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Enterprise AI Adoption

• Cohere is partnering with Accenture to bring their enterprise search capabilities to Accenture's clients, aiming to drive productivity gains.
• Mistral AI and Snowflake are collaborating to make Mistral's LLMs available through Snowflake, enabling enterprises to build AI apps within the security of Snowflake's platform.
• Deepspeed innovations are coming to Together AI Research to accelerate cloud infrastructure for generative AI.

Exploring AI Model Capabilities and Comparisons:

• Claude 3 is generating excitement for its reported superior performance across various cognitive tasks, surpassing GPT-4 according to some users. Discussions revolve around its capabilities in coding, function calling, and self-moderation in group chats, as showcased in a Twitter story.
• Opus, a model variant, is praised for its coding prowess, particularly in function calling. It achieved an impressive 800 score on the SAT Reading section, sparking conversations about avoiding memorization.
• Skepticism arises regarding the reliability of published benchmarks in capturing the full potential of newer models like GPT-4.

LangChain AI Discord Summary:

• LangChain Function Integration Discussion: LangChain Core Example provided a guide on how to use LangChain and OpenAI's ChatCompletion.create() to integrate function roles into messages, following an inquiry by @vishal5795.

• Partner Up for Paid Tech Gig: @mattew_999 is on the lookout for a technically inclined collaborator for a paid project, no further details on the partnership offered.

• Chain Partners Wanted, Issues Reported: Queries about new partnerships with LangChain sparked conversations, while @rajib2189 reported intermittent 502 errors on FastAPI hosted on AWS and served through an Apache server with Uvicorn.

• GPT-4 Fine-Tuning Interest Surfaces: One member, @8886600, expressed interest in obtaining access to GPT4.

Root Squashed at RunPod:

Discussions on RunPod revealing docker image limitations.

Bandwidth Performance in NVIDIA's Latest:

Comparison of SRAM bandwidth of H100 to A100 and RTX 4090's L1 bandwidth.

PyTorch Community Sparks Cooperation and Quantization Speed:

Importance of TensorOptions setting, bitsandbytes package for quantization speedup.

Optimization via Algorithms:

Efficiency improvements in flash_attention algorithm and sliding window attention bias.

CUDA Learning Path:

Guidance for newcomers to CUDA programming.

Ring the Alarm on Ring Attention:

Details on ring-attention issues and benchmarks.

Disclosure of Model File Placement Debate.

Seeking community input on standardization of model file locations.

In this section, various conversations from the Mistral community are highlighted. Discussions include comparing prices of GPT-4 Turbo with Mistral Large, seeking French speakers for text analysis using Mistral IA, setting up Mistral locally, fine-tuning challenges, correcting errors in Mistral prompts, and more. There are also inquiries about API errors, Mistral webhooks, hosting locations, JSON parsing issues, and chatbot functionalities. The community also discusses Mistral's commitment to open models, balancing open-source projects with commercial viability, multilingual model performance, and anticipation for future office hours. The section offers a glimpse into the diverse topics being discussed in the Mistral community.

Claude 3 Now Available for Pros:

• Claude 3 is now available for pro users, offering 5 daily queries with Claude 3 Opus and faster processing with Claude 3 Sonnet.

Partnership with Nothing's Phone (2a) Launch:

• New owners of Nothing's Phone (2a) can get 1 year of Perplexity Pro for free, with instructions on how to redeem the offer.

Links mentioned:

• Nothing Perplexity: Experience tech excitement.
• More than an OpenAI Wrapper: Perplexity Pivots to Open Source: Transitioning to open source technology.
• Cat Dont Care Didnt Ask GIF: Expressing indifference with a gif.
• GPTZero | The Trusted AI Detector for ChatGPT, GPT-4, & More: Advanced AI detector for various AI models.
• Perplexity Blog: Stay updated on Perplexity's blog for product updates and tips.
• Wolfram|Alpha: Making the world’s knowledge computable: Expert-level knowledge accessible to all.
• What is a token and how many tokens can Perplexity read at once?: Technical insights on token usage.
• Welcome to Live — Ableton Reference Manual Version 12 | Ableton: Introduction to Live by Ableton.
• Rabbit R1 and Perplexity AI dance into the future: Explanation of Rabbit R1 and Perplexity AI collaboration.

Seeking Capybara-34b Usage Guidance:

• @oemd001 inquired about using the Capybara-34b model with a chat template but strugged with the OpenAI template. .ben.com provided a suggestion with a specific template format: "template": "\n\nUSER: \nASSISTANT:".

Clarifying GENIE's Versatility:

• @pier1337 clarified that GENIE applies to any interactive world environment and not just 2D games, which was supported by @max_paperclips who mentioned that it could be used for other things besides the popular 2D game example.

Curiosity Around JEPA Applications:

• @max_paperclips considered creating a functional demonstration for JEPA as @pier1337 discussed the broad potential of JEPA, like patching images, with text and software media.

Troubles with Striped-Hyena Tokenizer:

• @mrgonao mentioned having issues with the striped-hyena nous tokenizer, which defaults to sentencepiece and then experiences breakdowns.

Training Large Language Models on Length Awareness:

• @hy3na_xyz pondered why LLMS like Mistral 8x7b don't understand word count limitations, engaging in a dialogue with @hexani about the potential need for numerous examples to train on length awareness.

Dive into Tree-Structured Retrieval with RAPTOR:

• @jerryjliu0 invites everyone to a webinar to learn about RAPTOR, a paper featuring a novel tree-structured indexing and retrieval technique. The webinar is scheduled for Thursday at 9am PT and interested participants can register here.
• Understanding RAPTOR's Advantages: The technique presented in RAPTOR hierarchically clusters and summarizes information into a tree structure with various levels of detail. This method aims to overcome issues with naive top-k Retrieval Augmented Generation (RAG), which struggles with questions that require understanding of higher-level concepts.

Latent Space ▷ #ai-announcements (4 messages):

• New Podcast Episode Alert: @swyxio announced that the latest podcast episode is live, featuring <code>@776472701052387339</code>. Find the tweet with the podcast here.
• Podcast Episode Hits Hacker News: @swyxio mentioned that the podcast with Soumith is also featured on Hacker News.

Latent Space ▷ #llm-paper-club-west (82 messages🔥🔥):

• Welcome Aboard Paper Club: @eugeneyan and @youngphlo showed support and welcomed @swyxio who volunteered to take on the task of surveying model serving papers.
• Paper Teaser Excitement: @swizec expressed enthusiasm about the start of the model serving paper, saying it included topics they'd been curious about.
• Speculative Decoding on GPUs: @swyxio and @rj_rms discussed speculative decoding's use of GPU cycles to improve performance when memory is the bottleneck, while @shivdinho queried its dependence on hardware configurations.
• Model Serving with No Trade-offs: @swyxio recommended Fireworks AI blog post covering faster model serving with FireAttention through quantization.
• The Waifu-Driven Performance Theory: @swyxio humorously attributes coding dedication to the so-called waifu research department, emphasizing how community-driven research can lead to performance advances, such as seen in the Aphrodite Engine by PygmalionAI.

• Exploring Positional Embeddings and ALiBi Concerns: Discussion on the efficiency of T5 simplified positional embeddings, Resonance RoPE paper introduction.
• AGI and Compute Horsepower: Debate on the role of compute power in advancing towards AGI.
• vLLM Batching Internals Clarification: Clarification on vLLM's handling of batching internally.
• Government Inquiry on AI Regulation: Shared link for public consultation on regulating open source AI.
• Ternary Neural Networks Exploration: Discussion on the inefficiency of Ternary Neural Networks compared to full-precision NNs.
• Contributions Welcomed for Fused Triton Kernels: Inquiries about contributing to gpt-neox for fused triton kernels and MOE configs.
• Team Expansion for Tensor Expression Integration: Offer to integrate Tensor Expressions into gpt-neox with access to H100 GPUs.
• Focus on Basic TE Support Before Tackling Convergence: Priority on adding TE support and subsequent convergence with fp8 in gpt-neox.
• Results Mismatch Mystery on SQuADv2: Unexpected results with SQuADv2, examination of GPT-2 vs. GPT-3 performance.
• IQ Versions for LLMs Proposed: Proposal for variations of 'IQ' versions of LLMs and adding system prompts for performance enhancement.
• Open Source LLMs Pressure-tested: Pressure-testing various open-source LLMs using Gregory Kamradt's analysis.
• In Search of the Best AI for Storytelling: Inquiry about the best AI for storytelling with specific system specifications.
• Evaluating LLMs' Ability to Perform Arithmetic: Drafting a blog post on benchmarking LLMs on basic arithmetic operations.

Quest for More RAM:

• User @jason_2065 discovers that 64GB of RAM is inadequate for Smaug 34B model

Crash Test Dummies:

• User @goldensun3ds faces crashes when loading Smaug with GPU layers

Ultra-Smaug 128B:

• Community yet to test models larger than 70B

Vying for Velocity:

• User @jason_2065 reports slow token/sec rate on Smaug

Overnight Challenge:

• User @goldensun3ds commits to running large context overnight

Syntax Struggles for default_system_message:

• User @nxonxi expresses difficulty with syntax in different operating environments

Clarifying the Role of default_system_message.py:

• User @1sbefore explains the role of default_system_message.py

Triple Encoder Text Model in Question:

• User @top_walk_town discusses text encoder structures

Unique Velocity Sampling in Flows:

• User @pseudoterminalx highlights a research trick on velocity training

Google's Model Distillation Method Revealed:

• User @pseudoterminalx shares Google's distillation method

On the Utility of T5 for Diffusion Models:

• Users discuss using T5 in diffusion models

Efforts and Challenges in Low Resolution Adaptation:

• User @astropulse shares excitement for low resolution adaptation project

• LangChain and Function Implementation Assistance:
  • @vishal5795 inquired about integrating function roles into messages using LangChain and OpenAI's ChatCompletion.create().
  • @chester3637 provided detailed Python example using LangChain.
• Seeking Tech Task Partner:
  • @mattew_999 announced the search for a partner to work on tech tasks, a paid opportunity.
• Inquiry About New Partnerships:
  • @earduman2 asked about LangChain's openness to new chain partnerships.
• FastAPI Sporadic Issues:
  • @rajib2189 reported sporadic 502 errors using FastAPI under heavy load.
• Interest in GPT-4 Fine-Tuning Access:
  • @8886600 expressed interest in GPT-4 fine-tuning capabilities and willingness to pay for an API key.
• Injecting Humor into AI Art:
  • @neil6430 experimented with ML Blocks to create an amusing image.
• Lutra Revolutionizes Workflow Automation:
  • @polarbear007 introduced Lutra.ai, a platform to transform English instructions into code.
• Raptor Reveals Secrets of Long Context RAG:
  • @andysingal shared a Medium article on building Long Context RAG with RAPTOR and Langchain.
• ChromaDB joins LM Studio:
  • @vic49 provided a GitHub link to ChromaDB Plugin for LM Studio.
• Opus catches attention in coding community:
  • User @pantsforbirds found Opus promising for coding.
• Peer approval for Opus in function calling:
  • @res6969 heard high praise for Opus's performance in function calling.
• GPT-4 excels in medical knowledge:
  • @thebaghdaddy found GPT-4 significantly better in technical knowledge of medicine.
• Script...testing:
  • @iron_bound discussed adding device IDs to a script for testing purposes.
• Sampling Code Introduced with Glitches:
  • @jamesmel shared a GitHub Pull Request for sampling code with some errors to be investigated.
• Benchmarks Completed for Striped and Zigzag:
  • @iron_bound reported specific memory usage for two CUDA devices.
• Opening Up the Axolotl Training:
  • A link was shared to OpenAccess-AI-Collective's Axolotl GitHub repository.
• Troubleshooting Ring Attention and Sampling Logic:
  • Discussions focused on debugging custom attention library and sampling code logic.
• Clarifying AI Terminology:
  • @simonw detailed the difference between prompt injection and jailbreaking in a blog post.

The section discusses a Microsoft blog post about state-backed actors using OpenAI's large language models (LLMs) for cyber activities, including reconnaissance and spear phishing. It also touches on the risks associated with AI, such as prompt injection, and proposes access control as a mitigation strategy. Additionally, it highlights the challenges of preventing invisible prompt injections and provides links to related resources.