Model ID
LiquidAI/LFM2.5-350M
Capabilities
LFM2.5 350M
LFM2.5 350M is a compact hybrid model designed for general-purpose language tasks and efficient on-device deployment. It features 350M parameters across 16 layers, combining 10 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 6,656-dimensional SwiGLU feed-forward network. Trained on 28T tokens through extended pre-training and multi-stage reinforcement learning, it supports a 32K-token context window and multilingual text generation across English, Arabic, Chinese, French, German, Japanese, Korean, Portuguese, and Spanish.
About this model
LFM2.5 350M is a compact hybrid model designed for general-purpose language tasks and efficient on-device deployment. It features 350M parameters across 16 layers, combining 10 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 6,656-dimensional SwiGLU feed-forward network. Trained on 28T tokens through extended pre-training and multi-stage reinforcement learning, it supports a 32K-token context window and multilingual text generation across English, Arabic, Chinese, French, German, Japanese, Korean, Portuguese, and Spanish.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (LiquidAI/LFM2.5-350M). 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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