Fix fine-tuning notebooks intros (#194)

The notebook intros were outdated and mentioned the 6B model, while the actual code already runs the 176B model. This led to confusion among our users in Discord.

examples/prompt-tuning-personachat.ipynb

@@ -11,9 +11,9 @@
     "\n",
     "# Distributed Bloom for Text Generation using Prompt Tuning\n",
     "\n",
-    "In this example, we show how to use [prompt tuning](https://aclanthology.org/2021.emnlp-main.243.pdf) to adapt a test 6B version of the [BLOOM](https://huggingface.co/bigscience/bloom) model for a specific downstream task. We will run this model in a decentralized fashion using [Petals](https://github.com/bigscience-workshop/petals). Petals servers will maintain the BLOOM blocks (they are kept unchanged during adaptation), and the gradient descent will learn a few prefix tokens stored on a Petals client.\n",
+    "In this example, we show how to use [prompt tuning](https://aclanthology.org/2021.emnlp-main.243.pdf) to adapt the [BLOOM](https://huggingface.co/bigscience/bloom) model for a specific downstream task. We will run this model in a decentralized fashion using [Petals](https://github.com/bigscience-workshop/petals). Petals servers will maintain the BLOOM blocks (they are kept unchanged during adaptation), and the gradient descent will learn a few prefix tokens stored on a Petals client.\n",
     "\n",
-    "We will adapt the BLOOM model for the chatbot task using the [Personachat](https://huggingface.co/datasets/bavard/personachat_truecased) dataset. For a given dialogue context, the model has to provide a relevant answer.\n",
+    "We will adapt BLOOM for the task of creating a chatbot with a specific personality using the [Personachat](https://huggingface.co/datasets/bavard/personachat_truecased) dataset. For a given dialogue context, the model has to provide a relevant answer.\n",
     "\n",
     "To use this notebook in Colab:\n",
     "\n",

examples/prompt-tuning-sst2.ipynb

@@ -11,9 +11,9 @@
     "\n",
     "# Distributed Bloom for Text Classification using Prompt Tuning\n",
     "\n",
-    "In this example, we show how to use [prompt tuning](https://aclanthology.org/2021.emnlp-main.243.pdf) to adapt a test 6B version of the [BLOOM](https://huggingface.co/bigscience/bloom) model for a specific downstream task. We will run this model in a decentralized fashion using [Petals](https://github.com/bigscience-workshop/petals). Petals servers will maintain the BLOOM blocks (they are kept unchanged during adaptation), and the gradient descent will learn a few prefix tokens stored on a Petals client.\n",
+    "In this example, we show how to use [prompt tuning](https://aclanthology.org/2021.emnlp-main.243.pdf) to adapt the [BLOOM](https://huggingface.co/bigscience/bloom) model for a specific downstream task. We will run this model in a decentralized fashion using [Petals](https://github.com/bigscience-workshop/petals). Petals servers will maintain the BLOOM blocks (they are kept unchanged during adaptation), and the gradient descent will learn a few prefix tokens stored on a Petals client.\n",
     "\n",
-    "We will adapt the BLOOM model for the classification task using the [SST-2 dataset](https://nlp.stanford.edu/sentiment/). This dataset is a binary classification task, where the goal is to predict whether a sentence is positive or negative. The SST-2 dataset is a subset of the Stanford Sentiment Treebank, and it is available in the [Hugging Face Datasets](https://huggingface.co/datasets) library.\n",
+    "We will adapt BLOOM for the classification task using the [SST-2 dataset](https://nlp.stanford.edu/sentiment/). This dataset is a binary classification task, where the goal is to predict whether a sentence is positive or negative. The SST-2 dataset is a subset of the Stanford Sentiment Treebank, and it is available in the [Hugging Face Datasets](https://huggingface.co/datasets) library.\n",
     "\n",
     "To use this notebook in Colab:\n",
     "\n",