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 $19.25 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