Model ID
CohereLabs/tiny-aya-fire
Capabilities
Tiny Aya Fire
Tiny Aya Fire is a 3.35B-parameter multilingual language model optimized for South Asian 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 for efficient long-range interaction. It supports around 8K context length and uses RoPE for positional encoding. Trained across 70+ languages, it is designed for strong regional performance while remaining efficient for local deployment and downstream adaptation.
About this model
Tiny Aya Fire is a 3.35B-parameter multilingual language model optimized for South Asian 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 for efficient long-range interaction. It supports around 8K context length and uses RoPE for positional encoding. Trained across 70+ languages, it is designed for strong regional performance while remaining efficient for local deployment and downstream adaptation.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (CohereLabs/tiny-aya-fire). 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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