hf-bf16-gguf-conversion-jobs

Development

Use when converting Hugging Face SafeTensors checkpoints into split BF16 GGUF model repos with skippy-quantize on Hugging Face Jobs or a local machine, then publishing the artifact to Hugging Face.

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HF BF16 GGUF Conversion Jobs

Use this skill when the source artifact is a Hugging Face checkpoint repo and the target artifact is a split BF16 GGUF model repo. The operational tool is skippy-quantize; do not use convert_hf_to_gguf.py, hf_to_gguf.py, or a wrapper that shells out to either script. Treat hf_to_gguff.py as the same forbidden path if it appears in old notes or logs.

Preconditions

Confirm the source checkpoint repo, revision, tokenizer files, target repo, output basename, expected split count, and desired split size before spending HF Jobs credits.
Build the standalone binary with just skippy-quantize-standalone-release-build for local runs or in the job image/script for HF Jobs.
Use --output-type bf16 unless the experiment explicitly records a different target precision.
Prefer a split output with --window-size 1 for first full-model runs. Raise the window only after a smaller fixture proves the memory and I/O budget.
Publish only complete windows, write per-window records, and resume from the first missing target shard after cancellation.

Local Workflow

Create a manifest:

target/release/skippy-quantize init-convert \
  --source /path/to/checkpoint \
  --target /path/to/output-repo \
  --target-prefix BF16 \
  --output-basename <model>-BF16 \
  --output-type bf16 \
  --expected-splits <N> \
  --window-size 1 \
  --manifest /tmp/skippy-convert.json

Dry-run the next conversion window before spending I/O:

target/release/skippy-quantize convert-job \
  --source /path/to/checkpoint \
  --target /path/to/output-repo \
  --target-prefix BF16 \
  --output-basename <model>-BF16 \
  --output-type bf16 \
  --expected-splits <N> \
  --window-size 1 \
  --manifest /tmp/skippy-convert.json \
  --max-memory 32G \
  --dry-run

Run until complete:

target/release/skippy-quantize run-convert \
  --manifest /tmp/skippy-convert.json \
  --max-memory 32G \
  --split-max-size 50G \
  --stream-buffer-bytes 8388608 \
  --spool-dir /tmp/skippy-convert-output \
  --record-dir /tmp/skippy-convert-records \
  --json-event-file /tmp/skippy-convert-status.json \
  --json-event-interval-seconds 120 \
  --json-event-window 8

Validate and publish:

target/release/skippy-quantize verify-job \
  --manifest /tmp/skippy-convert.json \
  --json

hf repo create <org>/<target-repo> --type model --private
hf upload <org>/<target-repo> /path/to/output-repo . --repo-type model

HF Jobs Workflow

Mount the source checkpoint and target model repo rather than downloading the whole checkpoint into the job filesystem:

hf jobs uv run \
  --namespace meshllm \
  --flavor cpu-upgrade \
  --timeout 3d \
  --secrets HF_TOKEN \
  --volume hf://models/<source-repo>:/mnt/checkpoint \
  --volume hf://models/<target-repo>:/mnt/target \
  --env SKIPPY_QUANTIZE_OUTPUT=json \
  --env PYTHONUNBUFFERED=1 \
  --detach \
  /path/to/skippy_convert_job.py \
  -- \
  --source /mnt/checkpoint \
  --target /mnt/target \
  --target-prefix BF16 \
  --output-basename <model>-BF16 \
  --expected-splits <N> \
  --split-max-size 50G \
  --max-memory 32G

The job script should only build or install skippy-quantize, create the manifest if missing, run run-convert, verify the job, and upload sidecars. It must not call the old Python converter.

Monitoring

Use both HF Jobs status and skippy-quantize status:

hf jobs inspect <job-id> --namespace meshllm
hf jobs logs <job-id> --namespace meshllm --tail 120
target/release/skippy-quantize status --manifest /tmp/skippy-convert.json --json

For agents, prefer polling /tmp/skippy-convert-status.json over ingesting full logs. Healthy snapshots show phase movement through running, publishing, and complete, with only the last few high-level events retained. Stop and diagnose if the same window restarts without a new published shard or memory stays pinned near the hardware limit.

Record Keeping

Record the job id, exact command, source revision, target repo commit, split count, split size, memory budget, tokenizer notes, and follow-ups in the experiment card or phase iteration card before promoting the artifact.