README.md

metadata

title: Zero Forgetting Benchmarks
emoji: 🧠
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 6.9.0
app_file: app.py
pinned: false
license: apache-2.0
short_description: Zero forgetting in LLM fine-tuning — 4 benchmarks
tags:
  - continual-learning
  - catastrophic-forgetting
  - fine-tuning
  - lora
  - qlora
  - mistral
  - llm
  - adapters
  - benchmark
  - zero-forgetting
  - gradient-stability
  - spectral-norm
  - peft
  - parameter-efficient-fine-tuning
  - sequential-training
  - domain-adaptation
  - multi-domain
  - knowledge-retention
  - backward-transfer

Zero Forgetting in LLM Fine-Tuning — Benchmark Results

ModelBrew AI — the only commercial continual learning solution for LLM fine-tuning. Patent pending.

The Problem: Catastrophic Forgetting

Every time you fine-tune a large language model on new data, it forgets what it already knew. This is called catastrophic forgetting — one of the most well-documented and least solved problems in deep learning. The standard workarounds (separate models per domain, retraining from scratch, RAG, EWC, experience replay, knowledge distillation) are expensive, fragile, or never made it to production.

The Solution

ModelBrew is a small continual learning adapter (~0.1% additional parameters) that prevents catastrophic forgetting during fine-tuning. Train one model sequentially across domains — medical, legal, financial, code, enterprise — and it retains all prior knowledge. Works with any LoRA/QLoRA setup on any open-source model.

4 Benchmarks on Mistral-7B — Zero Forgetting

Benchmark 1: Multi-Seed Research (5 domains, 3 seeds)

• Domains: Medical → Legal → Financial → Code → Science
• -0.17% drift vs +43% forgetting with naive LoRA
• Naive LoRA crashed at step 43 (gradient norm 263). ModelBrew: peak gradient norm under 6
• Spectral norm locked at 1.0

Benchmark 2: Walmart Enterprise (4 domains)

• Customer Service → Product Knowledge → HR Policy → Financial Analytics
• BERTScores 0.82–0.94 across all domains retained

Benchmark 3: Salesforce Enterprise (5 domains)

• CRM Operations → Sales Ops → Reporting & Analytics → Customer Support → Admin & Dev
• Positive backward transfer — retention BERTScores improved with each domain (0.889 → 0.907)
• The model gets better at old domains as it learns new ones

Benchmark 4: Dental Stress Test (8 domains, 2 seeds)

• 8 sequential domains — the longest chain tested
• Peak gradient norms stable (3.8–6.1). Zero crashes. Zero NaN losses.

Key Results

• Zero catastrophic forgetting across all 4 benchmarks
• Spectral norm locked at 1.0 — gradient stability guaranteed by construction
• No replay buffers, no EWC, no knowledge distillation — none of the standard CL machinery needed
• 98.9% gradient norm reduction vs standard LoRA on Mistral-7B
• Works with LoRA, QLoRA, any open-source LLM (Mistral, LLaMA, etc.)

What's Shipped

• Live product processing real training runs
• 196 automated tests with CI pipeline
• US patent pending (provisional filed February 2026)
• 7 technical reports
• Free tier — no credit card needed

Links

• Live Product: ModelBrew Dashboard
• API: ModelBrew API
• Contact: fourwheels2512@gmail.com

Keywords

Catastrophic forgetting, continual learning, continual fine-tuning, sequential fine-tuning, LLM fine-tuning, LoRA, QLoRA, parameter-efficient fine-tuning, PEFT, domain adaptation, multi-domain training, knowledge retention, backward transfer, gradient stability, spectral normalization, Mistral-7B, zero forgetting, adapter, continual learning benchmark, enterprise fine-tuning, ModelBrew AI

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Kiran Nayudu — ModelBrew AI — Patent Pending (US Provisional, February 2026)