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agk4444
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Model Details
Model Provider
agk4444
Model Tree
Qwen/Qwen2.5-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
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)
| 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.
python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigfrom peft import PeftModelimport 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.
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigfrom peft import PeftModelimport 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))