Rnj 1 Rnj 1 is a family of 8B parameter open weight, dense models trained from scratch by Essential AI, optimized for code and STEM with capabilities on par with SOTA open weight models. These models perform well across a range of programming languages and boast strong agentic capabilities (e.g., inside agentic frameworks like mini SWE agent), while also excelling at tool calling. They additionally exhibit strong capabilities in math and science. Herein, rnj 1 refers to the base model, while rnj 1 instruct refers to the post trained instruction tuned model. Capabilities We evaluate Rnj 1 models against models of comparable size. In addition to accuracy, we also show the FLOPs used in pre training for each model. Benchmark Results Base Model rnj 1 Instruct Model rnj 1 instruct rnj 1 instruct is strong at code, math, and STEM tasks. It also performs well within agentic frameworks such as mini swe agent and has stellar tool use abilities. We report published numbers when possible, and when unavailable they are internal reproductions. Pre training FLOPs were estimated using 6nt, where n is the number of parameters and t is the token budget. All Evals under the Env bucket were evaluated…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy