README.md

---
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         |