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Model: nnpy/Nape-0
Source: Original Platform

README.md

@@ -0,0 +1,41 @@
+---
+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         |