[AINews] $100k to predict LMSYS human preferences in a Kaggle contest • ButtondownTwitterTwitter

Large Language Model (LLM) Advancements and Challenges

• Discussions around LLaMA 3 achieving 1040k context length, Hermes 2 Pro with advanced QA and Function Calling, and llm.c hitting 167K tokens/second. However, quantization seems to hurt LLaMA 3 quality.
• Exploring how LLMs handle multilingual and multimodal tasks, showcasing capabilities and challenges faced in these areas.

Unsloth AI Enables Near-Full Finetuning:

The Unsloth AI community explored possibilities for near-full finetuning by setting all parameters except layernorms to trainable, outperforming standard Hugging Face implementations. Discussions also covered dataset formatting for optimization and unofficial full finetuning tactics. Key resources included Unsloth's Colab notebooks and finetuning guides.

Retrieval Augmented Generation (RAG):

Guides on building efficient RAG data stacks and LangChain's RAG integration for intelligent applications. Discussions on RAG's role in LlamaIndex's introspective agents.

Optimizing Training Pipelines:

Axolotl improved data preprocessing parallelism. Leveraging DeepSpeed Stage 3 and Flash Attention for efficient large model training.

The AI community on various Discord channels engaged in a range of discussions covering topics such as the potential of GPT-5 pricing strategies, the nostalgia for older AI models like GPT-3 and Codex, challenges in fine-tuning models, and debates on the multilingual capabilities of LLMs. Community members also shared resources, tips, and insights related to model optimizations, code enhancements, performance improvements, and upcoming AI events and hackathons. The ongoing exchange of ideas and experiences reflects a vibrant and collaborative community focused on advancing AI technologies in diverse and innovative ways.

AI and Data Handling Strategies

• Members of the Unsloth AI community continue to explore and experiment with various AI-assisted techniques and data handling strategies.
• Discussions include tackling sentiment analysis models, dataset structuring for preference optimization, and adapting full parameter finetuning approaches.

Technical Support and Collaborative Efforts

• Technical issues such as GGUF file optimization, sentiment analysis model creation guidance, and dataset formatting are actively addressed and discussed.
• Community members share unofficial tactics for full finetuning, highlight the importance of correct dataset structuring, and exchange insights on improving AI model performances.

Valuable Resources and Mentions

• Various resources, from Google Colab notebooks to research papers and Hugging Face model repositories, are shared to aid in model development and optimization.
• The collaborative atmosphere in the community drives contributions, discussion advancements, and collective learning for all members.

GGUF Conversion Issues with Llama 3 Identified: A critical issue regarding fine-tuning data loss during conversion to GGUF format was highlighted. Discussions and suggestions have not yet resolved the problem.

Lora Adapter Merging Problems: Merging Lora adapters with GGUF models resulted in partial fine-tuning data loss. Suggestions to use separate adapters did not yield expected outcomes and worsened with combined GGUF and Lora.

Inference and Fine-tuning Strategies Shared for Llama 3: Users shared strategies for fine-tuning Llama 3 using the original INSTRUCT model and discussed necessary steps for successful completion and server initiation.

Possible Compatibility Issues with Llama.cpp for Llama 3: Concerns were raised about compatibility between llama.cpp and the newly released Llama 3.

Seeking Help and Roadmaps for Finetuning Models: New users sought step-by-step guidance for models like Gemma and Llama, with community members pointing to resources and suggesting exploring AI/ML courses on platforms like YouTube.

Channel Collaboration, Model Specialization, Data Considerations, and AI Use Case Discussions: Community members discussed collaboration channels, model specialization challenges, data considerations, and differences between business and experimental use cases. They emphasized the importance of knowledge sharing and strategic planning.

FAQs Removal, GPU Debates, Video to Anime Conversion, and Text/Image Upscaling Inquiries: Conversations included the disappearance of the /faq command, debates on GPU choices and futureproofing, inquiries about video to anime conversion benchmarks, and discussions on text/image manipulation tools like Davinci Resolve.

Member Discussions on Gradient Details, Hessian Calculation, and Debugging Procedures: Topics ranged from gradient computations requiring specific settings, Hessian-vector calculations, estimating Hessian diagonals, Triton kernel implementation challenges, and debugging queries in PyCharm.

Member Experiences with GPU Selection, Video Conversion, and AI Tool Recommendations: Conversations covered member experiences with GPU choices for AI tasks, video to anime conversion benchmarks, and recommendations for AI text and image manipulation tools like ComfyUI.

Member Experiences with Custom PyTorch/CUDA Extensions, Kernel Modules, and Debugging in PyTorch Development: Topics included queries and tips on installing custom CUDA extensions, managing CUDA setups in PyTorch, and debugging challenges in PyTorch development, showcasing member experiences and solutions.

Improving Issue Triage on CUDA MODE Discord:

• Discussion on handling server issues with a proposed bot for GitHub issue management.

Torch Compile Optimization for Variable Lengths:

• Troubleshooting the use of <code>torch._dynamo.mark_dynamic(inputs, index=1)</code> for dynamic sequence lengths in PyTorch 2.2 & 2.3.

GreenBitAI Toolkit and BitBlas for Large Language Models (LLMs):

• Introduction of GreenBitAI's toolkit and BitBlas with a focus on binary operations.

CUDA and Memory Optimization Discussions:

• Achievements in optimizing CUDA kernels for improved token processing speeds in PyTorch.

LLM Studio Integration Challenges and Solutions:

• Issues integrating llama.cpp with LM Studio, addressing file version compatibility and resolution of FileNotFoundError.

Overall, the discussions revolve around enhancing performance and addressing technical challenges in PyTorch development.

LM Studio ▷ #amd-rocm-tech-preview

• A new CLI tool, lms, has been introduced for managing LLMs and running the local server on AMD ROCm Preview Beta, now open source on GitHub. Users can download the latest LM Studio 0.2.22 ROCm Preview to utilize lms, which comes with the additional benefit of having OpenCl pre-packaged for new users.
• A member noted that the prompt is included in the API response, a known issue in the latest build. The LM Studio team rapidly acknowledged and confirmed an imminent fix was pushed live, which users have verified.
• Participants are discussing running ROCm on Linux, with one sharing their experience of using ROCm on Mesa's opencl implementation and hoping for a Linux-supported ROCm build, while another suggested using lm-studio to download models for local llama.cpp build could be a workaround.

LM Studio ▷ #model-announcements

• The latest update to LM Studio includes a significant improvement for llama.cpp addressing Llama 3 and BPE model issues. BPE-fix tagged versions of Llama 3 8B, 70B instruct, and Phi-3 models are available to download at the provided Hugging Face links.

A new version of Nous Research AI's Hermes, Hermes 2 Pro, has been released with capabilities in good QA, function calling, and JSON mode with vision multimodal. The LLM model with function calling validates answers by external function/tool calls, not just simulating answers. The Glaive function-calling dataset V2 structure was shared for training models with function-calling, emphasizing advanced LLM applications. Users experienced exceptional inference speeds with Hermes 2 Pro utilizing llama.cpp on devices like Android with 8GB RAM. Solutions were provided for using Hermes 2 Pro function-calling with CrewAI and LocalAI, including examples on GitHub and Jupyter notebooks.

The Discord channels related to Nous Research AI, Modular (Mojo 🔥), and World-sim were active with various discussions and updates. Members discussed modifications for enabling ChatML, finetuning large language models (LLM), free generic datasets, upcoming gaming updates, AI consciousness, language race with Mojo, and more. Modular celebrated community contributions in Mojo 24.3 release while introducing MAX Engine Extensibility. The importance of community contributions and the challenges of simulating consciousness were also topics of conversation.

The Modular (Mojo 🔥) Discord channel discussions highlighted the promising advancements within the CHERI ecosystem. The Capability Hardware Enhanced RISC Instructions (CHERI) technology offers potential in enhancing hardware security, possibly nullifying a significant portion of vulnerability exploits. Adopting CHERI could lead to a paradigm shift in software development, enabling lightning-fast inter-process communication and efficient UNIX-style programming. Furthermore, discussions pointed towards CHERI's impact on sandboxes and traditional security measures, speculating about potential redundancies and the prominence of microkernels. The community shared relevant links for further exploration of CHERI's capabilities and developments.

The HuggingFace community is actively engaged in multiple channels, discussing topics ranging from model interpretability and auto-training to voice synthesis models and model deployment. Members exchange recommendations for voice synthesis models, discuss challenges in model conversion and fine-tuning, share insights on deploying LLMs in production, and request tools like a parquet converter-bot. Furthermore, the community delves into topics like evaluating refined prompts, Kolmogorov-Arnold Networks, and the significance of RAG in LangChain's email drafting. Various models like FinBERT for financial sentiment analysis, LongCap for image captioning, and RARR for model attribution are also introduced and discussed. Additionally, community members highlight the importance of React agents, explore reasoning and acting in LLMs, study the intersection of graph ML and LLMs, and share strategies for fine-tuning models efficiently. Through curated collections and fine-tuned models, the community collaborates to enhance the capabilities and applications of AI models across different domains.

In this section, members from the HuggingFace and LlamaIndex channels engage in various discussions related to Auto-Train Configs, Merging Diffusion Pipelines Technique, Seeking Examples for Partial Diffusion, Availability of Partial Diffusion for Testing, Building an Optimized RAG Data Stack, RAG Pipeline Guide, Natural Language Filters for Airbnb Listings, LlamaIndex 10.34 Release Features, Launch of LlamaIndex 0.10.34 with Huggingface Support, Seeking Financial Analysis Application, Customizing MongoDB with LlamaIndex, LLamacpp Parallel Request Deadlock, Setting Up Trulens with Llama Index, Memory Load Issues with LlamaIndex, and more. Various tools, tutorials, and models are discussed highlighting advancements and challenges in the AI field.

The Eleuther discussion group delved into various topics related to AI research, including concerns about leaked information in benchmark datasets for large language models (LLMs) and utilizing chatbot conversations for model improvement. Additionally, a study was referenced showcasing a transformer model's high chess performance without domain-specific enhancements or explicit search algorithms. The group also engaged in playful exchanges related to time travel and noted selective responses from a member after a period of absence from the server. Overall, the discussions highlighted the continuous exploration of innovative approaches and challenges in the field of AI research.

OpenInterpreter

• A member shared a link to Open Interpreter local installation documentation, including instructions for Ollama, Jan.ai, and Llamafile, with emphasis on dolphin-mixtral.
• Members discussed using the --profile 01 command in Open Interpreter to avoid repetitive steps and plans.
• An invite was extended to join a team for the Microsoft Open Source AI hackathon in Seattle.
• Inquiries were made about hosting an Open Interpreter server for others to connect to, with guidance on using specific commands.
• Guidance was sought on setting up a local Open Interpreter model for access by mobile devices, with links provided to GitHub documentation for Android device setup and running Open Interpreter locally.

The AI community discussions featured updates on Stable Diffusion shifting focus due to hardware constraints, emphasis on properly training with CLIP to avoid biases. Discussions in various Discord channels covered topics like dataset choices, interpretability, new tools, integration struggles, hackathon announcements, and interview requests. Interconnects channel talked about reward model competitions, ensembling models, and technical details on algorithms like PPO and value functions in RLHF. Additionally, Coherer, Mozilla AI, and LangChain channels discussed implementations, code interpreter SDK launch, and embedding discrepancies.

tinygrad (George Hotz)

• One member inquired about progress, confirming substantial progress made two days ago.
• A member shared enthusiasm for their first commit to the project, expressing joy upon successful commitment.

Learn-Tinygrad

• The utility of blobfile in examples/llama.py clarified, as load_tiktoken_bpe depends on it.
• Troubleshooting issues with generating the forward pass compute graph for a neural network, including tips on resolving errors and installing necessary libraries.
• Installation of pydot and graphviz to resolve errors related to graph visualization and commands.
• Suggestion to update the documentation regarding resolving specific errors.

AI21 Labs (Jamba) Announcements

• Launch of Jamba-Instruct, an instruction-tuned version of the hybrid SSM-Transformer Jamba model, with a focus on quality and performance for commercial use.
• Encouragement to read the detailed blog post for deeper insights into Jamba-Instruct.

Jamba

• Announcement of Jamba-Instruct's launch and exploration of larger context windows for potential use cases.

Alignment Lab AI

• Discussion on quantization impact on LLaMA 3's quality compared to LLaMA 2 and risks associated with precision reduction in larger models.

Skunkworks AI Off-Topic

• Mention of funding opportunities for Skunkworks projects and availability of fast compute grants.

Datasette - LLM (@SimonW)

• Member's frustration with managing scattered 7B localmodels and the need for an LLM to assist with organization.