metadata
title: README
emoji: 🏆
colorFrom: indigo
colorTo: green
sdk: static
pinned: true
How to
Please stick to the following guidelines when uploading resources.
How to upload models
- Create a model repo using the following naming schema:
<use-case>-<pre-trained-model-name>-<training-type>-<version>- E.g., du-qwen3-4b-sft-v0
- Keep the
<pre-trained-model-name>as simple as possible (e.g., Apertus-8B-Instruct-2509 can become Apertus-8b) - Mention the complete pre-trained-model-name in the model card
- Important: If this model will be deployed and used in production please append the "latest" keyword to the name and remove it from the repo that is currently marked as "latest"
- Upload the files to the created repo
- Write a model card with at least the following information
- Use Case (e.g., DU, NLG, ...)
- Pre-Trained Model Name
- Training Type (e.g., SFT, DPO, ...)
- Used Hyperparameters
- Version
- Used dataset(s)
- Where was it trained (e.g., Fireworks AI, Google Colab, ...)
- If trained on Colab please add the Link to the Notebook (both to Colab and GitLab)
- Add the repo to an existing collection, or create a new one if none applies
How to upload datasets
- Create a dataset repo using the following naming schema:
<use-case>-<training-type>-<version> - Upload the files to the created repo
- Write a dataset card with at least the following information
- Summary
- Supported tasks
- Dataset structure
- Dataset creation
- Example for a dataset card: https://huggingface.co/datasets/novomind/nlg-sft-v1