Upload Concept Memory base model

README.md

@@ -23,6 +23,7 @@ not a language model pretrained from scratch. Its text encoder is
 - `model.pt`: PyTorch state dictionary and training metadata.
 - `config.json`: encoder, task, and training run metadata.
 - `concept_memory/`: the minimal model runtime modules needed for inference.
+- `example.py`: a runnable end to end Choice prediction example.
 
 ## Run a prediction
 
@@ -32,6 +33,12 @@ Use Python 3.11 or newer. Install the runtime dependencies:
 pip install torch transformers huggingface_hub
 ```
 
+Run the standalone example with:
+
+```bash
+python example.py
+```
+
 Download this model repository. It contains only the small `concept_memory`
 runtime package needed to load the checkpoint, rather than the full training
 project. Then construct a question with the criteria you want the model to
@@ -100,8 +107,10 @@ is not directly compatible with `transformers.AutoModel.from_pretrained`.
     "noul"
   ],
   "encoder": "sentence-transformers/all-mpnet-base-v2",
-  "epochs": 10,
-  "batch_size": 16,
-  "seed": 42
+  "epochs": 50,
+  "batch_size": 32,
+  "seed": 42,
+  "model_version": "base-20261003-171325Z",
+  "generated_at": "2026-10-03T17:13:25Z"
 }
 ```

config.json

@@ -10,7 +10,9 @@
     "noul"
   ],
   "encoder": "sentence-transformers/all-mpnet-base-v2",
-  "epochs": 10,
-  "batch_size": 16,
-  "seed": 42
+  "epochs": 50,
+  "batch_size": 32,
+  "seed": 42,
+  "model_version": "base-20261003-171325Z",
+  "generated_at": "2026-10-03T17:13:25Z"
 }

example.py

@@ -0,0 +1,57 @@
+"""Download the Hugging Face checkpoint and run one typed Choice prediction."""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import torch
+from huggingface_hub import snapshot_download
+
+
+REPO_ID = "Syigen/rgcm"
+
+
+def main() -> None:
+    repo_path = Path(snapshot_download(REPO_ID))
+    # The model repo ships this small runtime package beside the checkpoint.
+    sys.path.insert(0, str(repo_path))
+
+    from concept_memory import ConceptMemoryModel
+    from concept_memory.types import Question
+
+    checkpoint = repo_path / "model.pt"
+    payload = torch.load(checkpoint, map_location="cpu", weights_only=False)
+
+    model = ConceptMemoryModel.from_pretrained(
+        payload["encoder"],
+        decision_size=payload["decision_size"],
+        max_length=payload["max_length"],
+    )
+    model.load_state_dict(payload["model_state_dict"])
+    model.eval()
+
+    question = Question(
+        id="intent",
+        type="choice",
+        instructions="Which team should handle this customer request?",
+        criteria=[
+            {"id": "billing", "description": "Payment or subscription issues"},
+            {"id": "technical", "description": "Bugs or integration problems"},
+            {"id": "sales", "description": "Pricing or account questions"},
+        ],
+    )
+
+    with torch.inference_mode():
+        result = model.predict(
+            "Stripe integration keeps failing after several attempts.", [question]
+        )[0]
+
+    criteria = question.normalized_criteria()
+    predicted = criteria[result.prediction.item()]
+    print(f"Prediction: {predicted.id} - {predicted.description}")
+    print("Probabilities:", result.probabilities.tolist())
+
+
+if __name__ == "__main__":
+    main()

model.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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-size 437320470
+oid sha256:58255c1ecc04e59a5e5545788ce320fcce3695db0231e839b81a9a14dcf2911e
+size 441699103