Update README.md

README.md

@@ -12,15 +12,15 @@ Falcon-40b-chat-oasst1 is a chatbot-like model for dialogue generation. It was b
 
 - **Model Type:** Causal decoder-only
 - **Language(s) (NLP):** English (primarily)
-- **Base Model:** [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) (License: [TII Falcon LLM License](https://huggingface.co/tiiuae/falcon-40b#license), commercial use ok-ed)
-- **Dataset:** [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) (License: [Apache 2.0](https://huggingface.co/datasets/OpenAssistant/oasst1/blob/main/LICENSE), commercial use ok-ed)
+- **Base Model:** [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) (License: [TII Falcon LLM License](https://huggingface.co/tiiuae/falcon-40b#license))
+- **Dataset:** [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) (License: [Apache 2.0](https://huggingface.co/datasets/OpenAssistant/oasst1/blob/main/LICENSE))
 - **License:** Inherited from the above "Base Model" and "Dataset"
 
 ## 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 10 hours and was executed on a workstation with a single A100-SXM NVIDIA GPU with 37 GB of available memory
+- The run took approximately 10 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-40b-chat-oasst1/blob/main/finetune_falcon40b_oasst1_with_bnb_peft.ipynb) for the code and hyperparams used to train the model.