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Fine-tuning launchpad

Fine-tuning Llama 3.1 8B

Base on HF: meta-llama/Llama-3.1-8B  ·  Model page →

Tokenizer
Llama-3 BPE (128K vocab)
License
Llama 3.1 Community License. Commercial use allowed up to 700M MAU. AUP applies; legal should read it once.
Ecosystem
Highest in class. First-class support across HuggingFace TRL, Axolotl, Unsloth, LLaMA-Factory, llama.cpp, vLLM, MLX.

If you're new to fine-tuning a small open model, start here. Llama 3.1 8B has the biggest community of training recipes, the best inference support, and the most quantization formats. You'll find a working LoRA recipe for almost any domain.

Recommended training stacks

  • Unsloth - fastest single-GPU LoRA / QLoRA. Fits a 24GB GPU comfortably for 8B at 4K-8K context.
  • Axolotl - most flexible. YAML config, supports SFT/DPO/ORPO/KTO in one pipeline.
  • HuggingFace TRL - clean Python API for custom training loops.

Watch out for

  • Special tokens - <|begin_of_text|>, <|eot_id|> etc. must be preserved. Most frameworks handle this; check if you're rolling your own dataloader.
  • Acceptable-use policy - Meta's AUP excludes some applications even with commercial use. Check before fine-tuning a use-case-specific variant.
  • Chat template strictness - drift from the official template hurts eval scores noticeably.