ServiceNow AI Releases Apriel-1.5-15B-Thinker: An Open-Weights Multimodal Reasoning Model that Hits Frontier-Level Performance on a Single-GPU Budget

ServiceNow AI Research Lab has launched Apriel-1.5-15B-Thinker, a 15-billion-parameter open-weights multimodal reasoning mannequin educated with a data-centric mid-training recipe—continuous pretraining adopted by supervised fine-tuning—with out reinforcement studying or desire optimization. The mannequin attains an Artificial Analysis Intelligence Index rating of 52 with 8x value financial savings in comparison with SOTA. The checkpoint ships underneath an MIT license on Hugging Face.

So, What’s new in it for me?

• Frontier-level composite rating at small scale. The mannequin stories Artificial Analysis Intelligence Index (AAI) = 52, matching DeepSeek-R1-0528 on that mixed metric whereas being dramatically smaller. AAI aggregates 10 third-party evaluations (MMLU-Pro, GPQA Diamond, Humanity’s Last Exam, LiveCodeBench, SciCode, AIME 2025, IFBench, AA-LCR, Terminal-Bench Hard, τ²-Bench Telecom).
• Single-GPU deployability. The mannequin card states the 15B checkpoint “suits on a single GPU,” concentrating on on-premises and air-gapped deployments with fastened reminiscence and latency budgets.
• Open weights and reproducible pipeline. Weights, coaching recipe, and analysis protocol are public for unbiased verification.

Ok! I received it however what’s it’s coaching mechanism?

Base and upscaling. Apriel-1.5-15B-Thinker begins from Mistral’s Pixtral-12B-Base-2409 multimodal decoder-vision stack. The analysis staff applies depth upscaling—growing decoder layers from 40→48—then projection-network realignment to align the imaginative and prescient encoder with the enlarged decoder. This avoids pretraining from scratch whereas preserving single-GPU deployability.

CPT (Continual Pretraining). Two levels: (1) combined textual content+picture information to construct foundational reasoning and doc/diagram understanding; (2) focused artificial visible duties (reconstruction, matching, detection, counting) to sharpen spatial and compositional reasoning. Sequence lengths lengthen to 32k and 16k tokens respectively, with selective loss placement on response tokens for instruction-formatted samples.

SFT (Supervised Fine-Tuning). High-quality, reasoning-trace instruction information for math, coding, science, software use, and instruction following; two extra SFT runs (stratified subset; longer-context) are weight-merged to kind the ultimate checkpoint. No RL (reinforcement studying) or RLAIF (reinforcement studying from AI suggestions).

Data word. ~25% of the depth-upscaling textual content combine derives from NVIDIA’s Nemotron collection.

O’ Wow! Tell me about it’s outcomes then?

Key textual content benchmarks (move@1 / accuracy).

• AIME 2025 (American Invitational Mathematics Examination 2025): 87.5–88%
• GPQA Diamond (Graduate-Level Google-Proof Question Answering, Diamond cut up): ≈71%
• IFBench (Instruction-Following Benchmark): ~62
• τ²-Bench (Tau-squared Bench) Telecom: ~68
• LiveCodeBench (practical code correctness): ~72.8

Using VLMEvalKit for reproducibility, Apriel scores competitively throughout MMMU / MMMU-Pro (Massive Multi-discipline Multimodal Understanding), LogicVista, MathVision, MathVista, MathVerse, MMStar, CharXiv, AI2D, BLINK, with stronger outcomes on paperwork/diagrams and text-dominant math imagery.

Lets Summarize all the pieces

Apriel-1.5-15B-Thinker demonstrates that cautious mid-training (continuous pretraining + supervised fine-tuning, no reinforcement studying) can ship a 52 on the Artificial Analysis Intelligence Index (AAI) whereas remaining deployable on a single graphics processing unit. Reported task-level scores (for instance, AIME 2025 ≈88, GPQA Diamond ≈71, IFBench ≈62, Tau-squared Bench Telecom ≈68) align with the mannequin card and place the 15-billion-parameter checkpoint in essentially the most cost-efficient band of present open-weights reasoners. For enterprises, that mixture—open weights, reproducible recipe, and single-GPU latency—makes Apriel a sensible baseline to guage earlier than contemplating bigger closed programs.

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