Native
Transformers, vLLM, SGLang, or fine-tuning
Use the exact LiquidAI/LFM2.5-2.6B ID.
Liquid AI model record · Source checked August 5, 2026
LFM2.5-2.6B puts agent tool use into an on-device-sized model.
Liquid AI publishes a 2.69B dense, text-only checkpoint with 131,072-token context and native tool calling. It ships in native, GGUF, MLX, and ONNX formats. That record supports local evaluation—not a promise that every phone, harness, task, or Flowith account is ready for it.
Verified model record
Checkpoint
LiquidAI/LFM2.5-2.6B, post-trained for agentic workloads
Architecture
2.69B-parameter dense, text-only LFM2.5 model
Context
131,072 tokens in the official model card
Published formats
Native weights, GGUF, MLX, and ONNX
Recommended work
Tool use, data extraction, RAG, and long-context workflows
Published caution
Liquid AI does not recommend it for agentic coding or knowledge-heavy tasks
Flowith status
Not established by the provider release; verify the live workspace separately
Format decision
Choose the package from the target runtime
GGUF
llama.cpp and compatible local applications
Choose a quantization only after measuring quality and memory.
MLX
Apple Silicon local inference
Measure unified-memory pressure with the intended context.
ONNX
Cross-platform edge and hardware-accelerated deployment
Validate the exported graph and target execution provider.
Continue by implementation decision
LFM2.5-2.6B questions, answered
LFM2.5-2.6B is Liquid AI's 2.69B-parameter dense, text-only model post-trained for agentic workloads, native tool calling, and long-context work.
The official model card lists 131,072 tokens. Usable context on a device still depends on runtime, quantization, KV-cache memory, latency, and workload acceptance tests.
Liquid AI names the ready-to-use checkpoint LFM2.5-2.6B and describes it as post-trained for agentic workloads. The separate LFM2.5-2.6B-Base checkpoint is intended for fine-tuning. Use those exact names rather than inventing an Instruct ID.
Use native weights for Transformers or GPU serving, GGUF for llama.cpp-style local inference, MLX for Apple Silicon, and ONNX for cross-platform edge deployment. Confirm the exact repository and runtime version before installation.
Yes. Liquid AI documents native tool calling with tool definitions, a model-produced call, external execution, and a returned tool result. Your application still owns validation, permissions, execution, retries, and approval.
This page does not claim a Flowith integration. Provider weights and documentation do not establish availability or entitlements in a Flowith workspace; check the live model selector.