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July 25th, 2023 - Byte-Sized Brilliance: Decoding the Epochs of ML Evolution
Manage episode 372191015 series 3485608
Contenuto fornito da Marcus Edel. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Marcus Edel o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.
- The case for 4-bit precision: k-bit Inference Scaling Laws
- No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models
- PUMA: Secure Inference of LLaMA-7B in Five Minutes
- Optimized Network Architectures for Large Language Model Training with Billions of Parameters
- A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis
Capitoli
1. Intro (00:00:00)
2. The case for 4-bit precision: k-bit Inference Scaling Laws (00:01:28)
3. No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models (00:04:15)
4. PUMA: Secure Inference of LLaMA-7B in Five Minutes (00:06:02)
5. Optimized Network Architectures for Large Language Model Training with Billions of Parameters (00:08:33)
6. A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis (00:09:53)
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