Athene v2 Chat & Agent by NexusFlow - SoTA general LLM fine-tuned from Qwen 2.5 72B excels at Chat + Function Calling/ JSON/ Agents Nexusflow/athene-v2-6735b85e505981a794fb02cc
Orca Agent Instruct by Microsoft - 1 million instruct pairs covering text editing, creative writing, coding, reading comprehension, etc - permissively licensed microsoft/orca-agentinstruct-1M-v1
If I am correct and the LLM model changes the 'shape' of the data as it learns, then I should be able to track and utilize those shape changes as a backpropagation training mechanism, right? Well guess what, I can do that! Entropy, Sparsity, and Density, this is how I can measure the shape of the data the LLM model is creating. Nodes, Clusters, and Edges, these are the mechanisms within the neural network the LLM model updates as it learns these concepts. I measure the effects of these updates, via Entropy, Sparsity, and Density. Check out more in this video: https://youtu.be/jADTt5HHtiw
2 replies
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Reacted to erikkaum's
post with ππ₯9 days ago
β¨ Unified 3D generation & text understanding. β¨ 3D meshes as plain text for seamless LLM integration. β¨ High-quality 3D outputs rivaling specialized models.
Reacted to sayakpaul's
post with πβ€οΈ9 days ago
It's been a while we shipped native quantization support in diffusers π§¨
We currently support bistandbytes as the official backend but using others like torchao is already very simple.
This post is just a reminder of what's possible:
1. Loading a model with a quantization config 2. Saving a model with quantization config 3. Loading a pre-quantized model 4. enable_model_cpu_offload() 5. Training and loading LoRAs into quantized checkpoints