---
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