Fine-Tune & Continual Learning
It Remembers Everything
CRMA is a lightweight adapter that mathematically constrains fine-tuning — your model learns new domains without overwriting what it already knows. One model, unlimited domains, zero forgetting.
3 free runs/day on TinyLlama. Pro from $1/M tokens. See pricing
In practice: Mistral-7B kept performance flat across 5 new domains while naive LoRA lost up to 43% on previous tasks.
Teach your AI new things. It remembers everything.
Most AI models forget old knowledge every time you train them on something new. CRMA fixes that.
Upload your data
Medical notes, legal docs, code, anything.
Train with CRMA
CRMA guards the model so it can learn without forgetting.
Done. Nothing lost.
Your model knows the new stuff AND still remembers the old stuff.
Built for teams that can't afford to forget.
Any organization training models across multiple domains, departments, or data sources — without wanting to retrain from scratch every time.
Clinical NLP
Train on radiology reports, then clinical notes, then pathology — without losing prior specialties. Built by a healthcare practitioner who hit this problem firsthand.
Multi-Practice Firms
Fine-tune on contract review, then case law, then regulatory filings. Each practice area improves without degrading the others.
Cross-Asset Intelligence
Equities research, fixed income, credit analysis — one model that learns sequentially across asset classes without catastrophic forgetting.
Multi-Department AI
Support tickets, internal docs, product specs, HR policies. Add departments over time without retraining or managing dozens of separate models.
Regulated Industries
When data can't leave your network, ModelBrew ships as a Docker container. Same API, same results — runs on your infrastructure with zero external calls.
Production Pipelines
Plug into existing CI/CD. Upload data per domain, choose standard FT or continual learning, track per-domain metrics and drift over time via API.
Three lines to your first training run.
Works with any JSONL dataset. Or use the web UI — no code needed. · Full API docs →
Three papers. Real experiments. Ongoing research.
CRMA is built on active research — not a wrapper around existing tools. We publish our methodology, run multi-seed experiments at scale, and continuously improve the algorithm. Patent pending (US provisional filed Feb 2026).
98.9% Gradient Reduction at 7B Scale
LoRA crashes at step 43 on Mistral-7B (grad norm 263). CRMA holds at 3.0. 39-84% stability improvement across 3 runs.
Preprint · 3 independent runs · Mistral-7B
Read paper →Six CL Methods Tested — Six Failures
EWC, replay, gradient projection, knowledge distillation, O-LoRA, 10-component stacks. Best result: 58.4% forgetting. We tested them all so you don’t have to.
Preprint · v2-v7 experiments · TinyLlama & Mistral-7B
Read paper →-0.16% Drift Across 5 Domains at 7B
Modular adapters on a constrained backbone. No replay, no EWC, no growing memory. 3-seed validated, >3,500× improvement over naive. Patent pending.
Preprint · 3-seed avg · 5 domains · Mistral-7B
Read paper →Current Research & Development
Multi-seed experiments across 3 random seeds on Mistral-7B. 5 real-world domains (medical, legal, financial, code, science). Results reproducible across seeds.
Enhanced reasoning via self-distillation fine-tuning (SDFT). Scale testing beyond 7B. Head-to-head benchmark against O-LoRA and other academic CL methods.
Real-time continual learning (streaming updates). Agent fine-tuning with tool-use preservation. Automatic domain boundary detection.
Numbers, not promises.
CRMA has been tested across multiple model scales and domains. Here's what the benchmarks show.
| Method | Forgetting | Overhead | Price/M tokens | CL Support |
|---|---|---|---|---|
| CRMA | -0.16% drift | None | $1-3 | Built-in |
| Naive LoRA | +43% (7B) / +225% (1.1B) | None | Varies | No |
| DOC (Academic SOTA) | -0.6 BWT | Projection matrices | Research only | Research only |
| OpenAI | No CL | N/A | $3-25 | No |
| Mistral / Together | No CL | N/A | $0.48-9 | No |
How we measure: "Forgetting" = change in holdout loss on previously learned domains after training on new ones. Negative = the model got slightly better (ideal). Positive = knowledge was lost. Measured across 5 real-world domains (medical, legal, financial, code, science) on Mistral-7B, averaged over 3 random seeds.
Per-Domain Drift After 5 Sequential Domains
Each domain was trained sequentially. Drift measures how much earlier domains degraded after all 5 were trained. Negative = slight improvement (positive transfer).
| Domain | CRMA | Frozen | Naive LoRA |
|---|---|---|---|
| Medical | -0.09% | +1.39% | +128.0% |
| Legal | -0.17% | +1.87% | +37.1% |
| Financial | -0.13% | +1.75% | +18.9% |
| Code | -0.14% | +1.59% | +14.6% |
| Science | +0.01% | +1.68% | -0.05% |
| Average | -0.16% | +1.95% | +43.0% |
Key insight: CRMA shows slight negative drift on 4 of 5 domains — positive transfer, not suppression. If CRMA were simply freezing weights, it would match the Frozen column (~+1.7%). Instead it’s 10-100× lower. 3-seed avg, Mistral-7B.
View full benchmark data & methodology
CRMA Internal (Mistral-7B, 5 domains, 3-seed avg): CRMA Modular -0.16% drift, Frozen +1.95%, Naive +43.0%. No replay required.
Academic comparison: DOC (LLaMA-7B): -0.6 BWT with orthogonal projection. O-LoRA: -1.9 BWT. EWC: ~30% accuracy drop with Fisher matrix storage. Naive sequential: +95% to +351%.
Pricing (March 2026): CRMA $1-3/M tokens with gradient visibility. OpenAI GPT-4o $25/M (loss only, no CL). Mistral $4-9/M. Together $0.48-2.90/M. Google Vertex varies.
CRMA results are from internal benchmarks using holdout evaluation. Academic results use different metrics — direct comparison should be interpreted with caution. Individual results vary by dataset and configuration.
Pay only for what you use.
No subscriptions. Load $10 in credits, pay only for tokens used. Start free with 3 runs per day.
- 3 runs per day
- TinyLlama-1.1B only
- Fine-tuning mode
- Download adapter ZIPs
- Real-time training progress
- All models (Mistral, Gemma, TinyLlama)
- Fine-tuning + continual learning
- Priority GPU access
- Cost estimates before each run
- Credits never expire — balance rolls over
Fine-Tuning
| Small models (≤3B params) | $1 / M tokens |
| Large models (3B-16B params) | $3 / M tokens |
Continual Learning
| Small models (≤3B params) | $2 / M tokens |
| Large models (3B-16B params) | $5 / M tokens |
Credits & Balance
| Minimum credit purchase | $10 |
| Credits roll over | Never expire |
| Failed jobs | Auto-refunded |
Example: Fine-tune TinyLlama on 500 medical Q&A pairs
| Estimated tokens | ~135K tokens |
| Rate (≤3B) | $1 / M tokens |
| Computed cost | $0.14 |
| Deducted from balance | $0.14 |
Example: Continual learning on Mistral-7B — 4 domains
| ~800 samples per domain × 4 domains | ~865K tokens |
| Rate (3B-16B CL) | $5 / M tokens |
| Computed cost | $4.33 |
| Deducted from balance | $4.33 |
You only pay for tokens used. Load $10 to start — credits never expire, unused balance rolls over.
Refund Policy: If a training job fails due to a system error, your credits are automatically refunded — no action needed. Unused credits are non-refundable and non-transferable. All payments are processed securely by Stripe — we never see your card details. By purchasing credits, you agree to our Terms of Service.
Built for regulated industries.
Enterprise-grade security from day one. Every layer — from API to storage — is hardened for healthcare, financial, and regulated deployments.
Encryption at Rest
All model checkpoints and training data encrypted with AES-256 (Fernet). Secure delete enabled — no residual data on disk.
Security Headers
HSTS, X-Frame-Options DENY, Content-Type nosniff, XSS protection, strict Referrer-Policy, and Permissions-Policy on every response.
Audit Logging
Every API call logged with user, action, IP, and timestamp. Full audit trail for compliance reviews and incident response.
Role-Based Access
RBAC with granular permissions. Admin, user, and read-only roles. API keys separated from session tokens.
GDPR & Data Rights
One-click data export and account deletion. Your data, your control. Full compliance with data protection regulations.
Hardened Runtime
Non-root containers, health checks, safe model loading (no arbitrary code execution), and sanitized error responses.
Built by a practitioner, not a lab.
Zero catastrophic forgetting — proven at 7B parameters. ModelBrew AI is on a mission to make fine-tuning stable, accessible, and production-ready.
ModelBrew AI
Based in Frederick, Maryland. We build mathematically constrained fine-tuning technology that lets AI teams train on new data without losing what their models already know. Our platform runs on serverless GPUs — no infrastructure to manage, no MLOps team required.
Kiran Nayudu
Healthcare practitioner who built CRMA after watching fine-tuned models forget critical knowledge with every training run. Combines deep domain expertise in regulated industries with hands-on ML engineering — from research to deployed API.
Learn about fine-tuning and continual learning.
Deep dives into the technology behind stable fine-tuning, and why it matters for production AI.
What Is Fine-Tuning? Why It Matters and How It's Changing AI
Fine-tuning explained for a broader audience — real-world use cases in healthcare, legal, code, and finance.
Read more →What Are LoRA and QLoRA? A Practical Guide to Efficient Fine-Tuning
How LoRA and QLoRA democratized fine-tuning on consumer GPUs — and the stability problems they don’t solve.
Read more →How CRMA Solves Continual Learning
Stable backbone, swappable domain adapters, near-zero forgetting. No replay buffers, no growing memory.
Read more →Catastrophic Forgetting: The Silent Killer of Fine-Tuned Models
Why every fine-tuning run destroys prior knowledge, and what the research says about fixing it.
Read more →CRMA vs LoRA: What's the Difference?
Side-by-side comparison of standard LoRA and CRMA — when you need each, and what happens when you don’t use CL.
Read more →The Cost of Forgetting: Why Retraining From Scratch Is Unsustainable
The real-world compute, time, and quality costs of not having continual learning in your ML pipeline.
Read more →Get in touch.
Questions about CRMA, partnership inquiries, or just want to chat about fine-tuning? We'd love to hear from you.
Roadmap
Fine-Tuning
Continual Learning
Enhanced Reasoning
Agent Training
Real-Time CL
Stop losing what your model already knows.
Start with 3 free runs on TinyLlama. No credit card, no setup, no infrastructure to manage.
Legal Disclaimers & Legal Notices ▼
No Warranty. CRMA is provided "AS IS" without warranties. Not guaranteed to be uninterrupted or error-free.
Benchmarks. All metrics are from internal experiments under controlled conditions. Results are not guarantees — individual results vary by dataset, model, and configuration. Academic comparisons use different benchmarks.
AI Outputs. Fine-tuned models may produce inaccurate or harmful outputs. Users are responsible for validation. Not for medical, legal, or financial decisions without human review.
Liability. ModelBrew AI's total liability shall not exceed amount paid in the preceding 12 months. No liability for indirect or consequential damages.
IP. CRMA is protected by U.S. provisional patent (filed Feb 2026). Third-party names used for identification only.
Data. Your training data is used only for your job, stored temporarily, deleted after completion. We never train on your data. See Privacy Policy.
Research. Papers are pre-publication drafts, not yet peer-reviewed. Some experiments are single-seed.
Third-Party Services. Built on Modal, Stripe, Streamlit, Hugging Face. We're not responsible for their outages. Stripe handles payments — we never see your card.
Governing Law. State of Maryland, USA. Exclusive jurisdiction: Frederick County courts.
By using CRMA you agree to these disclaimers, our Terms, and Privacy Policy. Contact: info@modelbrew.ai.