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Definition of flashedtention
THE Flashed is an optimization technique that allows Reduce consumption memory of transformers modelswhile accelerating the processing of long text sequences.
Why is flashedtention crucial?
- Decreases the LLMS calculation time By avoiding unnecessary memory access.
- Allows you to use longer sequences Without explosion in memory consumption.
- Accelerates generation and language understanding tasks.
Concrete examples
🔹 GPT-4 Turbo uses a Optimization inspired by flasing To answer more quickly.
🔹 Llama 3 should integrate Flashed 2 To optimize the treatment of long sequences.
Advantages and challenges
| Benefits | Challenge |
|---|---|
| 🚀 reduction in inference time | ❗ Integration complexity in certain models |
| 🔋 less memory consumption | ⚙️ Compatible only with certain architectures |
| 🏗️ allows you to treat long sequences | 🔄 Still in the adoption phase by industry |
The future of flashedtention
✅ Generalized integration into future LLMS.
✅ Optimization for models requiring a long context.
✅ Acceleration of conversational assistants.
