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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+tags:
+  - lora
+  - peft
+  - onnxruntime-genai
+  - agent-memory
+  - qwen2
+---
+
+# PSM Memory — Qwen2.5-0.5B LoRA adapters + ONNX Runtime GenAI export
+
+Three task-specific LoRA adapters trained on `Qwen/Qwen2.5-0.5B-Instruct` for the PSM
+("memory as a cognitive skill") project, plus a production ONNX export with all three adapters
+as swappable deltas over one shared base graph.
+
+## Adapters
+
+| Task | Directory | Gate score | LoRA config |
+|---|---|---|---|
+| Storage decision (store/ignore/update/etc.) | `lora/storage/` | 0.84 (100-case coding-agent gate) | r=16, alpha=32 |
+| Retrieval-plan (recall/context planning) | `lora/retrieval_plan/` | 0.935 | r=16, alpha=32 |
+| Consolidation (merge/update/conflict resolution) | `lora/consolidation/` | 0.826 | r=16, alpha=32 |
+
+All three share identical LoRA config (target_modules: q/k/v/o/gate/up/down_proj), which is what
+allows them to share one base ONNX graph in `onnx/`.
+
+## ONNX Runtime GenAI export (`onnx/`)
+
+`onnx/model.onnx` is the base model traced with LoRA branches kept genuinely separate (not merged),
+via Olive's `--use_dynamo_exporter` + `ExtractAdapters` pass. `onnx/adapters/*.onnx_adapter` are the
+three swappable adapter deltas. Confirmed via `Microsoft.ML.OnnxRuntimeGenAI`
+(`Adapters.LoadAdapter` / `Generator.SetActiveAdapter`) at **exact parity with the PyTorch baseline**
+(0.84 on the storage gate, parse_valid_rate 1.00).
+
+Produced by the repeatable conversion pipeline at
+`psm-model/scripts/convert_adapters_onnx.py` in the source repo — see that script for the exact
+Olive/transformers version pinning required (Olive 0.13.0 needs `transformers==4.48.3` for its
+dynamo-exporter Cache-compatibility patch; this machine's default `transformers` 5.x will silently
+break the export otherwise).
+
+```python
+import onnxruntime_genai as og
+
+model = og.Model("onnx")
+tokenizer = og.Tokenizer(model)
+adapters = og.Adapters(model)
+adapters.load("onnx/adapters/storage.onnx_adapter", "storage")
+
+params = og.GeneratorParams(model)
+params.set_search_options(do_sample=False, max_length=4096)
+generator = og.Generator(model, params)
+generator.set_active_adapter(adapters, "storage")
+# ... encode prompt, generate ...
+```
+
+## Raw PEFT adapters (`lora/`)
+
+Standard PEFT adapter directories (`adapter_config.json` + `adapter_model.safetensors`), loadable via
+`peft.PeftModel.from_pretrained(base_model, "lora/storage")` etc. against the base
+`Qwen/Qwen2.5-0.5B-Instruct` model.