metadata

language:
  - en
datasets:
  - garage-bAInd/Open-Platypus
library_name: transformers
pipeline_tag: text-generation
license: cc-by-nc-sa-4.0

SOLAR-Platypus-10.7B-v2

Model Details

Model Developers Kyujin Han (kyujinpy)

Input Models input text only.

Output Models generate text only.

Model Architecture
SOLAR-Platypus-10.7B-v2 is an auto-regressive language model based on the Llama2 architecture.

Base Model
upstage/SOLAR-10.7B-v1.0

Training Dataset
garage-bAInd/Open-Platypus.

Notice

While training, I used Q-LoRA.
The lora_r values is 64.

Q-LoRA config

LoRA_r: 64
LoRA_alpha: 16
LoRA_dropout: 0.05
LoRA_target_modules: [gate_proj, up_proj, down_proj, q_proj, k_proj, v_proj]

Prompt

## Human:

## Assistant:

Model Benchmark

Open leaderboard

Follow up as link.

Model	Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
SOLAR-Platypus-10.7B-v1	58.62	61.69	84.23	60.37	51.58	82.79	11.07
SOLAR-Platypus-10.7B-v2	55.25	59.39	83.57	59.93	43.15	81.45	4.02
upstage/SOLAR-10.7B-v1.0	66.04	61.95	84.60	65.48	45.04	83.66	55.50

Implementation Code

### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "kyujinpy/SOLAR-Platypus-10.7B-v2"
OpenOrca = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)