Models
Hugging Face releases SmolLM at 135M, 360M and 1.7B parameters
Three base models trained on the newly released SmolLM-Corpus, with published memory footprints from 109.78 MB to 3422.76 MB.
Alibaba builds Qwen2's 0.5B and 1.5B sizes for phones, earphones and glasses
Alibaba built Qwen2-0.5B and Qwen2-1.5B for smartphones, earphones and smart glasses, with 32K context and Apache 2.0 weights.
Apple's MobileCLIP-S0 encodes an image in 1.5 ms on an iPhone 12 Pro Max
Apple timed its image-text models on an iPhone and released four variants, the weights and the reinforced DataCompDR dataset.
TensorOpera releases Fox-1, a 1.6B model trained on 3 trillion tokens
TensorOpera published a 1.6B model under Apache 2.0 and reports it ahead of Gemma-2B and Qwen1.5-1.8B on a six-benchmark average.
Apple Intelligence pairs a 3-billion-parameter on-device model with a server model
Apple reports 0.6 ms per prompt token and 30 tokens per second on an iPhone 15 Pro for a model compressed to an average of 3.7 bits per weight.
PowerInfer-2 runs a 47B model on a OnePlus 12 at 11.68 tokens per second
Shanghai Jiao Tong University researchers report a 47B model decoding at 11.68 tokens per second on a OnePlus 12, with weights streamed from flash.
Apple releases OpenELM at 270M to 3B, with parameters spread unevenly across layers
Apple published four models from 270M to 3B parameters with the full training framework and code to run them through MLX on Apple silicon.
Microsoft runs Phi-3-mini offline on an iPhone 14 at over 12 tokens per second
The 3.8-billion-parameter model takes about 1.8 GB at 4-bit and scores 69 percent on MMLU, which Microsoft compares to Mixtral 8x7B and GPT-3.5.
Octopus v3 picks an action from an image and a query in under 1B parameters
A technical report describes a sub-billion multimodal agent that turns an image and a text query into a function call, shown through ten worked examples.
Octopus v2 is a 2B model that calls Android APIs with one token per function
A 2B Gemma fine-tune gives every Android API its own token, and the authors report 99.524% accuracy and 0.38 seconds per call, ahead of GPT-4.
Octopus fine-tunes a 2B model to 93 percent on API function calls
Stanford and Harvard authors fine-tuned open 2B to 7B models on 20,000 RapidAPI functions and report up to 97 percent function call accuracy.
MeRino designs sub-100M language models that run 4.9 times faster than OPT-350M
Researchers design 52M to 64M parameter transformers by maximising entropy under a compute budget, matching OPT-350M accuracy on an NVIDIA Jetson Nano.