lfm2.5-ADA-GGUF

lfm2.5-ADA-GGUF

GGUF quantizations of ray0rf1re/lfm2.5-ADA-merged — the ray0rf1re/lfm2.5-ADA LoRA adapter merged into its base model LiquidAI/LFM2.5-1.2B-Thinking — converted and quantized with llama.cpp.

Files

If you're not sure which to grab: Q4_K_M for speed/low RAM, Q6_K if you want quality closest to the unquantized model, Q5_K_M as a middle ground.

Usage (llama.cpp)

# CLI chat
llama-cli -m lfm2.5-ADA-GGUF-Q4_K_M.gguf -p "Hello!" -cnv

# OpenAI-compatible local server
llama-server -m lfm2.5-ADA-GGUF-Q4_K_M.gguf -c 4096

Also works directly in LM Studio, Ollama (via a Modelfile pointing at the .gguf), and any other llama.cpp-based runtime.

Provenance

• Base model: LiquidAI/LFM2.5-1.2B-Thinking
• LoRA adapter: ray0rf1re/lfm2.5-ADA
• Merged full-precision model: ray0rf1re/lfm2.5-ADA-merged
• Quantized with: llama.cpp, convert_hf_to_gguf.py (f16) → llama-quantize

Persona benchmark

Automated persona-fidelity benchmark against same-size reference models (see persona_bench.py for methodology). Heuristic score is free/deterministic keyword+repetition scoring; no LLM-judge pass was run for this benchmark.