hugging-face-model-trainer – DevOps & Infrastructure skill for AI agents

Hugging Face Model Training

When to Use

Use for planning and running language-model SFT, DPO or GRPO training with

TRL or Unsloth, and for preparing GGUF conversion. The bundled Unsloth example

is a text-training example; it is not an implementation of vision training or

streaming support. Read [the detailed guide](references/detailed-guide.md) and

only the method-specific references needed for the task.

Before Execution

The user must approve the exact training data and model revisions, destination,

hardware, duration and compute budget before a paid job. Training authorization

does not automatically authorize public Hub repositories, telemetry, tracking

Spaces, or uploading private datasets. Confirm current account eligibility,

quotas and prices through the actual provider; do not infer them from old tables.

Use whichever supported Jobs interface is actually available. Inspect its current

schema before submitting. The string $HF_TOKEN is not a universal secret

interpolation protocol. Use the host's documented secret binding without printing

credentials, putting them in source, or changing an unrelated login session.

Read-only operations need no write token. Grant write access only to an approved

artifact destination.

Validate sample values, types, chat roles, model chat templates and the complete

bounded training subset before reserving compute. Column names alone do not prove

compatibility. GRPO additionally needs an explicit, tested reward function. Pin

reviewed dependency versions and immutable model/dataset revisions. Review any

required remote model code separately; default to trust_remote_code=False.

Workflow

1. Confirm the method and baseline; inspect the bundled example before adapting it.

2. Run a bounded pilot with an explicit maximum number of steps and examples.

Verify memory use on the actual sequence length, precision and optimizer.

Multiple GPUs require a supported distributed launch; their memory is not

automatically a shared pool.

3. Use measured pilot duration and a current user-supplied rate with

scripts/estimate_cost.py --measured-hours 0.1 --hourly-rate 2.

The numbers in this example are arithmetic inputs, not provider prices.

4. Choose a deadline within the approved budget. Configure private durable

checkpoints or an approved storage destination before an ephemeral job starts.

Uploading to the Hub is one option; public publishing is never required.

5. Inspect job state and sanitized logs. Check checkpoint recovery and evaluate

on held-out data before calling a run successful. Do not resubmit a job with an

uncertain outcome until its status is known.

Examples

For SFT, adapt scripts/train_sft_example.py to the approved model and text or

chat dataset. For preference learning inspect scripts/train_dpo_example.py;

for online rewards inspect scripts/train_grpo_example.py. These are examples

requiring environment-specific verification, not certified production jobs.

For local conversion inspect scripts/convert_to_gguf.py before use. Review and

pin llama.cpp and its dependencies. Keep intermediate artifacts until conversion

and any separately approved upload succeed. Respect base-model, adapter and data

licenses; a generated model card cannot grant rights you do not have.

Limitations

Unsloth performance and supported architectures depend on versions, kernels,

hardware and workload; percentages are not guarantees. The cost helper performs

arithmetic only, not memory sizing, runtime prediction or live quoting. GPU

training, model loading, uploads and external tracking have not been executed as

part of this source review. References may show older API versions: resolve them

against your pinned environment before executing. Do not disable the MPS memory

watermark as a generic out-of-memory workaround.