PlatYi-34B-200k-Q-FastChat

Model Details

Model Developers Kyujin Han (kyujinpy)

Input Models input text only.

Output Models generate text only.

Model Architecture
PlatYi-34B-200k-Q-FastChat is an auto-regressive language model based on the Yi-34B transformer architecture.

Blog Link
Blog: [Coming soon...]
Github: [Coming soon...]

Base Model
01-ai/Yi-34B-200K

Training Dataset
garage-bAInd/Open-Platypus.

Notice
While training, I used QLoRA.
lora_r values is 64.

Apply prompting
References by FastChat.

Model Benchmark

Open leaderboard

• Follow up as link.

Model	Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
PlatYi-34B-200k-Q-FastChat	67.85	64.93	84.46	77.13	48.38	80.74	51.48
PlatYi-34B-Llama-Q-FastChat	68.31	66.31	85.25	78.37	53.62	82.16	44.35
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-200k-Q-FastChat"
OpenOrca = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)

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Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	67.85
AI2 Reasoning Challenge (25-Shot)	64.93
HellaSwag (10-Shot)	84.46
MMLU (5-Shot)	77.13
TruthfulQA (0-shot)	48.38
Winogrande (5-shot)	80.74
GSM8k (5-shot)	51.48