by AGK FIRE INC A QLoRA fine-tune of Qwen/Qwen3-8B that tutors for the SAT (math + reading & writing). Trained on ~17k SAT-style examples (60% math, 40% reading & writing), one epoch, 2048-token training length, with full step-by-step solutions plus hint-style answers. 160 held-out questions (100 math, 60 reading & writing). Identical prompt and decoding for both: greedy, 400 new tokens, thinking off, same Answer: X format instruction. Strict scoring = unparseable counted wrong. Reports: evalbase.json, evaltuned.json. The tuned adapter also answers far more math questions (97/100 vs 71/100) — base adapter overfit toward math. More RW training data is recommended before the teacher is…
Independent publisher
Gopi Adusumilli
agk4444
Models in Library2
Datasets in Library0
Models on Hugging Face5
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by AGK FIRE INC A Qwen3-8B fine-tune that tutors for the SAT (math + reading/writing), packaged as a Q4KM GGUF for local inference with llama.cpp, Ollama, LM Studio, and friends. Works through SAT questions step by step and finishes with a clear Answer: X line. Trained on SAT-style math and reading/writing questions with step-by-step solutions. Held-out eval: 160 questions, identical prompt and decoding for both (greedy, 400 tokens, thinking off), strict scoring = unparseable counted wrong. the adapter overfit toward math and wants more RW data before the teacher is frozen The tuned adapter was chosen as the quantization target. Q4KM accuracy on the same suite: run the "Measure the…