Restructure documentation, use phi-3-mini, include structured generat…

…ion example, add feature matrix to models

docs/cookbook/deploy-using-modal.md

@@ -6,7 +6,7 @@ In this guide we will show you how you can use Modal to run programs written wit
 
 ## Build the image
 
-First we need to define our container image. We download the Mistral-7B-v0.1 model from HuggingFace as part of the definition of the image so it only needs to be done once.
+First we need to define our container image. We download the Mistral-7B-v0.1 model from HuggingFace as part of the definition of the image so it only needs to be done once (you need to provide an [access token](https://huggingface.co/settings/tokens))
 
 ```python
 from modal import Image, App, gpu
@@ -21,6 +21,8 @@ outlines_image = Image.debian_slim(python_version="3.11").pip_install(
 )
 
 def import_model():
+    import os
+    os.environ["HF_TOKEN"] = "YOUR_HUGGINGFACE_TOKEN"
     import outlines
     outlines.models.transformers("mistralai/Mistral-7B-Instruct-v0.2")
 
@@ -73,7 +75,7 @@ schema = """{
 To make the inference work on Modal we need to wrap the corresponding function in a `@app.function` decorator. We pass to this decorator the image and GPU on which we want this function to run (here an A100 with 80GB memory):
 
 ```python
-@app.function(image=outlines_image, gpu=gpu.A100(memory=80))
+@app.function(image=outlines_image, gpu=gpu.A100(size='80GB'))
 def generate(
     prompt: str = "Amiri, a 53 year old warrior woman with a sword and leather armor.",
 ):

docs/cookbook/index.md

@@ -6,3 +6,5 @@
 - [Chain Of Density](chain_of_density.md): Summarize documents using chain of density prompting and JSON-structured generation.
 - [Playing Chess](models_playing_chess.md): Make Mistral-7B play chess against itself using regex-structured generation.
 - [SimToM](simtom.md): Improve LLMs' Theory of Mind capabilities with perspective-taking prompting and JSON-structured generation.
+- [Q&A with Citations](qa-with-citations.md): Answer questions and provide citations using JSON-structured generation.
+- [Knowledge Graph Generation](knowledge_graph_extraction.md): Generate a Knowledge Graph from unstructured text using JSON-structured generation.