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PlatYi-34B-Llama-Q

Model Details

Model Developers Kyujin Han (kyujinpy)

Input Models input text only.

Output Models generate text only.

Model Architecture
PlatYi-34B-Llama-Q 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.

Notice

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

Model Benchmark

Open leaderboard

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Model Average ARC HellaSwag MMLU TruthfulQA Winogrande GSM8K
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"
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. 71.13
AI2 Reasoning Challenge (25-Shot) 65.70
HellaSwag (10-Shot) 85.22
MMLU (5-Shot) 78.78
TruthfulQA (0-shot) 53.64
Winogrande (5-shot) 83.03
GSM8k (5-shot) 60.42
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Safetensors
Model size
34.4B params
Tensor type
FP16
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Dataset used to train kyujinpy/PlatYi-34B-Llama-Q

Evaluation results