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---
library_name: peft
tags:
- meta-llama
- code
- instruct
- databricks-dolly-15k
- Llama-2-70b-hf
datasets:
- databricks/databricks-dolly-15k
base_model: meta-llama/Llama-2-70b-hf
---
### Finetuning Overview:
**Model Used:** meta-llama/Llama-2-70b-hf
**Dataset:** Databricks-dolly-15k
#### Dataset Insights:
The Databricks-dolly-15k dataset is an impressive compilation of over 15,000 records, made possible by the hard work and dedication of a multitude of Databricks professionals. It has been tailored to:
- Elevate the interactive capabilities of ChatGPT-like systems.
- Provide prompt/response pairs spanning eight distinct instruction categories, inclusive of the seven categories from the InstructGPT paper and an exploratory open-ended category.
- Ensure genuine and original content, largely offline-sourced with exceptions for Wikipedia in particular categories, and free from generative AI influences.
The contributors had the opportunity to rephrase and answer queries from their peers, highlighting a focus on accuracy and clarity. Additionally, some data subsets feature Wikipedia-sourced reference texts, marked by bracketed citation numbers like [42].
#### Finetuning Details:
Using [MonsterAPI](https://monsterapi.ai)'s user-friendly [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm), the finetuning:
- Stands out for its cost-effectiveness.
- Was executed in a total of 17.5 hours for 3 epochs with an A100 80GB GPU.
- Broke down to just 5.8 hours and `$19.25` per epoch, culminating in a combined cost of `$57.75` for all epochs.
#### Hyperparameters & Additional Details:
- **Epochs:** 3
- **Cost Per Epoch:** $19.25
- **Total Finetuning Cost:** $57.75
- **Model Path:** meta-llama/Llama-2-70b-hf
- **Learning Rate:** 0.0002
- **Data Split:** Training 90% / Validation 10%
- **Gradient Accumulation Steps:** 4
---
### Prompt Structure:
```
### INSTRUCTION:
[instruction]
[context]
### RESPONSE:
[response]
```
Loss metrics
Training loss (Blue) Validation Loss (orange):
---
license: apache-2.0
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