docs: add mellea byoa tutorial

tutorials/README.md

@@ -24,6 +24,7 @@ Step-by-step walkthroughs covering adapter invocation, pipeline construction, an
 | [Bring Your Own Adapter](guides/bring_your_own_adapter.md) | Train, compose, and use custom adapters |
 | [Compare Inference Throughput](guides/compare_inference_throughput.md) | Compare LoRA vs aLoRA based models in an inference race setup |
 
+
 ## Learning Paths
 ### Composing Models
 
@@ -60,7 +61,7 @@ Best for: Seeing how adapters compose into multi-step applications
 Best for: Custom adapter development
 
 1. [Bring Your Own Adapter Guide](guides/bring_your_own_adapter.md)
-2. [Configure Your Own Adapter Guide](guides/configure_your_own_adapter.md)
+2. [Configure Your Own Adapter Guide](guides/mellea_bring_your_own_adapter.md)
 3. [Compose Your Checkpoint](notebooks/compose_granite_switch.ipynb) 
 
 

tutorials/guides/mellea_bring_your_own_adapter.md

@@ -0,0 +1,84 @@
+# Bring Your Own Adapter with Mellea
+
+This guide explains how to configure your own adapter with Mellea to be used by Granite Switch model.
+
+## Overview
+
+Together, Mellea + Granite Switch + vLLM provide a production-ready inference stack for adapter-based AI applications that can utilize custom adapters.
+- See [Mellea With Granite Switch](mellea_with_granite_switch.md) for a detailed explanation of how granite-switch and Mellea work together.
+- See [Bring Your Own Adapter](bring_your_own_adapter.md) for info on how to train your own adapter.
+- See Mellea's [Lora and aLoRA adapters](https://docs.mellea.ai/advanced/lora-and-alora-adapters) for info on how to train your own custom adapters using Mellea.
+
+## Prerequisites
+
+1. **A composed Granite Switch model** with the adapters you need
+2. **A running vLLM server** serving that model
+
+## Installation
+
+```bash
+pip install mellea
+```
+
+## Quick Example
+
+By default, calling custom adapters requires utilizing lower level interfaces in Mellea:
+
+```python
+import json
+
+from mellea.backends.model_options import ModelOption
+from mellea.backends.openai import OpenAIBackend
+from mellea.stdlib.context import ChatContext
+from mellea.stdlib.components import Message, Intrinsic
+import mellea.stdlib.functional as mfuncs
+
+# 1. Initialize the Mellea Backend
+backend = OpenAIBackend(
+    model_id="path/to/your/granite-switch-model",  # Local files or Huggingface model id
+    base_url="http://localhost:8000/v1",  # vLLM server
+    api_key="unused",  # vLLM doesn't require auth by default
+    load_embedded_adapters=True
+)
+
+# By default, load_embedded_adapters will autoload the adapters for the provided switch model.
+# If you need to explicitly load adapters from another location (that are supported by the running vLLM
+# server / model), you can use `backend.register_embedded_adapter_model` as shown in the "Mellea With Granite Switch"
+# example.
+
+# 2. Use Your Custom Adapter
+# Your custom adapter likely requires certain inputs. Here, the example assumes a simple
+# chat / conversation is enough.
+context = ChatContext().add(Message("assistant", "Hello there, how can I help you?"))
+action = Intrinsic("<your-custom-adapter-name>")
+
+out, _ = mfuncs.act(
+    action,
+    context,
+    backend,
+    model_options={ModelOption.TEMPERATURE: 0.0},
+    strategy=None,
+)
+
+# Adapter / Intrinsic processing in Mellea utilizes the io.yaml format forcing the output
+# to be a json. See the "Bring Your Own Adapter" linked example above.
+result = json.loads(str(out))
+print(result)
+```
+
+If you want to define helper functions so that your adapters operate similarly to the high-level
+intrinsic wrapper functions in Mellea, you can do the following:
+
+```python
+# Continuing with imports / code from above.
+from typing import Any
+
+from mellea.backends.adapters import AdapterMixin
+from mellea.stdlib.components.intrinsic._util import call_intrinsic
+# Importing from a private API is fragile. You can imitate the same functionality below without it as well.
+
+# Replace the return type with the actual type.
+def your_custom_functionality(context: ChatContext, backend: AdapterMixin) -> Any:
+    result_json = call_intrinsic("<your-custom-adapter-name>", context, backend)
+    return result_json["<your-io-yaml-output-field(s)>"]
+```

tutorials/guides/mellea_with_granite_switch.md

@@ -38,8 +38,7 @@ See [PREREQUISITES.md](../PREREQUISITES.md) for detailed setup instructions.
 ## Installation
 
 ```bash
-# Install Mellea from source
-pip install "git+https://github.com/generative-computing/mellea.git@main"
+pip install mellea
 ```
 
 ## Quick Example