| name | hf-training |
| description | Fine-tune models on Hugging Face: set up a training run with transformers or TRL (SFT, DPO), run it locally or as an HF Job on cloud GPU, and push the checkpoint to the Hub. Use for fine-tuning, LoRA, or PEFT training of LLMs or vision models.
|
| stack | ml |
Hugging Face Training
The path from a base model + dataset to a trained checkpoint on the Hub.
Choose the tool
| Goal | Tool |
|---|
| Supervised fine-tune an LLM | trl.SFTTrainer (LoRA by default via PEFT) |
| Preference optimization (RLHF-style) | trl.DPOTrainer |
| Plain transformers training | transformers.Trainer |
| Sentence embeddings | sentence-transformers |
Prefer TRL + PEFT (QLoRA) when GPU memory is limited — it trains large
models on one consumer GPU.
Minimal SFT skeleton
from transformers import AutoModelForCausalLM, AutoTokenizer
from datasets import load_dataset
from trl import SFTTrainer, SFTConfig
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B", load_in_4bit=True)
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B")
ds = load_dataset("ns/dataset", split="train")
trainer = SFTTrainer(
model=model,
args=SFTConfig(output_dir="./out", per_device_train_batch_size=1,
gradient_accumulation_steps=8, max_seq_length=2048),
train_dataset=ds,
tokenizer=tokenizer,
)
trainer.train()
Then push the checkpoint:
hf upload <ns>/<model-name> ./out --type model --commit-message "sft run"
Run on cloud GPU (HF Jobs)
When local hardware is insufficient, run the same script as a Job:
hf jobs run python:3.12 "pip install trl peft && python train.py" \
--flavor l4x1 --env-file .env --secrets HF_TOKEN
hf jobs logs <job-id> -f
Check hardware options with hf jobs hardware, and pin the cheapest flavor
that fits (l4x1, a10g, a100 depending on model size).
When to use
Fine-tuning or LoRA training (e.g. NANOG1, GEKO-style efficient fine-tuning)
that ends in a Hub checkpoint.
Pitfalls
- Reproduce the run: pin model revision, dataset revision, and seeds (see
sci/reproducible-experiments).
- Push the final checkpoint + a model card; don't leave it in a throwaway job.
- Don't regenerate provider model data — train on your own dataset.
Adapted from huggingface/skills (Apache-2.0).
