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---
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](https://www.youtube.com/channel/UCAq9THVHhPK0Zv4Xi-88Jmg).
* **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](mailto:[email protected])
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Brillibits__Instruct_Llama70B_Dolly15k)
| 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 |
|