"""ModelBrew AI — Zero Forgetting Benchmark Results Dashboard""" import gradio as gr import plotly.graph_objects as go # ── Brand colors ── BLUE = "#1F4E79" GREEN = "#4CAF50" RED = "#E53935" GOLD = "#F9A825" GRAY = "#757575" # ── Data ── SEEDS = ["Seed 0", "Seed 42", "Seed 1234"] NAIVE_FORGET = [38.1, 41.7, 49.0] MODELBREW_DRIFT = [-0.03, -0.10, -0.37] WALMART_DOMAINS = ["Customer Service", "Product Knowledge", "HR Policy", "Financial Analytics"] WALMART_GEN = [0.92, 0.94, 0.88, 0.83] WALMART_RET = [None, 0.83, 0.86, 0.82] SF_DOMAINS = ["CRM Ops", "Sales Ops", "Reporting", "Support", "Admin & Dev"] SF_GEN = [0.882, 0.897, 0.890, 0.885, 0.897] SF_RET = [None, 0.889, 0.891, 0.897, 0.907] SF_GN = [3.68, 2.15, 3.16, 2.53, 2.11] SF_LOSS = [1.33, 1.05, 1.24, 0.96, 0.66] DENTAL_DOMAINS = [f"Domain {i+1}" for i in range(8)] DENTAL_MB_GN = [3.8, 4.2, 5.1, 4.5, 5.5, 4.8, 6.1, 5.2] DENTAL_NAIVE_GN = [4.8, 5.6, 6.3, 6.9, 7.2, 8.1, 8.8, 9.4] def make_seed_chart(): fig = go.Figure() fig.add_trace(go.Bar( name="Naive LoRA (forgetting)", x=SEEDS, y=NAIVE_FORGET, marker_color=RED, text=[f"+{v}%" for v in NAIVE_FORGET], textposition="outside", )) fig.add_trace(go.Bar( name="ModelBrew (drift)", x=SEEDS, y=[abs(v) for v in MODELBREW_DRIFT], marker_color=GREEN, text=[f"{v}%" for v in MODELBREW_DRIFT], textposition="outside", )) fig.update_layout( title="Benchmark 1: Multi-Seed Research — 5 Domains on Mistral-7B", yaxis_title="Knowledge Lost (%)", barmode="group", template="plotly_white", height=450, legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="center", x=0.5), ) return fig def make_walmart_chart(): fig = go.Figure() fig.add_trace(go.Bar( name="Gen BERTScore", x=WALMART_DOMAINS, y=WALMART_GEN, marker_color=BLUE, text=[f"{v:.2f}" for v in WALMART_GEN], textposition="outside", )) ret_vals = [v if v else 0 for v in WALMART_RET] ret_text = [f"{v:.2f}" if v else "—" for v in WALMART_RET] fig.add_trace(go.Bar( name="Retention BERTScore", x=WALMART_DOMAINS, y=ret_vals, marker_color=GREEN, text=ret_text, textposition="outside", )) fig.update_layout( title="Benchmark 2: Walmart Enterprise — 4 Domains on Mistral-7B", yaxis_title="BERTScore", yaxis_range=[0.5, 1.0], barmode="group", template="plotly_white", height=450, legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="center", x=0.5), ) return fig def make_salesforce_chart(): fig = go.Figure() fig.add_trace(go.Scatter( name="Gen BERTScore", x=SF_DOMAINS, y=SF_GEN, mode="lines+markers+text", marker=dict(size=10, color=BLUE), text=[f"{v:.3f}" for v in SF_GEN], textposition="top center", )) ret_x = SF_DOMAINS[1:] ret_y = [v for v in SF_RET if v] fig.add_trace(go.Scatter( name="Retention BERTScore", x=ret_x, y=ret_y, mode="lines+markers+text", marker=dict(size=10, color=GREEN), text=[f"{v:.3f}" for v in ret_y], textposition="bottom center", line=dict(dash="dot"), )) fig.update_layout( title="Benchmark 3: Salesforce Enterprise — 5 Domains, Positive Backward Transfer", yaxis_title="BERTScore", yaxis_range=[0.85, 0.92], template="plotly_white", height=450, legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="center", x=0.5), ) return fig def make_salesforce_gn_chart(): fig = go.Figure() fig.add_trace(go.Bar( name="Peak Gradient Norm", x=SF_DOMAINS, y=SF_GN, marker_color=BLUE, text=[f"{v:.2f}" for v in SF_GN], textposition="outside", )) fig.add_hline(y=263, line_dash="dash", line_color=RED, annotation_text="Naive LoRA: 263 (crashed)", annotation_position="top left") fig.update_layout( title="Salesforce — Gradient Stability (Naive LoRA crashed at 263)", yaxis_title="Peak Gradient Norm", template="plotly_white", height=400, ) return fig def make_dental_chart(): fig = go.Figure() fig.add_trace(go.Scatter( name="Naive LoRA", x=DENTAL_DOMAINS, y=DENTAL_NAIVE_GN, mode="lines+markers", marker=dict(size=8, color=RED), line=dict(width=2), )) fig.add_trace(go.Scatter( name="ModelBrew", x=DENTAL_DOMAINS, y=DENTAL_MB_GN, mode="lines+markers", marker=dict(size=8, color=GREEN), line=dict(width=2), )) fig.update_layout( title="Benchmark 4: Dental Stress Test — 8 Sequential Domains", yaxis_title="Peak Gradient Norm", template="plotly_white", height=450, legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="center", x=0.5), ) return fig def make_summary_chart(): experiments = ["Research\n(5 domains)", "Walmart\n(4 domains)", "Salesforce\n(5 domains)", "Dental\n(8 domains)"] naive = [43.0, None, None, None] ours = [0.17, 0, 0, 0] fig = go.Figure() fig.add_trace(go.Bar( name="Naive LoRA Forgetting", x=["Research\n(5 domains)"], y=[43.0], marker_color=RED, text=["+43.0%"], textposition="outside", width=0.3, )) fig.add_trace(go.Bar( name="ModelBrew Drift", x=experiments, y=[0.17, 0, 0, 0], marker_color=GREEN, text=["-0.17%", "Zero", "Zero\n(positive transfer)", "Zero"], textposition="outside", width=0.3, )) fig.update_layout( title="Zero Forgetting Across All 4 Benchmarks", yaxis_title="Knowledge Lost (%)", barmode="group", template="plotly_white", height=450, legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="center", x=0.5), ) return fig OVERVIEW_MD = """ # Zero Forgetting in LLM Fine-Tuning **Every fine-tuning run destroys what the model already knew.** Train on medical, then legal — medical is gone. ModelBrew is a continual learning adapter (~0.1% additional parameters) that solves catastrophic forgetting. Train one model on domain after domain — **it keeps everything.** --- ### 4 Benchmarks on Mistral-7B. Zero Forgetting. Every Single One. | Benchmark | Domains | Seeds | Result | |-----------|---------|-------|--------| | **Research** | 5 (Medical → Legal → Financial → Code → Science) | 3 | **-0.17% drift** vs +43% naive forgetting | | **Walmart** | 4 (Customer Service → Product → HR → Finance) | 1 | **BERTScores 0.82–0.94** across all domains | | **Salesforce** | 5 (CRM → Sales → Reporting → Support → Admin) | 1 | **Positive backward transfer** (0.889 → 0.907) | | **Dental** | 8 sequential domains | 2 | **Gradient norms stable**, zero explosions | - Spectral norm locked at **1.0** across every experiment - Naive LoRA crashed at step 43 with gradient norm **263**. Ours: peak under **6** - No replay buffers. No EWC. No knowledge distillation. No retraining from scratch. --- ### What This Means Right now every AI team in the world throws away learned knowledge every time they fine-tune. That's billions of dollars in wasted compute and a fundamental barrier to AI that actually builds on what it knows over time. - A hospital trains one model across radiology, pathology, cardiology — it keeps learning, never forgets - A legal AI learns new case law without losing old precedent - Models in developing countries accumulate knowledge across languages and domains on limited hardware --- ### What's Shipped - **Live product** — processing real training runs today - **196 automated tests** — CI pipeline on GitHub Actions - **US patent pending** — provisional filed February 2026 - **7 technical reports** — from 50+ failed experiments to the working method - **Free tier** — try it right now, no credit card needed Google published Nested Learning at NeurIPS 2025. Meta has Sparse Memory Finetuning. Neither is available to use. **This is.** --- **[Try it live](https://mhc-finetune-saas-zrtokzlkbnue9zsk7jfgad.streamlit.app)** | **[API](https://fourwheels2512--crma-finetune-fastapi-app.modal.run/docs)** | **Patent Pending (US Provisional, Feb 2026)** *Kiran Nayudu — ModelBrew AI — fourwheels2512@gmail.com* """ # ── Build app ── with gr.Blocks( title="ModelBrew AI — Zero Forgetting Benchmarks", ) as demo: gr.Markdown("# ModelBrew AI — Zero Forgetting Benchmark Results") gr.Markdown("*4 independent benchmarks on Mistral-7B. Zero catastrophic forgetting across all of them.*") with gr.Tabs(): with gr.Tab("Overview"): gr.Markdown(OVERVIEW_MD) gr.Plot(make_summary_chart) with gr.Tab("Research Benchmark"): gr.Markdown(""" ### Multi-Seed Research — 5 Domains, 3 Seeds Medical → Legal → Financial → Code → Science on Mistral-7B. Repeated across 3 random seeds to confirm reproducibility. Naive LoRA destroyed **38–49%** of prior knowledge with every new domain. ModelBrew drifted less than **0.4%**. The negative sign means the model actually *improved* on old domains. Naive LoRA **crashed at step 43** with gradient norm 263. ModelBrew completed every run with peak gradient norm under 6. Spectral norm locked at 1.0. """) gr.Plot(make_seed_chart) with gr.Tab("Walmart Enterprise"): gr.Markdown(""" ### Walmart Enterprise — 4 Domains Customer Service → Product Knowledge → HR Policy → Financial Analytics. One model. Four enterprise domains. All retained. The final model answers questions across all four with **BERTScores of 0.82–0.94**. """) gr.Plot(make_walmart_chart) with gr.Tab("Salesforce Enterprise"): gr.Markdown(""" ### Salesforce Enterprise — 5 Domains, Cumulative Adapter CRM Operations → Sales Ops → Reporting & Analytics → Customer Support → Admin & Dev. Retention BERTScores went **UP** with each new domain — 0.889 → 0.891 → 0.897 → 0.907. The model gets *better* at old domains as it learns new ones. **Positive backward transfer.** Peak gradient norms stayed between **2.1 and 3.7**. Zero gradient explosions. """) gr.Plot(make_salesforce_chart) gr.Plot(make_salesforce_gn_chart) with gr.Tab("Dental Stress Test"): gr.Markdown(""" ### Dental Stress Test — 8 Sequential Domains, 2 Seeds The longest chain we've tested. Eight sequential domains on Mistral-7B. Peak gradient norms stayed between **3.8 and 6.1** across all 8 domains. Naive LoRA gradient norms grew monotonically to **9.4**. Spectral norm: **1.0** throughout. Zero crashes. Zero NaN losses. """) gr.Plot(make_dental_chart) with gr.Tab("Salesforce Details"): gr.Markdown(""" ### Salesforce — Full Per-Domain Breakdown | Domain | Training Loss | Gen BERTScore | Retention BERTScore | Peak Grad Norm | |--------|:---:|:---:|:---:|:---:| | 1. CRM Operations | 1.33 | 0.882 | — | 3.68 | | 2. Sales Ops | 1.05 | 0.897 | 0.889 | 2.15 | | 3. Reporting & Analytics | 1.24 | 0.890 | 0.891 | 3.16 | | 4. Customer Support | 0.96 | 0.885 | 0.897 | 2.53 | | 5. Admin & Dev | 0.66 | 0.897 | 0.907 | 2.11 | **Key findings:** - Retention BERTScores *improved* as domains accumulated — evidence of positive backward transfer - Training loss decreased across domains (1.33 → 0.66) — the model learns faster with more accumulated knowledge - Peak gradient norms stayed between 2.1–3.7 — zero gradient explosions, zero NaN losses - Final adapter answers questions from all 5 Salesforce domains """) gr.Markdown("---") gr.Markdown( "*ModelBrew AI — Patent Pending (US Provisional, Feb 2026) — " "[Try it live](https://mhc-finetune-saas-zrtokzlkbnue9zsk7jfgad.streamlit.app) — " "fourwheels2512@gmail.com*" ) demo.launch(theme=gr.themes.Soft(primary_hue="blue"))