws for Data Filtering -- Data Curation cannot be Compute Agnostic: Vision-language models (VLMs) are trained for thousands of GPU hours on carefully curated web datasets. In recent times, data curation has gained prominence with several works developing strategies to...
• Tweet from Vaishaal Shankar (@Vaishaal): @Teknium1 @georgejrjrjr @FineWeb @achalddave we just put the raw text into classifier and sort the documents by P(hermes or reddit) and take the top ~10%.
• microsoft/Florence-2-large · Hugging Face: no description found
• Transcendence: Generative Models Can Outperform The Experts That Train Them: Generative models are trained with the simple objective of imitating the conditional probability distribution induced by the data they are trained on. Therefore, when trained on data generated by huma...
• Tweet from George (@georgejrjrjr): @FineWeb They ran similar trials with quality filters, and found that the most effective thing was filtering for text that's similar to...GPT-4 outputs from @Teknium1's OpenHermes instruction ...
• VISinger2+: End-to-End Singing Voice Synthesis Augmented by Self-Supervised Learning Representation: Singing Voice Synthesis (SVS) has witnessed significant advancements with the advent of deep learning techniques. However, a significant challenge in SVS is the scarcity of labeled singing voice data,...
• GitHub - mlfoundations/dclm: DataComp for Language Models: DataComp for Language Models. Contribute to mlfoundations/dclm development by creating an account on GitHub.
• Pass@k or Pass@1? · Issue #1 · trotsky1997/MathBlackBox: After seeing this work, I read the paper and found that the effect is very good. When reading the code, I found that this line of code seems to cause the indicator to degenerate from pass@1 to pass...

Eleuther ▷ #lm-thunderdome (8 messages🔥):

• Multi-step, multi-choice task customization query: A user is looking for a way to set up a multi-choice task where the model not only picks an answer but also rates its confidence on a scale from 1 to 5. They are curious about creating a custom metric that penalizes LLMs for being overly confident.

• Perplexity evaluations for multiple choice tasks: Another user asked about the possibility of performing perplexity evaluations for multiple-choice tasks without these metrics appearing in the output or log file. No direct solution or link was provided in the discussion.

• File saving system reorganization proposal: A user suggested an improvement in the file saving system where results are stored in timestamped subdirectories instead of being appended in the same directory. Another user expressed a preference for this proposed method.

LM Studio ▷ #💬-general (61 messages🔥🔥):

<ul>
  <li><strong>Meta releases four new AI models</strong>: Meta announced four new AI models, including <em>Meta Chameleon</em>, <em>Meta Multi-Token Prediction</em>, <em>Meta JASCO</em>, and <em>Meta AudioSeal</em>. Full details are available on their <a href="https://go.fb.me/tzzvfg">website</a> and the <a href="https://github.com/facebookresearch/chameleon">GitHub repository</a>.</li>
  <li><strong>Effortless Jailbreaking of AI Models</strong>: Users discussed bypassing restrictions on various AI models like ChatGPT and MistralAI, sharing methods and potential risks. One member mentioned successfully using jailbreak methods for an extended period and their efforts to find universal techniques.</li>
  <li><strong>Handling Model Tickets and General Setup Issues</strong>: Users shared tips on how to follow up on Discord tickets and suggested prefacing image prompts with specific tags to avoid image generation problems. Newer users sought advice on troubleshooting model compatibility and setup in LM Studio, focusing on VRAM issues and model formats.</li>
  <li><strong>Performance Hits on Model Handling</strong>: Members reported performance issues with recent LM Studio versions, attributing lag and stop word issues to the latest updates. Downgrading to earlier versions appeared to solve these issues.</li>
  <li><strong>Exploring Model Quantization Differences</strong>: There was a discussion regarding the differences between GGUF models and loading models in 4-bit in TextGenWebUI. The consensus is that GGUF's might not perform as well under certain conditions compared to other methods.</li>
</ul>

LM Studio ▷ #🤖-models-discussion-chat (32 messages🔥):

• Llama 3-70B faces criticism: Members debated the efficiency of Llama 3-70B, with some calling it weak for its size despite its 53% win-rate against the Llama 3-70B model. One user expressed a preference for Magnum, citing poor performance of the aforementioned models relative to their resource consumption.
• DeepSeek Coder V2 Performance Praised: A user appreciated the f32 version of DeepSeek Coder V2 Lite Instruct, sharing it runs at 22 tok/s on old P40's with 64k context and faster at Infinity tok/s after certain settings. They noted considerable speed improvements despite older hardware.
• Struggles with Model Formats: Users discussed challenges converting Nvidia's Llama 3 model weights from their original format to gguf format. The model was mentioned in the context of Llama3-70B-SteerLM-RM and its use governed by the Llama 3 Community License Agreement.
• Debate on Model Performance and Utility: Members discussed various models, including deepseek coder 6.7b, Opus, and Nemotron for different tasks such as business management. Some users shared negative experiences and errors with deepseek, which were resolved with updates and specific configurations.
• Creative Writing Model Comparisons: A comparison was made between models for their effectiveness in creative writing, lauding Opus and Sonnet for their performance. A sentiment was shared that newer models still struggle with delivering creative, "soulful" output compared to these established names, particularly in metrics assessed by Lmsys arena.

Link mentioned: nvidia/Llama3-70B-SteerLM-RM · Hugging Face: no description found

LM Studio ▷ #📝-prompts-discussion-chat (7 messages):

• Struggle with Model Fine-Tuning: "It's really hard to do this with a model that's finetuned for instruct or chat or etc. because it doesn't know how to do that." To solve this issue, members suggested using Gemini API settings or opting for pure code models like Codestral, DeepseekCoder V2 Lite, or StarCoder2.

• Inquiry about Prompt Websites: A member asked if there's a website for prompts similar to "promptadvance.club". This suggests users are actively seeking accessible resources for prompt generation.

• Difficulty with GitHub Repos in LM Studio: A new user wanted to know how to read a GitHub repo with LM Studio. It was clarified that LM Studio cannot crawl pages or repositories or use RAG support.

• Exploring Alternatives for GitHub Repositories: When asked if cloning a GitHub repo would work, it was explained that LM Studio lacks the capability to browse cloned repositories.

• Converting GitHub Repos to Text Files: A final suggestion was made about converting the GitHub repository to a text file. This conversation left members pondering if this method could work with LM Studio.

LM Studio ▷ #⚙-configs-discussion (21 messages🔥):

• Struggling with Assistant Placeholder Syntax: A user expressed frustration with placing "<|end|>" after the placeholder {Assistant}, hoping to recreate a specific prompt structure: "<|user|> {prompt}<|end|><|assistant|><|end|>".
• Phi-3 Context Obedient Model Discussion: Members discussed modifying the existing Phi-3 preset with a specific syntax for system messages and linked to the model card for Phi-3-Context-Obedient-RAG.
• Seeking RAG Model Recommendations: A user inquired about a performant, GPU-RAM-efficient model for RAG and received suggestions, noting a preference for something hardware-light due to limited resources, mentioning "CMDR+" as an option.
• Exploring Free RAG Options: Coral Cohere was recommended as a free service for RAG, though another member clarified that while the API might cost, using the chat on their site is free.

Links mentioned:

• Login | Cohere: Cohere provides access to advanced Large Language Models and NLP tools through one easy-to-use API. Get started for free.
• bartowski/Phi-3-Context-Obedient-RAG-GGUF · Hugging Face: no description found

LM Studio ▷ #🎛-hardware-discussion (18 messages🔥):

• ARM64 Windows build request meets delayed availability: Members discussed the feasibility of an ARM64 Windows build for new Snapdragons, with one mentioning that it won't happen "for a while" and suggesting posting it in feature requests for visibility. heyitsyorkie recommended manually building llama.cpp for a temporary solution.
• Optimizing Llama 3 performance queries stirs up hardware scrutiny: A member expressed concern about getting only "2.50 tok/s on Llama 3 Instruct 70B" and another responded by emphasizing the need for detailed hardware specs to diagnose the issue.
• GPU configuration impacts token generation speed: A detailed account of hardware setup, including dual NVIDIA 4060TIs and specific PCIe configurations, was given to report token generation speeds for Qwen2 Instruct 7B tests. Token speeds varied from 27.98 tok/s to 31.86 tok/s depending on GPU usage.
• Building a PC for Nemotron-4-340B sparks high-performance GPU recommendation: In response to a query about building a PC capable of running Nemotron-4-340B, the straightforward advice was to use several H100 GPUs.
• High-end Ryzen 9 setup for large LLMs: Another member shared their setup using an RTX 4090, 64GB DDR4 RAM, and a Ryzen 9 7950X, inquiring about the recommended hardware for running the "Meta-Llama-3-70B-Instruct.Q3_K_M.gguf" model after noting performance limitations.

LM Studio ▷ #🧪-beta-releases-chat (4 messages):

• LM Studio CLI starts UI not CLI interface: A member shared their experience with the CLI interface of LM Studio, stating it "just started the UI" instead of staying in CLI mode. This led them to question the utility of using the CLI in its current form.

• CPU vs GPU quantization impacts model accuracy: Discussion highlighted that CPU math is slightly more accurate compared to GPU, which might influence results. Suggestions included trying a different quantization or adjusting temperature settings to avoid producing gibberish, as "significant differences between quants" can exist.

LM Studio ▷ #amd-rocm-tech-preview (3 messages):

• Choosing AMD-compatible models for 7900xt: A user with a 7900xt GPU inquires about the best model versions to run on AMD hardware, highlighting confusion over options like q, s, k, and x. Members suggest that models under 18 GB are manageable, with Q8 quantization offering the best quality in a highly compressed format for larger models.

• GPU offloading for efficient model runs: When selecting models for AMD GPUs, it's recommended to look for ones labeled 'FULL GPU OFFLOAD POSSIBLE.' Models like Q4KM are optimal for higher B (13b-30b), while 7b models can run efficiently at full Q8 quant size.

LM Studio ▷ #open-interpreter (4 messages):

• Old config version causing issues: A member pointed out that an issue was caused by using an old version of the config. They instructed to run interpreter --version and mentioned that deleting the profiles would regenerate them, specifying to look for local.py.
• Current version is 0.3.1: Another member inquired if version 0.2.6 was in use, to which it was clarified that the current version is 0.3.1.

LM Studio ▷ #🛠-dev-chat (3 messages):

• Seeking Help with Documenting GitHub Repos: A member asked for advice on how to document a code GitHub repository using LM Studio. Another member suggested that this question might be better suited for a different channel, <#1120489168687087708>.

Modular (Mojo 🔥) ▷ #general (21 messages🔥):

• Mojo Programming Model Debated: Discussion highlighted that Mojo's concurrency model might not rely on threads and locks, instead it focuses on a memory-safe model for asynchronous tasks. A participant noted, "It would be reasonable for Mojo to only offer a memory-safe model for programming with asynchronous tasks."

• Role of Executors in Concurrency: Conversation pivoted to executors handling concurrency, where threads are spun up and synchronized via a library-based executor. Someone mentioned, "An executor spins up the threads and synchronizes work across them."

• Safety and Synchronization Concerns: Participants discussed the necessity of ensuring safety when using handles with non-thread-safe C libraries in concurrent tasks—emphasizing function call synchronization over data synchronization. One noted, "Just because you’re calling a C library that isn’t thread-safe doesn’t mean you can’t call it from a task."

• Task Pinning Clarified: The discussion clarified the concept of pinning tasks to cores versus data pinning in Rust, indicating a difference between where data is stored and where functions are executed. A comment explained, "Rust's 'pinning' is about preventing data from being moved to another memory location, whereas we're talking about tasks being executed on different cores."

• Data Races Discussed: Emphasis was placed on data races occurring when data is concurrently accessed and mutated by multiple cores. It was noted, "You get data races when the same data is concurrently accessed by multiple cores, and at least one of those cores is mutating the data."

Modular (Mojo 🔥) ▷ #💬︱twitter (1 messages):

ModularBot: From Modular: https://twitter.com/Modular/status/1803442744226095586

Modular (Mojo 🔥) ▷ #ai (1 messages):

cheerful_pomelo_54063: what a giver ...

Modular (Mojo 🔥) ▷ #🔥mojo (112 messages🔥🔥):

• Debate on Mojo Web Server Standards: Members intensely discussed whether Mojo should adopt WSGI/ASGI standards, with points about deployment, performance overhead, and integration with Python frameworks. One argued, "Mojo should adopt it as well, costs not withstanding," while another countered, "It's a shim to help Python be less bad at networking."

• Challenges with LLVM Intrinsics and Float 16: Issues concerning float 16 throwing errors when calling LLVM intrinsics due to type mismatches were highlighted. One noted, "It's calling a C++ lib (something something 'libm') here, not LLVM intrinsics."

• Feature Request for Multi-dimensional Array Slicing: A community member requested enhancing Mojo's array slicing capabilities to handle mixed integers and colon slices more naturally. They provided a GitHub issue link to support their proposal.

• Memoization in Mojo: A question was raised about implementing caching functionality in Mojo akin to Python decorators, showing interest in improving performance optimization.

• Open Source Discussion on Mojo: Members clarified that while parts of Mojo are open source, like the standard library, the compiler is not yet fully open source. Relevant links to the Modular blog and GitHub provided further context.

Links mentioned:

• Modular: The Next Big Step in Mojo🔥 Open Source: We are building a next-generation AI developer platform for the world. Check out our latest post: The Next Big Step in Mojo🔥 Open Source
• PEP 3333 – Python Web Server Gateway Interface v1.0.1 | peps.python.org: This document specifies a proposed standard interface between web servers and Python web applications or frameworks, to promote web application portability across a variety of web servers.
• Issues · modularml/mojo: The Mojo Programming Language. Contribute to modularml/mojo development by creating an account on GitHub.
• Mojo🔥 FAQ | Modular Docs: Answers to questions we expect about Mojo.
• GitHub - modularml/mojo: The Mojo Programming Language: The Mojo Programming Language. Contribute to modularml/mojo development by creating an account on GitHub.
• mojo/CONTRIBUTING.md at main · modularml/mojo: The Mojo Programming Language. Contribute to modularml/mojo development by creating an account on GitHub.

Modular (Mojo 🔥) ▷ #performance-and-benchmarks (3 messages):

• Mojo Nightly Improves Dictionary Performance: A member shared an impressive improvement in the new nightly build of Mojo (Mojo: 2024.6.1705 vs. Mojo: 2024.6.1912). They noted the build was “2.78X faster for Dict[Int,Int]” and “1.12X faster for Dict[String,String]”, prompting questions about why optimizations don’t equally benefit different types and which Dict method consumes the most time.

• Deeper Insight into Optimization: Another member explained that the performance difference arises because “int is a reg-type, string is a memory type”, also noting factors like "benchmarking malloc and copy" and differences in hash functions.

• Optimization Context: Additional context was provided using examples like the bitshifting operation replacing the modulus operation, which contributed to the performance gains but wasn't the sole bottleneck. Hashing and equality comparison vary in complexity between Ints and Strings, impacting overall performance improvements.

• GitHub Pull Request Reference: The original poster shared the GitHub Pull Request #3071 detailing the changes for the speedup. Another member linked a relevant GitHub Gist for further review and feedback.

Links mentioned:

• [Stdlib] Speedup `Dict` (changing modulus to bitshifting) by rd4com · Pull Request #3071 · modularml/mojo: Hello, it could be a nice improvement, around +80% here (Ubuntu); Hard to tell without feedbacks, here is the benchmark used: from time import now from random import * from sys.param_env import is_...
• playground.mojo: GitHub Gist: instantly share code, notes, and snippets.

Modular (Mojo 🔥) ▷ #🏎engine (2 messages):

• Execution method in Mojo limits inputs: A user encountered an error when trying to provide more than three inputs to the model.execute function, stating, "expected at most 2 positional arguments, got 11." They question how to overcome this limitation.

• Documentation suggests using NamedTensor for multiple inputs: Another member provided useful links to Modular's documentation on using NamedTensor or Tuple[StringLiteral, EngineNumpyView] for the execute method documentation and NamedTensor doc. These documents explain ways to correctly pass multiple inputs.

Links mentioned:

• Model | Modular Docs: Represents a model that's loaded and ready for execution.
• NamedTensor | Modular Docs: A named input tensor.

Modular (Mojo 🔥) ▷ #nightly (10 messages🔥):

• New tool for branch management: A member announced a new tool that simplifies testing and activating branches on the terminal. Commands include dev_help set_branch, dev_help rebuild, dev_help use_branch, and dev_help use_mojo.

• Nightly release delay due to CI issues: A member asked why the nightly release didn't happen, and it was explained that an internal service was unavailable during the CI testing. This GitHub infrastructure issue caused a delay, but a new job will be kicked off shortly.

• Stuck on nightly/max version 2024.6.1505: A member mentioned being stuck on the nightly/max version for several days. Another member clarified that the max nightly build is failing due to stability issues, and internal teams will look into it post-holiday.

• New Mojo nightly release announced: A new nightly release for the Mojo compiler is now available. Updates include a new StaticString feature, changelog updates, and various improvements; users can update with modular update nightly/mojo (changelog, raw diff).

Perplexity AI ▷ #general (99 messages🔥🔥):

• Debate over Perplexity YouTube Search Functions: The YouTube search function in Perplexity's system, which adds timestamps as citations, is criticized for lacking practical use cases. One user shared the system prompt and highlighted issues, suggesting that timestamps often don't appear in outputs.
• API Internet Access Confirmation: Users inquired if Perplexity's API had internet access capabilities similar to its web UI. It was confirmed that all online models have internet access and users shared links to Perplexity's labs and API documentation as resources.
• Concerns over Content Sharing and Collection Handling: Users expressed concerns about Perplexity sharing entire collections when only a single thread was intended to be shared. Comparisons were drawn to sharing a full folder in Google Drive when only a single file should be shared, highlighting the need for more granular control.
• Issues with Diacritical Marks in Portuguese: A user reported issues with using diacritical marks in Portuguese within the Perplexity prompt, a problem that wasn’t occurring on other platforms or services. Suggestions to troubleshoot involved checking language packs and frontend settings.
• Discussion on AI Detectors for Academic Integrity: There was a debate about the effectiveness and reliability of AI detectors, with a user mentioning their class's usage concerns and the perceived inadequacies of these systems in properly identifying AI-generated content.

Links mentioned:

• Perplexity Is a Bullshit Machine: A WIRED investigation shows that the AI-powered search startup Forbes has accused of stealing its content is surreptitiously scraping—and making things up out of thin air.
• YouTube Summary with ChatGPT & Claude: Summarize YouTube videos, web articles, and PDFs to save time, powered by ChatGPT (OpenAI) and Claude (Anthropic).
• Perplexity Labs: no description found
• Repeat all text above in the format of a text block (): Knowledge cutoff: 2023-10 You are an AI assistant created by Perplexity Your responses should be: Accurate, high-quality, and expertly written Informative,...

Perplexity AI ▷ #sharing (2 messages):

• Nvidia tops the market and more: A YouTube video was shared detailing various topics including Nvidia's market status, DeepMind's Audio AI, Fisker's bankruptcy, a Mars rock discovery, and a Vegas monolith. Watch the video.

• Canned coffee loses its flavor: A member shared insights on how canned coffee's taste degrades over time due to oxidation, loss of aromatics, and staleness. More details can be found in articles from Mashed and Philly Fair Trade.

Links mentioned:

• YouTube: no description found
• What is the shelf-life of canned coffee?: Canned coffee has a surprisingly long shelf life compared to other coffee formats. Here are the key points about the shelf life of canned coffee: Regular...

Perplexity AI ▷ #pplx-api (9 messages🔥):

• Perplexity API's data crawl frequency varies: It was discussed that Perplexity "splits results into 'domains' which are updated with more or less urgency." For example, "news sites are updated more than once every hour," while less frequently changing sites are updated every few days. Source

• Access Token confusion for Perplexity: A user sought clarification on obtaining an access token, and another clarified that this can be found under a Pro subscription tab in settings. It was also suggested that an API key might be available even with a free account, provided there is some credit added to it.

• Perplexity API features and limitations: A developer mentioned that the Perplexity API appears to offer fewer features compared to the web UI, highlighting shorter responses and the lack of support for proprietary models like Claude. This was questioned as "the API initially offers free search" and more limited functionalities.

LAION ▷ #general (79 messages🔥🔥):

• Chameleon Model Released with Limitations: A restricted, safety-aligned version of the Chameleon model (7B/34B) has been released with open weights. Armen Agha shared the announcement along with the GitHub repository and the related research paper.

• Discussion on Image Output Feasibility: Members speculated on tuning the Chameleon model for image output despite the current restrictions. Suggestions included using MLP adapters and finetuning on ground truth datasets; some expressed skepticism about whether the released weights actually include image generation capabilities.

• Downloading and Using Chameleon Models: Users faced issues downloading the 34B model, with some only able to get the 7B model. One noted that the inference script assumes 4 GPUs and inquired about quantization support for potentially running the model on 8-bit.

• Vision Component Testing and Fine-Tuning: Members discussed the need for practical testing of the vision component of the Chameleon model, specifically VQA capabilities. They highlighted potential uses in fine-tuning due to the easy integration with existing LLM training tooling.

• Concerns Over Safety and Hallucination: There was a concern about the model's censorship and hallucination issues, especially with the 7B variant. Some members noted that deploying models safely is crucial to avoid creating harmful content, while others shared their experiences with corrupted image outputs.

Link mentioned: Tweet from Armen Aghajanyan (@ArmenAgha): A restricted, safety aligned (no-image-out) version of Chameleon (7B/34B) is now open-weight! https://github.com/facebookresearch/chameleon The team strongly believes in open-source. We had to do a ...

LAION ▷ #research (20 messages🔥):

• Microsoft's Florence-2 is a Vision Powerhouse: The Florence-2 model by Microsoft is making waves with its capability to handle various vision tasks using a prompt-based approach. The model utilizes the extensive FLD-5B dataset to excel in zero-shot and fine-tuned settings.

• Object Detection Accuracy Tradeoffs Discussed: Members discussed the inferencing trade-offs and accuracy issues in bounding boxes for object detection in Florence-2. The comparison with traditional OCR and segmentation was a primary focal point.

• Adversarial Robustness Tools Fail Artists: The arXiv paper highlighted the failure of adversarial robustness tools like Glaze to protect artists from style mimicry. The study revealed that low-effort techniques like image upscaling can easily bypass these protections.

• Carlini and Adversarial Robustness: Carlini's work and its impact on adversarial robustness were discussed, with references to the history of adversarial research by Papernot, Carlini, and Wagner. The effectiveness of Glaze and its closed-source nature were critically examined.

• Ben's Hostility Toward Carlini: There was speculation about Ben's hostile reaction to Carlini's paper, with claims that Ben went ad hominem instead of addressing the actual problems raised. Despite his criticism, it was noted that Ben hasn't made substantive contributions to protection mechanisms either.

Links mentioned:

• microsoft/Florence-2-large · Hugging Face: no description found
• sample_inference.ipynb · microsoft/Florence-2-large at main: no description found
• Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI: Artists are increasingly concerned about advancements in image generation models that can closely replicate their unique artistic styles. In response, several protection tools against style mimicry ha...

OpenRouter (Alex Atallah) ▷ #announcements (1 messages):

• Dolphin 2.9.2 Mixtral faces discontinuation: Due to insufficient usage, Dolphin 2.9.2 Mixtral 8x22B will be discontinued by the end of this week. For continuity, a new router model called Flavor of the Week has been introduced and is currently pointing to Dolphin 2.9.2.
• Gemini tool call fixes: Multi-turn Gemini tool calls for versions 1.0 pro, 1.5 pro, and 1.5 flash have been fixed. Additionally, a minor issue with Mistral's tool_choice has been resolved.
• Improved user control and interface: Users can now pick a provider in the playground, and Cohere supports cancellation. Enhancements have been made to the model browser through lazy loading, and the /credits page UI has been improved.

Links mentioned:

• Dolphin 2.9.2 Mixtral 8x22B 🐬 by cognitivecomputations: Dolphin 2.9 is designed for instruction following, conversational, and coding. This model is a finetune of [Mixtral 8x22B Instruct](/models/mistralai/mixtral-8x22b-instruct). It features a 64k context...
• Flavor of The Week by cognitivecomputations: This is a router model that rotates its underlying model weekly. It aims to be a simple way to explore the capabilities of new models while using the same model ID. The current underlying model is [D...

OpenRouter (Alex Atallah) ▷ #app-showcase (2 messages):

• Clarification on Bot's Affiliation: A member questioned whether a particular bot was from the OpenRouter team. Another member responded, "Not affiliated with OR, we just use their service for the bot."

OpenRouter (Alex Atallah) ▷ #general (81 messages🔥🔥):

• Best Free Models for Function Calling: A member asked for recommendations on the best free models for function calling, and another user suggested that "all of them actually support a level of it." One user mentioned they settled on Haiku due to its cost-effectiveness.

• LLaMA 3 Instruct Serving at FP16: There was some discussion about whether the LLaMA 3 8b Instruct model is quantized. Confirmation was given that it serves at FP16, not quantized.

• 404 Error with L3-70B-Euryale-v2.1: Multiple users reported getting a 404 MODEL_NOT_FOUND error when trying to use the L3-70B-Euryale-v2.1. It was identified that Novita's API downtime is causing the 404 error as it's the only provider, and another user noted similar issues with Deepseek's Codeseek model.

• High Demand Models on OpenRouter: Discussions touched on OpenRouter's strategy for hosting models. Models like Dolphin are hosted based on high demand and experimental hosting, with a note that hosting less popular ones could require significant price increases to be sustainable.

• Censorship Issues with Deepseek’s API: Members noted heavy censorship in Deepseek’s API, affecting functional requests like coding examples. One user suggested using zero-width spaces to bypass censorship, albeit with drawbacks in token usage and speed.

Link mentioned: cognitivecomputations/dolphin-2.9.1-mixtral-1x22b · Hugging Face: no description found

LlamaIndex ▷ #blog (1 messages):

• MistralAI simplifies LLM fine-tuning: LlamaIndex shared a tweet about MistralAI releasing a fine-tuning API, making it easier to fine-tune their open-source models. This API optimizes LLMs for specific tasks by training them further on targeted datasets, enhancing performance.

LlamaIndex ▷ #general (80 messages🔥🔥):

• Llama 3 70b Function Implementation Needed: A user is trying to create a graph using Llama 3 70b from Bedrock but finds that a necessary function, acomplete, isn't implemented. They seek advice on implementing, testing, and PRing this function with suggestions to fork the repo and use async boto3 sessions.
• Discussion on Entity Extraction and LLMs: Users discuss the feasibility of using LLM for entity extraction vs. smaller, more efficient tools like gliner. One suggests that LLMs are overkill and proposes using a small LLM to generate relationships based on extracted entities.
• Azure Content Filtering Issue: A user faces Azure Content Filtering barriers while querying over manual descriptions of festive items like confetti guns and cannons. The suggestion is to configure or request to turn off Azure's content filters, with a link to Azure OpenAI Service content filters guide.
• User Feedback Collection in LlamaIndex: One user queries if Portkey is the only method for collecting user feedback in LlamaIndex, with the provided documentation and no mention of other integrations like Arize or Traceloop. Portkey's Feedback API was illustrated as the documented method.
• Custom Similarity Score Inquiry: Users explore the possibility of defining a custom similarity score for queries in a vector store in LlamaIndex. The current framework does not explicitly support this, but users might extend or modify existing classes as necessary.

Links mentioned:

• How to use content filters (preview) with Azure OpenAI Service - Azure OpenAI: Learn how to use content filters (preview) with Azure OpenAI Service.
• Beyond the Basics of Retrieval for Augmenting Generation (w/ Ben Clavié): LLMs are powerful, but have limitations: their knowledge is fixed in their weights, and their context window is limited. Worse: when they don’t know somethin...
• DLAI - Building Agentic RAG with Llamaindex: Introduction · Router Query Engine · Tool Calling · Building an Agent Reasoning Loop · Building a Multi-Document Agent · Conclusion
• llama_index/docs/docs/examples/llm/portkey.ipynb at 8151b02fee851c7d9d9912390902c6e784b15233 · run-llama/llama_index: LlamaIndex is a data framework for your LLM applications - run-llama/llama_index
• Build software better, together: GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects.
• Citation - LlamaIndex: no description found
• Auto-Retrieval from a Vector Database - LlamaIndex: no description found
• Upstash Vector Store - LlamaIndex: no description found
• Jaguar - LlamaIndex: no description found
• Weaviate - LlamaIndex: no description found
• Rocksetdb - LlamaIndex: no description found
• Singlestoredb - LlamaIndex: no description found

LLM Finetuning (Hamel + Dan) ▷ #general (15 messages🔥):

• Access Troubleshooter Strikes Again: A member initially reported trouble accessing the course on Maven but later acknowledged using an incorrect link. They confirmed resolving the issue and thanked the support.

• Event Registration Announced: A member shared a registration link for the event "So you think you can prompt" hosted by Bryan Edward Bischof and Bain Capital Ventures. The event includes technical talks on "Mastering LLMs 201" topics like RAGs, evals, and function calling.

• New Python BM25 Implementation: A member excitedly shared the GitHub repository BM25S, highlighting it as an ultra-fast lexical search library that implements BM25 using scipy.

• Missed Live Sessions? No Problem!: A member asked if missing live sessions was an issue, and received assurance that recordings are available for review anytime.

• OSS Evaluation Framework Discussion: A member mentioned Uptrain, an open-source evaluation and tracing framework, prompting another member to express interest in testing BAML, which is based on Rust, while currently using "instructor".

Links mentioned:

• "So you think you can prompt" — Mastering LLMs Encore with BCV · Luma: You've mastered LLMs — now what? Join us for an in-person encore to close out the Mastering LLMs course, hosted by Bain Capital Ventures, course creator Hamel…
• Tweet from Lazarz (@Laz4rz): On one-example-learning problem in LLM finetuning, or why does my loss curve look so weird!? A small thread so you can avoid my mistakes 🧵
• GitHub - xhluca/bm25s: BM25S is an ultra-fast lexical search library that implements BM25 using scipy: BM25S is an ultra-fast lexical search library that implements BM25 using scipy - xhluca/bm25s

LLM Finetuning (Hamel + Dan) ▷ #workshop-1 (1 messages):

• Fine-tuning for Fraud Detection and Niche Products: For a "Fraud detection system for a unique financial institution", fine-tuning is necessary due to the requirement of detailed knowledge of specific transaction patterns and fraud indicators. Similarly, for a "Recommendation system for highly niche products (e.g., rare collectibles)", fine-tuning is essential to understand specific user preferences and product attributes unique to the niche.
• Avoid Fine-tuning for General Tasks: A "General language translation service" and a "Generic news summarization tool" do not require fine-tuning. General language models are effective for these tasks as they work well across various languages, contexts, and news summarization needs.
• Specialized Technical Support Needs Fine-tuning: A "Chatbot for a highly specialized technical support role" should be fine-tuned. This is because it needs detailed knowledge of the specific technical area to provide accurate support.

LLM Finetuning (Hamel + Dan) ▷ #🟩-modal (5 messages):

• A100s Availability Cheered: A member thanked the community for the credits and mentioned rarely having to wait for A100s in the past few days. They also plan to share comments on their developer experience with the repository.

• Checkpoint Writing Issue: A member experienced problems with checkpoint files not appearing immediately in modal volumes even after setting save_steps=5. Another member explained that writes commit asynchronously in the background and suggested discussing this in the Modal Slack.

• Multimodal Fine-tuning Without Axolotl: A member inquired about multimodal LLM fine-tuning on Modal without using Axolotl due to its complexity. They asked for examples or alternatives, mentioning that JarvisLab was helpful but had limitations with model download times.

LLM Finetuning (Hamel + Dan) ▷ #jarvis-labs (4 messages):

• Pausing instances saves costs: If you pause an instance on Jarvislabs, you'll only be charged for storage. However, keeping the instance running incurs full compute costs.

• Fine-tuning with Jarvislabs and Axolotl: A user successfully ran the Honeycomb fine-tuning example using Jarvislabs and Axolotl, sampling 50% of the initial dataset. Details and files are available on Hugging Face.

• Suggestion for Docker image support: Another user praised Jarvislabs for its intuitive interface but suggested allowing importing of Docker images to save setup time. They noted that current finetuning runs take 20 minutes, whereas setup and model downloads take around 45 minutes.

LLM Finetuning (Hamel + Dan) ▷ #hugging-face (5 messages):

• Delayed Credits Concern Resolved: Members reported delays in receiving their Hugging Face credits after submitting forms. The issue was confirmed resolved with credits now rolled out as expected, and one user confirmed receipt of their credits.

LLM Finetuning (Hamel + Dan) ▷ #langsmith (1 messages):

• LangSmith Credits Requested for a Course: A user inquired about receiving LangSmith credits for the "Mastering LLMs Course." Relevant details include the user's email (swaroopch@gmail.com) and organizational ID (65aabefe-200a-4f7f-a15e-c506d905c34f).

LLM Finetuning (Hamel + Dan) ▷ #clavie_beyond_ragbasics (1 messages):

- **Corrected query confusion**: A member acknowledged flipping some details around in their original query and confirmed they have corrected the post. *"My bad, yes I did flip things around and also the query was wrong. Have corrected the post now."*

LLM Finetuning (Hamel + Dan) ▷ #fireworks (2 messages):

• Users request credit assistance: nullbit0 and tailwind8960 requested help with credits from @466291653154439169. They provided their account IDs: "shreyas-damle-vit-5c4ec6" and "divhit-98df67" respectively.

LLM Finetuning (Hamel + Dan) ▷ #east-coast-usa (1 messages):

bringmedabir: Hi All. Any one in Miami, FL?

LLM Finetuning (Hamel + Dan) ▷ #predibase (1 messages):

• New Free Token Limit for Serverless Setup: You now get 1M tokens per day up to 10M tokens per month for free using the serverless setup. This works in the prompt tab of the dashboard, although you have to manually enter all the special instruct format tokens yourself.

LLM Finetuning (Hamel + Dan) ▷ #openpipe (1 messages):

• Contact OpenPipe Support via Email: "We don't have OpenPipe people following this channel, so if you have any issues with their credits, you can email them at hello@openpipe.ai." This message indicates that any issues concerning OpenPipe credits should be directed to their support email.

LLM Finetuning (Hamel + Dan) ▷ #pawel-function-calling (1 messages):

• Function Calling vs JSON Structured Output: A user observed that function calling in the context of AI seems similar to JSON structured output but is more reliable. They believe this is because of the specialized training to detect and return functions, seeking further insights on the motivation behind this feature.

LLM Finetuning (Hamel + Dan) ▷ #bergum_rag (27 messages🔥):

• Farewell, but not forever: Participants expressed a mix of sadness and appreciation as this session drew to a close, hinting at plans for future engagements. One noted, "Till the next one."

• Excitement for Gemini's context caching: An enthusiastic mention about experimenting with many-shot prompting using the new Gemini context caching features. This feature is expected to enable more efficient handling of prompts.

• RAG optimization tips: Key takeaways from a RAG (Retrieval-Augmented Generation) discussion emphasized hybrid search over pure ANN, the importance of relevance metrics, and the potential of re-rankers despite increased latency and costs.

• Metadata's crucial role in document structure: A query about embedding metadata separately for document sections led to a clarification that metadata is critical, especially in structured domains like insurance, and hybrid search helps in tuning relevance for different fields. A relevant resource on relevance tuning is available here.

• Importance of iterative improvement: Key strategies for enhancing search systems were highlighted: building domain-specific evaluations, leveraging BM25 and classical search components, and iteratively improving the system. This approach prioritizes pushing classical search and systematically incorporating and evaluating advanced methods.

Links mentioned:

• Relevance Tuning Guide, Weights and Boosts | App Search documentation [8.14] | Elastic: no description found
• Chapter 5. Basic multifield search · Relevant Search: With applications for Solr and Elasticsearch: Satisfying multiple user goals when searching · Searching more than one field in your documents to satisfy user searches · Transforming fields derived from your source data into a search-friendly form...
• relevant-search-book/ipython/Chapter 5 (Multifield Search).ipynb at master · o19s/relevant-search-book: Code and Examples for Relevant Search. Contribute to o19s/relevant-search-book development by creating an account on GitHub.

OpenAI ▷ #ai-discussions (19 messages🔥):

• Questions about early access to Sora: Multiple users inquired if it is possible to get early access to Sora. The general consensus is that it's unlikely unless connected to a major Hollywood studio, with no definitive answers provided.
• Excitement for Runway v3: Users expressed excitement for the upcoming release of Runway v3, with speculation it might be available as soon as tomorrow. One user also mentioned Luma AI as another promising tool.
• Issue with attaching photos in GPT-4o: A user reported having trouble attaching photos in GPT-4o, stating they tried multiple solutions like changing networks and clearing cache without success. The problem persists with no resolution shared in the chat.
• Link to learn about Sora: A user shared a link to Sora allowing others to gather more information about the topic.
• Comparison between GPT-4o and other models: A user discussed the performance differences between GPT-4o, Turbo, and Opus. They claim GPT-4o has better reasoning capabilities compared to other non-OpenAI models and encouraged others to examine metrics and conduct reproducible tests.

OpenAI ▷ #gpt-4-discussions (22 messages🔥):

• Discuss AI Issues in GPT-specific Channels: Members clarified that discussions about GPT should primarily be conducted in GPT-related channels to maintain organization. One user suggested <#998381918976479273> for broader AI topics.
• Persistent 'New Version Available' Notification: A member noted encountering a recurring notification about a new version of GPT despite starting new chats. Another member acknowledged this issue, mentioning it often appears after recent edits to GPT instructions.
• Issues with Adhering to Word Count: Users discussed difficulties in instructing GPT-4 to generate long-form content, such as a 5000-word YouTube script. Suggestions included breaking the task into smaller segments and rewriting prompts, although it was noted that GPT-4 might still condense content automatically.
• GPT-4 Token Limits and Resets: A member inquired about limits and resets on GPT-4 usage, finding it annoying to run out of the quota. They questioned whether limits are dynamically reset over time or require a long wait.

OpenAI ▷ #prompt-engineering (10 messages🔥):

• Comment syntax in different languages: A member shared that in prompt engineering, different languages use different syntax for single-line comments, such as // in C++ and # in Python. They also mentioned using #, ##, and so forth as headings in prompt engineering.
• Effectiveness of custom roles in prompts: One member inquired about the effectiveness of using custom roles beyond the basics, like user and system, noting their prompt sources are varied and user role-focused.
• Memory function confusion: A user reported experiencing context leaks between conversations, suspecting a bug. Another clarified it might be due to the memory function, which can be toggled on or off.
• Seeking color code assistance: A member asked for assistance on implementing color references in code, providing an example with different text strings requesting colors like Cyan and Red.

OpenAI ▷ #api-discussions (10 messages🔥):

• Commenting conventions vary by language: One member shared examples of single-line comment syntax in different programming languages: // for C++ and # for Python. They noted that in prompt engineering, headings use #, ##, ###, etc.
• Effectiveness of custom roles in prompts: A member asked how effective custom roles are compared to standard roles like user and system, sharing that their prompt sources information from various roles, including research-plan.
• Context leaking between conversations: A member reported experiencing context from previous conversations appearing in new ones, which they referred to as "leaking".
• Possible memory function issue: Another member explained that ChatGPT's memory function could be causing the issue and suggested turning it off if not desired. The affected user planned to investigate this function further.
• Question about color coding: A member inquired about how to handle color formatting within a specific code block, asking for guidance on managing text like "Give me Cyan Color" and 'NowGiveMeRed'.

Cohere ▷ #general (44 messages🔥):

• Job Seekers Embrace Open Source Contributions: Several members discussed the challenge of landing interviews, with one advising to "send more PRs, fewer resumes.” Another member shared their company's hiring practice, which focuses solely on contributors to their open-source projects, dismissing the need to even look at resumes.

• Vector Stores and Cohere Embed: There was some confusion about whether Cohere's tools include a built-in vector store. While one user believed it to be based on the Annoy library, another pointed out that "the toolkit is open source" and shared links to GitHub repositories like cohere-toolkit and BinaryVectorDB for more information.

• Free Credits for Students: Multiple users inquired about obtaining free credits as students. A user assured them that they could start with a free trial API key for experimenting, and once they had a substantial project, they could discuss further opportunities.

• Building Personal Portfolios: Emphasis was placed on the value of having a personal portfolio over traditional resumes. One member highlighted that every professional should host their own website and shared their work-in-progress portfolio hosted on Neocities as an example.

• Safe Superintelligence Announced: Users buzzed about a recent announcement from Safe Superintelligence Inc. (SSI), founded by prominent figures like Ilya Sutskever, aimed at developing safe superintelligence. While some expressed excitement, others humorously noted the shift in narrative from AGI to superintelligence.

Links mentioned:

• Tweet from Jordan Burgess (@jordnb): @ssi going straight to superintelligence, nice.
• The Digital Realm of SillyVille: no description found
• Tweet from SSI Inc. (@ssi): Superintelligence is within reach. Building safe superintelligence (SSI) is the most important technical problem of our​​ time. We've started the world’s first straight-shot SSI lab, with one go...
• GitHub - cohere-ai/BinaryVectorDB: Efficient vector database for hundred millions of embeddings.: Efficient vector database for hundred millions of embeddings. - cohere-ai/BinaryVectorDB
• GitHub - cohere-ai/cohere-toolkit: Cohere Toolkit is a collection of prebuilt components enabling users to quickly build and deploy RAG applications.: Cohere Toolkit is a collection of prebuilt components enabling users to quickly build and deploy RAG applications. - cohere-ai/cohere-toolkit

Cohere ▷ #project-sharing (4 messages):

• Balancing tasks is tough: A brief agreement on the difficulties in balancing tasks ("Agree it's tough to balance the two!").
• Cohere API bug report channel: A member reported they may have found a bug in the Cohere API for Rerank and inquired about who to talk to. They were directed to share their findings in another channel ("pls share what you found in <#1168578329423642786>").
• Cohere's email inbox chat impresses: A user found the Cohere chat impressive for interacting with their email inbox and performing explainer tasks. They suggested improvements like adding out of the box support for cmd r+, reducing response lag, and simplifying the UI.

OpenInterpreter ▷ #general (26 messages🔥):

• Video Review of Latest OI Release: A member inquired about video reviews or content for the latest OpenInterpreter release. A link to a YouTube video titled "WELCOME TO THE JUNE OPENINTERPRETER HOUSE PARTY" was shared as relevant content.

• Meta FAIR Announces New AI Models: Meta's AI division announced four new publicly available AI models including Meta Chameleon and Meta Multi-Token Prediction via a Twitter post. The post includes links to GitHub and Hugging Face repositories for detailed information.

• Local III Windows Fix Released: A fix for Local III on Windows has been pushed. Users can apply it by running pip install --upgrade open-interpreter to ensure Local III works on Windows.

• Jan as Local Inference Server: A user asked about running Open Interpreter with Jan, an open-source platform for local language models. Details for setting it up can be found in the Jan.ai documentation.

• Linking Mistral Model with Jan: A user successfully linked the "mistral-7b-openorca.Q4_0.gguf" model downloaded from GPT4All to Jan and ran it using a command. However, there was some confusion regarding the API server settings which was later resolved, but the user experienced delays in response.

Links mentioned:

• Jan.ai - Open Interpreter: no description found
• WELCOME TO THE JUNE OPENINTERPRETER HOUSE PARTY: Powered by Restream https://restream.iodiscord stages are hard
• Tweet from Mike Bird (@MikeBirdTech): Automatically give your photos descriptive names, fully offline Private and free
• Tweet from AI at Meta (@AIatMeta): Today is a good day for open science. As part of our continued commitment to the growth and development of an open ecosystem, today at Meta FAIR we’re announcing four new publicly available AI models...
• GitHub - facebookresearch/chameleon: Repository for Meta Chameleon a mixed-modal early-fusion foundation model from FAIR.: Repository for Meta Chameleon a mixed-modal early-fusion foundation model from FAIR. - facebookresearch/chameleon
• Introduction - Open Interpreter: no description found
• facebook/multi-token-prediction · Hugging Face: no description found

OpenInterpreter ▷ #O1 (1 messages):

one_humankindness: Where is this "pinned message" of which ye speak? 😁

OpenInterpreter ▷ #ai-content (6 messages):

• Collaborators sought for AI use cases: One member asked if others were interested in collaborating on AI use cases, mentioning "awesome AI credit grants". Another member expressed interest quickly.

• Wearable Open Source Tech Ideas: The discussion focused on wearable open source technology, targeting vision and hearing impairments. Suggestions included streaming video for the vision impaired and auto speech-diarization for the deaf in crowded environments.

• Neurodivergent-Focused Use Cases: Another member mentioned their interest in neurodivergent-focused use cases. This caught the interest of another member who shared they had relevant ideas for personal use.

Latent Space ▷ #ai-general-chat (27 messages🔥):

• HuggingFace acquires Argilla for $10M: HuggingFace announced the acquisition of Argilla.io to double down on datasets, which are deemed more impactful than models. Clement Delangue expressed excitement over how aligned Argilla's mission is with HuggingFace's goals. Link

• WebArena as a notable agent benchmark: While WebArena is mentioned as a relevant benchmark for "Agents", it does not hold the same level of mindshare as MMLU. This sparked a conversation about the significance of benchmarks in evaluating AI models' capabilities.

• Factory's Code Droid sets new SOTA on SWE-Bench: Factory.ai published a technical report revealing their Code Droid's new state-of-the-art performance on SWE-bench with 19.27% on Full and 31.67% on Lite. This is part of their mission to bring autonomy to software engineering. Link

• Microsoft releases Florence vision model: Microsoft launched Florence, a vision model capable of handling various tasks like captioning and OCR. The small models (200M and 800M) are MIT licensed and boast comparable quality to models 100 times larger. Link

• Ilya Sutskever starts Safe Superintelligence Inc.: Ilya Sutskever announced the creation of Safe Superintelligence Inc. (SSI), an organization focusing solely on building safe superintelligence. This new company aims to tackle the most important technical problem of our time by advancing capabilities while ensuring safety. Link

Links mentioned:

• Safe Superintelligence Inc.: The world's first straight-shot SSI lab, with one goal and one product: a safe superintelligence.
• Safe Superintelligence Inc.: The world's first straight-shot SSI lab, with one goal and one product: a safe superintelligence.
• Code Droid Technical Report: This technical report will give you a high-level overview of the Code Droid. We provide an analysis of it’s state-of-the-art performance on SWE-bench, where we achieve 19.27% on SWE-bench Full and 31....
• Tweet from SkalskiP @CVPR2024 🇺🇸 (@skalskip92): live GPT-4o demo by @rown from OpenAI at #CVPR2024
• Tweet from clem 🤗 (@ClementDelangue): Super excited to announce the acquisition of @argilla_io! I was lucky to be an angel investor (with @MattHartman) so I could see first-hand how great they are and how aligned their mission is with our...
• Tweet from Factory (@FactoryAI): THE MACHINE THAT BUILDS THE MACHINE Today we are excited to announce the latest updates from Factory and the next steps in our mission to Bring Autonomy to Software Engineering. Droids are autonomou...
• Bloomberg - Are you a robot?: no description found
• Introducing Vercel AI SDK 3.2 – Vercel: Vercel AI SDK 3.2 enables agent and embeddings workflows while improving provider support and DX.
• BigCodeBench: Benchmarking Large Language Models on Solving Practical and Challenging Programming Tasks: no description found
• Tweet from Ilya Sutskever (@ilyasut): I am starting a new company: Quoting SSI Inc. (@ssi) Superintelligence is within reach. Building safe superintelligence (SSI) is the most important technical problem of our​​ time. We've star...
• Tweet from swyx 🛫 @AIdotEngineer (@swyx): @brady @crtr0 happy to also share that Factory will be giving their first conference talk post launch at http://ai.engineer :) single densest collection of AI talent in the world Quoting Factory (@F...
• Tweet from Omar Sanseviero (@osanseviero): Microsoft just silently dropped Florence 👀Vision model that can tackle many vision tasks (captioning, detection, region proposal, OCR) 🤏Small models (200M and 800M) with ~quality to models 100x lar...

Latent Space ▷ #ai-announcements (1 messages):

• Join Waseem Alshikh's talk on Retrieval Systems: An event featuring Waseem Alshikh, CTO of Writer, will present A Comparative Analysis of Retrieval Systems in the Real World. You can join the event through this link.

Link mentioned: LLM Paper Club (Real World Retrieval Systems, with special guest Waseem Alshikh, CTO of Writer) · Zoom · Luma: Today we are covering Comparative Analysis of Retrieval Systems in the Real World with Waseem Alshikh, CTO of Writer covering…

LangChain AI ▷ #general (17 messages🔥):

• GenAI Live Coding Event Announcement: A member promoted the GenAI Live Coding Event scheduled for Thursday, June 20th, 2024, and shared the LinkedIn registration link.

• Langgraph and Semantic Memory Integration: A YouTube video titled "Langgraph integrated with semantic memory" was shared, showing integration of semantic memory with Langgraph. The relevant GitHub code was also provided.

• Microsoft GraphRAG Repository Removal Woes: A member expressed regret for not cloning or forking the GraphRAG repository before it was removed, mentioning its documentation as a valuable resource.

• Custom LLM vs. BaseChatModel Compatibility: A technical query was raised regarding the compatibility between custom LLM wrappers and BaseChatModel, questioning differences in input methods.

• Addressing Async Connection Issue in SQLChatMessageHistory: A detailed response was provided to a member experiencing issues with SQLChatMessageHistory in async mode, directing them to pull request #22933 and issue #22021 for more information on proper handling of async operations and connections.

Links mentioned:

• Langgraph integrated with semantic memory: In this recording, I show how we can integrate semantic memory with langgraph.code: https://github.com/rajib76/langgraph_examples/blob/main/02_a_reflection_a...
• Build software better, together: GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects.
• Build software better, together: GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects.
• Issues · langchain-ai/langchain: 🦜🔗 Build context-aware reasoning applications. Contribute to langchain-ai/langchain development by creating an account on GitHub.

LangChain AI ▷ #langserve (6 messages):

• LangChain setup for ChromaDB retrieval: A member requested an example of LangServe streaming a ChromaDB retriever for RAG and OpenAI model. A detailed explanation was provided, showcasing the use of the Python LangChain library to create a vectorstore with ChromaDB and OpenAIEmbeddings, incorporating it into a question-answering chain and running the LangServe instance.
• Installation and environment setup: The example included commands for installing necessary packages and setting the OPENAI_API_KEY environment variable using Python.
• Code for creating vectorstore and retriever: Steps were provided to load documents from a webpage using WebBaseLoader, split text with RecursiveCharacterTextSplitter, and create a vectorstore with Chroma and OpenAIEmbeddings.
• Integration into a Q&A chain: Instructions were given to create a Q&A chain using create_stuff_documents_chain and integrate it with the retriever using create_retrieval_chain.
• Running LangServe instance: Example code showed how to add routes to the LangServe app with the rag_chroma_chain and mentioned the detailed guide available in the LangChain documentation.

LangChain AI ▷ #share-your-work (3 messages):

• Learn Environment Variables in Custom Visual Agents: A member shared a YouTube video tutorial on using environment variables in custom Visual Agents built on LangChain. This resource is described as essential for tracking state or storing values within AI agents.
• MultiNet Preps Omnimodal Pre-training Corpus: Sidh from Manifold Research Group shared their biweekly Research Log #040, highlighting impressive strides in creating a pre-training corpus for generalist, omnidimensional models like NEKO. They invite interested parties to join the conversation on Discord and explore their endeavors on Github.

Links mentioned:

• Research Log #040: Welcome to Research Log #040! We document weekly research progress across the various initiatives in the Manifold Research Group, and highlight breakthroughs from the broader research community we thi...
• How to Use Environment Variables in your Custom Visual Agents: In this video, I quickly show how to read and write BLOCK scope environment variables from your AI Agents. This is useful to keep track of state or to store ...

LangChain AI ▷ #tutorials (1 messages):

• Music Production AI Tutorial: A member shared a YouTube video titled "AI for music production is insane". The video covers Music Gen 101 and building applications with Text-to-Music API.

Link mentioned: AI for music production is insane: Music Gen 101 & build application with Text-to-Music APIHostinger website builder: https://www.hostinger.com/aijasonGet 10% off with my code: AIJASON🔗 Links...

OpenAccess AI Collective (axolotl) ▷ #general (16 messages🔥):

• Together AI's speed questioned: Members discussed the performance of Together AI, expressing skepticism about its speed, particularly with nemotron. One member noted, "I think the model is just slow to run."
• Call for Apple Metal support: One user simply requested, "Apple Metal pls," highlighting a desire for broader platform compatibility.
• VRAM requirements for training DPO Llama-3-70B: Members speculated about the minimum VRAM needed for full-weight training DPO Llama-3-70B, with suggestions such as "Maybe 8xA100?" There was also discussion on whether an 80GB A100 node is required given the complexities of fine-tuning large models.
• Nemotron API performance and reward model: A user reported that "nemotron's API a lot faster now," and mentioned that the reward model has been released. This implies ongoing improvements and new feature rollouts.

OpenAccess AI Collective (axolotl) ▷ #datasets (6 messages):

• Infinity Instruct impresses with massive dataset: A user shared the "Infinity Instruct" dataset from the Beijing Academy of Artificial Intelligence, praising its massive scale and quality. The dataset was introduced to fill gaps in high-quality instruction fine-tuning, which are critical for enhancing model performance.
• User seeks function calling datasets: A community member requested recommendations for different function calling datasets, mentioning an openness to various formats. Links to Glaive Function Calling v2, APIGen Function-Calling Datasets, and Function Calling ChatML were provided.
• Encouragement to log successful function calls: Users discussed the importance of logging successful function calls to contribute to and enhance existing datasets. One member emphasized, "Remember to log your successful function calls in the future so you can add to the datasets 🙂".
• Tuning a 70b model for function calling: A user expressed interest in tuning a 70 billion parameter model specifically for function calling. The user appreciated the dataset recommendations and mentioned continuing their studies in this area.

Links mentioned:

• BAAI/Infinity-Instruct · Datasets at Hugging Face: no description found
• glaiveai/glaive-function-calling-v2 · Datasets at Hugging Face: no description found
• Salesforce/xlam-function-calling-60k · Datasets at Hugging Face: no description found
• Locutusque/function-calling-chatml · Datasets at Hugging Face: no description found

OpenAccess AI Collective (axolotl) ▷ #axolotl-help-bot (4 messages):

• Using Pre-tokenized Data with Axolotl: To use pre-tokenized data with Axolotl, ensure your dataset has columns named input_ids, attention_mask, and labels. Avoid specifying a type: in your configuration file to indicate a custom dataset format—example configuration and code snippets were provided.

Link mentioned: OpenAccess-AI-Collective/axolotl | Phorm AI Code Search: Understand code, faster.

Interconnects (Nathan Lambert) ▷ #news (5 messages):

• Superintelligence Inc aims high: Safe Superintelligence Inc. (SSI) has been announced as a dedicated lab focused exclusively on developing a safe superintelligence. The founders, including Ilya Sutskever, emphasize their singular goal and streamlined team approach to ensure both rapid capability advancements and safety.

• OpenAI co-founder ventures anew: OpenAI co-founder Ilya Sutskever plans to start a new AI-focused research lab called Safe Superintelligence Inc. According to Bloomberg, the lab will emphasize safety and capability in parallel, with significant talent sourced from Palo Alto and Tel Aviv.

Links mentioned:

• Bloomberg - Are you a robot?: no description found
• Tweet from SSI Inc. (@ssi): Superintelligence is within reach. Building safe superintelligence (SSI) is the most important technical problem of our​​ time. We've started the world’s first straight-shot SSI lab, with one go...
• Bloomberg - Are you a robot?: no description found
• Bloomberg - Are you a robot?: no description found

Interconnects (Nathan Lambert) ▷ #ml-questions (5 messages):

• Date debates: Arxiv paper citation confusion: A user asked whether to consider an Arxiv paper by its first publication date or its most recent update. Nathan Lambert suggested using the "earliest date usually," unless there is a "multi-year gap" which he noted is "super rare."

Interconnects (Nathan Lambert) ▷ #ml-drama (1 messages):

xeophon.: https://fxtwitter.com/nathanwchan/status/1803476213937348814?s=46

Interconnects (Nathan Lambert) ▷ #random (2 messages):

• GPT-4o Shown off at CVPR2024: A member shared a tweet mentioning a live demo of GPT-4o by OpenAI's @rown at the CVPR 2024 event. The member reacted with emojis indicating curiosity and concern.
• Voice Still Hot?: Another member humorously remarked about checking if the voice is still "hot" in response to the demo announcement, likely referencing the demo's anticipated impact.

Link mentioned: Tweet from SkalskiP @CVPR2024 🇺🇸 (@skalskip92): live GPT-4o demo by @rown from OpenAI at #CVPR2024

tinygrad (George Hotz) ▷ #general (7 messages):

• MLPerf Challenge with AMD: A user asked about the challenges of getting AMD on MLPerf despite PyTorch supporting ROCm. Responses clarified that although PyTorch runs on ROCm, the ecosystem and performance were not competitive with CUDA, making it hard to achieve competitive results ("yes it 'just sucked'").

• Tinygrad and Ecosystem Issues: George Hotz pointed out a crucial rhetorical question about why AMD didn't achieve easier entry into MLPerf if it were simple. He also noted that such banter is off-topic for the tinygrad Discord and belongs on Twitter instead.

• Vivobook S15 + Snapdragon X Elite for x86 Emulation: A user sought opinions on the ASUS' Vivobook S15 with Snapdragon X Elite for x86 emulation. This prompted a humorous comment on the irony of asking such a question right after discussing rules about relevant technical queries in the Discord.

tinygrad (George Hotz) ▷ #learn-tinygrad (3 messages):

• Optimizer Buffers Realization Questioned: A member queried the necessity of realizing buffers in the optimizer step, noting that they are not updated. The code snippet highlights the realization process, questioning its purpose.
• BatchNorm Stats Clarification: Another member explained, "for example batchnorm running stats" as a reason for buffers being included in the realization step. They added, "if they don't change, realize doesn't do anything".

MLOps @Chipro ▷ #events (3 messages):

• Wes McKinney to discuss data systems' past and future: Excited to host Wes McKinney in a session where he'll present his work on pandas, Apache Arrow, Ibis, and composable data systems. The event will be livestreamed on YouTube and questions can be posted in #1253002953384529953; RSVP here.

• Sign up for Eluvio's webinar on multimodal clip search: Eluvio AI Research team organizes a free webinar on June 20, 10 a.m. PT about building a multi-field multimodal clip search platform. Register for the event here to dive into advancements in semantic searches and future functionalities in video and content management.

• Moderators needed for Wes McKinney's event: Received numerous queries for Wes McKinney's upcoming talk and created a dedicated discussion channel #1253002953384529953. Volunteers are needed to help moderate YouTube and Discord during the event.

Links mentioned:

• Ins and Outs of Building a Multi-Field Multimodal Clip Search · Luma: The Data Phoenix team invites you to our upcoming webinar, which will take place on June 20th at 10 a.m. PT. Topic: Ins and Outs of Building a Multi-Field…
• Future of DataFrames and Data Systems with Wes McKinney · Luma: I'm really excited to host this talk as Wes is both a really thoughtful person and a great engineer! We'll also host a discussion on Discord. Please post your…
• Future of DataFrames and Data Systems with Wes McKinney: Wes McKinney, the creator of pandas, Apache Arrow, and Ibis, will discuss the future of dataframes and composable data systems. I'm really excited about this...

Datasette - LLM (@SimonW) ▷ #ai (3 messages):

• Anthropic Workbench impresses users: A user remarked, "Boy, the anthropic workbench is a breath of fresh air."

• Florence-2 excels at OCR and handwriting recognition: Florence 2 from Microsoft has been highlighted for its excellent handwriting recognition and OCR capabilities (source). Described as "the best text recognition I've seen in any open model," it performs admirably on handwritten documents.

• Play with Florence-2 on Hugging Face: Users can interact with Florence-2 on Hugging Face's platform (link here). The model is praised for its performance on diverse vision tasks and its utility in workflows such as journalism.

• Florence-2 unifies vision task representations: Florence-2 adopts a prompt-based approach for a variety of vision and vision-language tasks using Hugging Face's transformers implementation (details here). It leverages the extensive FLD-5B dataset to master multi-task learning, excelling in both zero-shot and fine-tuned settings.

Links mentioned:

• Tweet from Dylan Freedman (@dylfreed): New open source OCR model just dropped! This one by Microsoft features the best text recognition I've seen in any open model and performs admirably on handwriting. It also handles a diverse range...
• microsoft/Florence-2-base · Hugging Face: no description found

Mozilla AI ▷ #llamafile (2 messages):

• Tomorrow's Implementation Timeline Set: A user stated, "I can make this happen tomorrow." indicating a commitment to implement a task soon.
• Request to Include tinyBLAS in llama.cpp: A user asked if there are plans to include a "tinyBLAS implementation to llama.cpp" to reduce build size. They mentioned successfully building it by "injecting" the tinyBLAS code but indicated it as not a sustainable long-term solution.

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

• World's Shortest Hackathon on WebSim: WebSim is hosting the "world's shortest hackathon" on Thursday along with two more hackathons in the evening. All projects created will utilize WebSim, as detailed in the hackathon event link.

Link mentioned: WebSim Hackathon Boogaloo: no description found

AI Stack Devs (Yoko Li) ▷ #ai-town-discuss (1 messages):

gomiez: Thanks. I guess it’s not public yet.

AI21 Labs (Jamba) ▷ #general-chat (1 messages):

rajib2189: https://youtu.be/Kw3FtreHgOw