All tags
Topic: "small-models"
not much happened this weekend
o3 o1 opus sonnet octave openai langchain hume x-ai amd nvidia meta-ai-fair hugging-face inference-time-scaling model-ensembles small-models voice-cloning fine-math-dataset llm-agent-framework benchmarking software-stack large-concept-models latent-space-reasoning mechanistic-interpretability planning speech-language-models lisa-su clementdelangue philschmid neelnanda5
o3 model gains significant attention with discussions around its capabilities and implications, including an OpenAI board member referencing "AGI." LangChain released their State of AI 2024 survey. Hume announced OCTAVE, a 3B parameter API-only speech-language model with voice cloning. x.ai secured a $6B Series C funding round. Discussions highlight inference-time scaling, model ensembles, and the surprising generalization ability of small models. New tools and datasets include FineMath, the best open math dataset on Hugging Face, and frameworks for LLM agents. Industry updates cover a 5-month benchmarking of AMD MI300X vs Nvidia H100 + H200, insights from a meeting with Lisa Su on AMD's software stack, and open AI engineering roles. Research innovations include Large Concept Models (LCM) from Meta AI, Chain of Continuous Thought (Coconut) for latent space reasoning, and mechanistic interpretability initiatives.
12/9/2023: The Mixtral Rush
mixtral hermes-2.5 hermes-2 mistral-yarn ultrachat discoresearch fireworks-ai hugging-face mistral-ai benchmarking gpu-requirements multi-gpu quantization gptq chain-of-thought min-p-sampling top-p-sampling model-sampling model-merging model-performance small-models reasoning-consistency temperature-sampling bjoernp the_bloke rtyax kalomaze solbus calytrix
Mixtral's weights were released without code, prompting the Disco Research community and Fireworks AI to implement it rapidly. Despite efforts, no significant benchmark improvements were reported, limiting its usefulness for local LLM usage but marking progress for the small models community. Discussions in the DiscoResearch Discord covered Mixtral's performance compared to models like Hermes 2.5 and Hermes 2, with evaluations on benchmarks such as winogrande, truthfulqa_mc2, and arc_challenge. Technical topics included GPU requirements, multi-GPU setups, and quantization via GPTQ. Benchmarking strategies like grammar-based evaluation, chain of thought (CoT), and min_p sampling were explored, alongside model sampling techniques like Min P and Top P to enhance response stability and creativity. Users also discussed GPTs' learning limitations and the adaptability of models under varying conditions, emphasizing min_p sampling's role in enabling higher temperature settings for creativity.