What is the best framework for fine-tuning LLMs in 2026?

As of October 30, 2026: Unsloth for fine-tuning on one GPU or a laptop (desktop app and CLI, LoRA, QLoRA, full fine-tuning and RL, with GGUF export); Axolotl for multi-GPU and multi-node training from YAML configs (FSDP2, DeepSpeed, context and expert parallelism); Hugging Face TRL when you write your own training code (SFTTrainer, DPOTrainer, GRPOTrainer, v1.17.0 on October 29, 2026); LlamaFactory for a no-code web UI over 100+ models; and ms-swift for very large MoE and multimodal models with Megatron parallelism. All five are Apache 2.0.

Unsloth vs Axolotl: which should I use?

Use Unsloth when the model fits on one GPU and you want the fastest start: a desktop app, notebooks, and Unsloth's claim of 2x faster training with 70% less VRAM. Use Axolotl when you need several GPUs or nodes, reproducible YAML configs, FSDP2 or DeepSpeed, and MoE expert parallelism. Many teams prototype in Unsloth and scale the same recipe in Axolotl.

Which framework supports reinforcement learning like GRPO and DPO?

All five do. TRL is the reference implementation (GRPOTrainer, DPOTrainer, KTOTrainer), and Unsloth, Axolotl, LlamaFactory and ms-swift build on or alongside it. ms-swift supports the widest list of GRPO variants, including DAPO, GSPO, SAPO, CISPO and RLOO.

Do I need a framework or a managed fine-tuning service?

Use a framework when you control GPUs and want full control of data, method and weights. Use a managed service billed per token when you want no infrastructure; compare them in our guide to fine-tuning platforms. The framework choice still matters there, because several services run the same open-source trainers underneath.

Quick Answer

Best LLM Fine-Tuning Frameworks 2026: Unsloth vs Axolotl

Published: October 30, 2026

The short answer

Choose by hardware and by how much code you want to write. The best LLM fine-tuning frameworks, as of October 30, 2026:

Framework	Best for	Interface	Scale	Licence · stars · latest release
Unsloth	One GPU, laptops, fastest start	Desktop app, CLI, notebooks	Single GPU to multi-GPU	Apache 2.0 · ~78,800 · Oct 28, 2026
Axolotl	Multi-GPU and multi-node runs	YAML config + CLI	FSDP2, DeepSpeed, N-D parallelism	Apache 2.0 · ~12,600 · v0.21.1 (Oct 27)
TRL (Hugging Face)	Custom training code, RL research	Python trainers	DDP, DeepSpeed ZeRO, FSDP	Apache 2.0 · ~19,600 · v1.17.0 (Oct 29)
LlamaFactory	No-code fine-tuning	Web UI (LlamaBoard) + CLI	Single to multi-GPU	Apache 2.0 · ~75,800 · v0.9.5 (May 30; commits Oct 29)
ms-swift (ModelScope)	Huge MoE and multimodal models	CLI, Web UI	Megatron TP/PP/CP/EP	Apache 2.0 · ~16,000 · v4.6.0 (Oct 13)

Stars and release dates are from GitHub on October 30, 2026.

1. Unsloth: best for one GPU

Unsloth now ships as a desktop app for Windows, macOS and Linux as well as a Python library, Docker image and Homebrew cask. It covers LoRA, QLoRA, full fine-tuning, pretraining and RL (GRPO, DPO), plus FP8, and exports to GGUF, NVFP4 and FP8 for local serving. Unsloth claims 2x faster training with 70% less VRAM and no accuracy loss; treat that as the vendor’s figure. It also runs models locally and connects them to Claude Code or Codex. Pick it for a first fine-tune, a single RTX or Mac, or any job where the model fits on one device.

2. Axolotl: best for multi-GPU and multi-node

Axolotl drives training from one YAML file, which makes runs reproducible and easy to review in a pull request. Recent releases added DeepSpeed Ulysses context parallelism, CPU-staged FSDP2 QLoRA loading (models larger than one GPU’s memory), Torch all-to-all expert parallelism for MoE models without DeepEP, and MoE expert quantization. Pick it when you have a cluster, train MoE models, or need the same recipe to run on 8 or 64 GPUs.

3. TRL: best when you write the training loop

TRL is Hugging Face’s post-training library: SFTTrainer, DPOTrainer, GRPOTrainer, KTOTrainer and more, each a thin wrapper over the Transformers trainer with DDP, DeepSpeed ZeRO and FSDP built in. Its README ships recipes for very large 2026 open-weight models, including LoRA on Mistral Large 4 on one node of eight B300s and an 8-node LoRA example for Muse Spark Open. Pick it for custom reward functions, new RL methods, or when you need to debug every step.

4. LlamaFactory: best no-code option

LlamaFactory fine-tunes 100+ language and vision-language models from a zero-code CLI or a Gradio web UI, with experiment tracking in LlamaBoard, TensorBoard, Weights & Biases, MLflow or SwanLab. Its last tagged release is from May 30, 2026, but the main branch is updated daily, so install from source for new models. Pick it when the person fine-tuning is an analyst, not an ML engineer.

5. ms-swift: best for very large models

ModelScope’s ms-swift supports 600+ text models and 300+ multimodal models, Megatron parallelism (tensor, pipeline, context, expert), the widest set of GRPO-family algorithms (GRPO, DAPO, GSPO, SAPO, CISPO, RLOO, Reinforce++), and inference and deployment via vLLM, SGLang and LMDeploy. Pick it for trillion-parameter-class MoE checkpoints or omni-modal models.

Picks by situation

One consumer GPU or a Mac: Unsloth.
A team cluster and reproducible configs: Axolotl.
A new RL reward or algorithm: TRL.
No Python on the team: LlamaFactory.
Fine-tuning a 500B+ MoE model: ms-swift or Axolotl.

Whatever you pick, the data matters more than the framework: a few thousand clean, task-specific examples beat a large noisy set. Start with our how to fine-tune LLMs guide, and compare per-token managed options in QLoRA fine-tuning platforms with token billing.

Last verified: October 30, 2026.