---

base_model: LiquidAI/LFM2.5-1.2B-Thinking

tags:

- gguf

- lora

- peft

- llama.cpp

- quantized

---

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

| Quant | Size | Notes |

|-------|------|-------|

| Q4_K_M | 697.04 MB | Smallest / fastest. Best choice for low-RAM or CPU-only inference (e.g. this pipeline's target, an i7-7660U). Some quality loss vs f16, usually not noticeable in casual chat. |

| Q5_K_M | 804.29 MB | Middle ground — noticeably better quality than Q4_K_M for a modest size/speed cost. Good default if you have the RAM to spare. |

| Q6_K | 918.24 MB | Largest / highest quality of the three, very close to f16 output. Use this if quality matters more than footprint or speed. |

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.

| Model | Avg heuristic (0-1) | Degenerate outputs |

|---|---|---|

| lfm2.5-ADA-GGUF-Q4_K_M (ours) | 0.558 | 0 / 8 |

| lfm2.5-ADA-GGUF-Q5_K_M (ours) | 0.532 | 0 / 8 |

| lfm2.5-ADA-GGUF-Q6_K (ours) | 0.557 | 0 / 8 |

| LFM2.5-1.2B-Thinking (base) | 0.731 | 0 / 8 |

| LFM2.5-350M | 0.618 | 0 / 8 |

| Qwen3-0.6B (~0.8B params) | 0.736 | 0 / 8 |

!chart_avg_heuristic

!chart_per_category