limit: None, provide_description: False, num_fewshot: 5, batch_size: None
|
Task
|
Version
|
Metric
|
Value
|
|
Stderr
|
|
hendrycksTest-college_chemistry
|
1
|
acc
|
0.4600
|
±
|
0.0501
|
|
|
|
acc_norm
|
0.4600
|
±
|
0.0501
|
|
hendrycksTest-high_school_chemistry
|
1
|
acc
|
0.5222
|
±
|
0.0351
|
|
|
|
acc_norm
|
0.5222
|
±
|
0.0351
|
|
hendrycksTest-college_biology
|
1
|
acc
|
0.7222
|
±
|
0.0375
|
|
|
|
acc_norm
|
0.7222
|
±
|
0.0375
|
|
hendrycksTest-high_school_biology
|
1
|
acc
|
0.7355
|
±
|
0.0251
|
|
|
|
acc_norm
|
0.7355
|
±
|
0.0251
|
|
winogrande
|
0
|
acc
|
0.7758
|
±
|
0.0117
|
This model was trained from base Mistral-7B-Instruct-v0.2 on 710 examples, 200 of which comes from camel-ai/biology set. The rest were scraped personally and consists of very long scientific articles and text books.
It beats Mistral-7B-Instruct-v0.2 in MMLU chemistry and biology. It should be able to generate mostly factual, basic and lengthy scientific text. I guess it could be "we have cosmopedia at home" for people who want to create cheap pretraining datasets from scratch.
Template:
[Context]
You are a helpful assistant. Read the instruction and write a response accordingly.
[User]
{prompt}
[Assistant]