metadata
license: llama2
datasets:
- databricks/databricks-dolly-15k
language:
- en
pipeline_tag: text-generation
Instruct_Llama70B_Dolly15k
Fine-tuned from Llama-2-70B，used Dolly15k for the dataset. 80% for training, 15% validation, 5% test. Trained for 1.5 epochs using QLora. Trained with 1024 context window.
Model Details
- Trained by: trained by Brillibits. See YouTube as well.
- Model type: Instruct_Llama70B_Dolly15k is an auto-regressive language model based on the Llama 2 transformer architecture.
- Language(s): English
- License for Instruct_Llama70B_Dolly15ks: llama2 license
Prompting
Prompt Template With Context
Write a 10-line poem about a given topic
Input:
The topic is about racecars
Output:
Prompt Template Without Context
Who was the was the second president of the United States?
Output:
Professional Assistance
This model and other models like it are great, but where LLMs hold the most promise is when they are applied on custom data to automate a wide variety of tasks
If you have a dataset and want to see if you might be able to apply that data to automate some tasks, and you are looking for professional assistance, contact me here
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 60.97 |
| ARC (25-shot) | 68.34 |
| HellaSwag (10-shot) | 87.21 |
| MMLU (5-shot) | 69.52 |
| TruthfulQA (0-shot) | 46.46 |
| Winogrande (5-shot) | 84.29 |
| GSM8K (5-shot) | 42.68 |
| DROP (3-shot) | 28.26 |