This is the vector-configuration finalist from Muta's ADTC fine-tuning campaign. It starts from Qwen/Qwen2.5-1.5B-Instruct, applies BF16 LoRA with rank 16 for 500 steps on a licence-clean multiple-choice math and science mixture, merges the adapter, and exports the result as Q4_K_M GGUF.

File

Muta-Tutor-Qwen2.5-1.5B-Q4_K_M.gguf

SHA-256: a750d00d458c6ab38925364ea1413db00648449180941e47025736d09922e1eb

bash

hf download timiiowolabi/Muta-Tutor-Qwen2.5-1.5B-ADTC-GGUF \
  Muta-Tutor-Qwen2.5-1.5B-Q4_K_M.gguf

Evaluation

The candidate and untuned control were evaluated with the same GGUF conversion, quantization, prompt format, and GCP CPU-proxy harness.

Measure	Control	Fine-tuned
ARC-Easy acc_norm, 500 samples	74.4%	77.8%
Scalar total score	65.3277	67.0475
Vector total score	82.4386	84.1387

The repository includes the training manifest, licence-clean dataset manifest, artifact hashes, and the full fine-tuning summary. Performance was measured on a cloud CPU proxy; temperature was unavailable, and peak RSS includes a 45 MiB estimate for the profiler root process. The secondary held-out battery is pending because its first attempt overlapped unrelated CPU work and was discarded.

Status

This remains a competition candidate. It still requires Muta's final embedded tutor template, live tutoring validation, the secondary held-out battery, and profiling on the physical target laptop.