| SUBMIT_TEXT = f""" | |
| # 🏎️ Submit | |
| Models added here will be queued for evaluation on the Intel Developer Cloud ☁️. Depending on the queue, your model may take up to 10 days to show up on the leaderboard. | |
| We will work to create greater transperancy as our leaderboard community grows. | |
| <hr> | |
| ## Review these steps before submitting your model | |
| ### 1) Make sure you can load your model and tokenizer using AutoClasses: | |
| ```python | |
| from transformers import AutoConfig, AutoModel, AutoTokenizer | |
| config = AutoConfig.from_pretrained("your model name", revision=revision) | |
| model = AutoModel.from_pretrained("your model name", revision=revision) | |
| tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision) | |
| ``` | |
| If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been improperly uploaded. | |
| Note: Make sure your model is public! | |
| Note: If your model needs `use_remote_code=True`, we do not support this option yet, but we are working on adding it. | |
| ### 2) Consider converting your model weights to [safetensors](https://huggingface.co./docs/safetensors/index) | |
| It's a new format for storing weights which is safer and faster to load and use. It will also allow us to add the number of parameters of your model to the `Extended Viewer`. | |
| ### 3) Make sure your model has an open license. | |
| This is a leaderboard for Open LLMs, and we'd love for as many people as possible to know they can use your model 🤗 A good example of an open source license is apache-2.0. | |
| Typically model licenses that are allow for commercial and research use tend to be the most attractive to other developers in the ecosystem. | |
| ### 4) Fill up your model card | |
| We use your model card to better understand the properties of your model and make them more easily discoverable for other users. | |
| Model cards are required to have mentions of the hardware, software, and infrastructure used for training - without this information | |
| we cannot accept your model as a valid submission. Remember, only models trained on these processors are eligle to participate in evaluation: | |
| Intel® Gaudi Accelerators, Intel® Xeon® Processors, Intel® Data Center GPU Max Series, Intel® ARC GPUs, and Intel® Core Ultra, | |
| ### 5) Select the correct precision | |
| Not all models are converted properly from `float16` to `bfloat16`, and selecting the wrong precision can sometimes cause evaluation error (as loading a `bf16` model in `fp16` can sometimes generate NaNs, depending on the weight range). | |
| ## In case of model failure | |
| If your model fails evaluation 😔, we will contact you by opening a new discussion in your model respository. Let's work together to get your model the love it deserves ❤️! | |
| <hr> | |
| """ |