গাইড লিঙ্ক ক্যুইজ · মাঝারি স্তর
লিকুইড এআই এবং লিকুইড ফাউন্ডেশন মডেল কুইজ
লিকুইড এআই-এর আসল ক্রমাগত-সময়ের গবেষণাকে তারিখের LFM2 হাইব্রিড আর্কিটেকচারের সাথে সংযুক্ত করুন এবং প্রান্ত অনুমানের জন্য স্কোপড রিপোর্টের ফলাফলগুলি।
প্রশ্ন 1 এর 8
What does “LFM” stand for in the LFM2 technical report?
প্রতিটি প্রশ্নের পূর্বরূপ দেখুন8 প্রশ্ন
আপনি কুইজ নেওয়ার সাথে সাথে উত্তর এবং ব্যাখ্যাগুলি উপস্থিত হয়।
- What does “LFM” stand for in the LFM2 technical report?
- Liquid Foundation Model
- Latent Feature Mapper
- Long-Form Memory
- Linear Forecasting Module
- According to the LFM2 technical report, what architecture did its hybrid backbone combine?
- Only a dense layer with no sequence processing
- Gated short convolutions with a small number of grouped-query attention blocks
- A vision-only encoder and a decision tree
- A single unchanging recurrent state with no attention
- What is the LFM2-8.3B mixture-of-experts model’s active parameter count?
- All 8.3 billion parameters are active for every token
- About 350 million total parameters
- About 1.5 billion active parameters
- The report gives no active parameter count
- What context length does the LFM2 report list for the model family?
- 2K tokens only
- 128K tokens for every variant
- The report lists only audio duration
- 32K টোকেন
- What deployment setting does the LFM2 report explicitly optimize its architecture for?
- On-device inference under latency, memory, and energy constraints, including phone- and laptop-class hardware
- Guaranteed private operation on every car and phone
- Cloud-only training on large GPU clusters
- A fixed constant-memory budget independent of context and model size
- What CPU speed comparison did the November 2025 LFM2 report make against similarly sized models?
- Exactly 2× higher accuracy on every benchmark
- Up to 2× faster prefill and decode
- A fixed 32× reduction in memory for every device
- A guaranteed 2× improvement over every Transformer workload
- What constraint guided the hardware-in-the-loop architecture search described in the LFM2 report?
- The need to maximize image resolution only
- A requirement to train exclusively on one dataset
- Edge-device latency and memory limits
- The number of labels available for medical diagnosis
- What is LFM2-ColBERT designed to provide?
- An audio-only speech synthesizer
- A general-purpose image decoder
- A controller for training liquid time-constant networks
- A low-latency encoder for queries and documents used in retrieval