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"Synthetic Data" is used in AI and LLM training !! • cheap • easy to produce • perfectly labelled data ~ derived from the real world data to replicate the properties and characteristics of the rela world data. It's used in training an LLM (LLMs

I think CHAT-GPT is doing great in AI sector and Google gemini,Grok Ai , perplexity left behind coz I used it all and found GPT better in every aspect from content to Diagnose to Image generation See I completely shift from Google to Gpt,

Random Thought : I was wondering, why ChatGPT weren't build on the Increment Learning modle.. Because I might destroy it's algo.... Let me expain.. In the world of machine learning, training models can be approached in two main ways, Batch Lea

deepseek r2 leaks • 97.3% cheaper than gpt-4o • 1.2t parameters, 78b active, hybrid moe • 5.2PB training data, 89.7% on C-Eval2.0 • better vision, 92.4% on COCO • 82% utilization in huawei ascend 910b new era 🔥

Will AI hit a wall? 🤔 Future advanced models need complex data—but is there enough available? Or are we running out of high-quality training data? What’s the solution? More synthetic data? Better datasets? Discuss! ⬇️

"Just fine-tuned LLaMA 3.2 using Apple's MLX framework and it was a breeze! The speed and simplicity were unmatched. Here's the LoRA command I used to kick off training: ``` python lora.py \ --train \ --model 'mistralai/Mistral-7B-Instruct-v0.2' \ -

Looking for a financer for new tech startups basically works on the agricultural CIBIL score with the help of live data available.

Many argue that Data Engineering is a part of data science and analytics. It's different from data science, but they work together closely. Data Engineers come first in the process. They gather and organize data. This data is then used by Data Scien