Base model: Qwen/Qwen3-0.6B-Base
Train file: /kaggle/input/datasets/anurhalizah/math-he/math_level45_train.parquet
Test file: /kaggle/input/datasets/anurhalizah/math-he/math_level45_test.parquet
Train rows used: 3994
Train subset size: 3994
Smoke loss eval rows: 200
Max sequence length: 2048
Epochs: 3
Learning rate: 0.0002
Precision: fp16
Merge method: manual_lora_cpu
Merged LoRA matrices: 196

Prompt Format

text

### System:
{system_prompt}

### Problem:
{problem}

### Solution:

System prompt:

text

You are a precise mathematical problem solver.
Follow this exact output contract:
Solve the problem step by step with concise reasoning.
Use valid LaTeX math notation for mathematical expressions.
Preserve LaTeX commands such as \frac{...}{...}, \sqrt{...}, x^{...}, subscripts, equations, and inequalities.
Put the final answer on its own last line exactly in this form: Final Answer: \boxed{...}
Do not use Markdown code fences.
Do not switch to a different answer format.

Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

repo_id = "zannvznn/qwen3-0.6b-math-l45-qlora-merged-fp16-v2"
tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
)