The Alternative to RAG
Near-Zero Forgetting
RAG looks up answers every time. ModelBrew bakes knowledge directly into the model. Train across multiple sequential domains — your model keeps what it learns, with prior-task drift within measurement noise on our 3-seed Mistral-7B benchmark. No vector database, no retrieval pipeline.
3 free runs/day on TinyLlama. Pro from $3.99/M tokens. See pricing
Mistral-7B, 5 sequential domains, 3 seeds. Per-seed MODULAR and NAIVE ranges are disjoint at every seed. All forgetting numbers are conditional on correct inference-time routing.
RAG retrieves. ModelBrew remembers.
RAG systems look up answers from documents every time — slow, fragile, and expensive to maintain. ModelBrew trains the knowledge directly into the model weights. No vector database. No chunking pipeline. Your model just knows it.
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.
Teams training models across multiple domains — without retraining from scratch every time.
Clinical NLP
Train on radiology reports, then clinical notes, then pathology — without forgetting 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.
Dataset Optimizer
Clean your fine-tuning dataset before training. 60+ validator codes, AI-judge scoring with score-floor-gated rewrite, structural pair audit + judge-based polarity sample, tool-call validation, jailbreak + military-OPSEC + industry-specific PII detection — all in your browser. Free, no signup.
60+ validator codes
Format, schema, length, dedup (exact + near + semantic), encoding, GPT-slop, refusals, repetition, mislabel detection. Every flag points back to a row index.
AI judge + rewrite
Four-axis judge with calibration exemplars; optional 14-dim and G-Eval rubrics. Rewriter preserves every number, URL, named entity, and acronym — verified by a fact-diff before the row ships.
DPO / ORPO structural audit
Eight structural defect codes — identity pairs, near-duplicate chosen, both-refusals, both-too-short, extreme length bias, sycophantic chosen, refusal-as-chosen, missing prompt. The pair-level checks row-level scanning misses.
Tool-call validation
OpenAI tool_calls and Anthropic tool_use shape detection. Missing-required-arg and wrong-arg-type are critical; unknown-arg is a warning. Built for shipping agentic fine-tunes.
Jailbreak · OPSEC · typed PII
Eight jailbreak categories (prompt injection, role bypass, system extraction, encoding attacks). Six military OPSEC codes (MGRS, EDIPI, classification markings, DTG, lat/long, network refs). Nine industry-specific PII detectors (medical: MRN/DEA/ICD-10/NPI, financial: CUSIP/SWIFT/ABA, legal: bar number/Bates) on top of the standard 10-type regex PII pass.
Proven at 100,000 rows
250 rows / sec on a single worker, peak RSS under 1.5 GB. End-to-end scan of 100k OASST1 and 100k military corpora. Real benchmark, not a marketing number.
Supports JSONL, CSV, and JSON · Up to 50MB · No account needed
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 comes from original research — not a wrapper around existing tools. We publish our methodology, run multi-seed experiments, and update the algorithm based on results. Patent pending (US provisional filed Feb 2026).
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 →Near-Zero Forgetting on Mistral-7B Across 5 Domains
Modular LoRA on a spectrally bounded CRMA backbone: −0.17% ± 0.17 MODULAR drift vs +42.96% ± 5.5 NAIVE forgetting across 3 seeds. Per-seed ranges disjoint. Validated on 5 models across 4 architecture families. Patent pending.
Preprint · 3 seeds · 5 domains · Mistral-7B & Gemma-2-9B
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 | -0.17% drift | None | $1-3 | Built-in |
| Naive LoRA | +43% (7B) / +225% (1.1B) | None | Varies | No |
| 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 | −0.56% | +2.22% | +149.6% |
| Legal | −0.55% | +1.83% | +34.3% |
| Financial | +0.59% | +1.74% | +17.8% |
| Code | −0.51% | +2.78% | +13.0% |
| Science | +0.20% | +1.17% | +0.08% |
| 3-seed Avg | −0.17% | +1.95% | +42.96% |
Key insight: CRMA drift is on the order of an order of magnitude lower than FROZEN (∼1.95%), and two orders of magnitude lower than naive sequential LoRA (∼43%). The 3-seed average of the per-domain values reconciles to −0.17% at the bottom row. 3-seed average across seeds 0, 42, 1234; Mistral-7B.
View full benchmark data & methodology
CRMA Internal (Mistral-7B, 5 domains, 3-seed avg): CRMA Modular −0.17% ± 0.17 drift, Frozen +1.95% ± 0.64, Naive +42.96% ± 5.5. Per-seed MODULAR and NAIVE ranges are disjoint. No replay, no EWC, no knowledge distillation.
Gemma-2-9B inference ablation: 98/100 with CRMA (Wilson 95% CI [93.0%, 99.5%]) vs 38/100 without (Wilson 95% CI [29.0%, 47.8%]). Same weights, same questions, only CRMA toggled.
Pricing (April 2026): ModelBrew FT $3.99/M, all 7–9B models, with gradient visibility + built-in Dataset Optimizer. CL is in closed beta and not available for self-serve purchase at this time. OpenAI GPT-4.1 $3.00/M (no CL, FT only on their models). Together/Fireworks/OpenPipe $0.48-0.50/M (FT only, no cleaner, no CL). Mistral La Plateforme $1.00/M.
Head-to-head baselines: We have not run head-to-head comparisons against published CL methods (O-LoRA, InfLoRA, Lewandowski et al.) on our protocol. This is the single largest gap in our research; it is acknowledged openly in the paper. Our internal controls compare NAIVE vs FROZEN vs MODULAR on identical data.
CRMA results are from internal benchmarks using holdout evaluation. All forgetting-prevention numbers are conditional on correct inference-time routing.
Pay only for what you use.
No subscriptions. Sign up and get 75 credits free ($7.50). Load $20 in credits when you're ready, pay only for tokens used. 3 free training runs per day on TinyLlama.
- 75 credits free at signup ($7.50)
- 3 runs per day on TinyLlama-1.1B
- Fine-tuning mode
- Download adapter ZIPs
- Real-time training progress
- All models (Mistral-7B, Llama-3.1-8B, Saul-7B, Qwen3-8B, Gemma-2-9B)
- Fine-tuning + continual learning
- Priority GPU access
- Cost estimates before each run
- Credits never expire — balance rolls over
All 7–9B models (Mistral-7B, Llama-3.1-8B, Saul-7B, Qwen3-8B, Gemma-2-9B)
| Fine-Tuning | $3.99 / M tokens |
| Continual Learning | Closed beta — contact us |
| Clean with AI (Dataset Optimizer) | 50 credits per 200 rows |
Credits & Balance
| Minimum credit purchase | $20 |
| Credits roll over | Never expire |
| Failed jobs | Auto-refunded |
Example: Fine-tune Mistral-7B on 500 medical Q&A pairs
| Estimated tokens | ~135K tokens |
| Rate (Fine-Tuning) | $3.99 / M tokens |
| Computed cost | $0.54 |
| Deducted from balance | $0.54 |
Example: Continual learning on Mistral-7B — 5 domains
Continual learning is currently in closed beta and not available for self-serve purchase. Request access if you'd like to evaluate it on your data.
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.
Production security. We lock down the API, storage, and runtime for healthcare, financial, and regulated teams.
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.
Near-zero catastrophic forgetting — validated on Mistral-7B and Gemma-2-9B across 5 sequential domains (3 seeds). ModelBrew AI makes continual fine-tuning practical, accessible, and pilot-ready (SFT path); preference-tuning surface (SimPO/DPO) in beta.
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. Background in regulated industries and hands-on ML engineering. Built CRMA from first experiment to deployed API.
Learn about fine-tuning and continual learning.
Technical articles about stable fine-tuning and why it matters for production AI.
Why RAG Falls Short — And What Happens When You Bake Knowledge Into the Model
Everyone is building RAG pipelines. We took a different path: train knowledge directly into the model weights, across sequential domains, with near-zero forgetting.
Read more →DPO vs SimPO in 2026: Which Preference-Tuning Method Should You Use?
Side-by-side comparison of Direct Preference Optimization and SimPO — when each works, the trade-offs, and how ModelBrew picks the right one for your dataset.
Read more →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 made fine-tuning possible 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, enterprise pricing, or fine-tuning? Reach out.
Roadmap
Fine-Tuning
Continual Learning
Enhanced Reasoning
Agent Training
Real-Time CL
Ditch the vector database. Teach your model directly.
Start with 3 free runs on TinyLlama. No credit card, no setup, no retrieval pipeline 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, and 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.