POV:Frank (d/acc) 🎩 💜 pfp

POV:Frank (d/acc) 🎩 💜

@scalinglaw.eth

441 Following
260 Followers
POV:Frank (d/acc) 🎩 💜 pfp
POV:Frank (d/acc) 🎩 💜
@scalinglaw.eth
Looking forward to testing Nous Research forge to compare with gpt-o1. 👀👀👀
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POV:Frank (d/acc) 🎩 💜 pfp
POV:Frank (d/acc) 🎩 💜
@scalinglaw.eth
I've joined get hyped waitlist! Join through the frame below and help me climb up the leaderboard! Powered by /beearly 🐝
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POV:Frank (d/acc) 🎩 💜 pfp
POV:Frank (d/acc) 🎩 💜
@scalinglaw.eth
Minted Doodles Certified Viral: Feline Hungry
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POV:Frank (d/acc) 🎩 💜 pfp
POV:Frank (d/acc) 🎩 💜
@scalinglaw.eth
Cooking flux models on Heurist. Will put link here. 👉😎
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POV:Frank (d/acc) 🎩 💜 pfp
POV:Frank (d/acc) 🎩 💜
@scalinglaw.eth
10 Moxie Passes available — mint yours to be eligible for upcoming airdrops, grants, Fan Tokens, and more! cc @betashop.eth @airstack.eth
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POV:Frank (d/acc) 🎩 💜 pfp
POV:Frank (d/acc) 🎩 💜
@scalinglaw.eth
CVPR paper: Instruct-Imagen: Image Generation with Multi-modal Instruction Innovations: - Multi-modal instruction for image generation: A new format that uses natural language to combine different modalities (text, edge, style, subject, etc.) to articulate complex generation intents in a uniform way. - Two-stage training approach for Instruct-Imagen: a) Retrieval-augmented training: Adapts a pre-trained text-to-image model to handle multi-modal inputs using retrieved similar (image, text) pairs. b) Multi-modal instruction-tuning: Fine-tunes the adapted model on diverse image generation tasks paired with multi-modal instructions. - Unified model architecture that can handle various image generation tasks - through multi-modal instructions, without task-specific designs. - Zero-shot generalization capability to unseen and more complex image generation tasks. - Adaptability to new tasks through fine-tuning on small datasets. source: https://arxiv.org/abs/2401.01952
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POV:Frank (d/acc) 🎩 💜 pfp
POV:Frank (d/acc) 🎩 💜
@scalinglaw.eth
Prepare for my summary of next CVPR2024 paper
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POV:Frank (d/acc) 🎩 💜 pfp
POV:Frank (d/acc) 🎩 💜
@scalinglaw.eth
CVPR 2024 Best Paper Award: Rich Human Feedback for Text-to-Image Generation - First dataset with detailed human feedback on generated images. - Rich Automatic Human Feedback (RAHF) Model (Multimodal Transformer model to predict rich human feedback) So, it enables that: - Finetuning generative models using predicted scores. - Region inpainting using predicted implausibility heatmaps. - Using aesthetic scores for classifier guidance in diffusion models. https://arxiv.org/abs/2312.10240
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POV:Frank (d/acc) 🎩 💜 pfp
POV:Frank (d/acc) 🎩 💜
@scalinglaw.eth
I will post the gem papers I found at the CVPR 2024 conference daily here. They are either (1) innovative in new model architectures, (2) new ways to facilitate model development, or (3) fun applications with full potential.
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POV:Frank (d/acc) 🎩 💜 pfp
POV:Frank (d/acc) 🎩 💜
@scalinglaw.eth
Minted an NFT for contributing to @jessepollak's baldness. Onchain Summer Buildathon is based. Mint yours by joining the Onchain Summer Buildathon. https://letsgetjessebald.com/token/854?ref_code=63421e6a18
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POV:Frank (d/acc) 🎩 💜 pfp
POV:Frank (d/acc) 🎩 💜
@scalinglaw.eth
what the most innovative features on recaster?
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POV:Frank (d/acc) 🎩 💜 pfp
POV:Frank (d/acc) 🎩 💜
@scalinglaw.eth
https://github.com/e-p-armstrong/augmentoolkit/tree/master this tool make my life much easier to generate synthetic data!
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POV:Frank (d/acc) 🎩 💜 pfp
POV:Frank (d/acc) 🎩 💜
@scalinglaw.eth
Join me on Zora!
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POV:Frank (d/acc) 🎩 💜 pfp
POV:Frank (d/acc) 🎩 💜
@scalinglaw.eth
https://x.com/theroaringkitty/status/1789807772542067105?s=46&t=vwylJwbnwWDZdlCwZXX_Gw “back”
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POV:Frank (d/acc) 🎩 💜 pfp
POV:Frank (d/acc) 🎩 💜
@scalinglaw.eth
Mint Fun times ahead !!!
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POV:Frank (d/acc) 🎩 💜 pfp
POV:Frank (d/acc) 🎩 💜
@scalinglaw.eth
https://farcaster.manifold.xyz/frame/535535856
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POV:Frank (d/acc) 🎩 💜 pfp
POV:Frank (d/acc) 🎩 💜
@scalinglaw.eth
When AMD GPU available from runpod, I will test the vllm inference performance. Looking forward it https://rocm.blogs.amd.com/artificial-intelligence/llm-inference-optimize/README.html
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POV:Frank (d/acc) 🎩 💜 pfp
POV:Frank (d/acc) 🎩 💜
@scalinglaw.eth
gm! generated from https://imagine.heurist.xyz/models/BluePencilRealistic-blue_pen5805
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POV:Frank (d/acc) 🎩 💜 pfp
POV:Frank (d/acc) 🎩 💜
@scalinglaw.eth
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POV:Frank (d/acc) 🎩 💜 pfp
POV:Frank (d/acc) 🎩 💜
@scalinglaw.eth
how's the MLX LLM models serving performance, for llama3-8b-4bit, ~ 20 tokens/s? any concurrency scheduling mechanism like continuous batching and page-attention optimization?
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