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
license: apache-2.0
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's user-friendly LLM finetuner, 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
$19.25per epoch, culminating in a combined cost of$57.75for 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