Model ID
LiquidAI/LFM2.5-230M
Capabilities
LFM2.5 230M
LFM2.5 230M is an ultra-compact hybrid model designed for general-purpose language tasks, tool use, data extraction, and efficient on-device deployment. It features 230M parameters across 14 layers, combining 8 double-gated convolution blocks with 6 GQA attention blocks. The architecture uses a 1,024-dimensional hidden size, 16 attention heads, 8 KV heads, and a 2,560-dimensional SwiGLU feed-forward network. Distilled from LFM2.5-350M and refined through multi-stage reinforcement learning, it supports a 32K-token context window and multilingual text generation across English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, and Spanish.
About this model
LFM2.5 230M is an ultra-compact hybrid model designed for general-purpose language tasks, tool use, data extraction, and efficient on-device deployment. It features 230M parameters across 14 layers, combining 8 double-gated convolution blocks with 6 GQA attention blocks. The architecture uses a 1,024-dimensional hidden size, 16 attention heads, 8 KV heads, and a 2,560-dimensional SwiGLU feed-forward network. Distilled from LFM2.5-350M and refined through multi-stage reinforcement learning, it supports a 32K-token context window and multilingual text generation across English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, and Spanish.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (LiquidAI/LFM2.5-230M). Released under LFM 1.0. Further reading: Paper · Blog
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Hardware
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
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Performance under load
Tokens/s (total)
Benchmarks measured by Vultr on B200 · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.
NVIDIA HGX B200
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