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Model: "llava"
not much happened today
llama-2-70b llama-2-7b mistral-7b qwen-1.5 llava microsoft mistral-ai ollama fine-tuning synthetic-data retrieval-augmented-generation embeddings hardware-optimization performance-benchmarks model-memory multimodality
The Reddit community /r/LocalLlama discusses fine-tuning and training LLMs, including tutorials and questions on training models with specific data like dictionaries and synthetic datasets with 25B+ tokens. Users explore retrieval-augmented generation (RAG) challenges with models like mistral-7b and embedding generation for EEG brain activity. Discussions include hardware optimization for running llama-2-70b locally under budget constraints, and performance benchmarks for qwen-1.5 models. There is interest in extending LLM capabilities, such as converting llama-2-7b into a vision-capable model like llava and improving model memory for longer context retention.
Google Solves Text to Video
mistral-7b llava google-research amazon-science huggingface mistral-ai together-ai text-to-video inpainting space-time-diffusion code-evaluation fine-tuning inference gpu-rentals multimodality api model-integration learning-rates
Google Research introduced Lumiere, a text-to-video model featuring advanced inpainting capabilities using a Space-Time diffusion process, surpassing previous models like Pika and Runway. Manveer from UseScholar.org compiled a comprehensive list of code evaluation benchmarks beyond HumanEval, including datasets from Amazon Science, Hugging Face, and others. Discord communities such as TheBloke discussed topics including running Mistral-7B via API, GPU rentals, and multimodal model integration with LLava. Nous Research AI highlighted learning rate strategies for LLM fine-tuning, issues with inference, and benchmarks like HumanEval and MBPP. RestGPT gained attention for controlling applications via RESTful APIs, showcasing LLM application capabilities.
12/26/2023: not much happened today
llava exllama2 meta-ai-fair google-deepmind gpu-offloading vram-utilization model-conversion moe-models multimodality model-performance hardware-configuration model-saving chatml installation-issues music-generation
LM Studio users extensively discussed its performance, installation issues on macOS, and upcoming features like Exllama2 support and multimodality with the Llava model. Conversations covered GPU offloading, vRAM utilization, MoE model expert selection, and model conversion compatibility. The community also addressed inefficient help requests referencing the blog 'Don't Ask to Ask, Just Ask'. Technical challenges with ChromaDB Plugin, server vs desktop hardware performance, and saving model states with Autogen were highlighted. Discussions included comparisons with other chatbots and mentions of AudioCraft from meta-ai-fair and MusicLM from google-deepmind for music generation.