Update README.md

README.md

@@ -1,6 +1,6 @@
 This Model is 8bit Version of EleutherAI/gpt-j-6B. It is converted by Facebook's bitsandbytes library. The original GPT-J takes 22+ GB memory for float32 parameters alone, and that's before you account for gradients & optimizer. So for finetuning on single GPU This model is converted into 8bit.
 
-Here's how to run it: [![colab](https://colab.research.google.com/drive/1KNf5siQdM7ILQM-pHsP6gNVPKl1SJdU1)
+Here's how to run it: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1KNf5siQdM7ILQM-pHsP6gNVPKl1SJdU1)
 __The [original GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B/tree/main)__ takes 22+ GB memory for float32 parameters alone, and that's before you account for gradients & optimizer. Even if you cast everything to 16-bit, it will still not fit onto most single-GPU setups short of A6000 and A100. You can inference it [on TPU](https://colab.research.google.com/github/kingoflolz/mesh-transformer-jax/blob/master/colab_demo.ipynb) or CPUs, but fine-tuning is way more expensive.
 Here, we apply several techniques to make GPT-J usable and fine-tunable on a single GPU with ~11 GB memory:
 - large weight tensors are quantized using dynamic 8-bit quantization and de-quantized just-in-time for multiplication