language, license, datasets, model-index
| language |
license |
datasets |
model-index |
|
|
apache-2.0 |
|
| name |
results |
| llmdo-Mistral-7B-case-c-v1 |
| task |
dataset |
metrics |
source |
| type |
name |
| text-generation |
Text Generation |
|
| name |
type |
config |
split |
args |
| AI2 Reasoning Challenge (25-Shot) |
ai2_arc |
ARC-Challenge |
test |
|
|
| type |
value |
name |
| acc_norm |
62.03 |
normalized accuracy |
|
|
|
|
| task |
dataset |
metrics |
source |
| type |
name |
| text-generation |
Text Generation |
|
| name |
type |
split |
args |
| HellaSwag (10-Shot) |
hellaswag |
validation |
|
|
| type |
value |
name |
| acc_norm |
83.55 |
normalized accuracy |
|
|
|
|
| task |
dataset |
metrics |
source |
| type |
name |
| text-generation |
Text Generation |
|
| name |
type |
config |
split |
args |
| MMLU (5-Shot) |
cais/mmlu |
all |
test |
|
|
| type |
value |
name |
| acc |
62.69 |
accuracy |
|
|
|
|
| task |
dataset |
metrics |
source |
| type |
name |
| text-generation |
Text Generation |
|
| name |
type |
config |
split |
args |
| TruthfulQA (0-shot) |
truthful_qa |
multiple_choice |
validation |
|
|
|
|
|
| task |
dataset |
metrics |
source |
| type |
name |
| text-generation |
Text Generation |
|
| name |
type |
config |
split |
args |
| Winogrande (5-shot) |
winogrande |
winogrande_xl |
validation |
|
|
| type |
value |
name |
| acc |
79.08 |
accuracy |
|
|
|
|
| task |
dataset |
metrics |
source |
| type |
name |
| text-generation |
Text Generation |
|
| name |
type |
config |
split |
args |
| GSM8k (5-shot) |
gsm8k |
main |
test |
|
|
| type |
value |
name |
| acc |
39.8 |
accuracy |
|
|
|
|
|
|
|
Model Details
- Model Description: This model is test for data ordering.
- Developed by: Juhwan Lee
- Model Type: Large Language Model
Model Architecture
This model is based on Mistral-7B-v0.1. We fine-tuning this model for data ordering task.
Mistral-7B-v0.1 is a transformer model, with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
Dataset
We random sample Open-Orca dataset. (We finetune the 100,000 dataset)
Guthub
https://github.com/trailerAI
License
Apache License 2.0
Detailed results can be found here
| Metric |
Value |
| Avg. |
62.16 |
| AI2 Reasoning Challenge (25-Shot) |
62.03 |
| HellaSwag (10-Shot) |
83.55 |
| MMLU (5-Shot) |
62.69 |
| TruthfulQA (0-shot) |
45.82 |
| Winogrande (5-shot) |
79.08 |
| GSM8k (5-shot) |
39.80 |