11 Open-Source Frameworks for Fine-Tuning, Serving, and Deploying LLMs

Large Language Models (LLMs) have revolutionized AI, but taking a model from its pre-trained state to a production-ready application is a complex journey.

This guide explores 11 essential open-source frameworks designed to streamline the entire LLM lifecycle, from fine-tuning to serving and deployment.

We delve into foundational tools like Hugging Face Transformers, memory-optimization powerhouses like DeepSpeed and Unsloth, and comprehensive toolkits like LLaMA Factory.

For deployment, we cover high-performance inference servers such as VLLM and LiteLLM, as well as platforms like OpenLLM and SkyPilot that simplify deployment across cloud environments.

Whether you need to slash VRAM usage, accelerate training with LoRA, or serve models with an OpenAI-compatible API, this article will help you navigate the landscape and select the perfect framework for your project.

Table of Contents:

1. Hugging Face Transformers: Popular framework for general fine‑tuning of language models

2. DeepSpeed: Framework from Microsoft for memory optimization and multi‑GPU fine‑tuning

3. LLaMA Factory: complete fine‑tuning toolkit with support for acceleration methods, adapters (LoRA, QLoRA), distributed training, quantization, web UI, and monitoring

4. Unsloth: Focused on fast fine‑tuning with low VRAM usage; claims up to 2× speedups and 70–80% less memory

5. Colossal AI: Designed to make LLMs cheaper, faster, and more accessible using powerful parallel training strategies and memory optimizations

6. Axolotl: Enables post‑training adjustments via YAML config files with minimal code, supports full‑finetuning and adapters like LoRA/QLoRA

7. LiteLLM: Lightweight inference and serving framework with high‑speed performance (e.g., flash attention)

8. VLLM: VLLM supports lightweight inference and OpenAI‑style API, and advanced memory management

9. OpenLLM: Model‑serving and deployment platform offering unified APIs (REST/gRPC) and seamless BentoML integration

10. FastChat: End‑to‑end framework for training and serving chat‑style language models.

11. SkyPilot: Enables running AI jobs across AWS, GCP, Azure, and Kubernetes with a unified interface

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1. Hugging Face Transformers: Popular framework for general fine‑tuning of language models

Hugging Face Transformers provides the Trainer API, which offers a comprehensive set of training features for fine-tuning any of the models on the Hub.

Trainer is an optimized training loop for Transformers models, making it easy to start training right away without manually writing your own training code. Pick and choose from a wide range of training features in TrainingArguments, such as gradient accumulation, mixed precision, and options for reporting and logging training metrics.

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2. DeepSpeed: Framework from Microsoft for memory optimization and multi‑GPU fine‑tuning

DeepSpeed empowers ChatGPT-like model training with a single click, offering 15x speedup over SOTA RLHF systems with unprecedented cost reduction at all scales.

DeepSpeed is an easy-to-use deep learning optimization software suite that powers unprecedented scale and speed for both training and inference. With DeepSpeed, you can:

• Train/Inference dense or sparse models with billions or trillions of parameters

• Achieve excellent system throughput and efficiently scale to thousands of GPUs

• Train/Inference on resource-constrained GPU systems

• Achieve unprecedented low latency and high throughput for inference

• Achieve extreme compression for an unparalleled inference latency and model size reduction with low costs.

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3. LLaMA Factory: complete fine‑tuning toolkit with support for acceleration methods