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allenai/
BAR-2x7B-Code-SFT

Name: BAR-2x7B-Code-SFT
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BAR

BAR (Branch-Adapt-Route) is a modular post-training approach that extends a fully post-trained language model with new domain capabilities via independently trained Mixture-of-Experts. Rather than retraining a single model across all domains, BAR trains independent domain experts — each through its own mid-training, supervised finetuning (SFT), and reinforcement learning pipeline — and composes them into a unified model via an MoE architecture with lightweight router training.

All BAR models are built on top of Olmo 2 7B.

Models in the BAR suite

BAR-7B — initial fully post-trained 7B dense model (the starting point)
BAR-2x7B-Base — 2-expert MoE (anchor + base pre-trained model)
BAR-2x7B-Math-SFT — math expert after mid-training and SFT
BAR-2x7B-Math — math expert after mid-training + SFT + RLVR
BAR-2x7B-Code-SFT — code expert after mid-training and SFT
BAR-2x7B-Code — code expert after mid-training + SFT + RLVR
BAR-2x7B-Tool-Use — tool use expert (SFT only)
BAR-2x7B-Safety — safety expert (SFT only)
BAR-5x7B — final 5-expert MoE combining all experts with a trained router

Results

Model	Overall	Knowledge	Reasoning	Chat	Math	Code	Tool Use	Safety
BAR-7B	31.3	28.5	29.8	48.9	23.6	11.8	25.3	51.3
BAR-2x7B-Math-SFT	36.8	28.8	31.2	40.9	41.9	20.5	21.6	72.7
BAR-2x7B-Math	39.3	29.0	30.8	42.5	55.8	22.1	19.8	75.4
BAR-2x7B-Code-SFT	38.5	28.8	29.1	40.1	25.5	49.3	19.7	77.3
BAR-2x7B-Code	38.8	28.5	29.2	41.0	26.9	50.4	19.8	75.3
BAR-2x7B-Tool-Use	37.2	28.5	28.7	39.3	21.8	16.9	46.4	79.1
BAR-2x7B-Safety	35.6	28.7	28.8	38.1	22.4	15.7	21.1	94.6
BAR-5x7B	49.1	28.4	30.8	38.7	56.2	49.9	45.6	94.0

Scores are unweighted averages across benchmarks within each category. See the paper for per-benchmark results and full evaluation details.

License

This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines.

allenai/BAR-2x7B-Code-SFT

BAR

Models in the BAR suite

Results

License

allenai/
BAR-2x7B-Code-SFT