Update README.md

README.md

@@ -14,36 +14,37 @@ tags:
 - LoRA
 ---
 
-Open source falcon 7b large language model fine tuned on SQuAD dataset for question and answering.
-
-QLoRA technique used for fine tuning the model on consumer grade GPU
-SFTTrainer is also used.
-
-Dataset used: SQuAD
-Dataset Size: 87278
-Training Steps: 500
-
+# 🚀 Falcon-7b-QueAns
 
-
-# 🚀 Falcon-7b-chat-oasst1
-
-Falcon-7b-chat-oasst1 is a chatbot-like model for dialogue generation. It was built by fine-tuning [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) on the [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) dataset. This repo only includes the LoRA 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) dataset. 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](https://huggingface.co/tiiuae/falcon-7b) (License: [Apache 2.0](https://huggingface.co/tiiuae/falcon-7b#license))
-- **Dataset:** [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) (License: [Apache 2.0](https://huggingface.co/datasets/OpenAssistant/oasst1/blob/main/LICENSE))
+- **Base Model:** [Falcon-7B] (License: [Apache 2.0])
+- **Dataset:** [SQuAD](https://huggingface.co/datasets/squad) (License: [cc-by-4.0])
 - **License(s):** Apache 2.0 inherited from "Base Model" and "Dataset"
 
 ## Model Details
 
-The model was fine-tuned in 8-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 6.25 hours and was executed on a workstation with a single A100-SXM NVIDIA GPU with 37 GB of available memory. See attached [Colab Notebook](https://huggingface.co/dfurman/falcon-7b-chat-oasst1/blob/main/finetune_falcon7b_oasst1_with_bnb_peft.ipynb) for the code and hyperparams 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 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. 
 
 ### Model Date
 
-May 30, 2023
+July 06, 2023
+
+
+Open source falcon 7b large language model fine tuned on SQuAD dataset for question and answering.
+
+QLoRA technique used for fine tuning the model on consumer grade GPU
+SFTTrainer is also used.
+
+Dataset used: SQuAD
+Dataset Size: 87278
+Training Steps: 500
+
+
 
 
 ## Training procedure