cog-hf-template 0.0.13

Cog template for Hugging Face.

cog-huggingface-template

Tools to help deploy Hugging Face models to replicate.

Requirements

• GPU environment
• Cog

Installation

PyPI Install

pip install cog_hf_template

Dev Install

Clone the repo:

git clone https://github.com/replicate/cog-huggingface-template
cd cog-huggingface-template
pip install -e .

Run tests

pip install pytest

pytest -vv tests

Usage

NOTE (Oct 19) - examples are out of date. Will update soon.

For now, see the examples directory for a working example.

Mirroring Weights from HF to Replicate Storage

Here's a quick tip on how to mirror weights from Hugging Face to GCP

You can use a minimal install of this package to avoid the extra dependencies...

# required deps for mirroring weights/CLI
pip install --no-cache huggingface-hub==0.18.0 google-cloud-storage==2.12.0 hf-transfer==0.1.3 fire==0.5.0

# Minimal version of the package
pip install --no-deps --no-cache --upgrade cog-hf-template

You will also need pget installed

curl -o /usr/local/bin/pget -L "https://github.com/replicate/pget/releases/download/v0.0.2/pget" && chmod +x /usr/local/bin/pget

Then you can run the following command to mirror the weights:

cog-hf-template mirror-weights \
    openai/whisper-large-v3 \
    your-bucket \
    --gcp-project-name your-project \
    --config_save_path whisper_large_v3_config.json

See cog-hf-template mirror-weights --help for more details.

In the example above, we mirror openai/whisper-large-v3 to the bucket your-bucket in the project your-project. We also save the model config to whisper_large_v3_config.json so that we can use it later to load the model (as you'll need to know the files to download).

The config looks like this:

{
  "hf_model_id": "openai/whisper-large-v3",
  "task": "automatic-speech-recognition",
  "gcp_bucket_weights": "gs://your-bucket/openai/whisper-large-v3/1940b900b8d4d8fdf72a31be85a0d223b9488e00",
  "trust_remote_code": true,
  "remote_filenames": [
    ".gitattributes",
    "README.md",
    "added_tokens.json",
    "config.json",
    "flax_model.msgpack",
    "generation_config.json",
    "merges.txt",
    "model.fp32-00001-of-00002.safetensors",
    "model.fp32-00002-of-00002.safetensors",
    "model.safetensors",
    "model.safetensors.index.fp32.json",
    "preprocessor_config.json",
    "pytorch_model.bin",
    "pytorch_model.bin.index.fp32.json",
    "pytorch_model.fp32-00001-of-00002.bin",
    "pytorch_model.fp32-00002-of-00002.bin",
    "special_tokens_map.json",
    "tokenizer_config.json",
    "vocab.json"
  ]
}

Note the files are accessible through the delivery URLs, and will the gs://your-bucket prefix will be replaced by the appropriate prefix before being downloaded.

import json
from pathlib import Path

from cog_hf_template.download_utils import maybe_pget_weights

config = json.loads(Path("whisper_large_v3_config.json").read_text())
maybe_pget_weights(
    path="whisper_large_v3",
    remote_path=config["gcp_bucket_weights"],
    remote_filenames=config["remote_filenames"],
)