---

license: apache-2.0

base_model: Qwen/Qwen2.5-1.5B-Instruct

pipeline_tag: text-generation

language:

- en

tags:

- gguf

- llama-cpp

- lora

- education

- mathematics

- science

---

Muta Tutor Qwen2.5 1.5B Q4_K_M

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

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.