| | --- |
| | language: |
| | - en |
| | license: mit |
| | --- |
| | |
| | Nape-0 |
| |
|
| | Nape series are small models that tries to exihibit much capabilities. |
| | The model is still in training process. This is very early preview. |
| |
|
| | You can load it as follows: |
| |
|
| | ``` |
| | from transformers import LlamaForCausalLM, AutoTokenizer |
| | tokenizer = AutoTokenizer.from_pretrained("nnpy/Nape-0") |
| | model = LlamaForCausalLM.from_pretrained("nnpy/Nape-0") |
| | ``` |
| |
|
| | ## Training |
| | It took 1 days to train 3 epochs on 4x A6000s using native deepspeed. |
| |
|
| | ``` |
| | assistant role: You are Semica, a helpful AI assistant. |
| | user: {prompt} |
| | assistant: |
| | |
| | ``` |
| | # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
| | Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_nnpy__Nape-0) |
| |
|
| | | Metric | Value | |
| | |-----------------------|---------------------------| |
| | | Avg. | 30.93 | |
| | | ARC (25-shot) | 32.68 | |
| | | HellaSwag (10-shot) | 58.68 | |
| | | MMLU (5-shot) | 24.88 | |
| | | TruthfulQA (0-shot) | 38.99 | |
| | | Winogrande (5-shot) | 57.3 | |
| | | GSM8K (5-shot) | 0.08 | |
| | | DROP (3-shot) | 3.89 | |
| |
|
