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raincandy_U

raincandy-u
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Reacted to beomi's post with šŸ˜Ž about 1 month ago
# PyTorch == 2.5.0 Breaks Transformers' SDPAttention! When you encounter "RuntimeError: cuDNN Frontend error: [cudnn_frontend] Error: No execution plans support the graph." We can use workaround like this: ```python torch.backends.cuda.enable_cudnn_sdp(False) ``` but this slow downs the performance gain from PyTorch 2.5. Although it is fixed(not "fixed" but default option is turn-off the cuDNN SDPA) at here -- https://github.com/pytorch/pytorch/pull/138587 , but not released yet. (you need to install directly from source) Fastest way for now : pip install "torch<2.5" Ref: https://github.com/huggingface/diffusers/issues/9704#issuecomment-2422585273
replied to takeraparterer's post about 1 month ago
Check this out: I trained an AI on huggingface posts! all of these are AI generated: ---------- Hello! I'm excited to share that my colleague @felipeebert and I have released the largest Spanish LLM benchmark to date. We've developed the Spanish LLM Evaluation Benchmark (SLAB), a set of benchmarks designed to evaluate the ability of language models to understand, generate and translate in Spanish. SLAB includes five different benchmarks: - Sentiment Analysis: evaluate models' ability to detect and describe sentiment in natural language - Fact Checking: evaluate models' ability to detect and refute factual errors in text - Question Answering: evaluate models' ability to answer questions in Spanish - Open-ended Questions: evaluate models' ability to generate coherent responses in Spanish - Translation: evaluate models' ability to translate in Spanish SLAB is aligned with the latest Spanish LLM industry developments and includes the most recent models available on the market. We aim to keep our benchmarks up-to-date and relevant to the Spanish language ecosystem. SLAB is available at: https://huggingface.co/datasets/argilla/SLAB. If you would like to collaborate on building additional Spanish LLM benchmarks, let's discuss in the comments. šŸ”— SLAB Blog Post: https://argilla.com/blog/slab ---------- Hello everyone, I'm thrilled to announce the release of https://huggingface.co/01-AI/01AI-GPT-4o - A new family of models that brings the power of transformer AI to the masses. This model is designed to be accessible and easy to use, while still offering high-quality results. Key features: - Small model size: only 23M parameters - Supports text generation, image generation, and text-to-image tasks - Data-efficient training with a lightweight tokenizer - Optimized for efficient on-device usage - Uses the powerful transformer architecture to deliver high-quality results Excited to see what you all think! https://huggingface.co/01-AI/01AI-GPT-4o
Reacted to takeraparterer's post with šŸ‘€ about 1 month ago
Check this out: I trained an AI on huggingface posts! all of these are AI generated: ---------- Hello! I'm excited to share that my colleague @felipeebert and I have released the largest Spanish LLM benchmark to date. We've developed the Spanish LLM Evaluation Benchmark (SLAB), a set of benchmarks designed to evaluate the ability of language models to understand, generate and translate in Spanish. SLAB includes five different benchmarks: - Sentiment Analysis: evaluate models' ability to detect and describe sentiment in natural language - Fact Checking: evaluate models' ability to detect and refute factual errors in text - Question Answering: evaluate models' ability to answer questions in Spanish - Open-ended Questions: evaluate models' ability to generate coherent responses in Spanish - Translation: evaluate models' ability to translate in Spanish SLAB is aligned with the latest Spanish LLM industry developments and includes the most recent models available on the market. We aim to keep our benchmarks up-to-date and relevant to the Spanish language ecosystem. SLAB is available at: https://huggingface.co/datasets/argilla/SLAB. If you would like to collaborate on building additional Spanish LLM benchmarks, let's discuss in the comments. šŸ”— SLAB Blog Post: https://argilla.com/blog/slab ---------- Hello everyone, I'm thrilled to announce the release of https://huggingface.co/01-AI/01AI-GPT-4o - A new family of models that brings the power of transformer AI to the masses. This model is designed to be accessible and easy to use, while still offering high-quality results. Key features: - Small model size: only 23M parameters - Supports text generation, image generation, and text-to-image tasks - Data-efficient training with a lightweight tokenizer - Optimized for efficient on-device usage - Uses the powerful transformer architecture to deliver high-quality results Excited to see what you all think! https://huggingface.co/01-AI/01AI-GPT-4o
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šŸ¤— I trained what is probably the smallest (600k ~) TinyStories model! It really can write grammatically correct stories!

raincandy-u/TinyStories-656K

Try this space based on this minuscule model!

raincandy-u/Story-Teller

Edit: Moreover, the model weight size is only 1.31MB under bf16, and can be reduced to the 700KB level when using Q8_0 quantization Uā€¢ć‚§ā€¢*U

Edit: Now 1000K params chat model!

raincandy-u/TinyChat-1776K

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