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SUBMIT_TEXT = f""" |
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# 🏎️ Submit |
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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. |
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We will work to create greater transperancy as our leaderboard community grows! |
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## First steps before submitting a model |
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### 1) Make sure you can load your model and tokenizer using AutoClasses: |
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```python |
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from transformers import AutoConfig, AutoModel, AutoTokenizer |
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config = AutoConfig.from_pretrained("your model name", revision=revision) |
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model = AutoModel.from_pretrained("your model name", revision=revision) |
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tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision) |
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``` |
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If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been improperly uploaded. |
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Note: make sure your model is public! |
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Note: if your model needs `use_remote_code=True`, we do not support this option yet but we are working on adding it, stay posted! |
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### 2) Convert your model weights to [safetensors](https://huggingface.co/docs/safetensors/index) |
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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`! |
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### 3) Make sure your model has an open license! |
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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. |
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Typically model licenses that are allow for commercial and research use tend to be the most attractive to other developers in the ecosystem! |
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### 4) Fill up your model card |
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We use your model card to better understand the properties of your model and make them more easily discoverable for other users. |
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Model cards are required to have mentions of the hardware, software, and infrastructure used for training - without this information |
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we cannot accept your model as a valid submission. Remember, only models trained on these processors are eligle to participate in evaluation: |
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Intel® Gaudi Accelerators, Intel® Xeon® Processors, Intel® Data Center GPU Max Series, Intel® ARC GPUs, and Intel® Core Ultra |
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### 5) Select the correct precision |
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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). |
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## In case of model failure |
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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 ❤️! |
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""" |