Update README.md

README.md

@@ -53,13 +53,10 @@ pipeline_tag: text-generation
 
 Heavily inspired by [Hivemind's GPT-J-6B with 8-bit weights](https://huggingface.co/hivemind/gpt-j-6B-8bit), this is a version of [bigscience/bloom](https://huggingface.co/bigscience/bloom-6b3) a ~6 billion parameters language model that you run and fine-tune with less memory.
 
-Here, we also apply [LoRA (Low Rank Adaptation)](https://arxiv.org/abs/2106.09685) to reduce model size. The original version takes \~353GB memory, this version takes **\~180GB**.
-
-Our main goal is to generate a model compressed enough to be deployed in a traditional Kubernetes cluster.
+Here, we also apply [LoRA (Low Rank Adaptation)](https://arxiv.org/abs/2106.09685) to reduce model size.
 
 ### How to fine-tune
-
-In this [notebook](https://nbviewer.org/urls/huggingface.co/joaoalvarenga/bloom-8bit/raw/main/fine-tuning-example.ipynb) you can find an adaptation from [Hivemind's GPT-J 8-bit fine-tuning notebook](https://colab.research.google.com/drive/1ft6wQU0BhqG5PRlwgaZJv2VukKKjU4Es) to fine-tune Bloom 8-bit with a 3x NVIDIA A100 instance.
+TBA
 
 ### How to use
 
@@ -222,10 +219,4 @@ tokenizer = BloomTokenizerFast.from_pretrained(model_name)
 prompt = tokenizer("Given a table named salaries and columns id, created_at, salary, age. Creates a SQL to answer What is the average salary for 22 years old:", return_tensors='pt')
 out = model.generate(**prompt, min_length=10, do_sample=True)
 tokenizer.decode(out[0])
-```
-
-
-
-
-
-
+```