ServoVo AI issues Apriel-1.5-15B-Mondition: The model of the Multimodal Openal Multimodal

Serviconow Ai Research Lab Issued APREL-1.5-15B-MONEYMultimodal consulting model 15 Training in the middle The planning of recipe-on-continued record is followed by supervis-outside To validate reading or selecting a selection. The model receives analyzing score of analyzing the Index Score of 52 according to 8x costs compared to Sota. Ships to check under the MIT License on the face of face.

So, what's new to me?

• Frontier-Level Comotion Score in small scale. Model Reports A symptom of analyzing intelligence (AIAI) = 52sympathy Deepseek-R1-0528 to that mixed metric while very small. AAI covers the third party test (MMLU-Pro, GPQA Diamond, the last human examination, LiveCode, Scicode, AA-LCR, Terminal-Bench Telecom).
• Single-GPu control. The model card says that 15b checkpoint “suits one GPU,” to the building and passing of air by fixed memory and latency budgets.
• Open metals and pipeline that emerges. Weight, training recipe, and the test protocol is a community of independent verification.

OK! I found it but what is the training method?

Basis and upside. Apriel-1.5-15B-think first from Pixtral-12b-709 Multimodal Decodal Decodel-Vision. Research team is working the rising depth-INCREASING Recodes Defaults from 40 → 48 Network reset Synchronization of Encoder with increased decoder. This avoids pretending to be from the beginning while maintaining Pup-GPu.

CPT (continuing to be). The two categories: (1) Consolidated text + for photo data for the building consultation and writing / understanding; . The sequence length is increasing in 32 and 16K tokens, with selected placement of loss in the tokens to respond to formatized instruction samples.

SFT (good direction). High quality, consulting – tracking statistical data, codes, science, use tools, and the following instructions; Two additional sffts in Run (Subset Used; Long-Term Code) Weight – Compilation to create a storage checkpoint. No rl (strengthening the verification) or rlaif (learning reinforcement from AI reports).

Data Note. ~ 25% of Dealth-UPSCALING Text MIX Based on Invidi's Nemotron Collection

O 'Wow! Tell me about its effects?

Key text Benchmarks (PASS @ 1 / accuracy).

• AIME 2025 (American Mathematics Avloy 2025): 87.5-88%
• GPPAQA Diamonds (Graduate-Level Google Proogram Proof responds, Diamond separation): ≈71%
• IFBEK (followed to teach): ~ 62
• ²²-bench (Tau-Squared Bench) Telecom: ~ 68
• LiveCodebelch (service code repairs): ~ 72.8

Use Vlmevalkit Reproduction, Apriel scores compete with MMMU / MMMMU-Pro (Massive Multimodal Understanding), Logicvista, Mathvision, Mathvision, Mathvista, Mathxiv, Ai2D, BlinkBy getting strong results in the documents / drawings and statistical statistics.

Lets summise everything

Apriel-1.5-15b – Thinking of a specialized internal training (the coming of one + is good guidance, nothing strengthening 52 in the Indeficial Teaknery Index. Reported Task-Level scores (for example, AID 2025 ≈88, GPQA Diamond Businesses, including open weights, re-renewed recipe, and Single-GPU Latency-enabled Apriel Practical Base Preferences before the thinking of the large closed programs.

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