Dataset Card for LLMFineTuningBench

A dataset of over 30,000 LLM fine-tuning experiments, capturing detailed performance metrics from jobs run on high-performance computing (HPC) clusters. It spans a wide range of models, fine-tuning methods, and hardware configurations, and is intended to support research on predictive resource allocation, performance optimization, and cost estimation for LLM fine-tuning workloads.

Dataset Details

Dataset Description

The rapid adoption of Large Language Models (LLMs) has led to a surge in fine-tuning activities to adapt these models for specific tasks. However, selecting the optimal hardware and software configuration for fine-tuning is a complex, multi-dimensional problem that can lead to significant resource wastage and computational overhead. To address this challenge, we introduce a comprehensive dataset of over 15,000 LLM fine-tuning experiments. The dataset captures detailed performance metrics from jobs executed on high-performance computing clusters, covering a wide range of models, fine-tuning methods, and hardware configurations.

Dataset Sources

The dataset was collected by systematically exploring the parameter space of LLM fine-tuning across different hardware and software configurations on a high-performance cluster equipped with GPUs. This exploration was conducted using IBM's ado (accelerated discovery orchestrator) framework. ado features multiple actuators for executing experiments and collecting data.

The data for LLM Fine-tuning benchmarking was collected by the SFTTrainer actuator. Under the hood, this actuator wraps the fms-hf-tuning library, which itself builds on the SFTTrainer API from Hugging Face Transformers. The data is collected using AIM (AI Metadata) to capture profiling metadata during training runs.

• Repository: ado SFTTrainer

Uses

This dataset is intended to support the development of predictive models that map a proposed job configuration (model, hardware, method) to expected throughput and resource utilization — enabling "cost-calculator" or recommendation tools that guide users toward efficient fine-tuning setups.

Direct Use

• Transfer learning approaches
• Performance prediction models
• Handling categorical configuration space expansion
• Sample-efficient model building strategies

See ado Autoconf for examples of how this data was used for building predictive models for automated configuration of resources for fine-tuning workloads

Dataset Structure

These columns are produced from the data collected by the SFTTrainer actuator. The parameter descriptions have been sourced from the ado SFTTrainer actuator documentation.

In case of any discrepancy, please refer to the actuator documentation for the most accurate and up-to-date information. Key columns in the dataset:

Column	Description
model_name	Name of the base LLM (e.g., llama3-8b, mixtral-8x7b-instruct-v0.1)
method	Fine-tuning technique used: full, lora, or gptq-lora
number_gpus	Number of GPUs allocated for the job
gpu_model	GPU model used (e.g., L40S)
batch_size	Number of samples processed per training step
tokens_per_sample	Sequence length of each sample
is_valid	Binary flag (1 or 0) indicating whether the run completed successfully
dataset_tokens_per_second	Primary throughput metric — tokens processed per second from the dataset; the most reliable performance measure

Other columns capture additional metrics such as GPU memory utilization, GPU compute utilization, GPU power draw, and total training runtime.

Dataset Creation

Data Collection and Processing

We benchmarked 30,190 jobs, varying parameters of SFTTrainer including the base model, fine-tuning method, number of GPUs, and batch size. Training data consisted of random text, since the primary goal was to measure computational performance rather than model accuracy — the resulting fine-tuned model weights were discarded after each run.

Citation [optional]

If you use this dataset, please cite it as:

@misc{lotito26finetuning,
  title={LLM Fine-Tuning Benchmark Dataset},
  author={Lotito, Daniele and Vassiliadis, Vassilis and  
          Pomponio, Alessandro and Venugopal, Srikumar and Johnston, Michael},
  howpublished={Hugging Face Datasets},
  url = {https://huggingface.co/datasets/ibm-research/LLMFineTuningBench/},
  year={2026}
}

Contact Information

For any comments or questions, please email Daniele Lotito, Vassilis Vassiliadis and Srikumar Venugopal.