All of our models share the following capabilities:
Model Families
Choose a model based on your desired functionalities. Each individual model card has specific details on deployment and customization.
Text Models
Chat, tool calling, structured output, and classification.
Vision Models
Image understanding with LFM backbones and custom encoders.
Audio Models
Interleaved audio/text models for TTS, ASR, and voice chat.
Liquid Nanos
Task-specific models for extraction, summarization, RAG, and translation.
Model Formats
All LFM2 models are available in multiple formats for flexible deployment:- GGUF — Best for local CPU/GPU inference on any platform. Use with llama.cpp, LM Studio, or Ollama. Append
-GGUFto any model name. - MLX — Best for Mac users with Apple Silicon. Leverages unified memory for fast inference via MLX. Browse at mlx-community.
- ONNX — Best for production deployments and edge devices. Cross-platform with ONNX Runtime across CPUs, GPUs, and accelerators. Append
-ONNXto any model name.
Quantization
Quantization reduces model size and speeds up inference with minimal quality loss. Available options by format:- GGUF — Supports
Q4_0,Q4_K_M,Q5_K_M,Q6_K,Q8_0,BF16, andF16.Q4_K_Moffers the best balance of size and quality. - MLX — Available in
3bit,4bit,5bit,6bit,8bit, andBF16.8bitis recommended. - ONNX — Supports
FP32,FP16,Q4, andQ8(MoE models also supportQ4F16).Q4is recommended for most deployments.