code/support_check/model_finetune/qwen3-finetune.py

import ast
import json
import os
import sys
from datetime import datetime

os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
from unsloth import FastLanguageModel
import torch
model_name = "unsloth/Qwen3-8B"
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = model_name, 
    max_seq_length = 8192,   # Context length - can be longer, but uses more memory
    load_in_4bit = False,     # 4bit uses much less memory
    load_in_8bit = False,    # A bit more accurate, uses 2x memory
    full_finetuning = False, # We have full finetuning now!
    # token = "hf_...",      # use one if using gated models
)
model = FastLanguageModel.get_peft_model(
    model,
    r = 32,           # Choose any number > 0! Suggested 8, 16, 32, 64, 128
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 32,  # Best to choose alpha = rank or rank*2
    lora_dropout = 0, # Supports any, but = 0 is optimized
    bias = "none",    # Supports any, but = "none" is optimized
    # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
    use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
    random_state = 3407,
    use_rslora = False,   # We support rank stabilized LoRA
    loftq_config = None,  # And LoftQ
)

with open(f"/home/mshahidul/readctrl/data/finetuning_data/dataset_for_sft_support_check_list.json") as f:
    data = json.load(f)
from datasets import Dataset
dataset = Dataset.from_list(data)

from unsloth.chat_templates import standardize_sharegpt
dataset = standardize_sharegpt(dataset)

def formatting_prompts_func(examples):
   convos = examples["conversations"]
   texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos]
   return { "text" : texts, }


def parse_label_array(raw_text):
    text = (raw_text or "").strip()
    if not text:
        return []

    if "```" in text:
        text = text.replace("```json", "").replace("```", "").strip()

    start = text.find("[")
    end = text.rfind("]")
    if start != -1 and end != -1 and end > start:
        text = text[start : end + 1]

    parsed = None
    for parser in (json.loads, ast.literal_eval):
        try:
            parsed = parser(text)
            break
        except Exception:
            continue

    if not isinstance(parsed, list):
        return []

    normalized = []
    for item in parsed:
        if not isinstance(item, str):
            normalized.append("not_supported")
            continue
        label = item.strip().lower().replace("-", "_").replace(" ", "_")
        if label not in {"supported", "not_supported"}:
            label = "not_supported"
        normalized.append(label)
    return normalized


def extract_conversation_pair(conversations):
    user_prompt = ""
    gold_response = ""
    for message in conversations:
        role = message.get("role") or message.get("from")
        content = message.get("content", "")
        if role == "user" and not user_prompt:
            user_prompt = content
        elif role == "assistant" and not gold_response:
            gold_response = content
    return user_prompt, gold_response


def generate_prediction(user_prompt):
    prompt = tokenizer.apply_chat_template(
        [{"role": "user", "content": user_prompt}],
        tokenize=False,
        add_generation_prompt=True,
    )
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    with torch.inference_mode():
        outputs = model.generate(
            **inputs,
            max_new_tokens=128,
            do_sample=False,
            temperature=0.0,
            use_cache=True,
        )
    generated_tokens = outputs[0][inputs["input_ids"].shape[1] :]
    return tokenizer.decode(generated_tokens, skip_special_tokens=True).strip()

dataset = dataset.map(formatting_prompts_func, batched = True)

split_dataset = dataset.train_test_split(test_size = 0.1, seed = 3407, shuffle = True)
train_dataset = split_dataset["train"]
eval_dataset = split_dataset["test"]

from trl import SFTTrainer, SFTConfig
trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = train_dataset,
    eval_dataset = eval_dataset,
    args = SFTConfig(
        dataset_text_field = "text",
        per_device_train_batch_size = 8,
        gradient_accumulation_steps = 2, # Use GA to mimic batch size!
        warmup_steps = 5,
        num_train_epochs = 3, # Set this for 1 full training run.
        # max_steps = 30,
        learning_rate = 2e-4, # Reduce to 2e-5 for long training runs
        logging_steps = 1,
        per_device_eval_batch_size = 8,
        bf16 = True,
        tf32 = True,
        optim = "adamw_8bit",
        weight_decay = 0.01,
        lr_scheduler_type = "linear",
        seed = 3407,
        report_to = "none", # Use this for WandB etc
    ),
)
trainer_stats = trainer.train()

save_dir = f"/home/mshahidul/readctrl_model/support_checking_vllm/{model_name.split('/')[-1]}"
os.makedirs(save_dir, exist_ok=True)
# Export merged model weights in FP16 format.
model.save_pretrained_merged(
    save_dir,
    tokenizer,
    save_method = "merged_16bit",
)
tokenizer.save_pretrained(save_dir)

FastLanguageModel.for_inference(model)
model.eval()

model_info_dir = "/home/mshahidul/readctrl/code/support_check/model_info"
os.makedirs(model_info_dir, exist_ok=True)

timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
model_tag = model_name.split("/")[-1].replace(".", "_")

results = []
exact_match_correct = 0
label_correct = 0
label_total = 0
parsed_prediction_count = 0

for idx, sample in enumerate(eval_dataset):
    conversations = sample.get("conversations", [])
    user_prompt, gold_text = extract_conversation_pair(conversations)
    if not user_prompt:
        continue

    gold_labels = parse_label_array(gold_text)
    pred_text = generate_prediction(user_prompt)
    pred_labels = parse_label_array(pred_text)

    if pred_labels:
        parsed_prediction_count += 1

    exact_match = bool(gold_labels) and pred_labels == gold_labels
    if exact_match:
        exact_match_correct += 1

    sample_label_correct = 0
    for pos, gold_label in enumerate(gold_labels):
        if pos < len(pred_labels) and pred_labels[pos] == gold_label:
            sample_label_correct += 1

    label_correct += sample_label_correct
    label_total += len(gold_labels)

    results.append(
        {
            "sample_index": idx,
            "gold_labels": gold_labels,
            "predicted_labels": pred_labels,
            "raw_prediction": pred_text,
            "exact_match": exact_match,
            "label_accuracy": (
                sample_label_correct / len(gold_labels) if gold_labels else None
            ),
        }
    )

total_samples = len(results)
exact_match_accuracy = exact_match_correct / total_samples if total_samples else 0.0
label_accuracy = label_correct / label_total if label_total else 0.0

accuracy_summary = {
    "model_name": model_name,
    "model_save_dir": save_dir,
    "dataset_path": "/home/mshahidul/readctrl/data/finetuning_data/dataset_for_sft_support_check_list.json",
    "seed": 3407,
    "test_size": 0.1,
    "test_samples_evaluated": total_samples,
    "parsed_prediction_count": parsed_prediction_count,
    "exact_match_accuracy": exact_match_accuracy,
    "label_accuracy": label_accuracy,
    "exact_match_correct": exact_match_correct,
    "label_correct": label_correct,
    "label_total": label_total,
    "timestamp": timestamp,
}

predictions_path = os.path.join(
    model_info_dir,
    f"{model_tag}_test_inference_{timestamp}.json",
)
accuracy_path = os.path.join(
    model_info_dir,
    f"{model_tag}_test_accuracy_{timestamp}.json",
)

with open(predictions_path, "w", encoding="utf-8") as f:
    json.dump(results, f, ensure_ascii=False, indent=2)

with open(accuracy_path, "w", encoding="utf-8") as f:
    json.dump(accuracy_summary, f, ensure_ascii=False, indent=2)

print(f"Saved test inference to: {predictions_path}")
print(f"Saved test accuracy to: {accuracy_path}")
print(f"Exact match accuracy: {exact_match_accuracy:.4f}")
print(f"Label accuracy: {label_accuracy:.4f}")

# model.push_to_hub(f"Translation_Evaluator_Qwen3_14B_v1", )
# tokenizer.push_to_hub(f"Translation_Evaluator_Qwen3_14B_v1")
# print(f"Model pushed to Hugging Face Hub")