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LFM2.5-24B Adapter: From Launch to Local GGUF

Liquid AI released LFM2.5-24B Pi and Claude Code adapters trained with RL to cut tool calls by ~39%, followed by a GGUF version enabling local execution of the LoRA adapter.

2026-10-30 ~ 2026-10-30 · 2 episodes · 8 posts

Episode 1 · LFM2.5-24B Harness Adapters Cut Tool Calls ~39% via LoRA+RL (2026-10-30, 6 posts)

The Liquid AI team (with training pipeline details from QGallouedec) has released a "LFM2.5-24B Harness adapters" collection on Hugging Face's harness-hub, offering LoRA adapters for LFM2.5-24B-A2B tailored to two open-source agent frameworks—Pi and Claude Code—along with GGUF versions. Training uses LoRA + RL: first bootstrapping from a strong model, then reinforcement learning, all doable on a single GPU in 24 hours.

Confirmed

  • The collection includes both Pi and Claude Code LoRA adapters plus GGUF versions, now live on Hugging Face
  • Official claim: 39–40% fewer tool calls in the pi harness with no loss in accuracy/success rate
  • An independent comparison by @yb2698: on pass@1, the Pi run (lr 1e-5) dropped 8 percentage points below baseline at 110 steps, with comparison data for the claude-code run also posted; overall takeaway is that 100 steps is the sweet spot for Pi, yielding roughly a 37% reduction in tool calls
  • Training cost: 24 hours on a single GPU

Why it matters

  • The work shows that low-cost RL fine-tuning can meaningfully improve the runtime efficiency of open-source agent frameworks by cutting unnecessary tool calls, directly lowering inference costs for agentic development
  • With adapters customized per harness and GGUF versions included, deployment on local setups and community replication are easy, offering the open-source agent ecosystem a reusable template for efficiency optimization

Episode 2 · LFM2.5 Pi LoRA Adapter Released in GGUF for Local llama.cpp Use (2026-10-30, 2 posts)

The team released a GGUF version of the LFM2.5-24B-A2B Pi LoRA agent adapter (only 37MB in F16), enabling local execution via llama.cpp. Tests on the Pi harness show good results, with more harnesses planned.