---
language: en
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- sat
- tutoring
- qlora
- peft
- education
---

# SAT Tutor — Qwen2.5-7B LoRA Adapter

Developed by **AGK FIRE INC**.

A fine-tuned SAT tutor built on **Qwen/Qwen2.5-7B-Instruct** with QLoRA (4-bit). It answers SAT Math and Reading & Writing questions with step-by-step explanations, and can also give hint-only nudges (first step only) so the student finishes the problem themselves.

## Training

- **Method:** QLoRA — 4-bit NF4, LoRA rank 16 / alpha 32 (~40.4M trainable params, 0.53% of 7.6B)

- **Data:** 15,000 examples from public SAT sources:

 - `ndavidson/sat-math-chain-of-thought` — step-by-step math solutions (deduplicated, correct-only)

 - `betterMateusz/SAT_Writting_Reading_Assessment_Question_Bank` — official-style reading/writing questions with rationales

 - `emozilla/sat-reading` — passage comprehension

- **Format:** ~75% full step-by-step solutions ending in `Answer: X`; ~25% hint-only responses

- **Run:** 1 epoch, 938 steps, 1024-token context, fp16, 2× Kaggle T4 (~7h)

## Evaluation (held-out official questions)

| Section | Score |
|---|---|
| SAT Math (100 AGIEval SAT questions) | 70/77 parsed = **90.9%** |
| Reading & Writing (60 official-bank questions) | 45/59 parsed = **76.3%** |

"Parsed" = responses ending in the `Answer: X` marker the tutor was trained to emit. The base model scores 0 on this grader because it never learned the marker format — the fine-tune teaches the tutor format and the reasoning holds up.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel
import torch

base = "Qwen/Qwen2.5-7B-Instruct"
tok = AutoTokenizer.from_pretrained(base, trust_remote_code=True)
bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
                         bnb_4bit_compute_dtype=torch.float16)
model = AutoModelForCausalLM.from_pretrained(
    base, quantization_config=bnb, device_map="auto", trust_remote_code=True)
model = PeftModel.from_pretrained(model, "agk4444/sat-tutor-qwen2.5-7b")
model.eval()

msgs = [{"role": "user", "content":
  "SAT Math practice question:\n\nIf 3x + 5 = 20, what is x?\nA) 3 B) 5 C) 7 D) 15"}]
prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
inp = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(inp["input_ids"], attention_mask=inp.get("attention_mask"),
                     max_new_tokens=400, do_sample=False,
                     pad_token_id=tok.eos_token_id)
print(tok.decode(out[0][inp["input_ids"].shape[1]:], skip_special_tokens=True))
```

---

© 2026 AGK FIRE INC. Released under Apache 2.0.