Model ID
CohereLabs/tiny-aya-global
Capabilities
Tiny Aya Global
Tiny Aya Global is a 3.35B-parameter multilingual language model designed for balanced performance across diverse languages. It uses a 36-layer transformer with 2,048 hidden size, 16 attention heads, and 4 KV heads. The architecture interleaves sliding window attention (4,096 window) with periodic global attention layers to maintain efficiency and full-context interaction. It supports around 8K context length and uses RoPE for positional encoding. Trained on 70+ languages, it is optimized for multilingual understanding, instruction tuning, and deployment in compute-constrained environments.
About this model
Tiny Aya Global is a 3.35B-parameter multilingual language model designed for balanced performance across diverse languages. It uses a 36-layer transformer with 2,048 hidden size, 16 attention heads, and 4 KV heads. The architecture interleaves sliding window attention (4,096 window) with periodic global attention layers to maintain efficiency and full-context interaction. It supports around 8K context length and uses RoPE for positional encoding. Trained on 70+ languages, it is optimized for multilingual understanding, instruction tuning, and deployment in compute-constrained environments.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (CohereLabs/tiny-aya-global). Released under CC-BY-NC-4.0. Further reading: Paper · Blog
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Hardware
MI325X
2,048 GB
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
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Performance under load
Tokens/s (total)
Benchmarks measured by Vultr on MI325X · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.
AMD Instinct MI325X
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