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# 🚀 FOOLPROOF KAGGLE TRAINING SCRIPT FOR CODELENS
#
# Step 1: Factory Reset your Kaggle environment to clear any broken libraries.
# Step 2: Create a code cell and run EXACTLY this (do not restart session after):
# !pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
# !pip install --no-deps trl peft accelerate bitsandbytes
# Step 3: Run this entire script in the next cell!
# =================================================================================
import os
import torch
from datasets import load_dataset
from trl import SFTTrainer, SFTConfig
from transformers import TrainingArguments
from unsloth import FastLanguageModel
from unsloth.chat_templates import get_chat_template
# 1. Load the Model (Qwen-2.5-Coder-7B)
max_seq_length = 2048 # Reduced to 2048 to prevent CUDA Out Of Memory on Kaggle T4
dtype = None # Auto detects float16/bfloat16
load_in_4bit = True # 4bit quantization to fit on Kaggle T4 GPUs
print("Loading model...")
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "unsloth/Qwen2.5-Coder-7B-Instruct",
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = load_in_4bit,
)
# Apply LoRA adapters
model = FastLanguageModel.get_peft_model(
model,
r = 16, # 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 = 16,
lora_dropout = 0,
bias = "none",
use_gradient_checkpointing = "unsloth",
random_state = 3407,
use_rslora = False,
loftq_config = None,
)
# 2. Format the Dataset
tokenizer = get_chat_template(
tokenizer,
chat_template = "chatml", # ChatML format
)
def formatting_prompts_func(examples):
convos = examples["messages"]
texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos]
return { "text" : texts, }
import glob
print("Finding dataset...")
dataset_files = glob.glob("/kaggle/input/**/dataset.jsonl", recursive=True)
if not dataset_files:
dataset_files = glob.glob("/kaggle/working/**/dataset.jsonl", recursive=True)
if not dataset_files:
raise FileNotFoundError("Could not find dataset.jsonl! Make sure you clicked 'Add Input' and uploaded it.")
data_file_path = dataset_files[0]
print(f"Loading dataset from: {data_file_path}")
dataset = load_dataset("json", data_files=data_file_path, split="train")
dataset = dataset.map(formatting_prompts_func, batched = True,)
# 3. Setup Trainer
trainer = SFTTrainer(
model = model,
processing_class = tokenizer,
train_dataset = dataset,
args = SFTConfig(
dataset_text_field = "text",
max_length = max_seq_length,
dataset_num_proc = 2,
packing = False, # Can make training 5x faster for short sequences.
per_device_train_batch_size = 1, # Reduced to 1 to prevent OOM
gradient_accumulation_steps = 8, # Increased to keep effective batch size the same
warmup_steps = 5,
max_steps = 60, # Increase this to ~300 for a real run
learning_rate = 2e-4,
fp16 = not torch.cuda.is_bf16_supported(),
bf16 = torch.cuda.is_bf16_supported(),
logging_steps = 1,
optim = "adamw_8bit",
weight_decay = 0.01,
lr_scheduler_type = "linear",
seed = 3407,
output_dir = "outputs",
),
)
# 4. Train!
print("Starting training...")
trainer_stats = trainer.train()
# 5. Save the fine-tuned model
print("Saving model to LoRA adapters...")
model.save_pretrained("codelens_reviewer_lora")
tokenizer.save_pretrained("codelens_reviewer_lora")
print("✅ Training complete! Model saved to codelens_reviewer_lora")
print("To push to Hugging Face, run: model.push_to_hub('your_username/codelens_reviewer_lora', token='...')")
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