slashml · pr-test1 · Oct 18, 2025 · Oct 18, 2025 · Oct 18, 2025 · Oct 18, 2025
diff --git a/about.mdx b/about.mdx
@@ -8,23 +8,47 @@ description: Deploy open source AI models to AWS, GCP, and Azure in minutes
 
 Magemaker is a Python tool that simplifies the process of deploying open source AI models to your preferred cloud provider. Instead of spending hours digging through documentation, Magemaker lets you deploy Hugging Face models directly to AWS SageMaker, Google Cloud Vertex AI, or Azure Machine Learning.
 
+## Current Features
+
+- **Multi-Cloud Deployment**: Deploy to AWS SageMaker, GCP Vertex AI, and Azure ML
+- **Hugging Face Integration**: Deploy any public or gated Hugging Face model
+- **Custom Model Support**: Deploy fine-tuned models from S3 or local paths
+- **Fine-Tuning**: Train AWS SageMaker JumpStart models on your data
+- **Interactive CLI**: User-friendly dropdown menus for AWS deployments
+- **YAML Configuration**: Infrastructure as Code for reproducible deployments
+
 ## What we're working on next
 
+- Interactive deployment support for GCP and Azure
+- Hugging Face model fine-tuning
+- GCP and Azure fine-tuning support
 - More robust error handling for various edge cases
 - Verbose logging
 - Enabling / disabling autoscaling
 - Enhanced multi-cloud support features
 
 Do submit your feature requests at https://magemaker.featurebase.app/
 
-## Known issues
+## Known Issues and Limitations
+
+### Platform Support
+- Python 3.13 is not supported due to an Azure SDK compatibility issue: https://github.com/Azure/azure-sdk-for-python/issues/37600
+- Python 3.11 or 3.12 is required
 
+### Deployment Limitations
+- Interactive model deployment from dropdown menu is only available for AWS (GCP and Azure require YAML files)
 - Querying within Magemaker currently only works with text-based models
 - Deleting a model is not instant, it may show up briefly after deletion
-- Deploying the same model within the same minute will break
-- Hugging-face models on Azure have different Ids than their Hugging-face counterparts. Follow the steps specified in the quick-start guide to find the relevant models
-- For Azure deploying models other than Hugging-face is not supported yet. 
-- Python3.13 is not supported because of an open-issue by Azure. https://github.com/Azure/azure-sdk-for-python/issues/37600
+- Deploying the same model within the same minute will cause conflicts
+
+### Azure-Specific Issues
+- Hugging Face models on Azure have different IDs than their Hugging Face counterparts (follow the steps in the quick-start guide to find model IDs)
+- Only Hugging Face models are supported for Azure deployments
+
+### Fine-Tuning Limitations
+- Fine-tuning is only supported for AWS SageMaker JumpStart models
+- Hugging Face model fine-tuning is not yet implemented
+- GCP and Azure fine-tuning are not yet supported
 
 
 If there is anything we missed, do point them out at https://magemaker.featurebase.app/

diff --git a/concepts/deployment.mdx b/concepts/deployment.mdx
@@ -15,11 +15,15 @@ When you run the `magemaker --cloud [aws|gcp|azure|all]` command, you'll get an
 magemaker --cloud [aws|gcp|azure|all]
 ```
 
+<Note>
+  **Important:** Interactive model deployment from the dropdown menu is currently only supported for AWS. For GCP and Azure deployments, you must use YAML configuration files.
+</Note>
+
 This method is great for:
 
-- First-time users
-- Exploring available models
-- Testing different configurations
+- First-time users (AWS only)
+- Exploring available models (AWS only)
+- Testing different configurations (AWS only)
 
 ### YAML-based Deployment
 
@@ -62,7 +66,7 @@ deployment: !Deployment
   destination: gcp
   endpoint_name: opt-125m-gcp
   instance_count: 1
-  machine_type: n1-standard-4
+  instance_type: n1-standard-4
   accelerator_type: NVIDIA_TESLA_T4
   accelerator_count: 1
 
@@ -104,7 +108,7 @@ models:
     source: huggingface
 ```
 
-### Advanced Configuration
+### Advanced Configuration with Prediction Parameters
 
 ```yaml
 deployment: !Deployment
@@ -125,6 +129,24 @@ models:
       max_new_tokens: 250
 ```
 
+### Custom Model Deployment
+
+Deploy a fine-tuned or custom model from S3 or local path:
+
+```yaml
+deployment: !Deployment
+  destination: aws
+  endpoint_name: my-custom-model
+  instance_count: 1
+  instance_type: ml.m5.xlarge
+
+models:
+  - !Model
+    id: google-bert/bert-base-uncased
+    source: custom
+    location: s3://my-bucket/my-model/  # or local path
+```
+
 ## Cloud-Specific Instance Types
 
 ### AWS SageMaker Types

diff --git a/concepts/fine-tuning.mdx b/concepts/fine-tuning.mdx
@@ -7,6 +7,14 @@ description: Guide to fine-tuning models with Magemaker
 
 Fine-tuning allows you to adapt pre-trained models to your specific use case. Magemaker simplifies this process through YAML configuration.
 
+<Warning>
+  **Important Limitations:**
+  - Fine-tuning is currently **only supported for AWS SageMaker JumpStart models**
+  - Hugging Face model fine-tuning is NOT yet implemented
+  - GCP and Azure fine-tuning are NOT yet supported
+  - Training data must be uploaded to S3
+</Warning>
+
 ### Basic Command
 
 ```sh
@@ -17,6 +25,8 @@ magemaker --train .magemaker_config/train-config.yaml
 
 ### Basic Training Configuration
 
+Example for AWS SageMaker JumpStart model:
+
 ```yaml
 training: !Training
   destination: aws
@@ -26,27 +36,30 @@ training: !Training
 
 models:
 - !Model
-  id: your-model-id
-  source: huggingface
+  id: tensorflow-tc-bert-en-uncased-L-12-H-768-A-12-2
+  version: 1.0.0
+  source: sagemaker
 ```
 
-### Advanced Configuration
+### Advanced Configuration with Hyperparameters
 
 ```yaml
 training: !Training
   destination: aws
   instance_type: ml.p3.2xlarge
   instance_count: 1
   training_input_path: s3://your-bucket/data.csv
+  output_path: s3://your-bucket/model-output/
   hyperparameters: !Hyperparameters
     epochs: 3
     per_device_train_batch_size: 32
     learning_rate: 2e-5
-    weight_decay: 0.01
-    warmup_steps: 500
-    evaluation_strategy: "steps"
-    eval_steps: 500
-    save_steps: 1000
+
+models:
+- !Model
+  id: tensorflow-tc-bert-en-uncased-L-12-H-768-A-12-2
+  version: 1.0.0
+  source: sagemaker
 ```
 
 ## Data Preparation
@@ -107,19 +120,21 @@ hyperparameters: !Hyperparameters
   batch_size: 32
 ```
 
-### Advanced Tuning
+### Supported Hyperparameters
+
+The following hyperparameters are supported and will be merged with the model's default hyperparameters:
 
 ```yaml
 hyperparameters: !Hyperparameters
   epochs: 3
-  learning_rate: 
-    min: 1e-5
-    max: 1e-4
-    scaling: log
-  batch_size:
-    values: [16, 32, 64]
+  per_device_train_batch_size: 32
+  learning_rate: 2e-5
 ```
 
+<Note>
+  Any hyperparameters not specified will use the SageMaker JumpStart model's default values. Additional hyperparameters beyond those defined in the schema may not be supported.
+</Note>
+
 ## Monitoring Training
 
 ### CloudWatch Metrics

diff --git a/concepts/models.mdx b/concepts/models.mdx
@@ -5,8 +5,13 @@ description: Guide to supported models and their requirements
 
 ## Supported Models
 
+Magemaker currently supports two model sources for deployment:
+
+1. **Hugging Face Models** - Deploy any public or gated model from Hugging Face Hub
+2. **Custom Models** - Deploy your own fine-tuned models from S3 or local paths
+
 <Note>
-Currently, Magemaker supports deployment of Hugging Face models only. Support for cloud provider marketplace models is coming soon!
+AWS SageMaker JumpStart models are supported for **fine-tuning only**, not for direct deployment via Magemaker. Cloud provider marketplace models (GCP Vertex AI Model Garden, Azure ML Model Catalog) are not yet supported for deployment.
 </Note>
 
 ### Hugging Face Models
@@ -65,17 +70,19 @@ We plan to add support for the following model sources:
 #### GCP Vertex AI
 1. **Small Models** (n1-standard-4)
    ```yaml
-   machine_type: n1-standard-4
+   instance_type: n1-standard-4
    ```
 2. **Medium Models** (n1-standard-8 + GPU)
    ```yaml
-   machine_type: n1-standard-8
+   instance_type: n1-standard-8
    accelerator_type: NVIDIA_TESLA_T4
    accelerator_count: 1
    ```
-3. **Large Models** (a2-highgpu-1g)
+3. **Large Models** (g2-standard-12 + GPU)
    ```yaml
-   machine_type: a2-highgpu-1g
+   instance_type: g2-standard-12
+   accelerator_type: NVIDIA_L4
+   accelerator_count: 1
    ```
 
 #### Azure ML
@@ -155,8 +162,19 @@ deployment: !Deployment
 models:
 - !Model
   id: your-model-id
-  source: huggingface|sagemaker # we don't support vertex and azure specific models yet
-  revision: latest  # Optional: specify model version
+  source: huggingface  # or 'custom' for fine-tuned models
+```
+
+### Custom Model Parameters
+
+For deploying custom or fine-tuned models:
+
+```yaml
+models:
+- !Model
+  id: google-bert/bert-base-uncased  # base model that was fine-tuned
+  source: custom
+  location: s3://my-bucket/my-model/  # S3 URI or local path
 ```
 
 ### Advanced Parameters

diff --git a/installation.mdx b/installation.mdx
@@ -3,11 +3,24 @@ title: Installation
 description: Configure Magemaker for your cloud provider
 ---
 
+## Prerequisites
+
+- Python 3.11 or 3.12 (Python 3.13 is NOT supported due to Azure SDK compatibility issues)
+- Cloud provider account(s):
+  - AWS for SageMaker
+  - GCP for Vertex AI
+  - Azure for Azure ML
+- Cloud CLI tools (recommended):
+  - AWS CLI (optional for AWS)
+  - Google Cloud SDK for GCP
+  - Azure CLI for Azure
+- Hugging Face account (for gated models like Llama)
 
 <Note>
-  For Macs, maxOS >= 13.6.6 is required. Apply Silicon devices (M1) must use Rosetta terminal. You can verify, your terminals architecture by running `arch`. It should print `i386` for Rosetta terminal.
+  For Macs, maxOS >= 13.6.6 is required. Apple Silicon devices (M1) must use Rosetta terminal. You can verify your terminal's architecture by running `arch`. It should print `i386` for Rosetta terminal.
 </Note>
 
+## Installation
 
 Install via pip:
 
@@ -47,7 +60,7 @@ magemaker --cloud gcp
 ### Azure Configuration
 
 - Follow this detailed guide for setting up Azure credentials:
-  [GCP Setup Guide](/configuration/Azure)
+  [Azure Setup Guide](/configuration/Azure)
 
 
 Once you have your Azure credentials, you can configure Magemaker by running:

diff --git a/mint.json b/mint.json
@@ -64,6 +64,7 @@
       "pages": [
         "concepts/deployment",
         "concepts/models",
+        "concepts/fine-tuning",
         "concepts/contributing"
       ]
     }

diff --git a/quick-start.mdx b/quick-start.mdx
@@ -8,18 +8,22 @@ title: Quick Start
 
 ## Interactive View
 
-1. Run Magemaker with your desired cloud provider:
+Run Magemaker with your desired cloud provider (the `--cloud` flag is required):
 
 ```sh
 magemaker --cloud [aws|gcp|azure|all]
 ```
 
 Supported providers:
 
-- `--cloud aws` AWS SageMaker deployment
-- `--cloud gcp` Google Cloud Vertex AI deployment
-- `--cloud azure` Azure Machine Learning deployment
-- `--cloud all` Configure all three providers at the same time
+- `--cloud aws` - AWS SageMaker deployment
+- `--cloud gcp` - Google Cloud Vertex AI deployment
+- `--cloud azure` - Azure Machine Learning deployment
+- `--cloud all` - Configure all three providers at the same time
+
+<Note>
+  **Important:** Interactive model deployment from the dropdown menu is currently only supported for AWS. For GCP and Azure deployments, you must use YAML configuration files with the `--deploy` flag.
+</Note>
 
 
 ### List Models
@@ -76,20 +80,15 @@ For GCP Vertex AI:
 deployment: !Deployment
   destination: gcp
   endpoint_name: facebook-opt-test
-  accelerator_count: 1
+  instance_count: 1
   instance_type: g2-standard-12
   accelerator_type: NVIDIA_L4
-  num_gpus: null
-  quantization: null
+  accelerator_count: 1
 
 models:
   - !Model
     id: facebook/opt-125m
-    location: null
-    predict: null
     source: huggingface
-    task: null
-    version: null
 ```
 
 For Azure ML:
@@ -137,18 +136,28 @@ Fine-tune models using the `train` command:
 magemaker --train .magemaker_config/train-config.yaml
 ```
 
-Example training configuration:
+<Warning>
+  **Training Limitations:** Fine-tuning is currently only supported for AWS SageMaker JumpStart models. Hugging Face model fine-tuning and GCP/Azure training are not yet implemented.
+</Warning>
+
+Example training configuration for AWS SageMaker JumpStart models:
 
 ```yaml
 training: !Training
-  destination: aws # or gcp, azure
-  instance_type: ml.p3.2xlarge # varies by cloud provider
+  destination: aws
+  instance_type: ml.p3.2xlarge
   instance_count: 1
   training_input_path: s3://your-bucket/data.csv
   hyperparameters: !Hyperparameters
     epochs: 3
     per_device_train_batch_size: 32
     learning_rate: 2e-5
+
+models:
+- !Model
+  id: tensorflow-tc-bert-en-uncased-L-12-H-768-A-12-2
+  version: 1.0.0
+  source: sagemaker
 ```
 {/* 
 ### Recommended Models