Update README.md

README.md

@@ -11,36 +11,43 @@ datasets:
 base_model: meta-llama/Llama-2-70b-hf
 ---
 
-For our finetuning process, we utilized the meta-llama/Llama-2-70b-hf model and the Databricks-dolly-15k dataset. 
-
-This dataset, a meticulous compilation of over 15,000 records, was a result of the dedicated work of thousands of Databricks professionals. It was specifically designed to further improve the interactive capabilities of ChatGPT-like systems.
-The dataset contributors crafted prompt / response pairs across eight distinct instruction categories. Besides the seven categories mentioned in the InstructGPT paper, they also ventured into an open-ended, free-form category. The contributors, emphasizing genuine and original content, refrained from sourcing information online, except in special cases where Wikipedia was the source for certain instruction categories. There was also a strict directive against the use of generative AI for crafting instructions or responses.
-The contributors could address questions from their peers. Rephrasing the original question was encouraged, and there was a clear preference to answer only those queries they were certain about.
-In some categories, the data comes with reference texts sourced from Wikipedia. Users might find bracketed Wikipedia citation numbers (like [42]) within the context field of the dataset. For smoother downstream applications, it's advisable to exclude these.
-
-Our finetuning leveraged the [MonsterAPI](https://monsterapi.ai)'s intuitive, no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). 
-
-This efficient process, surprisingly cost-effective, 
-was completed in just 17.5 hours for 3 epochs, running on an A100 80GB GPU. 
-Breaking it down further, each epoch took only 5.8 hours and cost a mere `$19.25`. The total cost for all 3 epochs came to `$57.75`.
-
-#### Hyperparameters & Run details:
-- Epochs: 3
-- Cost per epoch: $19.25
-- Total finetuning Cost: $57.75
-- Model Path: meta-llama/Llama-2-70b-hf
-- Dataset: databricks/databricks-dolly-15k
-- Learning rate: 0.0002
-- Number of epochs: 3
-- Data split: Training 90% / Validation 10%
-- Gradient accumulation steps: 4
+### 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.
+
+In an innovative approach, 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]. For an optimal user experience in downstream applications, it's recommended to remove these.
+
+#### 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
 
-license: apache-2.0
 ---
 
-######
+### Prompt Structure:
 
-Prompt Used: 
 
 ```
 ### INSTRUCTION:
@@ -57,4 +64,6 @@ Loss metrics
 Training loss (Blue) Validation Loss (orange):
 ![training loss](train-loss.png "Training loss")
 
+---
 
+license: apache-2.0