---
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
---

Note: This repo contains the base weights already merged with lora, pls check qblocks/llama2_70B_dolly15k repo for LORA adapters only


### 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):
![training loss](train-loss.png "Training loss")

---

license: apache-2.0