Update README.md links

README.md

@@ -81,7 +81,7 @@ To empower you in compressing models, Sparsify is made up of two components: the
 The Sparsify Cloud is a web application that allows you to create and manage Sparsify Experiments, explore hyperparameters, predict performance, and compare results across both Experiments and deployment scenarios.
 The Sparsify CLI/API is a Python package that allows you to run Sparsify Experiments locally, sync with the Sparsify Cloud, and integrate into your own workflows.
 
-To get started immediately, [create an account](https://account.neuralmagic.com/signup) and then check out the [Installation](https://github.com/neuralmagic/sparsify/edit/Sparsify-Alpha-README/README.md#installation) and [Quick Start](https://github.com/neuralmagic/sparsify/edit/Sparsify-Alpha-README/README.md#quick-start) sections of this README.
+To get started immediately, [create an account](https://account.neuralmagic.com/signup) and then check out the [Installation](https://github.com/neuralmagic/sparsify/blob/main/README.md#installation) and [Quick Start](https://github.com/neuralmagic/sparsify/blob/main/README.md#quick-start) sections of this README.
 With all of that setup, sparsifying your models is as easy as:
 
 ```bash
@@ -111,8 +111,8 @@ pip install sparsify
 Sparsify is tested on Python 3.8 and 3.10, ONNX 1.5.0-1.12.0, ONNX opset version 11+, and manylinux compliant systems.
 
 Sparsify requires a GPU with CUDA + CuDNN in order to sparsify neural networks. 
-We recommend you use a Linux system with a GPU that has a minimum of 16 GB of GPU Memory,  128 GB of RAM, 4 cores, and is CUDA-enabled. If you are sparsifying a very large model, you may need more RAM than the recommended 128 GB.
-If you encounter issues setting up your training environment, file a GitHub issue [here]( https://github.com/neuralmagic/sparsify/issues).
+We recommend you use a Linux system with a GPU that has a minimum of 16 GB of GPU Memory, 128 GB of RAM, 4 CPU cores, and is CUDA-enabled. If you are sparsifying a very large model, you may need more RAM than the recommended 128 GB.
+If you encounter issues setting up your training environment, file a GitHub issue [here](https://github.com/neuralmagic/sparsify/issues).
 
 ## Quick Start
 
@@ -155,9 +155,9 @@ Once you have located this, copy the command or the API key itself and run the f
 sparsify.login API_KEY
 ````
 
-For more details on locating the API_KEY, see the [Sparsify Cloud User Guide](https://github.com/neuralmagic/sparsify/docs/cloud-user-guide.md).
+For more details on locating the API_KEY, see the [Sparsify Cloud User Guide](https://github.com/neuralmagic/sparsify/blob/main/docs/cloud-user-guide.md).
 
-For more details on the `sparsify.login` command, see the [CLI/API Guide](https://github.com/neuralmagic/sparsify/docs/cli-api-guide.md).
+For more details on the `sparsify.login` command, see the [CLI/API Guide](https://github.com/neuralmagic/sparsify/blob/main/docs/cli-api-guide.md).
 
 ### Run an Experiment
 
@@ -166,7 +166,7 @@ They are the process of applying sparsification algorithms in One-Shot, Training
 All Experiments are run locally on your training hardware and can be synced with the cloud for further analysis and comparison.
 To run an Experiment, you can use either the CLI or the API depending on your use case.
 The Sparsify Cloud provides a UI for exploring hyperparameters, predicting performance, and generating the desired CLI/API command.
-For more info on generating commands from the Sparsify Cloud, see the [Sparsify Cloud User Guide](https://github.com/neuralmagic/sparsify/docs/cloud-user-guide.md).
+For more info on generating commands from the Sparsify Cloud, see the [Sparsify Cloud User Guide](https://github.com/neuralmagic/sparsify/blob/main/docs/cloud-user-guide.md).
 
 The general command for running an Experiment is:
 
@@ -175,11 +175,11 @@ sparsify.run EXPERIMENT_TYPE --use-case USE_CASE --model MODEL --data DATA --opt
 ```
 
 Where the values for each of the arguments follow these general rules:
-- EXPERIMENT_TYPE: one of `one-shot`, `training-aware`, or `sparse-transfer`; see the examples below for more details or the [CLI/API Guide](https://github.com/neuralmagic/sparsify/docs/cli-api-guide.md).
-- USE_CASE: the use case you're solving for such as `image-classification`, `object-detection`, `text-classification`, a custom use case, etc. A full list of supported use cases for each Experiment type can be found [here](https://github.com/neuralmagic/sparsify/docs/use-cases-guide.md).
-- MODEL: the model you want to sparsify which can be a model name such as `resnet50`, a stub from the [SparseZoo](https://sparsezoo.neuralmagic.com), or a path to a local model. For One-Shot, currently the model must be in an ONNX format. For Training-Aware and Sparse-Transfer, the model must be in a PyTorch format. More details on model formats can be found [here](https://github.com/neuralmagic/sparsify/docs/models-guide.md).
-- DATA: the dataset you want to use to the sparsify the model. This can be a dataset name such as `imagenette` or a path to a local dataset. Currently, One-Shot only supports NPZ formatted datasets. Training-Aware and Sparse-Transfer support PyTorch ImageFolder datasets for image classification, YOLOv5/v8 datasets for object detection and segmentation, and HuggingFace datasets for NLP/NLG. More details on dataset formats can be found [here](https://github.com/neuralmagic/sparsify/docs/datasets-guide.md).
-- OPTIM_LEVEL: the desired sparsification level from 0 (none) to 1 (max). The general rule is that 0 is the baseline model, <0.3 only quantizes the model, 0.3-1.0 increases the sparsity of the model and applies quantization. More details on sparsification levels can be found [here](https://github.com/neuralmagic/sparsify/docs/optim-levels-guide.md).
+- EXPERIMENT_TYPE: one of `one-shot`, `training-aware`, or `sparse-transfer`; see the examples below for more details or the [CLI/API Guide](https://github.com/neuralmagic/sparsify/blob/main/docs/cli-api-guide.md).
+- USE_CASE: the use case you're solving for such as `image-classification`, `object-detection`, `text-classification`, a custom use case, etc. A full list of supported use cases for each Experiment type can be found [here](https://github.com/neuralmagic/sparsify/blob/main/docs/use-cases-guide.md).
+- MODEL: the model you want to sparsify which can be a model name such as `resnet50`, a stub from the [SparseZoo](https://sparsezoo.neuralmagic.com), or a path to a local model. For One-Shot, currently the model must be in an ONNX format. For Training-Aware and Sparse-Transfer, the model must be in a PyTorch format. More details on model formats can be found [here](https://github.com/neuralmagic/sparsify/blob/main/docs/models-guide.md).
+- DATA: the dataset you want to use to the sparsify the model. This can be a dataset name such as `imagenette` or a path to a local dataset. Currently, One-Shot only supports NPZ formatted datasets. Training-Aware and Sparse-Transfer support PyTorch ImageFolder datasets for image classification, YOLOv5/v8 datasets for object detection and segmentation, and HuggingFace datasets for NLP/NLG. More details on dataset formats can be found [here](https://github.com/neuralmagic/sparsify/blob/main/docs/datasets-guide.md).
+- OPTIM_LEVEL: the desired sparsification level from 0 (none) to 1 (max). The general rule is that 0 is the baseline model, <0.3 only quantizes the model, 0.3-1.0 increases the sparsity of the model and applies quantization. More details on sparsification levels can be found [here](https://github.com/neuralmagic/sparsify/blob/main/docs/optim-levels-guide.md).
 
 #### Running One-Shot