Update README.md

README.md

@@ -1,6 +1,53 @@
 ---
 library_name: peft
 ---
+
+
+# 🚀 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. 
+
+## 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)
+- **License(s):** Apache 2.0 inherited from "Base Model" and "Dataset"
+
+
+## Why use Falcon-7B?
+
+* **It outperforms comparable open-source models** (e.g., [MPT-7B](https://huggingface.co/mosaicml/mpt-7b), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1) etc.), thanks to being trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
+* **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)). 
+* **It is made available under a permissive Apache 2.0 license allowing for commercial use**, without any royalties or restrictions.
+
+⚠️ **This is a finetuned version for specifically question and answering.** If you are looking for a version better suited to taking generic instructions in a chat format, we recommend taking a look at [Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct). 
+
+🔥 **Looking for an even more powerful model?** [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) is Falcon-7B's big brother!
+
+
+## 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. 
+
+### Model Date
+
+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