Cygnus X0 @Cygnus_X0

👨‍💻building…. Joined August 2024

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geni @geniixzy

a day ago

$166,000 in under 2 weeks starting from $500, i have nothing to sell you. ask me anything

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Today, we are releasing FineVision, a huge open-source dataset for training state-of-the-art Vision-Language Models: > 17.3M images > 24.3M samples > 88.9M turns > 9.5B answer tokens Here are my favourite findings:

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Satya Nadella @satyanadella

a day ago

AI is the defining technology of our time, and how we empower people to use it will shape our nation's future. Thank you to @realDonaldTrump and @FLOTUS for making this a national priority. At Microsoft, we're committed to doing our part, with new initiatives to empower…

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Cygnus X0 @Cygnus_X0

a day ago

10/ Want to visualize this? We can compress to 3D: ```python from sklearn.manifold import TSNE # Reduce 1,536 dimensions to 3 for plotting tsne = TSNE(n_components=3, random_state=42) reduced_vectors = tsne.fit_transform(vectors)

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CK @Vitamul

3 days ago

Zoom out. 20 years of virality. IP rights secured. ETH approaching new highs. A community that survived the worst. This is what the foundation of a multi billion meme brand looks like.

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𝚐𝔪𝟾𝚡𝚡𝟾 @gm8xx8

3 days ago

ShizhenGPT: Towards Multimodal LLMs for Traditional Chinese Medicine ShizhenGPT (7B / 32B, Qwen2.5 backbone) is the first multimodal LLM for Traditional Chinese Medicine, covering the Four Diagnostic Methods (vision, sound, smell, pulse). Trained on the largest TCM dataset…

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PyQuant News 🐍 @pyquantnews

3 days ago

OpenAI, Google, and Anthropic just published guides on: • Prompt engineering • Building agents • AI in business • 601 AI use cases 9 of the best guides you can't miss:

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Rohit Ghumare @ghumare64

3 days ago

This is how I cracked hiring

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Akshay 🚀 @akshay_pachaar

3 days ago

4 stages of training LLMs from scratch, clearly explained (with visuals):

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Akshay 🚀 @akshay_pachaar

3 days ago

Today, we are covering the 4 stages of building LLMs from scratch to make them applicable for real-world use cases. We'll cover: - Pre-training - Instruction fine-tuning - Preference fine-tuning - Reasoning fine-tuning The visual summarizes these techniques. Let's dive in!