MemOperator 0.6B Memory Write Gate
This PEFT LoRA adapter turns MemTensor/MemOperator-0.6B into a bilingual SKIP/SAVE sequence classifier for long-term-memory admission.
The model is intentionally tuned as a conservative gate: false saves are preferred over false skips. Use the threshold in classifier_metadata.json, rather than an implicit 0.5 threshold.
Evaluation
| Evaluation set | Accuracy | ROC-AUC | SAVE precision | SAVE recall |
|---|---|---|---|---|
| Held-out synthetic seed families (2,600 rows) | 89.73% | 96.89% | 86.66% | 93.92% |
| Small human-authored diagnostic set (60 rows) | 85.00% | 97.78% | 76.92% | 100.00% |
The 60-row diagnostic set is a small author-curated check, not a production benchmark. The synthetic test split isolates seed families but remains generator-domain data.
Usage
import json
import torch
from huggingface_hub import hf_hub_download
from peft import AutoPeftModelForSequenceClassification
from transformers import AutoTokenizer
model_id = "chris0809/memoperator-0.6b-memory-write-gate"
tokenizer = AutoTokenizer.from_pretrained(model_id)
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token
model = AutoPeftModelForSequenceClassification.from_pretrained(model_id)
model.config.pad_token_id = tokenizer.pad_token_id
metadata = json.loads(open(hf_hub_download(model_id, "classifier_metadata.json"), encoding="utf-8").read())
inputs = tokenizer("以后给我写周报时先写结论。", return_tensors="pt")
with torch.inference_mode():
save_probability = torch.softmax(model(**inputs).logits, dim=-1)[0, 1].item()
decision = "SAVE" if save_probability >= metadata["threshold"] else "SKIP"
Evaluate the gate on independently labeled traffic before using it for persistent memory. Do not store secrets merely because the classifier returns SAVE; deterministic privacy rules should run before this model.
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