FusionNet_34Bx2_MoE
Fine-tuned model on English language using MoE method.
Model description
The FusionNet_34Bx2_MoE is a model to experiment with the MoE method, which could significantly increase the performance of the original model. The FusionNet_34Bx2_MoE has 60.8B parameters, and this model is fine-tuned. Enjoy!
Usage
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("TomGrc/FusionNet_34Bx2_MoE")
model = AutoModelForCausalLM.from_pretrained("TomGrc/FusionNet_34Bx2_MoE")
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 77.07 |
| AI2 Reasoning Challenge (25-Shot) | 72.95 |
| HellaSwag (10-Shot) | 86.22 |
| MMLU (5-Shot) | 77.05 |
| TruthfulQA (0-shot) | 71.31 |
| Winogrande (5-shot) | 83.98 |
| GSM8k (5-shot) | 70.89 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard72.950
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard86.220
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard77.050
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard71.310
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard83.980
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard70.890