Tao Finetune Huggingface Model

tao-finetune-huggingface-model

Standalone install? If this session was not initialized by the TAO skill bank plugin, run the tao-setup skill first (host preflight, credentials, cross-skill discovery).

Local NVIDIA GPU fine-tuning for HuggingFace models, grounded in live-fetched documentation with curated references as a fallback safety net. One NGC container, a few focused scripts, one push to HF Hub. Follow the rules in this file; don't improvise.

Dedicated-model routing gate

Before Step 1 or any probe, image selection, package install, venv creation, or training-code generation, resolve model_id against the packaged model-owner registry. Use the absolute skill-bank root from which this file was loaded:

python <bank-root>/scripts/resolve_tao_model.py \
  --skill-bank <bank-root> \
  --model "$MODEL_ID" \
  --format json

The resolver matches model metadata, including huggingface_model_ids, network_arch, skill names, and legacy aliases. Routing is internal: a model ID and task are enough. Never require prompt boilerplate about skills, containers, or checkpoint formats.

• Exit 0: stop this workflow and follow the owning model skill's environment, action metadata, preflight, and checkpoint preparation.
• Exit 3: no packaged model skill owns the ID. This is the only result that permits Step 1 of the generic workflow.
• Any other nonzero exit: ownership discovery is broken or ambiguous. Stop and resolve that error; do not silently fall back to generic Hugging Face training.

Hugging Face hosting never overrides ownership. Do not use this workflow to bypass a matched skill or ask the user to prescribe its internal preparation. For example, nvidia/Cosmos3-Nano routes to tao-finetune-cosmos-reason.

Do not create a host training venv in this workflow. Its default execution path is the NGC container documented below; any venv-based training path requires an explicit user request.

Order of authority (highest first):

1. User input — explicit model_id, dataset_id, training_method, config.yaml overrides.
2. Live research — model card, HF repo example, author finetune script, HF task docs, paper; always fetched (Step 3 + references/research-priorities.md).
3. Curated references (references/*.md) — fallback when live research is silent/ambiguous.
4. Your training-data memory — last resort; suspect, cross-check against (2)/(3).

Conflict resolution between (2) and (3) and the source-line discrepancy note are in references/research-priorities.md.

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Inputs

Required:

• model_id — HuggingFace model ID, e.g. google/vit-base-patch16-224

Conditional credentials (read from the session environment — exported before launching or sourced from a user-approved env file):

• HF_TOKEN — only when the model/dataset is gated (read) or push_to_hub is on (write); public + public + push_to_hub: false needs none. Value never read — presence-only via [ -n "$HF_TOKEN" ].
• WANDB_API_KEY, WANDB_PROJECT — only when WandB is enabled; WANDB_MODE=disabled opts out.

Dataset — exactly one:

• dataset_id — HuggingFace dataset ID (source: hf)
• local_dataset_path — local folder or file (source: local); optional local_dataset_format ∈ {auto, imagefolder, coco, voc, jsonl, arrow, parquet, csv} (default: auto-detect).
• (omit) — agent recommends popular datasets (source: recommend)

Optional (have defaults):

• task_type — auto-detected from config + model card
• n_train=10000, n_eval=1000, n_epochs=3, lora_r=16
• output_dir=./output/<model_short_name>
• hf_model_repo — push target; if unset and HF_TOKEN has write access, auto-derived as <whoami>/<model_short_name>-finetuned.
• push_to_hub=True — set to False to skip
• skip_baseline=False — skip zero-shot baseline eval

Optional deliverables (off by default):

emit_progress_log: false   # output_dir/PROGRESS.md (per-step journal)
emit_report:       false   # reports/report.{pdf,html} with curves & samples
emit_unit_tests:   false   # tests/ with fake-data heterogeneous-batch tests

All values live in output_dir/config.yaml. Never hardcode in Python.

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Execution platform

This skill orchestrates what to run; the platform skills own how to run it on a GPU host — read them first.

Default platform: local-docker — build a one-off image (run-<short>:latest) and run it on the local Docker daemon. Ask only when the user explicitly needs a different backend (Brev remote GPU, SLURM/Kubernetes); then run that platform's Preflight first and route the Steps 4–5 docker run commands through it. The GPU-runtime and presence-only credential preflights (values never read), the canonical docker run flag set, discovery of the execution platforms from the installed platform skills (tao-run-on-docker / -slurm / -kubernetes / -brev, plus any external one; on a runtime that surfaces only the core router skills, read skills/platform/tao-run-on-*/SKILL.md frontmatter), and the workflow-specific flags (--entrypoint /bin/bash -lc, PYTORCH_CUDA_ALLOC_CONF, --name hft_train) are in references/workflow-intake-preflight.md.

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References — fallback safety net

Consulted only when live research is silent, ambiguous, or unavailable; live docs always win for the specific model and current API. Each step links the references it needs; full catalog in references/detailed-workflow.md.

Always-on: core-rules.md, error-playbook.md, compat-workarounds.md, model-discovery.md, dataset-recommendations.md, dataset-sources.md, dataset-patterns.md, hardware-container.md, research-priorities.md, cv-scripts.md, vlm-scripts.md, docker-runs.md, hub-push.md, pipeline-skill-template.md, deliverables.md. Opt-in (when their flag/need applies): progress-tracking.md, testing.md, reporting.md, workflow-intake-preflight.md, workflow-generate-train.md, workflow-push-rerun.md.

Rule: before falling back, log the live source you tried and why it was insufficient (config.yaml notes:, and PROGRESS.md if enabled). [FETCH LIVE] markers in cv-scripts.md / vlm-scripts.md are a research checklist, not code to inline — refetch the listed URL if a block has no Step 3 finding.

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Core rules

Non-negotiable behaviors. Short version (full enumeration — hallucinated-imports list, never-without-approval list, full error-recovery and hardware-sizing tables — in references/core-rules.md, consult before any training-time decision):

• Your HF-library knowledge is outdated. Fetch live docs (model card, HF repo example, task doc) before writing any ML code — don't generate trainer args / collator / transforms from memory (Step 3).
• Smoke-test on real data with --max_steps 1 before any full run; no batch launches without a verified smoke.
• Never silently substitute model_id, dataset_id, or training_method — if what the user asked for doesn't load, stop and ask.
• Error recovery is minimal-change. OOM → halve batch, double grad_accum, enable gradient checkpointing (no LoRA switch without approval); NaN → reduce LR 10×; flat loss → inspect collator; same error 3× → stop and ask. Don't loop.
• Dataset columns verified BEFORE the collator — rename in prepare_data.py; restructuring needed → stop and ask.
• Hardware-sizing thumb (bf16): ≤3B → 24 GB, 7–13B → 80 GB, 30B+ → multi-GPU or LoRA on 1× 80 GB, 70B+ → 8× 80 GB or LoRA. Full finetune won't fit and no LoRA requested → ask before switching.

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Workflow — 6 steps

Single pass, sequential; each step has a clear gate before the next begins.

Step 1 — Inspect & qualify

Goal: decide whether to proceed. Probe model + dataset, apply accept/reject, register applicable compat fixes, write the initial config.yaml.

Prerequisites: MODEL_ID, optional DATASET_ID / local_dataset_path, optional HF_TOKEN, OUTPUT_DIR (default ./output/<model_short_name>). Probes run in a CPU-only python:3.12-slim Docker container (bind-mounted .probe/ scratch) so the host needs no virtualenv — Docker must exist first. Docker-presence guard, container env, full probe invocation, and the model/dataset probe scripts are in references/workflow-intake-preflight.md, references/model-discovery.md, and references/dataset-sources.md.

Probe requirements:

• Model: load AutoConfig, read model-card tags, detect task from architectures + tags + card examples (fallback logging in model-discovery.md).
• Dataset: for recommended datasets, first present 3-5 choices from dataset-recommendations.md; for local data, bind-mount read-only and use dataset-sources.md format detection.
• Reject early if the model config fails, the task is out of scope, no recipe source exists, or the dataset cannot load / match the task schema.
• Evaluate compat-workarounds.md against the model/task; defer hardware-dependent rules to Step 2.

Write the initial config.yaml (model_id, task, dataset_id or local_dataset_path, research_sources: [] filled in Step 3, applicable_workarounds: from Step 1, notes: [] for reference fallbacks, push_to_hub: true default — annotated template in references/workflow-intake-preflight.md). Optionally rm -rf "$OUTPUT_DIR/.probe" once the gate is met.

Gate: config.yaml exists with model, dataset, task, applicable_workarounds; do not proceed if any field is missing.

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Step 2 — Hardware audit & NGC image

Goal: verify Docker + GPU + disk, pick the NGC PyTorch image live, finalize hardware-dependent compat rules.

2a. Audit (hard gate) — three checks (commands in references/workflow-intake-preflight.md):

1. GPU host runtime — tao-setup-nvidia-gpu-host's setup-nvidia-gpu-host.sh --backend docker --check-only; on fail, ask approval then re-run with --install --yes.
2. Free-disk soft-warn — override via MIN_DISK_GB (default 100 GB); recommend ≥ 100 GB for NGC base (~20 GB) + HF cache + checkpoints + data.
3. Conditional credential presence (values never read) — HF_TOKEN only when gated or push_to_hub is on; WANDB_* only when WandB is on.

Do not proceed to Step 4 on a hard-fail — Step 4's docker build pulls a 20+ GB NGC base, and a missing nvidia-container-toolkit only surfaces later as could not select device driver "" with capabilities: [[gpu]]. Record gpu_count, gpu_name, driver_major, vram_gb_per_gpu in config.yaml.

2b. Pick NGC image (live): from the NVIDIA Deep Learning Frameworks support matrix (https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html), PyTorch NGC container section, pick the highest-versioned image where Min driver ≤ detected driver_major and container CUDA ≤ host CUDA Toolkit (match closely so cuDNN / TensorRT line up). Do not reject an image for an aN/bN/rcN PyTorch tag — NGC validates the full image; pick the newest CUDA-aligned one and let compat-workarounds.md handle per-version issues. If the matrix is unreachable, use the fallbacks in references/hardware-container.md; default nvcr.io/nvidia/pytorch:24.09-py3 (driver ≥ 545; SDPA+GQA bug — if num_key_value_heads < num_attention_heads, set attn_implementation: "eager"). Record ngc_image in config.yaml.

2c. Re-evaluate hardware-dependent compat rules: re-run the compat-workarounds.md walk for entries whose detect needs hw; update applicable_workarounds: in place.

2d. Model-fit check: estimate param_bytes ≈ 2×param_count (bf16); if

60% of vram_gb_per_gpu × 1e9, recommend LoRA in the user-facing summary.

Gate: config.yaml has ngc_image, gpu_count, gpu_name, driver_major, vram_gb_per_gpu; hardware-dependent compat fixes recorded.

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Step 3 — Research the recipe

Goal: fetch the live recipe — training-data knowledge of transformers/trl/peft is suspect, so Step 3 is non-negotiable. Walk references/research-priorities.md in priority order (Priority 1 → 6); stop once you have, for the detected task:

• AutoModel / processor class
• Train + eval transforms
• Collator
• compute_metrics
• Hyperparameter hints (LR, batch size, epochs, scheduler)

Record findings in meta/recipe.md, append source URLs to config.yaml: research_sources:. A slot with no live finding falls back to the matching scaffold (cv-scripts.md / vlm-scripts.md), logged as "fallback to scaffold — no live source for <slot>" under notes:. Conflict-resolution rules are in references/research-priorities.md.

Gate: every required slot filled, with a source URL or scaffold-fallback note.

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Step 4 — Generate project & smoke-test

Goal: write all scripts, build the image, prepare data, run a 1-step smoke on real data (one docker build, two docker runs).

4a. Generate project files in output_dir/: config.yaml, Dockerfile, requirements.txt, prepare_data.py, train.py, run_eval.py, infer.py, optional merge_lora.py, optional tests/, .gitignore. Live Step 3 research is authority; cv-scripts.md / vlm-scripts.md give scaffold shape only. Apply every applicable_workarounds entry as a Dockerfile block, requirement pin, config override, or runtime env var. Hard rules: run_eval.py keeps that exact filename (avoids colliding with the HF evaluate package); every generated .py starts with the NVIDIA Apache-2.0 copyright header and any emitter fails when it is missing; emit_unit_tests: true generates and runs tests per references/testing.md. Script bodies, Dockerfile shape, and the emitter contract are in references/workflow-generate-train.md.

4b. Build, prepare, smoke — docker build -t run-<short>:latest ., then prepare_data and the --smoke --max_steps 1 run (references/docker-runs.md §1-3). Smoke pass criteria (in logs/smoke.log):

• No exception
• Loss is finite (not 0.0, not NaN)
• grad_norm > 0 at step 1

If emit_unit_tests: true, also run pytest tests/ in the container. Any failure → STOP.

4c. Preflight summary — before full training, print and verify: reference URL, dataset columns, Hub target, monitoring target, NGC image, hardware, smoke loss/grad norm.

Gate: project files written, image built, smoke PASSED, preflight has no blank fields.

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Step 5 — Train, evaluate, infer

Goal: baseline eval, full training, post-train eval, optional LoRA merge, 5 inference samples (all commands: references/docker-runs.md §4-8).

Multi-GPU: prepend torchrun --nproc_per_node=$gpu_count to python train.py.

While training streams, watch docker logs -f hft_train: loss should drop within 10-20 steps; flat loss (collator/label-masking bug), NaN (LR too high), and OOM all stop the run — recovery in references/core-rules.md. If emit_report: true, run report.py after Step 5e per references/reporting.md.

Gate: all of:

• checkpoints/final/ (or checkpoints/merged/ for LoRA) exists
• reports/eval_results.json has a numeric primary metric
• reports/baseline_results.json exists (unless skipped)
• reports/inference_samples/ has 5 samples
• wandb URL shows descending loss

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Step 6 — Push & emit rerun skill

Goal: publish the run and make it reproducible without re-research.

Push per references/hub-push.md (weights, model card, eval/baseline JSONs, config.yaml, Dockerfile, requirements.txt, inference samples, reports when emitted) unless push_to_hub: false is explicit. Emit <output_dir>/skills/run-<short>/SKILL.md from references/pipeline-skill-template.md — substitute every placeholder, include full YAML metadata + the NVIDIA copyright HTML comment, and make any emitter fail if those are missing.

Gate (Done criteria): all of:

• Step 5 gate met
• HF Hub repo exists at the resolved URL with weights + card + results/ (unless push_to_hub: false)
• <output_dir>/skills/run-<short>/SKILL.md exists, no <placeholder> left, with metadata + copyright HTML comment per pipeline-skill-template.md

Final message: wandb URL, HF Hub URL, baseline -> fine-tuned primary metric, reports/inference_samples/, and the rerun skill path.

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Error playbook

On a known runtime error, consult the symptom → minimal-fix table in references/error-playbook.md (NGC entrypoint, PyTorch/Transformers regressions, numpy ABI, Albumentations bbox, PEFT/checkpointing, LoRA target breadth, CV augmentation gaps, OOM at step 0) before redesigning anything. When a row there fires twice across runs, lift it into compat-workarounds.md with a detect rule — auto-applied in Step 1 before the error can fire.

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Communication style

• Terse. No filler, no restating the request; one-word answers when appropriate.
• Always include direct Hub and wandb URLs when referencing artifacts.
• On error: state what went wrong, why, what you changed — no menus.
• Never present "Option A/B/C" for a request with a clear answer. Act.