LFM2.5 Q4\_0 Checkpoints from Quantization-Aware Distillation
Article image or reusable cover for Hugging Face
Liquid AI has released optimized versions of its LFM2.5 model using a technique called Quantization-Aware Distillation (QAD).
This method takes a high-precision model and distills it into a compressed version that uses less memory and runs faster, without losing much quality — it recovers 97 percent of the original performance. The models come in four sizes (from 230 million to 2.6 billion parameters) and are tested on tasks such as reasoning, instruction following, and tool usage. The files can be used directly with llama.cpp or other tools that support the GGUF format.
Trained with Quantization-Aware Distillation (QAD): a high-precision teacher model is distilled into a quantized student model
Vibekollen prepared this summary with AI from the original publication. The content belongs to Hugging Face.
More to read
Lumaril, which develops AI software for optimizing industrial production by connecting to existing control systems, raised a $66M Series A at a $400M valuation
Techmeme 3 h ago
DeepSeek open sources Super-Agent as a research prototype, a proactive coding agent it says can monitor group chats for coding issues and suggest solutions
Techmeme 4 h ago
Insurance software maker Guidewire agrees to acquire Hyperexponential, which makes AI-powered insurance underwriting and pricing software, for $1.7B in cash
Techmeme 4 h ago
One word for AI agents in 2027
Nate Herk 8 h ago