RightNow-Arabic-0.5B-Turbo: An Open Sub-1B Arabic Language Model via Vocabulary Injection and Edge-First Deployment
This paper presents RightNow-Arabic-0.5B-Turbo, a 518M-parameter Arabic-specialized LLM built on Qwen2.5-0.5B using vocabulary injection and edge-first deployment. It achieves 35.9% mean accuracy on Arabic benchmarks, outperforming all same-class open models, and ties Falcon-H1-1.5B on COPA-ar at one-third the size. The quantized model is 398 MB and delivers 635 tokens/s on a single H100, enabling efficient edge deployment.
Article intelligence
Key points
- 518M-parameter Arabic LLM built on Qwen2.5-0.5B with vocabulary injection of 27,032 Arabic tokens.
- Achieves 35.9% mean accuracy on three Arabic benchmarks, surpassing all same-class open-source models.
- Matches Falcon-H1-1.5B on COPA-ar (58.4%) with one-third the parameters; recovers 67% of SILMA-9B's performance with 1/18 the parameters.
- Quantized to 398 MB (q4_k_m), achieving 635 tokens/s at batch size 1 on a single H100 for edge deployment.
Why it matters
This matters because 518M-parameter Arabic LLM built on Qwen2.5-0.5B with vocabulary injection of 27,032 Arabic tokens.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.28827] RightNow-Arabic-0.5B-Turbo: An Open Sub-1B Arabic Language Model via Vocabulary Injection and Edge-First Deployment
[Submitted on 10 Apr 2026]
Title:RightNow-Arabic-0.5B-Turbo: An Open Sub-1B Arabic Language Model via Vocabulary Injection and Edge-First Deployment
View a PDF of the paper titled RightNow-Arabic-0.5B-Turbo: An Open Sub-1B Arabic Language Model via Vocabulary Injection and Edge-First Deployment, by Jaber Jaber and 1 other authors
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Abstract:Open Arabic large language models split into two classes: sub-1B multilingual models that treat Arabic as an afterthought (Qwen2.5-0.5B, Falcon-H1-0.5B), and 7B-70B Arabic-specialized models that require a server to run (Jais, AceGPT, ALLaM, SILMA). The one published attempt at a sub-2B Arabic-specialized model, Kuwain-1.5B, never released its weights. We present RightNow-Arabic-0.5B-Turbo, a 518M-parameter Arabic-specialized decoder LLM built on Qwen2.5-0.5B. The pipeline adds 27,032 Arabic tokens via mean-subtoken initialization, continues pretraining on 504M Arabic tokens on 8xH100 with FSDP, FlashAttention varlen packing, and Liger fused kernels, then applies supervised fine-tuning on 129,116 Arabic instruction pairs with response-only loss masking, direct preference optimization on 6,750 Arabic preference pairs, and weight soup merging across three checkpoints. On three lm-evaluation-harness Arabic benchmarks (COPA-ar, Arabic HellaSwag, ArabicMMLU) the merged model reaches 35.9% mean accuracy, beats every same-class open model, ties Falcon-H1-1.5B on COPA-ar (58.4%) at one-third the size, and recovers 67% of SILMA-9B's mean at 1/18 the parameters. The edge build quantizes to 398 MB (q4_k_m) and delivers 635 tokens/s at batch size 1 on a single H100 via this http URL. All code (5,555 lines across 25 scripts), weights (bf16, int8, and four GGUF quantizations), and benchmark scripts are released at this https URL.
Comments: 12 pages, 7 tables, 4 figures, 1 algorithm. Weights: this https URL
Subjects:
Computation and Language (cs.CL); Machine Learning (cs.LG)
ACM classes: I.2.7
Cite as: arXiv:2605.28827 [cs.CL]
(or arXiv:2605.28827v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.28827
arXiv-issued DOI via DataCite
Submission history
From: Jaber Jaber [view email] [v1] Fri, 10 Apr 2026 00:56:50 UTC (66 KB)
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