Michael Conrad
m-conrad-202
·
AI & ML interests
Cherokee Language, NLP, TTS
Recent Activity
new activity
3 days ago
ResembleAI/chatterbox:IPA?
replied to
singhsidhukuldeep's
post
8 months ago
Good folks from @Microsoft Research have just released bitnet.cpp, a game-changing inference framework that achieves remarkable performance gains.
Key Technical Highlights:
- Achieves speedups of up to 6.17x on x86 CPUs and 5.07x on ARM CPUs
- Reduces energy consumption by 55.4–82.2%
- Enables running 100B parameter models at human reading speed (5–7 tokens/second) on a single CPU
Features Three Optimized Kernels:
1. I2_S: Uses 2-bit weight representation
2. TL1: Implements 4-bit index lookup tables for every two weights
3. TL2: Employs 5-bit compression for every three weights
Performance Metrics:
- Lossless inference with 100% accuracy compared to full-precision models
- Tested across model sizes from 125M to 100B parameters
- Evaluated on both Apple M2 Ultra and Intel i7-13700H processors
This breakthrough makes running large language models locally more accessible than ever, opening new possibilities for edge computing and resource-constrained environments.
replied to
singhsidhukuldeep's
post
8 months ago
Good folks from @Microsoft Research have just released bitnet.cpp, a game-changing inference framework that achieves remarkable performance gains.
Key Technical Highlights:
- Achieves speedups of up to 6.17x on x86 CPUs and 5.07x on ARM CPUs
- Reduces energy consumption by 55.4–82.2%
- Enables running 100B parameter models at human reading speed (5–7 tokens/second) on a single CPU
Features Three Optimized Kernels:
1. I2_S: Uses 2-bit weight representation
2. TL1: Implements 4-bit index lookup tables for every two weights
3. TL2: Employs 5-bit compression for every three weights
Performance Metrics:
- Lossless inference with 100% accuracy compared to full-precision models
- Tested across model sizes from 125M to 100B parameters
- Evaluated on both Apple M2 Ultra and Intel i7-13700H processors
This breakthrough makes running large language models locally more accessible than ever, opening new possibilities for edge computing and resource-constrained environments.
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