General Questions
What are LFM models?
What are LFM models?
LFM (Liquid Foundation Models) are a family of efficient language models built on a new hybrid architecture designed for fast training and inference. They range from 350M to 8B parameters and support text, vision, and audio modalities.
What context length do LFM models support?
What context length do LFM models support?
Most LFM models support a 32K token context length for extended conversations and document processing. The LFM2.5-8B-A1B supports a 128K token context length.
Which inference frameworks are supported?
Which inference frameworks are supported?
LFM models are compatible with:
- Transformers - For research and development
- llama.cpp - For efficient CPU inference, including iOS and Android apps
- vLLM - For high-throughput production serving
- MLX - For Apple Silicon optimization
- Ollama - For easy local deployment
Model Selection
Which model should I use for my use case?
Which model should I use for my use case?
- General chat/instruction following: LFM2.5-1.2B-Instruct (recommended)
- Vision tasks: LFM2.5-VL-1.6B
- Audio/speech: LFM2.5-Audio-1.5B
- Extraction tasks: LFM2-1.2B-Extract or LFM2-350M-Extract
- Edge deployment: LFM2-350M or LFM2-700M for smallest footprint
- Highest performance: LFM2.5-8B-A1B (MoE architecture, 128K context)
What is the difference between LFM2 and LFM2.5?
What is the difference between LFM2 and LFM2.5?
LFM2.5 models are updated versions with improved training that deliver higher performance while maintaining the same architecture. We recommend using LFM2.5 variants when available.
What are Liquid Nanos?
What are Liquid Nanos?
Liquid Nanos are task-specific models fine-tuned for specialized use cases like:
- Information extraction (LFM2-Extract)
- Translation (LFM2-350M-ENJP-MT)
- RAG question answering (LFM2-1.2B-RAG)
- Meeting summarization (LFM2-2.6B-Transcript)
Deployment
Can I run LFM models on mobile devices?
Can I run LFM models on mobile devices?
Yes! llama.cpp runs natively on iOS and Android with quantized GGUF models. See iOS & Android for embedding it in an app, and Vision & Audio for multimodal models.
What quantization formats are available?
What quantization formats are available?
- GGUF: For llama.cpp, LM Studio, Ollama, Atomic Chat (Q4_0, Q4_K_M, Q5_K_M, Q6_K, Q8_0, F16)
- MLX: For Apple Silicon (4-bit, 5-bit, 6-bit, 8-bit, bf16)
- ONNX: For cross-platform deployment with ONNX Runtime
How do I choose the right quantization level?
How do I choose the right quantization level?
- Q4_0 / 4-bit: Smallest size, fastest inference, some quality loss
- Q8_0 / 8-bit: Good balance of size and quality
- F16 / bf16: Full precision, best quality, largest size
Fine-tuning
Can I fine-tune LFM models?
Can I fine-tune LFM models?
Yes! Most LFM models support fine-tuning with TRL and Unsloth. Check the Model Library for trainability information.
What fine-tuning methods are supported?
What fine-tuning methods are supported?
- LoRA/QLoRA: Memory-efficient fine-tuning
- Full fine-tuning: For maximum customization
- SFT (Supervised Fine-Tuning): For instruction tuning
Still Have Questions?
- Join our Discord community for real-time help
- Check the Cookbook for examples
- See Troubleshooting for common issues