agentlans commited on
Commit
09fe4b6
·
verified ·
1 Parent(s): 23bfc4c

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +5 -3
README.md CHANGED
@@ -18,6 +18,8 @@ tags:
18
  - rslora
19
  - liger
20
  - sociology
 
 
21
  ---
22
  # Qwen2.5-1.5B-Instruct-Multiple-Choice-Maker
23
 
@@ -30,11 +32,11 @@ Special thanks to [mradermacher](https://huggingface.co/mradermacher) for quanti
30
 
31
  ### Training Details
32
  - **Data Source**:
33
- - The training dataset was generated from open-source sociology textbooks using a custom prompt powered by the [agentlans/Llama3.1-LexiHermes-SuperStorm](https://huggingface.co/agentlans/Llama3.1-LexiHermes-SuperStorm) model. The dataset contains 3,739 rows. Due to licensing restrictions, the dataset is not provided here.
34
- - Additional finetuning on [agentlans/finewebedu-multiple-choice](https://huggingface.co/datasets/agentlans/finewebedu-multiple-choice) dataset
35
  - **Training Method**:
36
  - Fine-tuning was conducted using [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) with rank 16 LoRA, alpha = 32, and rslora, leveraging the Liger kernel.
37
- - The additional finetuning was done with the same settings but with 0.2 LoRA dropout
38
 
39
  ### Potential Applications
40
  - **Education**: Automate the creation of multiple-choice questions for exams or quizzes.
 
18
  - rslora
19
  - liger
20
  - sociology
21
+ datasets:
22
+ - agentlans/finewebedu-multiple-choice
23
  ---
24
  # Qwen2.5-1.5B-Instruct-Multiple-Choice-Maker
25
 
 
32
 
33
  ### Training Details
34
  - **Data Source**:
35
+ - The training dataset was generated from open-source sociology textbooks using a custom prompt powered by the [agentlans/Llama3.1-LexiHermes-SuperStorm](https://huggingface.co/agentlans/Llama3.1-LexiHermes-SuperStorm) model. The dataset contains 3739 rows. Due to licensing restrictions, the dataset is not provided here.
36
+ - Additional finetuning on [agentlans/finewebedu-multiple-choice](https://huggingface.co/datasets/agentlans/finewebedu-multiple-choice) dataset.
37
  - **Training Method**:
38
  - Fine-tuning was conducted using [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) with rank 16 LoRA, alpha = 32, and rslora, leveraging the Liger kernel.
39
+ - The additional finetuning was done with the same settings but with 0.2 LoRA dropout.
40
 
41
  ### Potential Applications
42
  - **Education**: Automate the creation of multiple-choice questions for exams or quizzes.