Model ID
LiquidAI/LFM2.5-1.2B-Instruct
Capabilities
LFM2.5 1.2B Instruct
LFM2.5 1.2B Instruct is a compact hybrid model designed for general-purpose language tasks and efficient on-device deployment. It features 1.2B parameters across 16 layers, combining 10 double-gated convolution blocks with 6 GQA attention blocks. The architecture uses a 2,048-dimensional hidden size, 32 attention heads, 8 KV heads, and a 12,288-dimensional SwiGLU feed-forward network. Supporting extended context through positional scaling, it is optimized for fast edge inference and operates under 1GB of memory. It supports a 32K-token context window and multilingual text generation across eight languages.
About this model
LFM2.5 1.2B Instruct is a compact hybrid model designed for general-purpose language tasks and efficient on-device deployment. It features 1.2B parameters across 16 layers, combining 10 double-gated convolution blocks with 6 GQA attention blocks. The architecture uses a 2,048-dimensional hidden size, 32 attention heads, 8 KV heads, and a 12,288-dimensional SwiGLU feed-forward network. Supporting extended context through positional scaling, it is optimized for fast edge inference and operates under 1GB of memory. It supports a 32K-token context window and multilingual text generation across eight languages.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (LiquidAI/LFM2.5-1.2B-Instruct). 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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