How Long Does It Take to Fine Tune an LLM with a Suitable Dataset

Fine-tuning an LLM with a suitable dataset can take anywhere from a few hours to several days, depending on the complexity of the task and the size of the dataset.

A smaller dataset with a simple task can be fine-tuned in as little as a few hours.

The complexity of the task is a major factor in determining the fine-tuning time.

For example, a task that requires the model to understand and generate human-like text can take significantly longer than a task that only requires the model to classify text into categories.

The size of the dataset is also crucial, as larger datasets require more time and computational resources to fine-tune.

Recommended read: How to Fine Tune a Model

Fine-tuning a large language model can be a time-consuming process, but it's a crucial step in getting the most out of your model.

In some cases, you can fine-tune a 7B parameter LLM on a single GPU, which is a significant reduction in resources.

Using QLoRA with the best setting (r=256 and alpha=512) requires 17.86 GB and takes about 3 hours on an A100 for 50k training examples.

You can tune any large language model at this point, but this particular example uses the pretrained model "text-bison@002".

This step will take a few hours to complete, and you can track the progress using the pipeline job link in the result.

The result will show you the tuned model, which you can then use in your project.

The dataset you choose can be critical in fine-tuning an LLM. I used the Alpaca dataset, which contains 50k training examples, for my experiments.

Data quality is very important, and a smaller, curated dataset like LIMA can sometimes outperform a larger, synthetic one like Alpaca. For example, a 65B Llama model finetuned on LIMA noticeably outperformed a 65B Llama model finetuned on Alpaca.

Using the best configuration on LIMA, I got similar, if not better, performance than the 50x larger Alpaca dataset. Unfortunately, I don't have a good answer to the question of how important the dataset is, but knowledge is usually absorbed from the pretraining dataset.

See what others are reading: Fine-tuning Huggingface Model with Custom Dataset

QLoRA is a technique that can help you save memory during fine-tuning, but it comes with some trade-offs.

You can save up to 33% of GPU memory by using QLoRA, which is a significant reduction.

However, this comes at the cost of a 39% increase in training runtime.

QLoRA achieves this by quantizing the pretrained weights to 4-bit precision and using paged optimizers to handle memory spikes.

Here's a comparison of the training time and memory used with and without QLoRA:

The good news is that QLoRA barely affects the modeling performance, making it a feasible alternative to regular LoRA training.

Training Large Models on a Single GPU is definitely possible with the right techniques. LoRA is a key player in making this happen.

Using LoRA, we can finetune 7B parameter LLMs on a single GPU, which is a game-changer for many researchers and developers. This is made possible by QLoRA, a variant of LoRA that's optimized for efficiency.

A fresh viewpoint: Fine Tune Llama 2 with Lora

The best setting for QLoRA requires 17.86 GB of memory with AdamW, which is a relatively small amount considering the task at hand. With this setting, training a 7B parameter model on a single GPU takes about 3 hours on an A100, which is a significant improvement over other methods.

Having a single GPU available is a common constraint in many research and development settings, so this ability to finetune large models on a single GPU is a huge advantage. In the specific case mentioned, 50k training examples were used with the Alpaca dataset, which shows that this approach can be effective with a moderate-sized dataset.

For another approach, see: Ai Llm Training

The quality of the dataset can be very important. The Alpaca dataset, which I used for my experiments, contains 50k training examples, but it's a synthetic dataset that's probably not the best by today's standards.

Data quality can make a big difference in performance. A 65B Llama model finetuned on LIMA, a curated dataset with only 1k examples, noticeably outperformed a 65B Llama model finetuned on Alpaca.

The LIMA dataset is a great example of how smaller, high-quality datasets can be more effective than larger, lower-quality ones. Using the best configuration on LIMA, I got similar, if not better, performance than the 50x larger Alpaca dataset.

The pretraining dataset is where knowledge is usually absorbed. Instruction finetuning is more about guiding the LLM towards following instructions, rather than adding new knowledge.

The Alpaca dataset has a maximum length of 1304 tokens, which is relatively small compared to other datasets.

Suggestion: How to Fine Tune Llm to Teach Ai Knowledge

Sophia is a second-order optimization algorithm that promises to be particularly attractive for LLMs where Adam and AdamW are usually the dominant ones.

Compared to Adam, Sophia is 2× faster, which is a significant improvement in terms of training efficiency.

Full finetuning required at least 2 GPUs and was completed in 3.5 hours using 36.66 GB on each GPU.

The benchmark results from full finetuning were not very good, likely due to overfitting or suboptimal hyperparameters.

Interestingly, the author didn't run any RLHF experiments, but they're mentioned as a comparison point in this section.

Full finetuning took significantly longer than the method being discussed, highlighting the importance of efficient optimization techniques.

To fine-tune an LLM, you'll need to create a fine-tuning job. This involves specifying the model you want to fine-tune and the dataset you'll be using.

Fine-tuning jobs can be complex, but a good starting point is to create a simple one. For example, creating a fine-tuning job using a pre-trained model can take just a few minutes.

You can also create a fine-tuning job using a custom dataset, which can take longer depending on the size of the dataset.