---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: ceb8df1ff5e0c8af6a7c2e2ccbd02a538a9eee27d7f51b1af4fa2303ded8909a
  pageDirectory: concepts
  sources:
    - distributed-training-with-deepspeed-distributor-databricks-on-aws.md
  confidence: 0.9
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - fine-tuning-llama-2-7b-with-deepspeed-on-databricks
    - FL27WDOD
  citations:
    - file: distributed-training-with-deepspeed-distributor-databricks-on-aws.md
title: Fine-tuning Llama 2 7B with DeepSpeed on Databricks
description: An example notebook demonstrating how to fine-tune the Llama 2 7B Chat model using the DeepSpeedTorchDistributor on Databricks.
tags:
  - llama
  - fine-tuning
  - example-notebook
  - nlp
timestamp: "2026-06-19T18:37:50.052Z"
---

# Fine-tuning Llama 2 7B with DeepSpeed on Databricks

**Fine-tuning Llama 2 7B with DeepSpeed on Databricks** refers to the practice of adapting the 7‑billion‑parameter Llama 2 Chat model using the [DeepSpeed] library and the DeepSpeed distributor on the Databricks platform. DeepSpeed is an open‑source library developed by Microsoft that provides optimized memory usage, reduced communication overhead, and advanced pipeline parallelism, making it possible to train large models that would otherwise be limited by memory constraints on standard hardware. ^[distributed-training-with-deepspeed-distributor-databricks-on-aws.md]

## Overview

The DeepSpeed distributor is built on top of the [TorchDistributor] and is recommended for customers whose models require higher compute power but are constrained by available GPU memory. It is available in Databricks Runtime 14.0 ML and above. ^[distributed-training-with-deepspeed-distributor-databricks-on-aws.md]

Fine‑tuning a 7B‑parameter model like Llama 2 7B Chat benefits from DeepSpeed’s memory optimisations because the model, its gradients, and optimizer states often exceed the capacity of a single GPU. By sharding these components across multiple GPUs, DeepSpeed enables a training procedure that would be unattainable with standard data parallelism alone. ^[distributed-training-with-deepspeed-distributor-databricks-on-aws.md]

## Key Benefits for Fine‑tuning

DeepSpeed offers three main advantages for fine‑tuning large models such as Llama 2 7B:

- **Low GPU memory** – Techniques like ZeRO‑stage optimisation reduce the per‑GPU memory footprint, allowing larger batch sizes or larger models to fit.
- **Large model training** – Models with billions of parameters can be trained across multiple GPUs without requiring costly hardware upgrades.
- **Large input data handling** – During batch inference or training with long sequences, DeepSpeed efficiently manages memory and communication.

These capabilities are especially relevant when fine‑tuning Llama 2 7B, which typically requires multiple GPUs. ^[distributed-training-with-deepspeed-distributor-databricks-on-aws.md]

## Notebook Example

Databricks provides an example notebook titled **Fine‑tune Llama 2 7B Chat with DeepspeedTorchDistributor**. This notebook demonstrates the complete workflow: setting up the environment, configuring DeepSpeed, launching distributed training, and evaluating the fine‑tuned model. It serves as a starting point for users who want to apply the DeepSpeed distributor to their own fine‑tuning tasks. ^[distributed-training-with-deepspeed-distributor-databricks-on-aws.md]

## Requirements

To use the DeepSpeed distributor for fine‑tuning Llama 2 7B on Databricks, you need:

- A Databricks workspace on AWS.
- A cluster running **Databricks Runtime 14.0 ML** or above.
- Sufficient GPU resources (typically multiple GPUs such as A100 or H100).

The DeepSpeed library is pre‑installed in those runtimes, so no additional package installation is required. ^[distributed-training-with-deepspeed-distributor-databricks-on-aws.md]

## Related Concepts

- [DeepSpeed](/concepts/deepspeed.md) – The Microsoft open‑source library for memory‑optimised distributed training.
- [TorchDistributor](/concepts/torchdistributor.md) – The underlying PyTorch distributed trainer on which the DeepSpeed distributor is built.
- Llama 2 – Meta’s family of large language models, including the 7B Chat variant.
- Fine-tuning – The process of adapting a pre‑trained model to a specific task or domain.
- [Distributed Data Parallel (DDP)](/concepts/distributed-data-parallel-ddp.md) – A common parallelism strategy that DeepSpeed extends with memory optimisation.

## Sources

- distributed-training-with-deepspeed-distributor-databricks-on-aws.md

# Citations

1. [distributed-training-with-deepspeed-distributor-databricks-on-aws.md](/references/distributed-training-with-deepspeed-distributor-databricks-on-aws-6ba03a5a.md)
