Making it easier for users to get started with CTranslate2 models
Nobody has claimed this yet.
- Dominant language
- C++
- Stars
- 4.7k
- Forks
- 536
- Avg merge
- 12h 12m
- Merged PRs (30d)
- 4
Description
Problem
Currently, users must manually convert models before they can get started. This extra step creates unnecessary friction and makes onboarding more difficult than it needs to be.
Tasks
- Create an organization in HuggingFace and add the CTranslate2 developers
- Create a pipeline that automatically uploads to HuggingFace all the models supported converted to CTranslate2
- Add huggingface_hub as an optional dependency to the Python bindings and provide a mechanism to load models from HuggingFace using a prefix like hf://
Example:
generator = ctranslate2.Generator("hf://ctranslate2/llama3-8b", device="cpu")
If somebody wants to get this task, it will be great
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the Python bindings and the Generator entry point, then review how supported models are converted and where dependency configuration and automation are defined. The work is done when supported models are uploaded through the HuggingFace pipeline, huggingface_hub remains optional, and the hf:// loading mechanism works for the shown Generator example.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp, huggingface, python
- Domain
- api, ci-cd, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Active
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100