---
datasets:
- heegyu/wizard_vicuna_70k_v2
license: apache-2.0
---

Hyperparameters
- 3/8 epoch(3rd epoch checkpoing while 8epoch training)
- 1e-4 -> 1e-5 with cosine lr decay 
- batch size 128
- max sequence length 2048
- AdamW(weigth decay=0.01, b1=0.9, b2=0.99, grad_clip=1.0)
- no warmup
- BF16
- Base Model: [openlm-research/open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2)

```
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("heegyu/WizardVicuna-open-llama-3b-v2")
model = AutoModelForCausalLM.from_pretrained("heegyu/WizardVicuna-open-llama-3b-v2")

inputs = tokenizer(["Human: Hi, nice to meet you!\n\nAssistant: "], return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=16)
print(tokenizer.batch_decode(outputs, skip_special_tokens=False))
```

output: `['Human: Hi, nice to meet you!\n\nAssistant: Hello. Great to meet you too. Well, how can I assist you today?<|endoftext|>']`
# [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_heegyu__WizardVicuna-open-llama-3b-v2)

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | 34.11   |
| ARC (25-shot)         | 37.71          |
| HellaSwag (10-shot)   | 66.6    |
| MMLU (5-shot)         | 27.23         |
| TruthfulQA (0-shot)   | 36.8   |
| Winogrande (5-shot)   | 63.3   |
| GSM8K (5-shot)        | 0.99        |
| DROP (3-shot)         | 6.12         |