docs: add With Mellea section to BYOA guide and align terminology

tutorials/README.md

@@ -22,7 +22,7 @@ Step-by-step walkthroughs covering adapter function invocation, pipeline constru
 | Guide | Description |
 |-------|-------------|
 | [Using Mellea with Granite Switch](guides/mellea_with_granite_switch.md) | Connect Mellea to a Granite Switch model |
-| [Bring Your Own Adapter](guides/build_your_own_adapter.md) | Train, compose, and use custom adapters |
+| [Build Your Own Adapter](guides/build_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 |
 
 
@@ -57,11 +57,11 @@ Best for: Seeing how adapter functions compose into multi-step applications
 
 
 
-### Path 3: Bring Your Own Adapter
+### Path 3: Build Your Own Adapter
 
 Best for: Custom adapter function development
 
-1. [Bring Your Own Adapter Guide](guides/build_your_own_adapter.md)
+1. [Build Your Own Adapter Guide](guides/build_your_own_adapter.md)
 2. [Configure Your Own Adapter Guide](guides/mellea_build_your_own_adapter.md)
 3. [Compose Your Checkpoint](notebooks/compose_granite_switch.ipynb) 
 

tutorials/guides/build_your_own_adapter.md

@@ -1,4 +1,4 @@
-# Bring Your Own Adapter (BYOA)
+# Build Your Own Adapter (BYOA)
 
 This guide explains how to train your own adapter (aLoRA or LoRA) and compose it into a Granite Switch model.
 
@@ -183,7 +183,7 @@ The base model's tokenizer and generation assets (`generation_config.json`, `mer
 
 ## Step 4: Use the Composed Model
 
-> **Note:** Custom (BYOA) adapters are not supported by [Mellea](https://github.com/generative-computing/mellea). Mellea only supports the official IBM Granite Library adapters. To invoke your custom adapters, use the chat template directly as shown below.
+You can invoke your custom adapter directly via the model's chat template (with HuggingFace or vLLM), or through [Mellea](https://github.com/generative-computing/mellea). Note that Mellea's *high-level* wrappers (Guardian, RAG, Core) are specific to the official IBM Granite Library adapters; a custom (BYOA) adapter is invoked by name through Mellea's lower-level `Intrinsic` interface, as shown in the **With Mellea** section below.
 
 ### With HuggingFace
 
@@ -247,6 +247,52 @@ response = client.chat.completions.create(
 print(response.choices[0].message.content)
 ```
 
+### With Mellea
+
+Mellea can drive the same vLLM server. Its high-level wrappers (Guardian, RAG, Core) only cover the official IBM Granite Library adapters, but a custom adapter is invoked by name through the lower-level `Intrinsic` interface:
+
+```bash
+pip install 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
+
+# Connect to the vLLM server from the "With vLLM" section above.
+# load_embedded_adapters=True autoloads the adapters composed into the model.
+backend = OpenAIBackend(
+    model_id="./composed-model",
+    base_url="http://localhost:8000/v1",
+    api_key="unused",  # vLLM doesn't require auth by default
+    load_embedded_adapters=True,
+)
+
+# Invoke your custom adapter by name (the io.yaml `name` from Step 2/3).
+context = ChatContext().add(Message("assistant", "Paris is the capital of France."))
+action = Intrinsic("uncertainty")
+
+out, _ = mfuncs.act(
+    action,
+    context,
+    backend,
+    model_options={ModelOption.TEMPERATURE: 0.0},
+    strategy=None,
+)
+
+# The adapter's io.yaml `response_format` (see Step 3) forces JSON output, and its
+# `transformations` rename `score` to `certainty`.
+result = json.loads(str(out))
+print(result["certainty"])
+```
+
+For the full walkthrough — including helper functions that wrap your adapter so it behaves like Mellea's built-in intrinsics — see [Build Your Own Adapter with Mellea](mellea_build_your_own_adapter.md).
+
 ## Next Steps
 
 - **[Hello Adapter](../notebooks/hello_adapter.ipynb)** - minimal embedded-adapter invocation via the HuggingFace backend

tutorials/guides/compare_inference_throughput.md

@@ -87,4 +87,4 @@ raced simultaneously.
 
 - **[Hello Adapter](../notebooks/hello_adapter.ipynb)** - minimal embedded-adapter invocation via the HuggingFace backend
 - **[Using Mellea with Granite Switch](mellea_with_granite_switch.md)** - deeper Mellea integration details
-- **[Bring Your Own Adapter](build_your_own_adapter.md)** - train a custom adapter and compose it in
+- **[Build Your Own Adapter](build_your_own_adapter.md)** - train a custom adapter and compose it in

tutorials/guides/mellea_build_your_own_adapter.md

@@ -1,12 +1,12 @@
-# Bring Your Own Adapter with Mellea
+# Build 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](build_your_own_adapter.md) for info on how to train your own adapter.
+- See [Build Your Own Adapter](build_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
@@ -61,7 +61,7 @@ out, _ = mfuncs.act(
 )
 
 # 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.
+# to be a json. See the "Build Your Own Adapter" linked example above.
 result = json.loads(str(out))
 print(result)
 ```

tutorials/guides/mellea_with_granite_switch.md

@@ -247,7 +247,7 @@ print(f"Citations: {citations}")
 ## Next Steps
 
 - **[Hello Adapter](../notebooks/hello_adapter.ipynb)** - Minimal embedded-adapter invocation via the HuggingFace backend
-- **[Bring Your Own Adapter](build_your_own_adapter.md)** - Train a custom adapter and compose it in
+- **[Build Your Own Adapter](build_your_own_adapter.md)** - Train a custom adapter and compose it in
 - **[Compare Inference Throughput](compare_inference_throughput.md)** - Benchmark ALORA vs LoRA on a 6-step RAG pipeline
 - **[Mellea Repository](https://github.com/generative-computing/mellea)** - Full documentation
 - **[Granite Models](https://huggingface.co/ibm-granite)**