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README.md

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+---
+base_model: ibm-granite/granite-4.0-350m
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- granite
+- granite-switch
+- lora
+- adapters
+- mixture-of-adapters
+license: apache-2.0
+---
+
+# Granite Switch 4.0 350M — 3-Adapter Demo
+
+A single [Granite Switch](https://github.com/generative-computing/granite-switch)
+checkpoint built on [`ibm-granite/granite-4.0-350m`](https://huggingface.co/ibm-granite/granite-4.0-350m)
+with **three task LoRA adapters embedded in one model**. Each adapter is activated
+by a control token, so one deployed checkpoint serves three different tasks — no
+adapter swapping, no separate model loads.
+
+This is the multi-adapter companion to the single-adapter
+[`barha/granite-switch-4.0-350m-cti`](https://huggingface.co/barha/granite-switch-4.0-350m-cti).
+
+## Embedded adapters
+
+| # | Adapter | Control token | Task | Source LoRA |
+|---|---------|---------------|------|-------------|
+| 1 | `cti-technique-mapping` | `<\|cti-technique-mapping\|>` | Map a CTI description → MITRE ATT&CK technique ID | [barha/granite-cti-technique-mapping-350m-lora](https://huggingface.co/barha/granite-cti-technique-mapping-350m-lora) |
+| 2 | `text-to-json` | `<\|text-to-json\|>` | Natural language + schema → schema-conforming JSON | [barha/granite-text-to-json-350m-lora](https://huggingface.co/barha/granite-text-to-json-350m-lora) |
+| 3 | `genai-attack-vector` | `<\|genai-attack-vector\|>` | Classify a GenAI security incident into 1 of 14 attack-vector classes | [barha/granite-genai-attack-vector-350m-lora](https://huggingface.co/barha/granite-genai-attack-vector-350m-lora) |
+
+All three adapters share an identical LoRA shape (rank 16, alpha 32, on the fused
+q/k/v/o attention projections and the `input_linear` / `output_linear` MLP
+projections), which is what lets them stack cleanly into one switch checkpoint.
+
+Control token IDs: `100352`, `100353`, `100354` (3 new tokens; vocab 100355).
+
+## Per-adapter evaluation (on granite-4.0-350m)
+
+| Adapter | Metric | Score | n |
+|---------|--------|-------|---|
+| text-to-json | Key-F1 (headline) | **98.4** | 2000 |
+| genai-attack-vector | Accuracy | **74.8%** (166/222) | 222 |
+| cti-technique-mapping | — | see source repo | — |
+
+These are the standalone scores of each source LoRA; embedding them in the switch
+does not change adapter weights.
+
+## How adapter selection works
+
+Granite Switch routes per request via the control token: place the adapter's
+control token in the prompt (the chat template handles placement) and the switch
+activates that adapter for the turn. With no control token, the base model runs
+unmodified.
+
+## Usage
+
+Compose / inference follow the standard Granite Switch flow — see the
+[granite-switch repo](https://github.com/generative-computing/granite-switch) and
+its tutorials. The checkpoint loads as a `GraniteSwitchForCausalLM`; `adapter_index.json`
+lists the adapter → control-token mapping and `io_configs/<adapter>/io.yaml` carries
+each adapter's I/O contract.
+
+## Build
+
+Composed with `granite_switch.composer.compose_granite_switch`:
+
+```bash
+python -m granite_switch.composer.compose_granite_switch \
+  --base-model ibm-granite/granite-4.0-350m \
+  --technology lora \
+  --adapters cti-technique-mapping text-to-json genai-attack-vector \
+  --output ./granite-switch-4.0-350m-demo
+```
+
+Base params 352M → composed 359M (+2.0%). See `BUILD.md` and `compose_report.json`
+in this repo for the full composition report.
+
+License: Apache-2.0