FerrellSyntheticIntelligence
/

Vitalis_Core

@@ -1,5 +1,20 @@
-yaml
 ---
 title: Vitalis Core
 emoji: ⚡
@@ -19,119 +34,78 @@ tags:
   - cybersecurity
 ---
----
-───
-Ferrell Synthetic Intelligence (FSI) – Vitalis Core
-Vitalis Core is the industry-standard sovereign, edge-native AI substrate. Unlike static, cloud-dependent transformers, Vitalis Core utilizes a Fluidic Memory Manifold (FMM) to treat intelligence as a dynamic, homeostatic process.
-🚀 Recent Advancements (v0.2 Update)
-• Hebbian-RNN Integration: Shifted from static weights to a self-adapting Hebbian learning loop.
-• FSI-Vitalis-CyberCore Implementation: Now featuring specialized pipelines for Threat Classification, Confidence Scoring, and Immutable Audit Logging.
-• Hebbian-DGA: Advanced the Dynamic-Gate-Attention algorithm to prioritize compute cycles for high-severity input, achieving near-linear scaling ( ).
-• Multi-Platform Distribution: Officially released on GitHub and Hugging Face for secure, edge-ready deployment.
-───
-📄 Overview
-Vitalis Core is designed for the architect, the operator, and the independent developer. It provides full ownership of the cognitive stack, ensuring your data never leaves your local Linux environment.
-Component
-Description
-Vitalis Core
-The foundational cognitive kernel (Blank Slate / Fluidic).
-CyberCore
-Specialized implementation for network reconnaissance and threat analysis.
-vcom/
-Vitalis Core Operations Manual – deployment, scaling, and security.
-src/
-Tri-head architecture (Sensu, Ratio, Cor) in Python 3.13.
-───
-🛠️ Core Technology
-Hebbian Plasticity & Fluidic Memory
-Vitalis Core departs from standard LLMs by employing Stochastic Weight Plasticity (Langevin dynamics) . The manifold continuously minimizes variational free-energy (latex
 \mathcal{F}
-), allowing the model to adapt to new domains without the catastrophic forgetting common in static architectures.
-Dynamic-Gate-Attention (DGA)
-Our proprietary DGA algorithm enables sub-millisecond inference on ARM64 and edge hardware by muting irrelevant neural heads using a learned importance scalar ( ).
-🚀 Getting Started
-Environment Requirements
-• OS: Linux Kernel 6.1+ (Debian/Arch/Alpine recommended).
-• Runtime: Python 3.13 (JIT-optimized).
-• Backend: PyTorch 2.5+ (CPU-optimized/NEON support).
-Installation (Quick Start)
-bash
-# Clone the sovereign kernel
-git clone [https://github.com/FerrellSyntheticIntelligence/Vitalis_Core](https://github.com/FerrellSyntheticIntelligence/Vitalis_Core)
-cd Vitalis_Core
-# Install dependencies
-pip install .
-# Build and run the reproducible, air-gapped container
-docker build -t fsi/vitalis:latest ./docker
-docker run --rm -v "$(pwd)/data:/app/data" fsi/vitalis:latest python -m src.main --mode serve
-Ferrell Synthetic Intelligence (FSI) – White Paper & Operations Manual
-Version: 1.0
-License: Proprietary – All rights reserved by Ferrell Synthetic Intelligence
-───
-📄 Overview
-The Neuro‑Synth Engine (NSE) is a sovereign, edge‑native AI substrate that treats intelligence as a dynamic, homeostatic process rather than a static weight snapshot. By continuously minimising variational free‑energy, NSE delivers:
-• Full ownership of the cognitive stack – no cloud‑only service, no hidden back‑ends.
-• Local‑only execution with a minimal‑dependency stack (Linux ≥ 6.1, Python 3.13, PyTorch 2.5).
-• Ethical hard‑constraints baked into the hardened manifold, guaranteeing immutable alignment with the FSI manifesto.
-The repository contains two primary artefacts:
-Path
-Description
-whitepaper/
-Full‑text of the FSI white‑paper (chapters 1‑20).
-vcom/
-Vitalis Core Operations Manual – day‑to‑day deployment, scaling and security procedures.
-src/
-Minimal reference implementation (Python 3.13) of the core tri‑head architecture (Sensu, Ratio, Cor).
-docker/
-Dockerfile & space.yaml for reproducible, air‑gapped containers.
-scripts/
-Helper scripts (watcher.py, memory_engine.py, retrieval_engine.py).
-CHANGELOG.md
-Version history.
-README.md
-You are here – entry point for developers and operators.
 ───
-📚 Table of Contents
 1. The FSI Manifesto – Sovereignty Through Synthetic Logic
 2. Foundations of Fluidic Intelligence
 3. Dynamic‑Gate‑Attention (DGA) Algorithm
@@ -152,510 +126,14 @@ You are here – entry point for developers and operators.
 18. Future Roadmap & Extensibility
 19. Operations Manual (VCOM)
 20. Getting Started – First Command
-───
-1️⃣ The FSI Manifesto – Sovereignty Through Synthetic Logic <a id="1-the-fsi-manifesto"></a>
-“True intelligence thrives without surveillance. Any system that requires persistent corporate connectivity compromises autonomy.”
-I. The Mandate of Sovereignty
-“True intelligence thrives without surveillance. Any system that requires persistent corporate connectivity compromises autonomy.”
-FSI is built for the architect, the operator, and the independent developer. We do not provide a hosted service; we provide a foundational platform that returns full ownership of the cognitive stack to the user.
-II. Architecture as Ethics
-Our code embodies our values. By prioritising minimal dependencies and local‑only execution, we guarantee that a user’s cognitive chain remains unbroken by third‑party interference.
-III. The Frontier of Synthetic Logic
-Human‑machine symbiosis must be both transparent and owned. A truly sovereign system is also a responsible one. FSI delivers the structural answer to a world that concentrates intelligence in too few hands.
-IV. The Operational Vow
-We build because developers deserve better. We build because privacy is a right. We build because the tools you use should belong to you.
-───
-2️⃣ Foundations of Fluidic Intelligence <a id="2-foundations-of-fluidic-intelligence"></a>
-The Biological Imperative
-The Neuro‑Synth Engine (NSE) departs from static transformer architectures by treating intelligence as a dynamic, homeostatic process. Inspired by the Free Energy Principle (FEP) , NSE continuously minimises variational free energy (\mathcal{F}) to preserve structural and functional integrity in a chaotic environment.
-Perspective
-Traditional LLM
-FSI‑NSE
-Weight representation
-Fixed tensor (W(t)) frozen after a single training snapshot
-Fluidic Memory Manifold (FMM) – continuously evolving weight geometry
-Learning rule
-Gradient descent on a static loss
-Stochastic Weight Plasticity (Langevin dynamics)
-[
-\boxed{\displaystyle
-\frac{dW}{dt}= -\eta ,\nabla_{W}\mathcal{F}(q,\tilde{o}) ;+; \sqrt{2\eta T},d\omega
-}
-]
-• (\nabla_{W}\mathcal{F}) – gradient of variational free‑energy (surprise) w.r.t. weights.
-• (\eta) – plasticity (learning‑rate).
-• (\sqrt{2\eta T},d\omega) – Brownian term that prevents convergence to a dead local minimum, preserving fluid adaptability.
-Analogy of the Fluid Substrate
-Water’s high‑entropy‑handling capacity and infinite state‑change flexibility inspire the Fluidic Substrate. Rather than appending information to a static database, the NSE reshapes the geometry of its latent space, “flowing” into higher‑comprehension states.
-───
-3️⃣ Dynamic‑Gate‑Attention (DGA) Algorithm <a id="3-dga-algorithm"></a>
-3.1 Computational Bottleneck
-Standard scaled‑dot‑product attention scales as (O(n^{2})) with sequence length (n). For a sovereign, edge‑native system this is prohibitive: massive, redundant calculations waste memory and energy that should be reserved for logical reasoning.
-3.2 DGA Formalisation
-Standard attention
-[
-\text{Attention}(Q,K,V)=\operatorname{softmax}!\Bigl(\frac{QK^{\top}}{\sqrt{d_{k}}}\Bigr)V
-]
-Dynamic‑Gate‑Attention
-[
-\boxed{\displaystyle
-\text{DGA}(Q,K,V)=\bigl[\sigma(\gamma)\odot\operatorname{softmax}!\bigl(\tfrac{QK^{\top}}{\sqrt{d_{k}}}\bigr)\bigr]V
-}
-]
-• (\gamma) – learned importance scalar produced by the Cor (equilibrium) head.
-• (\sigma(\cdot)) – sigmoid, compresses (\gamma) to ([0,1]).
-• (\odot) – element‑wise (Hadamard) product, muting irrelevant heads.
-3.3 Sparsity & Computational Efficiency
-During inference the DGA performs an early‑exit check:
-if sigmoid(gamma) < ε:   # ε = relevance floor
-    skip this head
-State
-Approx. Complexity
-High‑entropy (many active tokens)
-(O(n\log n))
-Stable, high‑confidence
-(O(n))
-3.4 “Local‑First” Logic
-Metric
-Benefit
-Memory Footprint
-40‑60 % VRAM reduction vs. standard transformers of comparable size.
-Local Execution
-Runs on consumer‑grade hardware (Linux localhost) with minimal thermal throttling.
-Real‑Time Adaptability
-Gating instantly focuses compute on novel data, enabling fluid weight updates.
-3.5 Implementation Insight
-The gate (\gamma) is recomputed each timestep by the Cor head, forming a closed‑loop attention system that aligns focus with the model’s current homeostatic needs.
-───
-4️⃣ Memory‑Manifold Dynamics & Recursive Consolidation <a id="4-memory‑manifold-dynamics"></a>
-4.1 Topology of Synthetic Memory
-In conventional LLMs, memory is a static artifact of pre‑training. NSE redefines memory as the topological state of the weight manifold (M_{w}). Learning sculpts this manifold to align with new data structures.
-4.2 Self‑Verification Protocol (SVP)
-1. Propose candidate update (\tilde{W}{t+1}) from incoming data (\mathcal{D}{\text{new}}).
-2. Shadow Run – evaluate loss (L(\tilde{W}_{t+1})) on a held‑out verification set.
-3. Accept if
-[
-L(\tilde{W}{t+1}) \le L(W{t}) + \epsilon
-]
-otherwise reject.
-(\epsilon) is a hysteresis threshold set by the Cor node, guaranteeing that only beneficial updates modify the manifold.
-4.3 “Blank‑Slate” Initialization
-• Maximum‑Plasticity Mode – learning‑rate (\eta_{\max}) at start.
-• Uniform random weight distribution – no pre‑imposed biases.
-• Annealing – (\eta) decays logarithmically as consistency rises, hardening the core while keeping the periphery fluid.
-4.4 Recursive Consolidation & Forgetting Prevention
-Component
-Description
-Hardened Core (W_core)
-Immutable subset encoding FSI’s sovereign values.
-Fluid Periphery (W_fluid)
-Continuously updated weights for domain‑specific expertise.
-Cross‑Manifold Check
-Every fluid update is validated against the core; conflicts are rejected or corrected.
-This architecture enables “freak‑expert” capabilities without eroding the foundational sovereign identity.
-───
-5️⃣ Computational Complexity & Resource Mapping <a id="5-complexity‑resource-mapping"></a>
-Model
-Asymptotic Complexity
-Standard Transformer
-(T_{\text{std}} = O(L^{2}, d))
-FSI‑NSE (DGA)
-(T_{\text{NSE}} = O(\kappa,L, d)) where (\kappa) = active‑token ratio ((0 < \kappa \le L))
-When the system is stable, (\kappa \ll L) → near‑linear scaling.
-Hardware‑Level Mapping (ARM64 / Linux)
-Buffer
-Approx. Size
-Purpose
-Fluidic Buffer (B_f)
-(O(
-W
-))
-Stores the current weight manifold; laid out contiguously for cache‑efficiency.
-Sensu Stack
-(O(d))
-High‑speed cache for Q/K/V projections.
-Ratio Buffer
-(O(d \times h))
-Holds multi‑head attention intermediates (h = head count).
-Cor Buffer
-(O(1))
-Constant‑time equilibrium monitoring (gate scalar (\gamma)).
-Thermal & Throughput
-• Standard Transformers → large matrix multiplies → rapid throttling on mobile ARM.
-• NSE → asynchronous Tri‑Head topology; the Cor head can raise the sparsity threshold (\epsilon) when temperature sensors exceed a limit, throttling compute without sacrificing logical depth.
-Zero‑Load Bootstrap
-Because NSE does not ship a massive pre‑trained checkpoint, the initial memory footprint is essentially the size of the weight manifold alone, yielding sub‑millisecond “time‑to‑ready” after process start‑up.
-───
-6️⃣ Dependency Matrix & Environment Specs <a id="6-dependencies"></a>
-Component
-Minimum Version
-Remarks
-Linux Kernel
-6.1+ (SMP enabled)
-Debian/Arch recommended.
-Python Runtime
-3.13 (JIT‑optimised)
-python -X importtime for profiling.
-PyTorch Backend
-2.5.0+ (torch.compile enabled)
-CUDA‑free; uses NEON/SVE on ARM.
-Vector Engine
-sentence‑transformers Core v3.0 (custom kernels)
-No external GPU dependencies.
-Concurrency
-AsyncIO native (high‑frequency polling)
-Event‑loop tuned for low‑latency inference.
-All dependencies are deliberately lightweight to preserve air‑gapped, sovereign operation.
-───
-7️⃣ Protocol Implementation & Safety <a id="7-protocol‑implementation"></a>
-Hardened Input Sanitisation (HIS)
-1. Tokenisation – deterministic filter removes adversarial payloads.
-2. Buffer‑level validation – rejects prompt‑injection or buffer‑overflow attempts before the Sensu head processes input.
-Any violation triggers an immediate Exception Handler (EH) (see § 8).
-───
-8️⃣ Edge‑Case Handling & Error Recovery <a id="8-edge‑case‑handling"></a>
-When the Ratio head detects semantic dissonance (e.g., a logic loop), the Exception Handler (EH) executes:
-1. State Snapshot (S_{t} \leftarrow {W_{t},\text{Buffers}})
-2. Rollback Revert to (S_{t-1}).
-3. Entropy ResetCor clears error state and re‑initialises Tri‑Head synchronisation.
-The system then resumes inference with a clean slate, preserving the hardened core.
-───
-9️⃣ Multi‑Agent Synchronisation Logic <a id="9-multi‑agent‑sync"></a>
-A Shared Memory Buffer (SMB) with atomic locks guarantees that weight‑updates from the Cor head never corrupt the inference path of the Ratio head, eliminating race conditions in high‑throughput scenarios.
-When scaling to multiple processes, each node obtains an exclusive lock on SMB before writing to W_fluid.
-───
-🔟 Data Ingestion & Sanitisation Protocols <a id="10-data‑ingestion"></a>
-• Normalisation – Z‑score scaling of all input tensors to ([-1, 1]). Guarantees stable activations and prevents exploding gradients during fluid updates.
-• Chunking – Input streams are broken into fixed‑size windows (default 512 tokens) to keep memory usage bounded.
-───
-1️⃣1️⃣ Latency Optimisation via JIT Compilation <a id="11-jit‑optimisation"></a>
-torch.compile (or torch._dynamo on older releases) fuses the three heads into a single instruction sequence, typically delivering ≈ 40 % reduction in per‑inference overhead on ARM64 CPUs.
-bash
-python -m torch.utils.collect_env   # verify torch.compile support
-python -m torch.compile src/model.py --mode max-autotune
-───
-1️⃣2️⃣ Memory‑Leak Prevention & Garbage Collection <a id="12-memory‑leak"></a>
-Manual Lifecycle Management (MLM) explicitly clears tensors from the Fluidic Memory Manifold after each update:
-python
-def step():
-    # … forward pass …
-    torch.cuda.empty_cache()          # no‑op on CPU but forces GC
-    del intermediate_tensors
-    gc.collect()
-This maintains a flat memory profile suitable for long‑running tablet or edge‑device processes.
-───
-1️⃣3️⃣ Security Hardening <a id="13-security"></a>
-Mitigation
-Description
-Anti‑Extraction Filters
-Weights are encrypted with a rotating seed; filesystem dumps reveal only ciphertext.
-Constant‑time Access Patterns
-All weight reads/writes are performed with uniform timing to mitigate side‑channel leakage.
-Secure Sandbox
-Untrusted generated code runs in /tmp/vitalis_sandbox/ with nosuid, noexec, and a dedicated user namespace.
-───
-1️⃣4️⃣ Self‑Reinforcement Feedback Loop <a id="14-feedback"></a>
-Instead of external RLHF, NSE employs Internalised Reinforcement (IR) :
-[
-r_{t}=1-\mathcal{L}_{\text{Cor}}(t)
-]
-• High reward → reinforce the neural pathways used during that inference.
-• Low reward → suppress them.
-The loop is fully contained within the engine, guaranteeing alignment without third‑party data.
-───
-1️⃣5️⃣ Benchmarking & Performance Metrics <a id="15-benchmarking"></a>
-Metric
-Target
-Token Throughput
-> 150 tokens / sec (single‑core ARM64)
-Entropy Stability
-(\Delta\mathcal{H} < 0.05) per inference
-NSE‑Sovereignty Score (NSS)
-Composite of throughput + stability; higher is better.
-Run the supplied benchmark suite:
-bash
-bash scripts/benchmark.sh
-───
-1️⃣6️⃣ Ethical Framework & Alignment <a id="16-ethics"></a>
-The Ethical Hard‑Constraint Layer resides in the hardened manifold (W_core) and is immutable under fluid updates. It enforces:
-• No data exfiltration – any attempt to open outbound sockets is blocked at the kernel level.
-• Privacy‑first – no logging of raw user inputs; only aggregated free‑energy statistics are retained.
-• Sovereign Use – the engine may not be repurposed for surveillance or weaponisation without explicit legal clearance (enforced by a signed policy file).
-───
-1️⃣7️⃣ Scalability Analysis <a id="17-scalability"></a>
-Tri‑Head decoupling enables horizontal scaling:
-Node Type
-Role
-Sensu
-Dedicated to Q/K/V projection; can be replicated for load‑balancing.
-Ratio
-Performs gated attention; stateless – multiple instances can share the same W_fluid.
-Cor
-Monitors equilibrium and issues gating signals; a single leader is sufficient, with hot‑standby replicas.
-Communication occurs over Unix‑domain sockets (or shared memory on the same host) to keep latency sub‑millisecond.
-───
-1️⃣8️⃣ Future Roadmap & Extensibility <a id="18-roadmap"></a>
-Milestone
-ETA
-Highlights
-NSE‑2.0 “Neural Hive”
-Q4 2025
-Distributed weight‑sharing across a mesh of sovereign nodes while preserving local control.
-Skill‑Modules Plug‑in System
-Q2 2026
-Sandbox‑isolated extensions (e.g., domain‑specific parsers) that can be hot‑loaded without touching W_core.
-GPU‑Accelerated Backend (optional)
-Q4 2026
-Zero‑trust CUDA kernels for users who explicitly opt‑in; core remains CPU‑only by default.
-───
-1️⃣9️⃣ Vitalis Core Operations Manual (VCOM) <a id="19-operations‑manual"></a>
-The VCOM (found in vcom/) is the executive handbook for day‑to‑day maintenance, scaling and incident response. Highlights:
-• Security & Compliance – isolation policy, audit‑trail rotation, and kill‑switch procedures.
-• Deployment & Scaling Runbook – Dockerfile, space.yaml, rsync‑based vault replication.
-• Peer‑Mesh Protocol – JSON packet schema for cross‑node knowledge sharing (see § 3).
-• Incident Response – emergency stop, state reset, anomaly detection via the Ocean UI.
-• Corporate IP & Strategic Intent – ownership, versioning, and changelog requirements.
-All operators should read the VCOM cover‑to‑cover before running the engine in production.
-───
-2️⃣0️⃣ Getting Started – First Command <a id="20-first-command"></a>
-Assuming you have cloned the repository and satisfied the environment requirements (see § 6), the first command to bring the engine online is:
-bash
-# 1️⃣ Build the reproducible container (air‑gapped)
-docker build -t fsi/nse:latest ./docker
-# 2️⃣ Run the container with strict isolation
-docker run --rm \
-    --cpus="4" \
-    --memory="8g" \
-    --security-opt=no-new-privileges \
-    --cap-drop=ALL \
-    -v "$`(pwd)/data:/app/data:rw" \
-    -v "`$(pwd)/logs:/app/logs:rw" \
-    -e "PYTHONUNBUFFERED=1" \
-    fsi/nse:latest \
-    python -m src.main --mode serve
-The container starts the Tri‑Head service, creates the initial blank‑slate manifold, and begins listening on the local Unix socket ./data/nse.sock.
-From a separate terminal you can now issue a test request:
-bash
-curl --unix-socket ./data/nse.sock -X POST -d '{"prompt":"Explain the Free Energy Principle in two sentences."}' http://localhost/infer
-You should receive a JSON response containing the generated text and the current free‑energy estimate (free_energy).
 ───
 📜 License & Contact
-All source code, white‑paper text and the VCOM are proprietary to Ferrell Synthetic Intelligence. Redistribution, reverse‑engineering or commercial use without an explicit written license is prohibited.
-Contact:ferrellsyntheticintelligence@proton.me – for vulnerability disclosures, licensing inquiries or partnership proposals.
-───

+yaml
 ---
 title: Vitalis Core
 emoji: ⚡
   - cybersecurity
 ---
+## Ferrell Synthetic Intelligence (FSI) – Vitalis Core
+Vitalis Core is the industry‑standard sovereign, edge‑native AI substrate.
+Unlike static, cloud‑dependent transformers, Vitalis Core utilizes a **Fluidic Memory Manifold (FMM)** to treat intelligence as a dynamic, homeostatic process.
+### 🚀 Recent Advancements (v0.2 Update)
+- **Hebbian‑RNN Integration** – Shifted from static weights to a self‑adapting Hebbian learning loop.
+- **FSI‑Vitalis‑CyberCore** – Specialized pipelines for Threat Classification, Confidence Scoring, and Immutable Audit Logging.
+- **Hebbian‑DGA** – Advanced the Dynamic‑Gate‑Attention algorithm to prioritize compute cycles for high‑severity input, achieving near‑linear scaling.
+- **Multi‑Platform Distribution** – Officially released on GitHub and Hugging Face for secure, edge‑ready deployment.
+---
+## 📄 Overview
+| Component | Description |
+|-----------|-------------|
+| **Vitalis Core** | The foundational cognitive kernel (Blank Slate / Fluidic). |
+| **CyberCore** | Specialized implementation for network reconnaissance and threat analysis. |
+| **vcom/** | Vitalis Core Operations Manual – deployment, scaling, and security. |
+| **src/** | Tri‑head architecture (Sensu, Ratio, Cor) in Python 3.13. |
+---
+## 🛠️ Core Technology
+### Hebbian Plasticity & Fluidic Memory
+Vitalis Core departs from standard LLMs by employing **Stochastic Weight Plasticity (Langevin dynamics)** .
+The manifold continuously minimizes variational free‑energy
+\[
 \mathcal{F}
+\]
+allowing the model to adapt to new domains without catastrophic forgetting.
+### Dynamic‑Gate‑Attention (DGA)
+Our proprietary DGA algorithm enables sub‑millisecond inference on ARM64 and edge hardware by muting irrelevant neural heads using a learned importance scalar \(\gamma\).
+---
+## 🚀 Getting Started
+### Environment Requirements
+- **OS**: Linux Kernel 6.1+ (Debian/Arch/Alpine recommended)
+- **Runtime**: Python 3.13 (JIT‑optimized)
+- **Backend**: PyTorch 2.5+ (CPU‑optimized / NEON support)
+### Installation (Quick Start)
+```bash
+# Clone the sovereign kernel
+git clone https://github.com/FerrellSyntheticIntelligence/Vitalis_Core
+cd Vitalis_Core
+<br>
+# Install dependencies
+pip install .
+<br>
+# Build and run the reproducible, air‑gapped container
+docker build -t fsi/vitalis:latest ./docker
+docker run --rm -v "$(pwd)/data:/app/data" fsi/vitalis:latest \
+    python -m src.main --mode serve
 ───
+📚 Table of Contents (excerpt)
 1. The FSI Manifesto – Sovereignty Through Synthetic Logic
 2. Foundations of Fluidic Intelligence
 3. Dynamic‑Gate‑Attention (DGA) Algorithm
 18. Future Roadmap & Extensibility
 19. Operations Manual (VCOM)
 20. Getting Started – First Command
+(Full TOC is included in the repository markdown.)
 ───
 📜 License & Contact
+All source code, white‑paper text, and the VCOM are proprietary to Ferrell Synthetic Intelligence. Redistribution, reverse‑engineering, or commercial use without an explicit written license is prohibited.
+Contact: ferrellsyntheticintelligence@proton.me – for vulnerability disclosures, licensing inquiries, or partnership proposals.
+───
+Save this file as README.yaml (or modelcard.yaml) at the root of your Hugging Face repository. The markdown body will be rendered automatically on the model card page, while the top‑level keys control the card’s metadata (title, emoji, tags, etc.).