Add ConvMemory LoCoMo MPNet checkpoint

LICENSE

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+MIT License
+
+Copyright (c) 2026 ConvMemory contributors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+license: mit
+library_name: convmemory
+tags:
+- retrieval
+- memory
+- reranking
+- agents
+- convmemory
+- locomo
+pipeline_tag: feature-extraction
+---
+
+# ConvMemory LoCoMo MPNet
+
+This repository contains the public ConvMemory LoCoMo/MPNet checkpoint.
+
+ConvMemory is a lightweight learned memory reranker for long-term conversational and agent memory. It runs after vector search and before prompt construction:
+
+```text
+user query -> vector search top-k -> ConvMemory -> memory context
+```
+
+## Files
+
+- `model.pt`: ConvMemory checkpoint weights.
+- `config.json`: ConvMemory model and rerank configuration.
+- `manifest.json`: checksum and configuration manifest.
+- `LICENSE`: MIT license.
+
+## Usage
+
+Install ConvMemory from GitHub, or from PyPI after the next package release:
+
+```bash
+pip install git+https://github.com/pth2002/ConvMemory.git
+```
+
+Load directly from Hugging Face Hub:
+
+```python
+from convmemory import ConvMemory
+
+model = ConvMemory.from_pretrained("Purdy0228/ConvMemory-LoCoMo-MPNet")
+
+results = model.retrieve(
+    query="When is the hiking trip?",
+    memories=memories,
+    top_k=10,
+)
+```
+
+Use with the CCGE-LA conflict editor:
+
+```python
+from convmemory import ConvMemory
+
+model = ConvMemory.from_pretrained("Purdy0228/ConvMemory-LoCoMo-MPNet")
+model.load_ccge_editor("Purdy0228/ConvMemory-CCGE-LA")
+
+results = model.retrieve(
+    query=query,
+    memories=memories,
+    editor="ccge_la",
+    top_k=10,
+)
+```
+
+For systems with precomputed embeddings, skip encoder loading and pass embeddings directly:
+
+```python
+model = ConvMemory.from_pretrained("Purdy0228/ConvMemory-LoCoMo-MPNet", embedding_model=False)
+ranked = model.rerank_embeddings(
+    query_embedding=query_embedding,
+    memory_embeddings=memory_embeddings,
+    memory_ids=memory_ids,
+    memory_texts=memory_texts,
+    query=query,
+)
+```
+
+## Checkpoint Configuration
+
+| Field | Value |
+|---|---:|
+| Embedding backbone | `sentence-transformers/all-mpnet-base-v2` |
+| Embedding dimension | 768 |
+| Window size | 5 |
+| Stride | 1 |
+| Kernel size | 3 |
+| Hidden dimension | 256 |
+| Token MLP dimension | 32 |
+| Channel MLP dimension | 512 |
+| Candidate top-n | 500 |
+| Raw score fusion weight | 0.025 |
+
+## Intended Use
+
+- Retrieval-stage reranking for long-term conversational memory.
+- Agent memory selection after vector search.
+- Memory streams where missing relevant evidence is costly.
+
+## Limitations
+
+- This is not a vector database or end-to-end QA model.
+- It is not intended as a general web/document reranker.
+- The checkpoint is optimized for the MPNet embedding space; other embedding backbones require retraining or validation.
+- Scores are not calibrated by default.
+- No inference widget is provided; use the `convmemory` Python library.
+
+## Citation
+
+A formal citation will be added when a technical report is available.
+
+## Links
+
+- GitHub: https://github.com/pth2002/ConvMemory
+- CCGE-LA checkpoint: https://huggingface.co/Purdy0228/ConvMemory-CCGE-LA

config.json

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+{
+  "embedding_model": "sentence-transformers/all-mpnet-base-v2",
+  "format": "convmemory",
+  "model_config": {
+    "channel_mlp_dim": 512,
+    "embedding_dim": 768,
+    "extra_scalar_features": 5,
+    "hidden_dim": 256,
+    "kernel_size": 3,
+    "token_mlp_dim": 32,
+    "window_size": 5
+  },
+  "rerank_config": {
+    "candidate_top_n": 500,
+    "dca_router_block_size": 32,
+    "lexical_features": true,
+    "raw_weight": 0.025,
+    "stride": 1,
+    "window_size": 5
+  },
+  "version": 1
+}

manifest.json

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+{
+  "config_sha256": "8e02e303568177d3e45eab552e8b5eb2ff373da2ba86643235522e61e85e7bb5",
+  "embedding_model": "sentence-transformers/all-mpnet-base-v2",
+  "format": "convmemory",
+  "model_config": {
+    "channel_mlp_dim": 512,
+    "embedding_dim": 768,
+    "extra_scalar_features": 5,
+    "hidden_dim": 256,
+    "kernel_size": 3,
+    "token_mlp_dim": 32,
+    "window_size": 5
+  },
+  "model_pt_sha256": "2f96f5faf7f0cdbdb61df8bf511e5ebd276e57e84801a402fbce0dd522c05b4d",
+  "name": "convmemory-locomo-mpnet",
+  "notes": [
+    "Use with the convmemory Python package.",
+    "This checkpoint is trained for the MPNet embedding family.",
+    "LoCoMo-style results are in-domain for this checkpoint; do not treat them as broad OOD claims."
+  ],
+  "rerank_config": {
+    "candidate_top_n": 500,
+    "dca_router_block_size": 32,
+    "lexical_features": true,
+    "raw_weight": 0.025,
+    "stride": 1,
+    "window_size": 5
+  },
+  "status": "public ConvMemory LoCoMo/MPNet checkpoint",
+  "version": 1
+}

model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2f96f5faf7f0cdbdb61df8bf511e5ebd276e57e84801a402fbce0dd522c05b4d
+size 14605150