README.md

---
language:
- en
license: other
tags:
- llama
metrics:
- MMLU
- ARC
- HellaSwag
- TruthfulQA
model-index:
- name: Platypus-30B
 results:
 - task:
 type: text-generation
 name: Text Generation
 dataset:
 name: AI2 Reasoning Challenge (25-Shot)
 type: ai2_arc
 config: ARC-Challenge
 split: test
 args:
 num_few_shot: 25
 metrics:
 - type: acc_norm
 value: 64.59
 name: normalized accuracy
 source:
 url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lilloukas/Platypus-30B
 name: Open LLM Leaderboard
 - task:
 type: text-generation
 name: Text Generation
 dataset:
 name: HellaSwag (10-Shot)
 type: hellaswag
 split: validation
 args:
 num_few_shot: 10
 metrics:
 - type: acc_norm
 value: 84.24
 name: normalized accuracy
 source:
 url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lilloukas/Platypus-30B
 name: Open LLM Leaderboard
 - task:
 type: text-generation
 name: Text Generation
 dataset:
 name: MMLU (5-Shot)
 type: cais/mmlu
 config: all
 split: test
 args:
 num_few_shot: 5
 metrics:
 - type: acc
 value: 64.19
 name: accuracy
 source:
 url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lilloukas/Platypus-30B
 name: Open LLM Leaderboard
 - task:
 type: text-generation
 name: Text Generation
 dataset:
 name: TruthfulQA (0-shot)
 type: truthful_qa
 config: multiple_choice
 split: validation
 args:
 num_few_shot: 0
 metrics:
 - type: mc2
 value: 45.35
 source:
 url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lilloukas/Platypus-30B
 name: Open LLM Leaderboard
 - task:
 type: text-generation
 name: Text Generation
 dataset:
 name: Winogrande (5-shot)
 type: winogrande
 config: winogrande_xl
 split: validation
 args:
 num_few_shot: 5
 metrics:
 - type: acc
 value: 81.37
 name: accuracy
 source:
 url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lilloukas/Platypus-30B
 name: Open LLM Leaderboard
 - task:
 type: text-generation
 name: Text Generation
 dataset:
 name: GSM8k (5-shot)
 type: gsm8k
 config: main
 split: test
 args:
 num_few_shot: 5
 metrics:
 - type: acc
 value: 14.4
 name: accuracy
 source:
 url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lilloukas/Platypus-30B
 name: Open LLM Leaderboard
---

# 🥳 Platypus-30B has arrived! 

Platypus-30B is an instruction fine-tuned model based on the LLaMA-30B transformer architecture.

## Model Details

* **Trained by**: Cole Hunter & Ariel Lee
* **Model type:** **Platypus-30B** is an auto-regressive language model based on the LLaMA transformer architecture.
* **Language(s)**: English
* **License for base weights**: License for the base LLaMA model's weights is Meta's [non-commercial bespoke license](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md).

| Hyperparameter | Value |
|---------------------------|-------|
| \\(n_\text{parameters}\\) | 33B |
| \\(d_\text{model}\\) | 6656 |
| \\(n_\text{layers}\\) | 60 |
| \\(n_\text{heads}\\) | 52 |

## Training Dataset

Dataset of highly filtered and curated question and answer pairs. Release TBD.

## Training Procedure

`garage-bAInd/Platypus-30B` was instruction fine-tuned using LoRA on 4 A100 80GB. For training details and inference instructions please see the [Platypus-30B](https://github.com/arielnlee/Platypus-30B.git) GitHub repo.

## Reproducing Evaluation Results
Install LM Evaluation Harness:
```
git clone https://github.com/EleutherAI/lm-evaluation-harness
cd lm-evaluation-harness
pip install -e .
```
Each task was evaluated on a single A100 80GB GPU.

ARC:
```
python main.py --model hf-causal-experimental --model_args pretrained=garage-bAIdnd/Platypus-30B --tasks arc_challenge --batch_size 1 --no_cache --write_out --output_path results/Platypus-30B/arc_challenge_25shot.json --device cuda --num_fewshot 25
```

HellaSwag:
```
python main.py --model hf-causal-experimental --model_args pretrained=garage-bAIdnd/Platypus-30B --tasks hellaswag --batch_size 1 --no_cache --write_out --output_path results/Platypus-30B/hellaswag_10shot.json --device cuda --num_fewshot 10
```

MMLU:
```
python main.py --model hf-causal-experimental --model_args pretrained=garage-bAIdnd/Platypus-30B --tasks hendrycksTest-* --batch_size 1 --no_cache --write_out --output_path results/Platypus-30B/mmlu_5shot.json --device cuda --num_fewshot 5
```

TruthfulQA:
```
python main.py --model hf-causal-experimental --model_args pretrained=garage-bAIdnd/Platypus-30B --tasks truthfulqa_mc --batch_size 1 --no_cache --write_out --output_path results/Platypus-30B/truthfulqa_0shot.json --device cuda
```
## Limitations and bias

The base LLaMA model is trained on various data, some of which may contain offensive, harmful, and biased content that can lead to toxic behavior. See Section 5.1 of the LLaMA paper. We have not performed any studies to determine how fine-tuning on the aforementioned datasets affect the model's behavior and toxicity. Do not treat chat responses from this model as a substitute for human judgment or as a source of truth. Please use responsibly.

## Citations

```bibtex
@article{touvron2023llama,
 title={LLaMA: Open and Efficient Foundation Language Models},
 author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
 journal={arXiv preprint arXiv:2302.13971},
 year={2023}
}

@article{hu2021lora,
 title={LoRA: Low-Rank Adaptation of Large Language Models},
 author={Hu, Edward J. and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Chen, Weizhu},
 journal={CoRR},
 year={2021}
}
```
# [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_lilloukas__Platypus-30B)

| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 57.12 |
| ARC (25-shot) | 64.59 |
| HellaSwag (10-shot) | 84.24 |
| MMLU (5-shot) | 64.19 |
| TruthfulQA (0-shot) | 45.35 |
| Winogrande (5-shot) | 81.37 |
| GSM8K (5-shot) | 14.4 |
| DROP (3-shot) | 45.65 |

# [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_lilloukas__Platypus-30B)

| Metric |Value|
|---------------------------------|----:|
|Avg. |59.03|
|AI2 Reasoning Challenge (25-Shot)|64.59|
|HellaSwag (10-Shot) |84.24|
|MMLU (5-Shot) |64.19|
|TruthfulQA (0-shot) |45.35|
|Winogrande (5-shot) |81.37|
|GSM8k (5-shot) |14.40|