README.md

---
license: cdla-permissive-2.0
task_categories:
- tabular-regression
language:
- en
tags:
- llm-fine-tuning
- performance-modeling
- gpu-benchmarking
- throughput-prediction
- machine-learning
- research
- categorical-configuration-space
- transfer-learning
size_categories:
- n<1K
pretty_name: "LLM Fine-Tuning Performance Benchmark Dataset"
---

# LLM Fine-Tuning Performance Benchmark Dataset

## Dataset Summary

This dataset contains performance benchmarks for Large Language Model (LLM) fine-tuning across various hardware and software configurations. It includes throughput measurements (tokens per second) for 959 valid configurations, collected over 1000 GPU hours on a Kubernetes cluster. The dataset is designed for research on predictive performance modeling, specifically for evaluating methods that handle Categorical Configuration Space Expansion (CCSE) which occur when new values are introduced for categorical variables.

**Research Purpose**: This dataset enables evaluation of predictive model building approaches when the configuration space expands with new categorical values (e.g., new LLM models, GPU types, fine-tuning methods, or software versions).

## Dataset Description

### Overview

LLM fine-tuning is compute and memory intensive. This benchmark measures throughput across a configuration space with 7 variables (4 categorical, 3 numerical):

**Categorical Variables:**
- **LLM**: llama2-7b, granite-13b-v2, granite-3b-code-base-128k
- **Method**: Full fine-tuning, LoRA (Low-Rank Adaptation)
- **GPU**: NVIDIA A100-80GB, NVIDIA L40S-48GB
- **Version**: v2.0.0, v2.1.0 (software stack versions)

**Numerical Variables:**
- **#GPUs**: 1, 2, 4, 8
- **Batch Size**: 1, 2, 4, 8, 16, 32, 64, 128
- **Tokens per Sample**: 512, 1024, 2048, 4096, 8192

The full configuration space contains 3840 possible combinations. After excluding invalid configurations (batch size not divisible by #GPUs, memory constraints, hardware availability), 959 valid configurations were benchmarked.

### Data Collection
Data has been obtained with the software [accelerated discovery orchestrator (ado)]([https://ibm.github.io/ado/).
Ado is a platform for executing computational experiments at scale and analysing their results.
More specifically, the actuator [SFTTrainer](https://ibm.github.io/ado/actuators/sft-trainer/) has been used to collect data on IBM Research infrastructure.

- **Compute Time**: 1011 GPU hours (computed from `train_runtime * number_gpus`)
- **Methodology**: Each configuration was executed to measure throughput during a single epoch over a synthetic dataset
- **Metric**: Throughput = (total dataset tokens processed) / (epoch duration in seconds)


## Dataset Structure

### Main Dataset

The primary dataset file is `dataset.csv` containing all 959 benchmarked configurations.

### Task-Specific Datasets

The `task_datasets/` directory contains CSV files for 18 specific benchmark tasks, organized by the categorical variable causing the configuration space expansion:

**Naming Convention**: `{variable}_{generalization}_{target}.csv`
- `variable`: gpu, method, model, version
- `generalization`: least (generalized), most (specialized)
- `target`: specific value being predicted (e.g., g3b for granite-3b, l7b for llama2-7b)

## Data Fields

| Field | Type | Description |
|-------|------|-------------|
| `method` | string | Fine-tuning method: "full" or "lora" |
| `model_name` | string | LLM model: "llama2-7b", "granite-13b-v2", or "granite-3b-code-base-128k" |
| `gpu_model` | string | GPU type: "NVIDIA-A100-SXM4-80GB" or "NVIDIA-L40S-48GB" |
| `number_gpus` | float | Number of GPUs: 1.0, 2.0, 4.0, or 8.0 |
| `tokens_per_sample` | float | Tokens per training sample: 512.0, 1024.0, 2048.0, 4096.0, or 8192.0 |
| `batch_size` | float | Training batch size: 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, or 128.0 |
| `version` | string | [Foundation Model Stack](https://github.com/foundation-model-stack/foundation-model-stack) version: "v2.0.0" or "v2.1.0" |
| `dataset_tokens_per_second` | float | **Target variable**: Throughput in tokens/second |
| `train_runtime` | float | Training runtime in seconds for one epoch |

## Benchmark Tasks

The dataset supports 18 distinct prediction tasks for evaluating model building methods under Categorical Configuration Space Expansion (CCSE). Tasks are categorized by:

1. **Variable causing expansion**: LLM, GPU, Method, or Version
2. **Generalization level**: 
   - **Generalized (†)**: Source space includes all values of other categorical variables
   - **Specialized (★)**: Source space restricted to specific combinations

### LLM Expansion Tasks (6 tasks)

| Task | Source Space | Target | Source Size | Target Size |
|------|--------------|--------|-------------|-------------|
| † | {granite-13b, granite-3b}, *, *, * | llama2-7b | 614 | 345 |
| † | {granite-3b, llama2-7b}, *, *, * | granite-13b | 713 | 246 |
| † | {llama2-7b, granite-13b}, *, *, * | granite-3b | 614 | 345 |
| ★ | {granite-13b, granite-3b}, LoRA, A100, v2.1 | llama2-7b | 206 | 110 |
| ★ | {granite-3b, llama2-7b}, LoRA, A100, v2.1 | granite-13b | 220 | 96 |
| ★ | {llama2-7b, granite-13b}, LoRA, A100, v2.1 | granite-3b | 206 | 110 |

### GPU Expansion Tasks (4 tasks)

| Task | Source Space | Target | Source Size | Target Size |
|------|--------------|--------|-------------|-------------|
| † | *, LoRA, A100, v2.1.0 | L40S | 316 | 203 |
| ★ | llama2-7b, LoRA, A100, v2.1 | L40S | 110 | 74 |
| ★ | granite-13b, LoRA, A100, v2.1 | L40S | 96 | 55 |
| ★ | granite-3b, LoRA, A100, v2.1 | L40S | 110 | 74 |

### Method Expansion Tasks (4 tasks)

| Task | Source Space | Target | Source Size | Target Size |
|------|--------------|--------|-------------|-------------|
| † | *, LoRA, A100, v2.1.0 | Full | 316 | 264 |
| ★ | llama2-7b, LoRA, A100, v2.1 | Full | 110 | 101 |
| ★ | granite-13b, LoRA, A100, v2.1 | Full | 96 | 54 |
| ★ | granite-3b, LoRA, A100, v2.1 | Full | 110 | 110 |

### Version Expansion Tasks (4 tasks)

| Task | Source Space | Target | Source Size | Target Size |
|------|--------------|--------|-------------|-------------|
| † | *, LoRA, A100, v2.1.0 | v2.0 | 316 | 174 |
| ★ | llama2-7b, LoRA, A100, v2.1 | v2.0 | 110 | 60 |
| ★ | granite-13b, LoRA, A100, v2.1 | v2.0 | 96 | 40 |
| ★ | granite-3b, LoRA, A100, v2.1 | v2.0 | 110 | 74 |

**Note**: `*` indicates the entire domain is present in the source space.


## Considerations for Using the Data

### Research Context

This dataset is being used for **research purposes** to evaluate predictive modeling methods, particularly:
- Transfer learning approaches
- Performance prediction models
- Handling categorical configuration space expansion
- Sample-efficient model building strategies

### Data Characteristics

1. **Hardware-Specific**: Results are specific to NVIDIA A100-80GB and L40S-48GB GPUs
2. **Software-Specific**: Measurements taken with specific PyTorch library versions (v2.0.0, v2.1.0)
3. **Invalid Configurations Excluded**: 
   - Configurations where batch_size is not divisible by number_gpus
   - Configurations exceeding GPU memory limits
4. **Synthetic Dataset**: Throughput measured using synthetic training data
5. **Single Epoch**: Measurements represent single-pass throughput, not full training convergence

## Citation Information

If you use this dataset in your research, please cite:

```bibtex
@misc{lotito2026finetuning,
  title={LLM Fine-Tuning Performance Benchmark Dataset},
  author={Lotito, Daniele and Venugopal, Srikumar and 
          Vassiliadis, Vassilis and Pinto, Christian and 
          Pomponio, Alessandro and Johnston, Michael},
  howpublished={Hugging Face Datasets},
  url = {https://huggingface.co/datasets/ibm-research/LLM_Fine-Tuning_Performance/},
  year={2026}
}
```