PlatYi-34B-Llama-Q-v2
![](/kyujinpy/PlatYi-34B-Llama-Q-v2/resolve/main/PlatYi.png)
Model Details
Model Developers Kyujin Han (kyujinpy)
Input Models input text only.
Output Models generate text only.
Model Architecture
PlatYi-34B-Llama-Q-v2 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-v2 | 67.88 | 61.09 | 85.09 | 76.59 | 52.65 | 82.79 | 49.05 |
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-v2"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 67.88 |
AI2 Reasoning Challenge (25-Shot) | 61.09 |
HellaSwag (10-Shot) | 85.09 |
MMLU (5-Shot) | 76.59 |
TruthfulQA (0-shot) | 52.65 |
Winogrande (5-shot) | 82.79 |
GSM8k (5-shot) | 49.05 |
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Dataset used to train kyujinpy/PlatYi-34B-Llama-Q-v2
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard61.090
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard85.090
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard76.590
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard52.650
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard82.790
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard49.050