Jan v3 4B base instruct: a 4B baseline model for fine tuning Overview Jan v3 4B base instruct is a 4B parameter model obtained via post training distillation from a larger teacher, transferring capabilities while preserving general purpose performance on standard benchmarks. The result is a compact, ownable base that is straightforward to fine tune, broadly applicable and minimizing the usual capacity–capability trade offs. Building on this base, Jan Code , a code tuned variant, will be released soon. Model Overview This repo contains the BF16 version of Jan v3 4B base instruct , which has the following features: Type: Causal Language Models Training Stage: Pretraining & Post training Number of Parameters: 4B in total Number of Layers: 36 Number of Attention Heads (GQA): 32 for Q and 8 for KV Context Length: 262,144 natively . Intended Use A better small base for downstream work: improved instruction following out of the box, strong starting point for fine tuning, and effective lightweight coding assistance. Performance Quick Start Integration with Jan Apps Jan v3 demo is hosted on Jan Browser at chat.jan.ai . It is also optimized for direct integration with Jan Desktop, select the…
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