code/finetune/qwen3-32B.py

import os
import json
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "1"

from unsloth import FastLanguageModel
import torch
dataset_path = "/home/mshahidul/readctrl/data/finetuning_data/classifier_en_data.json"
lora_save_path = "/home/mshahidul/readctrl_model/qwen3-32B_classifier_en"
full_model_save_path = "/home/mshahidul/readctrl_model/full_model/qwen3-32B_classifier_en-bf16"
lora=True
# === Load base model ===
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Qwen3-32B",
    max_seq_length = 8192,
    load_in_4bit = False,
    load_in_8bit = False,
    full_finetuning = False,
    dtype = torch.bfloat16,
)

# === Prepare LoRA model ===
model = FastLanguageModel.get_peft_model(
    model,
    r = 32,
    target_modules = [
        "q_proj", "k_proj", "v_proj", "o_proj",
        "gate_proj", "up_proj", "down_proj"
    ],
    lora_alpha = 32,
    lora_dropout = 0,
    bias = "none",
    use_gradient_checkpointing = "unsloth",
    random_state = 3407,
    use_rslora = False,
    loftq_config = None,
)

# === Load non-reasoning dataset (Full dataset) ===
from datasets import load_dataset
from unsloth.chat_templates import standardize_sharegpt

print("Loading dataset...")
with open(f"{dataset_path}") as f:
    data = json.load(f)
from datasets import Dataset
dataset = Dataset.from_list(data)

# Standardize and apply chat formatting
dataset = standardize_sharegpt(dataset)
non_reasoning_conversations = [
    tokenizer.apply_chat_template(conv, tokenize=False)
    for conv in dataset["conversations"]
]

# === Prepare dataset for training ===
import pandas as pd
from datasets import Dataset

data = pd.Series(non_reasoning_conversations, name="text")
combined_dataset = Dataset.from_pandas(pd.DataFrame(data))
combined_dataset = combined_dataset.shuffle(seed=3407)

# === Training setup ===
from trl import SFTTrainer, SFTConfig

trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=combined_dataset,
    eval_dataset=None,  # Optional
    args=SFTConfig(
        dataset_text_field="text",
        per_device_train_batch_size=16,
        gradient_accumulation_steps=8,
        warmup_steps=5,
        num_train_epochs=1,
        max_steps=30,
        learning_rate=2e-4,
        logging_steps=1,
        optim="adamw_8bit",
        weight_decay=0.01,
        lr_scheduler_type="linear",
        seed=3407,
        report_to="none",
    ),
)

# === Train model ===
trainer_stats = trainer.train()


if lora==True:
    model.save_pretrained(lora_save_path)
    tokenizer.save_pretrained(lora_save_path)
else:
    model.save_pretrained_merged(
        full_model_save_path,
        tokenizer,
        save_method="merged_16bit",
    )