cyberneticlibrary
← library

Fine-tune language models on cloud

hugging-face-model-trainerskillsetup L3★0
Sheshiyer/skill-clusters ↗
What it does

Fine-tune language models (SFT/DPO/GRPO/reward) using TRL on Hugging Face Jobs

Best for

When fine-tuning LLMs (SFT/DPO/GRPO) on managed cloud GPUs without local infrastructure, with automatic Hub persistence.

Inputs
  • · training dataset
  • · model name from Hub
  • · training method (SFT/DPO/GRPO)
  • · HF_TOKEN
Outputs
  • · trained model pushed to Hub
  • · job ID
  • · Trackio monitoring dashboard
  • · training logs
Requires
  • · Hugging Face Jobs MCP
  • · TRL library
  • · HF_TOKEN with write permission
  • · Unsloth (optional, for memory efficiency)
Preconditions

HF pro/team/enterprise plan, dataset in supported format, HF_TOKEN, training timeout ≥1-2 hours

Failure modes
  • · timeout too short causing training loss
  • · HF_TOKEN not provided as secret causing 401
  • · dataset format mismatch (SFT needs messages, DPO needs chosen/rejected)
  • · no push_to_hub=True results in lost trained model
Trust signals
  • · Trackio real-time monitoring included
  • · example scripts (train_sft_example.py, train_dpo_example.py)
  • · cost estimator (estimate_cost.py) and dataset inspector provided