Document or link to existing best practices
Nobody has claimed this yet.
Assessment
- Difficulty
- 2/5
- Estimated time
- 1-2 days
- Newbie friendliness
- 68/100
- Issue type
- Documentation
- Clarity
- Mostly clear
- Activity status
- Quiet
- Tech stack
- pytorch
- Domain
- documentation, machine-learning
Research direction
Start by reviewing the linked Pixelverse issues, PyTorch export-format resource, and geoembeddings best-practices page, then check the project’s existing documentation structure. Create a page covering implementation-focused best practices for model weights, embeddings, visualization, and other pipeline decisions without duplicating geoembeddings.org; the listed resources should be linked and organized clearly.
Written by the indexing model from the issue text.
Description
To make our job easier, we should probably make a page that lists useful external resources on best practices for storing foundation model weights and embeddings. E.g.:
Foundation Models:
- Store as .pt2 or .safetensors?
Embeddings:
Visualization:
Anything else in the end-to-end pipeline to document? I can start a skeleton page with some of these as a start, and we can gradually fill them in. Key thing is to not duplicate or overlap with the scope of https://geoembeddings.org, so maybe focus on more technical/implementation details/decisions we're making.
- Dominant language
- Jupyter Notebook
- Stars
- 32
- Forks
- 0
- PR merge metrics
- No merged PRs in 30d
Contributor guide
No contributing guide indexed for this repository
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.
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