declare-lab

declare-lab / delta-Mem

Public

The official repo of the paper: delta-Mem: Efficient Online Memory for Large Language Models

46
1
69% credibility
Found May 13, 2026 at 46 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Delta-Mem provides code for training, evaluating, and demonstrating memory-augmented language models via lightweight adapters on top of base models like Qwen.

How It Works

1
🌐 Discover Delta-Mem

You stumble upon this project that upgrades AI to remember conversations like a real friend.

2
📥 Set up your playground

Run a simple setup script to get everything ready for chatting in minutes.

3
🚀 Launch the smart chat

Fire up the demo and start talking to an AI that holds onto every detail you share.

4
💬 Chat and see memory magic

Ask follow-up questions and feel amazed as it recalls earlier parts of your talk.

5
💾 Save your memories

Capture the conversation state so your AI picks up right where you left off next time.

😊 Smarter talks forever

Now you have a personal AI companion that builds wisdom from every chat you have.

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Star Growth

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AI-Generated Review

What is delta-Mem?

Delta-Mem adds efficient online memory to large language models like Qwen3 and SmolLM3, solving the KV cache explosion in long conversations by incrementally updating a compact state instead of replaying full history. Written in Python with Hugging Face Transformers, it provides bash-driven chat demos, SFT training scripts, and benchmarks on LoCoMo QA and MemoryAgentBench. Users get persistent recall across turns via lightweight adapters, plus CLI tools for quick sessions and official report-style evals.

Why is it gaining traction?

It stands out by enabling delta membership levels of memory retention with tiny param overhead, outperforming full-context baselines on long-range QA without the compute hit. Devs hook into the one-liner chat demo (`run_chat_demo.sh`) and model-specific training suites for Qwen3-4B or SmolLM3-3B, plus official GitHub actions for repro. As the official GitHub repository of the paper, it delivers delta membrane systems for stateful chats that feel magically persistent.

Who should use this?

LLM fine-tuners building chat agents for delta member login flows or memphis delta-style multi-turn trackers. Researchers probing memory in long-doc QA, like delta memorial hospital record retrieval or delta memphis atlanta event timelines. Teams needing delta membership levels without exploding inference costs.

Verdict

Solid pick for mem-aug experiments—fire up the chat demo or training suites today for real gains. With 46 stars and 0.70% credibility score, it's raw research code from the official GitHub releases page; great for prototypes, but add your own tests for production.

(198 words)

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