timiiowolabi/Muta-Tutor-Qwen2.5-1.5B-ADTC-GGUF
Muta Tutor Qwen2.5 1.5B Q4KM
Final submission: Muta-Tutor-Qwen2.5-1.5B-Q4_K_M-vocab32k.gguf
Muta's final ADTC submission is the vocabulary-pruned version of the fine-tuned Q4KM below. The tokenizer and embedding/output rows were cut from 151,936 to 32,000 tokens (merges 151,387 → 31,722). The kept set covers 100% of the 4.05M tokens in the training corpus. The weights and quantization are otherwise unchanged. The pruning manifest, with the source and artifact hashes, the corpus hashes and the tokenizer revision, is Muta-Tutor-Qwen2.5-1.5B-Q4_K_M-vocab32k.vocab-prune-manifest.json.
- Size: 830,115,168 bytes (source: 986,048,128)
- SHA-256:
3038036a93039e80d282581d451abfdf998cd2929d2d26720a2cc711bff33a94 - Source SHA-256:
a750d00d458c6ab38925364ea1413db00648449180941e47025736d09922e1eb
hf download timiiowolabi/Muta-Tutor-Qwen2.5-1.5B-ADTC-GGUF \
Muta-Tutor-Qwen2.5-1.5B-Q4_K_M-vocab32k.ggufSource model
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 Q4KM 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.ggufEvaluation
The candidate and untuned control were evaluated with the same GGUF conversion, quantization, prompt format, and GCP CPU-proxy harness.
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.
