Open weights
45 updates on Open weights.
Spectra ships ternary 3.9B models that match 4-bit quantisation at half the bits
Nolano AI trained 54 models from 99M to 3.9B parameters on the same 300B tokens in ternary, quantised and half-precision form to compare them by bit size.
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 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.
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.
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.
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.
Gemma 2B and 7B open the Gemma line, built on Gemini research
Google released Gemma 2B and 7B with an 8192-token context, weights on Kaggle and Hugging Face under a custom Gemma licence, not an open source one.
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.