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0b97a47048
Adds one transformers + PEFT + TRL ClusterTrainingRuntime per method that
fine-tunes ANY HuggingFace causal LM. The base model + dataset come from the
TrainJob's model/dataset initializers (hf:// or s3://, secretRef for private/gated
repos); every hyperparameter is read from the trainer container env that the Hanzo
Cloud broker sets. Adapters are merged into the base on completion, so one serving
path (KServe HuggingFace runtime) works for lora/qlora/full alike; output is
written to OUTPUT_DIR (the org's S3 Space when s3://).
- manifests/base/runtimes/hanzo_finetune.yaml 3 ClusterTrainingRuntimes (+kustomization)
- cmd/trainers/hanzo/finetune.py env-driven entrypoint (LoRA/QLoRA/full)
- cmd/trainers/hanzo/finetune_test.py pytest for the pure config/format helpers
- cmd/trainers/hanzo/{Dockerfile,requirements.txt,README.md}
- examples/yaml/hanzo-finetune-qlora.yaml example TrainJob (the broker's output)
kustomize build clean · ruff clean · 14 pytest pass. Image built in CI
(ghcr.io/hanzoai/finetune-runtime), not locally.
Co-authored-by: Hanzo Dev <[email protected]>
17 lines
569 B
Docker
17 lines
569 B
Docker
FROM pytorch/pytorch:2.9.1-cuda12.8-cudnn9-runtime
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WORKDIR /app
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# build-essential + git for bitsandbytes / source installs.
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RUN apt-get update && apt-get install -y --no-install-recommends build-essential git \
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&& rm -rf /var/lib/apt/lists/*
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# Install the trainer's Python dependencies first (layer-cached).
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COPY cmd/trainers/hanzo/requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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# The env-driven fine-tuning entrypoint (LoRA / QLoRA / full).
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COPY cmd/trainers/hanzo/finetune.py /app/finetune.py
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ENTRYPOINT ["python", "/app/finetune.py"]
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