| --- |
| license: apache-2.0 |
| library_name: peft |
| base_model: Qwen/Qwen3-8B |
| pipeline_tag: text-generation |
| language: |
| - en |
| tags: |
| - sat |
| - tutoring |
| - math |
| - qlora |
| - qwen3 |
| --- |
| |
| # SAT Tutor — Qwen3-8B adapter |
|
|
| 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. |
|
|
| ## Base vs tuned — held-out eval (2026-09-26) |
|
|
| 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: `eval_base.json`, `eval_tuned.json`. |
|
|
| | | Base (strict) | Tuned (strict) | |
| |---|---|---| |
| | Math | 57% (57/100) | **71%** (71/100) | |
| | Reading & Writing | 76.7% (46/60) | 66.7% (40/60) | |
| | Overall | 64.4% (103/160) | **69.4%** (111/160) | |
|
|
| The tuned adapter also answers far more math questions (97/100 vs 71/100) — base |
| refused 29 outright. Known weakness: reading & writing regressed 10 points, the |
| adapter overfit toward math. More RW training data is recommended before the |
| teacher is frozen. |
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| from peft import PeftModel |
| |
| tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", trust_remote_code=True) |
| model = AutoModelForCausalLM.from_pretrained( |
| "Qwen/Qwen3-8B", torch_dtype=torch.float16, |
| device_map="auto", trust_remote_code=True) |
| model = PeftModel.from_pretrained(model, "agk4444/sat-tutor-qwen3-8b") |
| model.eval() |
| |
| msgs = [{"role": "user", "content": "If x + 5 = 12, what is x?\n\n" |
| "End your response with your final answer on its own line in exactly " |
| "this format: **Answer: X** where X is A, B, C, or D."}] |
| prompt = tok.apply_chat_template(msgs, tokenize=False, |
| add_generation_prompt=True, |
| enable_thinking=False) |
| inp = tok(prompt, return_tensors="pt").to(model.device) |
| out = model.generate(**inp, max_new_tokens=400, do_sample=False) |
| print(tok.decode(out[0][inp["input_ids"].shape[1]:], skip_special_tokens=True)) |
| ``` |
|
|
| ## Quantized version |
|
|
| For local/phone inference (llama.cpp, Ollama, LM Studio), use the GGUF build: |
|
|
| **[agk4444/sat-tutor-qwen3-8b-gguf](https://huggingface.co/agk4444/sat-tutor-qwen3-8b-gguf)** |
| (`sat-tutor-qwen3-8b.Q4_K_M.gguf`, ~4.9 GB) |
|
|
| ## Limitations |
|
|
| - SAT-focused: off-topic questions get base-model-quality answers at best. |
| - Reading & writing trails math — see the eval table above. |
| - It can still make mistakes — double-check the math against the steps shown. |
|
|
| --- |
|
|
| © 2026 AGK FIRE INC. Released under Apache 2.0. |
|
|
