Model ID
LiquidAI/LFM2.5-2.6B
Capabilities
LFM2.5 2.6B
LFM2.5 2.6B is a compact hybrid model designed for tool use, instruction following, multi-step agentic tasks, and efficient on-device deployment. It features 2.69B parameters across 30 layers, combining 22 double-gated short convolution blocks with 8 GQA attention blocks. The architecture uses a 2,048-dimensional hidden size, 32 attention heads, 8 KV heads, and a 10,752-dimensional SwiGLU feed-forward network. Trained on 34T tokens with agentic reinforcement learning, it supports a 131K-token context window and multilingual text generation across 15 languages for efficient long-context agentic workloads.
About this model
LFM2.5 2.6B is a compact hybrid model designed for tool use, instruction following, multi-step agentic tasks, and efficient on-device deployment. It features 2.69B parameters across 30 layers, combining 22 double-gated short convolution blocks with 8 GQA attention blocks. The architecture uses a 2,048-dimensional hidden size, 32 attention heads, 8 KV heads, and a 10,752-dimensional SwiGLU feed-forward network. Trained on 34T tokens with agentic reinforcement learning, it supports a 131K-token context window and multilingual text generation across 15 languages for efficient long-context agentic workloads.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (LiquidAI/LFM2.5-2.6B). 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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