Research
65 updates on Research.
Stability AI trains Stable LM 2 1.6B on seven languages and 2 trillion tokens
The technical report details a 1.6B model pre-trained on seven languages and measures 127 tok/s for a 4-bit build on an M2 Mac mini.
BitNet b1.58 gives every weight three values and runs 3B in 2.22 GB
Microsoft trained models whose every weight is -1, 0 or 1, which replaces multiplication with addition, and reports parity with full-precision Llama from 3B.
MobiLlama is a fully transparent 0.5B model that runs in 770 MB on a phone
MBZUAI published a 0.5B model that shares one feed-forward block across all layers and reports 7.02 tok/s in 770 MB on a Snapdragon 685 phone.
Meta's MobileLLM trades width for depth and gains 2.7 and 4.3 points
Meta Reality Labs built 125M and 350M models around deep and thin layers and profiled them on an iPhone 13 through ExecuTorch.
TinyLLaVA's 3.1B model outscores 7B LLaVA-1.5 on seven of nine benchmarks
Beihang and Tsinghua researchers report a 3.1B vision-language model that beats the 7B LLaVA-1.5 on seven of nine image benchmarks.
MobileVLM V2 runs 1.7B, 3B and 7B vision models on 144 image tokens
Meituan and Zhejiang University report a 1.7B vision language model at 64.2 on six benchmarks and 51.63 tok/s on an NVIDIA Jetson Orin.
TinyLlama pretrains a 1.1B model on 3 trillion tokens
Singapore University of Technology and Design trained a 1.1B model on 3 trillion tokens with 16 A100-40G GPUs and released it under Apache 2.0.
Microsoft releases Phi-2, a 2.7B model it says matches models 25 times larger
The 2.7B base model was trained on 1.4 trillion tokens in 14 days on 96 A100 GPUs, and Microsoft says it matches models up to 25 times larger.
Apple researchers run models twice the size of available DRAM from flash
The LLM in a flash paper loads parameters from flash on demand and reports 4 to 5 times faster CPU and 20 to 25 times faster GPU inference.
Microsoft carries its textbook data recipe from code to reasoning with the 1.3B phi-1.5
The 1.3-billion-parameter model trains on 30B tokens of mostly synthetic data and posts reasoning scores above Llama2-7B in Microsoft evaluations.
MIT HAN Lab publishes TinyChatEngine for 4-bit LLMs on laptops and Raspberry Pi
The from-scratch C and C++ engine from Song Han's group at MIT runs 4-bit LLaMA2-7B on x86, Apple silicon, Raspberry Pi and Nvidia GPUs.
Microsoft trains phi-1 to 50.6 percent on HumanEval with 1.3B parameters
Microsoft Research reports 50.6 percent on HumanEval and 55.5 percent on MBPP for a 1.3B code model trained on under 7B tokens of textbook-quality data.