---
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://brillibits.com/en). See [YouTube](https://www.youtube.com/@Brillibits) 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](mailto:blakecmallory@gmail.com)
# [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         |