metadata
language:
- en
datasets:
- garage-bAInd/Open-Platypus
library_name: transformers
pipeline_tag: text-generation
license: cc-by-nc-sa-4.0
PlatYi-34B-Llama-Q-v3
Model Details
Model Developers Kyujin Han (kyujinpy)
Input Models input text only.
Output Models generate text only.
Model Architecture
PlatYi-34B-Llama-Q-v3 is an auto-regressive language model based on the Yi-34B transformer architecture.
Blog Link
Blog: [Coming soon...]
Github: [Coming soon...]
Base Model
chargoddard/Yi-34B-Llama
Training Dataset
garage-bAInd/Open-Platypus.
Fix some bugs
- Before model, there is some mistakes.
- I modified the templates and warmup_steps.
Notice
While training, I used Q-LoRA. The lora_r values is 64.
Model Benchmark
Open leaderboard
- Follow up as link.
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
|---|---|---|---|---|---|---|---|
| PlatYi-34B-Llama-Q-v3 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| PlatYi-34B-Llama-Q-v2 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| PlatYi-34B-Llama-Q | 71.13 | 65.70 | 85.22 | 78.78 | 53.64 | 83.03 | 60.42 |
| PlatYi-34B-Llama | 68.37 | 67.83 | 85.35 | 78.26 | 53.46 | 82.87 | 42.46 |
| Yi-34B-Llama | 70.95 | 64.59 | 85.63 | 76.31 | 55.60 | 82.79 | 60.80 |
| Yi-34B | 69.42 | 64.59 | 85.69 | 76.35 | 56.23 | 83.03 | 50.64 |
Implementation Code
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "kyujinpy/PlatYi-34B-Llama-Q-v3"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)