Update README.md

README.md

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 # 🚀 Falcon-7b-QueAns
 
-Falcon-7b-QueAns is a chatbot-like model for Question and Answering. It was built by fine-tuning [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) on the [SQuAD](https://huggingface.co/datasets/squad) dataset. This repo only includes the QLoRA adapters from fine-tuning with 🤗's [peft](https://github.com/huggingface/peft) package. 
+Falcon-7b-QueAns is a chatbot-like model for Question and Answering. It was built by fine-tuning [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) on the [SQuAD](https://huggingface.co/datasets/squad), [Adversarial_qa](https://huggingface.co/datasets/adversarial_qa), Trimpixel (Self-Made) datasets. This repo only includes the QLoRA adapters from fine-tuning with 🤗's [peft](https://github.com/huggingface/peft) package. 
 
 ## Model Summary
 
 - **Model Type:** Causal decoder-only
 - **Language(s):** English
 - **Base Model:** Falcon-7B (License: Apache 2.0)
-- **Dataset:** [SQuAD](https://huggingface.co/datasets/squad) (License: cc-by-4.0)
+- **Dataset:** [SQuAD](https://huggingface.co/datasets/squad) (License: cc-by-4.0), [Adversarial_qa](https://huggingface.co/datasets/adversarial_qa) (License: cc-by-sa-4.0), [Falcon-RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) (odc-by), Trimpixel (Self-Made)
 - **License(s):** Apache 2.0 inherited from "Base Model" and "Dataset"
 
 
@@ -51,21 +51,33 @@ Falcon-7b-QueAns is a chatbot-like model for Question and Answering. It was buil
 
 ## Model Details
 
-The model was fine-tuned in 4-bit precision using 🤗 `peft` adapters, `transformers`, and `bitsandbytes`. Training relied on a method called "Low Rank Adapters" ([LoRA](https://arxiv.org/pdf/2106.09685.pdf)), specifically the [QLoRA](https://arxiv.org/abs/2305.14314) variant. The run took approximately 4 hours and was executed on a workstation with a single T4 NVIDIA GPU with 15 GB of available memory. See attached [Colab Notebook] used to train the model. 
+The model was fine-tuned in 4-bit precision using 🤗 `peft` adapters, `transformers`, and `bitsandbytes`. Training relied on a method called "Low Rank Adapters" ([LoRA](https://arxiv.org/pdf/2106.09685.pdf)), specifically the [QLoRA](https://arxiv.org/abs/2305.14314) variant. The run took approximately 12 hours and was executed on a workstation with a single T4 NVIDIA GPU with 25 GB of available memory. See attached [Colab Notebook] used to train the model. 
 
 ### Model Date
 
-July 06, 2023
+July 13, 2023
 
 
-Open source falcon 7b large language model fine tuned on SQuAD dataset for question and answering.
-
+Open source falcon 7b large language model fine tuned on SQuAD, Adversarial_qa, Trimpixel datasets for question and answering.
 QLoRA technique used for fine tuning the model on consumer grade GPU
 SFTTrainer is also used.
 
+## Datasets
+
+1.
 Dataset used: SQuAD
-Dataset Size: 87278
-Training Steps: 500
+Dataset Size: 87599
+Training Steps: 350
+
+2.
+Dataset used: Adversarial_qa
+Dataset Size: 30000
+Training Steps: 400
+
+3.
+Dataset used: Trimpixel
+Dataset Size: 1757
+Training Steps: 400