This content originally appeared on HackerNoon and was authored by Lou El Idrissi
Would you like to experiment with an LLM? In this article, I share the lessons I learned while training an open-weight LLM using free-tier GPU and RAM resources. I have been exploring how lightweight instruction-tuned LLMs can reason over structured driving-risk signals and generate context-aware safety advice, a feature already integrated into many modern vehicles. But what if your car does not include those systems?
In this project, I am experimenting with the ability of a Smart Driving Assistant to identify and prioritize critical driving-risk factors, then generate context-aware interventions designed to help de-escalate dangerous situations. The goal is to provide drivers with accessible safety guidance regardless of the car model they own.
I also hope this article saves people working on similar projects a few hours by sharing practical workarounds for optimizing runtime, memory usage, and training stability on free-tier Google Colab environments.
Dataset structure :
For the initial phase of this project, I am using a simulated dataset of driving scenarios. Each scenario includes 8 factors classified into three categories: behavioral, physiological, and environmental. The input scenarios are based on the labels of Vicomtech’s Driver Monitoring Dataset, while the output advice are based on NHTSA guidelines. The factors are organized according to a rule-based system that prioritizes severe drowsiness and distraction indicators. If you are interested in experimenting with this setup, I will be glad to share the database upon request.
What model to use ?
For my use case, I needed an LLM that:
- Reason over structured driving-risk scenarios.
- Generate context-aware, instruction-style outputs designed to help de-escalate critical situations.
- Work alongside rule-based risk prioritization heuristics.
- Be fine-tuned within free-tier GPU and RAM constraints.
Although it is no longer state-of-the-art, Alpaca-LoRA 7B remains relevant for experimentation because:
- Alpaca is an instruction-tuned version of LLaMA.
- It supports parameter-efficient fine-tuning methods such as LoRA.
- It is an open-weight model and is well documented.
- It has a large ecosystem of repositories and community support.
Because it is instruction-tuned and LoRA-compatible, Alpaca-LoRA 7B can be trained within free-tier GPU and RAM limits despite its parameter size. Even so, training remained constrained by intermittent GPU availability and Colab session limits.
Set Up Alpaca-LoRA
First clone the alpaca-lora repository, then install the dependencies :
!git clone https://github.com/tloen/alpaca-lora.git
%cd alpaca-lora
\
!pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu118
!pip install bitsandbytes --upgrade
!pip install -r requirements.txt
!pip install -U transformers accelerate evaluate datasets
Free-tier Google Colab vs Kaggle :
I found some tasks to be straightforward when using Colab. Colab provides a clear file and folder structure when downloading repositories, which made navigating training files easier.
Uploading datasets and editing core training scripts was also simple and could be done either manually or directly through notebook kernels:
from google.colab import files
uploaded = files.upload() # upload your testing file
filename = list(uploaded.keys())[0] #get uploaded file name
print(f"file uploaded: {filename}")
One downside of Colab is that sessions can terminate without warning. To avoid losing progress, saving checkpoints continuously or mounting files on Google Drive becomes essential during training.
from google.colab import drive
drive.mount('/content/drive')
While using Kaggle, I encountered issues accessing a private repository for the base Alpaca model, even when using a Hugging Face personal access token. If you plan to switch between GPU providers, it is worth verifying that the base model can be accessed in both Google Colab and Kaggle environments beforehand.
In my tests, Kaggle appeared to provide more RAM on the free tier. For that reason, I trained the LoRA adapter weights on Colab while testing the base model and adapter weights on Kaggle.
Training script modifications :
I updated the original finetune.py script from the repository to match newer library versions and avoid compatibility issues during training.
from peft import (
...
prepare_model_for_kbit_training, # replaces prepare_model_for_int8_training, line 20
...
)
\
model = LlamaForCausalLM.from_pretrained(
...
dtype=torch.float16, # replaces torch_dtype, line 115
...
)
\
trainer = transformers.Trainer(
model=model,
train_dataset=train_data,
eval_dataset=val_data,
args=transformers.TrainingArguments(
...
eval_strategy="steps", # replaces evaluation_strategy, line 245
...
Stable Training Configuration :
After making those modifications to the finetune.py file, the training script was able to run successfully within the notebook environment. The values in the following were selected to ensure stability under free-tier GPU constraints rather than to optimize performance.
!python finetune.py \
--base_model 'baffo32/decapoda-research-llama-7B-hf' \
--data_path './training_data.jsonl' \
--val_data_path './testing_data.jsonl' \ # finetune supports splitting dataset into training and testing
--output_dir './lora-alpaca-output' \
--batch_size 8 \
--micro_batch_size 2 \
--num_epochs 3 \
--learning_rate 2e-4 \
--cutoff_len 256 \
--val_set_size 0 \ # you can your validation set size
--lora_r 16 \
--lora_alpha 16 \
--lora_dropout 0.05 \
--lora_target_modules '[q_proj,k_proj,v_proj,o_proj]' \
--train_on_inputs True \ # depends on use case
--group_by_length \
--resume_from_checkpoint './lora-alpaca-output/checkpoint-x'
Practical Optimizations for Stable Training :
In this pipeline, the main failure mode was GPU memory exhaustion and runtime instability, especially in free-tier environments like Colab. To keep training stable, I introduced a set of practical system-level optimizations.
**Controlled dataset size \ Training initially started with a reduced dataset (~10k samples) to validate the full pipeline before scaling. This helped ensure that errors were caught early without wasting GPU time.
GPU memory hygiene
To reduce memory fragmentation during repeated training runs in Colab, I periodically cleared the CUDA cache:
import torch
torch.cuda.empty_cache()
Data loading optimization
I used multiple CPU workers when loading data to reduce data bottlenecks and improve loading time. On the Colab free tier, setting num_workers above 2 triggered a warning, so I kept it at 2.
trainer = transformers.Trainer(
...
args=transformers.TrainingArguments(
dataloader_num_workers=2, # add parallel CPU workers for data loading, depends on hardware limit
...
**Batch size control via gradient accumulation \ I experimented with different batch sizes to balance memory usage and stable training. Smaller batches reduced memory pressure but introduced noisier gradients, while larger effective batches improved stability but slowed down updates.
Instead of increasing batch size directly, I used gradient accumulation:
--batch_size 8 \ # effective batch size
--micro_batch_size 2 \
Instead of increasing batch size directly, I used gradient accumulation:
effective batch size = micro-batch size × gradient accumulation steps
This allowed stable training without exceeding memory constraints.
...
args=transformers.TrainingArguments(
per_device_train_batch_size=micro_batch_size,
gradient_accumulation_steps=gradient_accumulation_steps,
warmup_steps=100,
)
Tokenization and sequence length management
I tokenized the dataset on CPU before training to avoid repeated preprocessing during each run of finetune.py. This reduced runtime and made training more stable across multiple iterations. It also allowed me to analyze the token distribution and estimate a reasonable maximum sequence length ahead of time.
Instead of relying on a fixed heuristic, I calculated the maximum token length in the dataset and added a small buffer to ensure no important information was truncated.
**Sequence length control \ Reducing sequence length has a direct impact on memory usage, since the attention mechanism scales quadratically with context length. In practice, this means that if the sequence length doubles, compute cost increases by roughly a factor of four.
Based on the dataset distribution, I selected a cutoff length that balanced memory constraints and information retention:
--cutoff_len 256 \
This value was chosen to preserve most of the input structure while keeping training feasible on free-tier GPUs.
Checkpoint strategy
Colab sessions are unstable, so saving checkpoints frequently is essential to avoid losing progress. I set save_steps=100 for safety, though 200–500 would likely have been sufficient for my dataset size.
trainer = transformers.Trainer(
…
args=transformers.TrainingArguments(
…
eval_strategy="steps", #if val_set_size > 0 else "no",
save_strategy="steps",
eval_steps=100, #if val_set_size > 0 else None, # change to eval per new update, set for more frequent weight saving
save_steps=100, # total data / batch size
output_dir=output_dir,
…
))
Conclusion:
This phase of the project was mainly an exercise in understanding how LLM fine-tuning works from the inside. Instead of focusing on achieving optimal performance, I focused on making each component of the pipeline work under real constraints and learning what actually affects training stability.
I started with Alpaca-LoRA because it is built on top of LLaMA and has a strong open-source ecosystem on Hugging Face. It was a practical entry point for experimenting with LoRA-based fine-tuning and understanding how these systems behave on limited hardware.
Since then, I’ve come across newer and more lightweight models like Qwen-based variants, which are more efficient and better suited for modern workflows. This project helped me build intuition before moving on to those newer tools.
This content originally appeared on HackerNoon and was authored by Lou El Idrissi
Lou El Idrissi | Sciencx (2026-05-28T06:58:21+00:00) How I Built a Stable Fine-Tuning Pipeline on Free Colab GPU. Retrieved from https://www.scien.cx/2026/05/28/how-i-built-a-stable-fine-tuning-pipeline-on-free-colab-gpu/
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