All tags
Topic: "community-engagement"
super quiet day
jamba-1.5 phi-3.5 dracarys llama-3-1-70b llama-3-1 ai21-labs anthropic stanford hugging-face langchain qdrant aws elastic state-space-models long-context benchmarking ai-safety virtual-environments multi-agent-systems resource-management community-engagement model-performance bindu-reddy rohanpaul_ai jackclarksf danhendrycks reach_vb iqdotgraph
AI21 Labs released Jamba 1.5, a scaled-up State Space Model optimized for long context windows with 94B parameters and up to 2.5X faster inference, outperforming models like Llama 3.1 70B on benchmarks. The Phi-3.5 model was praised for its safety and performance, while Dracarys, a new 70B open-source coding model announced by Bindu Reddy, claims superior benchmarks over Llama 3.1 70B. Discussions on California's SB 1047 AI safety legislation involve Stanford and Anthropic, highlighting a balance between precaution and industry growth. Innovations include uv virtual environments for rapid setup, LangChain's LangSmith resource tags for project management, and multi-agent systems in Qdrant enhancing data workflows. Community events like the RAG workshop by AWS, LangChain, and Elastic continue to support AI learning and collaboration. Memes remain a popular way to engage with AI industry culture.
12/10/2023: not much happened today
mixtral-8x7b-32kseqlen mistral-7b stablelm-zephyr-3b openhermes-2.5-neural-chat-v3-3-slerp gpt-3.5 gpt-4 nous-research openai mistral-ai hugging-face ollama lm-studio fine-tuning mixture-of-experts model-benchmarking inference-optimization model-evaluation open-source decentralized-ai gpu-optimization community-engagement andrej-karpathy yann-lecun richard-blythman gabriel-syme pradeep1148 cyborg_1552
Nous Research AI Discord community discussed attending NeurIPS and organizing future AI events in Australia. Highlights include interest in open-source and decentralized AI projects, with Richard Blythman seeking co-founders. Users shared projects like Photo GPT AI and introduced StableLM Zephyr 3B. The Mixtral model, based on Mistral, sparked debate on performance and GPU requirements, with comparisons to GPT-3.5 and potential competitiveness with GPT-4 after fine-tuning. Tools like Tensorboard, Wandb, and Llamahub were noted for fine-tuning and evaluation. Discussions covered Mixture of Experts (MoE) architectures, fine-tuning with limited data, and inference optimization strategies for ChatGPT. Memes and community interactions referenced AI figures like Andrej Karpathy and Yann LeCun. The community also shared resources such as GitHub links and YouTube videos related to these models and tools.