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Purdy0228/
ConvMemory-LoCoMo-MPNet

Name: ConvMemory-LoCoMo-MPNet
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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:

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:

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.

Purdy0228/ConvMemory-LoCoMo-MPNet

ConvMemory LoCoMo MPNet

Files

Usage

Checkpoint Configuration

Intended Use

Limitations

Citation

Links

Purdy0228/
ConvMemory-LoCoMo-MPNet