Open weights
45 updates on Open weights.
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.
Stability AI releases StableLM Zephyr 3B for edge devices
Stability AI tuned a 3B chat model with direct preference optimisation for edge devices and reports an MT-Bench score of 6.64.
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.
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.
RWKV trains like a transformer and runs with constant memory per token
The paper releases pretrained RNN weights from 169M to 14B parameters and reports constant time and memory per token during inference.
LaMini-LM distils models from 61M parameters up on 2.58M instructions
MBZUAI distilled 17 instruction models from 61M to 7B parameters on 2.58M synthetic instructions, and reports the 248M model matching LLaMA-7B.
Hugging Face's DistilBERT is 40% smaller than BERT and 71% faster on an iPhone
The 66M-parameter distilled BERT keeps 97% of its teacher on GLUE and weighs 207 MB inside a question-answering app on an iPhone 7 Plus.
TinyBERT shrinks BERT-base to 14.5M parameters and runs 9.4x faster
Huawei's distilled BERT keeps more than 96.8% of its teacher's GLUE score at 14.5M parameters, 7.5x smaller and 9.4x faster on inference.