Update README.md

README.md

@@ -11,30 +11,40 @@ datasets:
 base_model: tiiuae/falcon-180B
 ---
 
-For our finetuning process, we used the tiiuae/falcon-180B model and the Databricks-dolly-15k dataset. This dataset is a rich corpus of over 15,000 records, painstakingly created by the collaborative efforts of thousands of Databricks employees. The goal was to enable large language models to emulate the magical interactivity of ChatGPT.
+### Finetuning Overview:
 
-The contributors were asked to create prompt / response pairs spread across eight different instruction categories. This included the seven categories outlined in the InstructGPT paper, as well as an open-ended, free-form category. To ensure the uniqueness and authenticity of the data, contributors were instructed to abstain from using information from any online source, with the sole exception being Wikipedia (for specific subsets of instruction categories). They were also explicitly instructed to avoid using generative AI in formulating instructions or responses.
+**Model Used:** tiiuae/falcon-180B  
+**Dataset:** Databricks-dolly-15k  
 
-During the data generation process, contributors had the opportunity to answer questions posed by other contributors. They were prompted to rephrase the original question and encouraged to select only those questions they were confident they could answer correctly.
+#### Dataset Insights:
 
-In certain categories, contributors were asked to provide reference texts sourced from Wikipedia. These references (indicated by the context field in the dataset) may contain bracketed Wikipedia citation numbers (e.g. [42]). We recommend users to remove these for downstream applications.
+The Databricks-dolly-15k dataset represents a substantial collection of over 15,000 records, curated through the dedicated and collective efforts of numerous Databricks professionals. It's meticulously designed to:
 
-This finetuning process was carried out using [MonsterAPI](https://monsterapi.ai)'s no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm). The session lasted for 41.7 hours and costed us `$184.314`, running on 2x A100 80GB GPUs.
+- Enhance the magical interactivity of ChatGPT-like models.
+- Offer prompt/response pairs across eight different instruction categories, comprising the seven categories from the InstructGPT paper and an added open-ended category.
+- Ensure authenticity with restrictions against online sourcing (with the exception of Wikipedia for some categories) and the use of generative AI in crafting content.
 
-#### Hyperparameters & Run details:
-- Model Path: tiiuae/falcon-180B
-- Dataset: databricks/databricks-dolly-15k
-- Learning rate: 0.0002
-- Number of epochs: 1
-- Data split: Training: 90% / Validation: 10%
-- Gradient accumulation steps: 1
+During the dataset's creation, contributors responded to peer questions. A focus was placed on rephrasing the original queries and emphasizing accurate responses. Furthermore, certain data subsets incorporate Wikipedia references, identifiable by bracketed citation numbers like [42]. For optimal results in subsequent applications, users are advised to remove these references.
 
-license: apache-2.0
----
+#### Finetuning Details:
+
+Our finetuning harnessed the capabilities of [MonsterAPI](https://monsterapi.ai)'s no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm):
 
-######
+- **Duration:** The session spanned 41.7 hours.
+- **Cost:** The entire process cost `$184.314`.
+- **Hardware Utilized:** 2x A100 80GB GPUs.
 
-Prompt Used: 
+#### Hyperparameters & Additional Details:
+
+- **Model Path:** tiiuae/falcon-180B
+- **Learning Rate:** 0.0002
+- **Epochs:** 1
+- **Data Split:** Training 90% / Validation 10%
+- **Gradient Accumulation Steps:** 1
+
+---
+
+### Prompt Used: 
 
 ```
 ### INSTRUCTION:
@@ -44,4 +54,16 @@ Prompt Used:
 
 ### RESPONSE:
 [response]
-```
+```
+
+Loss metrics
+
+Training loss:
+![training loss](train-loss.png "Training loss")
+
+
+
+---
+
+license: apache-2.0
+