AI . META . COM {}

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We are analyzing https://ai.meta.com/blog/meta-llama-quantized-lightweight-models/.

Title:
Introducing quantized Llama models with increased speed and a reduced memory footprint
Description:
As our first quantized models in this Llama category, these instruction-tuned models retain the quality and safety of the original 1B and 3B models, while achieving 2-4x speedup.
Website Age:
34 years and 5 months (reg. 1991-01-21).

Matching Content Categories {📚}

Technology & Computing
Photography
Education

Content Management System {📝}

What CMS is ai.meta.com built with?

Custom-built

No common CMS systems were detected on Ai.meta.com, and no known web development framework was identified.

Traffic Estimate {📈}

What is the average monthly size of ai.meta.com audience?

🌟 Strong Traffic: 100k - 200k visitors per month

Based on our best estimate, this website will receive around 100,019 visitors per month in the current month.
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How Does Ai.meta.com Make Money? {💸}

We can't see how the site brings in money.

Earning money isn't the goal of every website; some are designed to offer support or promote social causes. People have different reasons for creating websites. This might be one such reason. Ai.meta.com might be earning cash quietly, but we haven't detected the monetization method.

Keywords {🔍}

models, llama, quantization, model, quantized, lora, spinquant, performance, qat, meta, memory, training, average, quality, accuracy, executorch, partners, arm, footprint, today, mobile, devices, adaptors, results, weve, developers, qlora, bit, approach, research, read, resources, size, quantizationaware, stateoftheart, posttraining, method, cpus, similar, open, community, utilize, scheme, cpu, quantize, post, speed, reduced, safety, original,

Topics {✒️}

meta ai news latest updates delivered prioritized short-context applications instruction-tuned models apply kleidi ai kernels adb binary-based approach generative post-training quantization vanilla post-training quantization integrated foundational components fine-tuned llama models employ quantization-aware training open source repository foundational model meta memory usage reduced post-training quantization low-precision environments supervised fine-tuning low-rank adaptation direct preference optimization highly efficient model decode latency improved prefill latency improved memory usage compared reduce memory usage reduced memory footprint fine-tune llama executorch llama instructions quantization-aware training current quantization scheme quantization scheme involves model size compared privacy model size decreased newsletter fine-tuned 1b resource-constrained devices prefill/decoding speed learn rotation matrices sharing quantized versions research breakthroughs quantization scheme similar compute resources achieving 2-4x speedup small calibration dataset achieved 10x growth token dynamic quantization specifically enable quantization 2023 read post limited runtime memory