metadata

language:
  - en
tags:
  - llama
license: apache-2.0
metrics:
  - MMLU
  - ARC
  - HellaSwag
  - TruthfulQA
  - ReClor

🥳 Platypus30B has arrived!

Metric	Value
MMLU (5-shot)	64.2
ARC (25-shot)	76.7
HellaSwag (10-shot)	84.3
TruthfulQA (0-shot)	37.4
ReClor (0-shot)	70

Model Description

Platypus30B is an instruction fine-tuned LlaMa model.

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Usage

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Model Details

Trained by: [Ariel Lee & Cole Hunter, LINK TO WEBSITES]
Model type: Platypus30B 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.

Hyperparameter	Value
$n_\text{parameters}$	33B
$d_\text{model}$	6656
$n_\text{layers}$	60
$n_\text{heads}$	52

Training

Training Dataset

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

Training Procedure

lilloukas/Platypus30b was instruction fine-tuned using lora [CITE REPO] on 2 A100 80GB with the following configuration:

Hyperparameter	Value
learning_rate	---
batch_size	---
microbatch_size	---
warmup_steps	---
epochs	---
weight_decay	---
optimizer	---
weight_decay	---
cutoff_len	---
lora_target_modules	---

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

@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}
}