// Audit a Hugging Face dataset before training to confirm splits, columns, format, sample rows, distributions, and duplicates. Triggered before any training/fine-tuning script runs, when a user mentions a new dataset, or when you hit a KeyError / format mismatch in a training job.
Set up training and inference on AMD Ryzen AI Max+ 395 / Strix Halo (gfx1151, RDNA 3.5) with TheRock nightly ROCm wheels. Triggered when the host has gfx1151, when `rocminfo` shows Strix Halo, or when the user mentions Strix Halo / Ryzen AI Max / gfx1151 / 128GB unified memory.
Recreate the ml-intern-kit Python environment on a new machine (laptop, rented GPU box, Docker, fresh checkout). Triggered when the user is on a new host, sees ImportError on a core dep (torch/transformers/trl/peft/accelerate), or wants to install flash-attn / unsloth / bitsandbytes after the fact.
Evaluate a trained or downloaded language model with `lm-eval-harness` standard tasks (arc, hellaswag, gsm8k, mmlu, truthfulqa, ifeval, ...). Triggered when the user wants to benchmark, eval, or compare a model — pre- or post-training.
Submit a training/inference script to Hugging Face Jobs (`hf jobs run`). Triggered when the user wants to run training in the cloud, scale beyond local hardware, or kick off a multi-hour fine-tune. Enforces pre-flight: hub_model_id required, ≥2h timeout for training, single-job validation before batch.
Run a literature-first crawl before writing ANY ML training/fine-tuning/inference code. Spawns an Explore sub-agent that mines papers, citation graphs, methodology sections, and matched HF datasets to produce a ranked list of training recipes attributed to specific published results. Triggered when the user asks to fine-tune, train, or improve a model, or when the user names a task/benchmark and you need a recipe before coding.
Direct Preference Optimization (DPO) fine-tune with TRL `DPOTrainer`. Triggered when the user wants to align a model on preferences / pairwise comparisons / chosen-vs-rejected data, or improve an existing SFT checkpoint with a preference dataset.
| name | inspect-dataset |
| description | Audit a Hugging Face dataset before training to confirm splits, columns, format, sample rows, distributions, and duplicates. Triggered before any training/fine-tuning script runs, when a user mentions a new dataset, or when you hit a KeyError / format mismatch in a training job. |
Never train on a dataset you haven't audited. Looking at the data is the single most reliable way to avoid format mismatches, KeyError-on-step-1 failures, and silently-bad runs.
train_sft.py / train_dpo.py / train_grpo.py invocation.KeyError, ValueError: column not found, or
bad samples.python scripts/inspect_dataset.py <repo_id> # default audit
python scripts/inspect_dataset.py <repo_id> --split train --n 5
python scripts/inspect_dataset.py <repo_id> --config wikitext-2-raw-v1
The script prints schema, row counts per split, length statistics on text /
prompt / messages columns, duplicate counts, label distribution, and n sample
rows.
| Method | Required columns |
|---|---|
| SFT | messages (chat) or text or prompt+completion |
| DPO | prompt, chosen, rejected |
| GRPO | prompt (+ a reward function) |
If the columns don't match the method, stop. Tell the user; don't silently remap or substitute the dataset.
max_seq_length choice.messages already wrapped in a list of dicts vs.
flat text).