docs: publish official Mark AI model card and benchmarks

README.md

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+---
+language:
+- en
+- pt
+license: agpl-3.0
+tags:
+- mark-ai
+- systems-engineering
+- linux-kernel
+- rust
+- cpp
+- c
+- reverse-engineering
+- robotics
+- electrical-engineering
+- mla
+- moe
+- mtp
+- gguf
+- ollama
+pipeline_tag: text-generation
+---
+
+---
+language:
+- en
+- pt
+license: agpl-3.0
+tags:
+- mark-ai
+- systems-engineering
+- linux-kernel
+- rust
+- cpp
+- c
+- reverse-engineering
+- robotics
+- electrical-engineering
+- mla
+- moe
+- mtp
+- gguf
+- ollama
+pipeline_tag: text-generation
+inference: false
+---
+
+# Mark-Engineer-V2-8B: High-Density Cognitive Systems Architecture
+
+Mark-Engineer-V2-8B is a specialized 8.01-billion-parameter Mixture-of-Experts (MoE) foundation model designed for low-level systems engineering, operating system kernels, distributed consensus, firmware, compiler toolchains, cybersecurity (AppSec P0/Red Team), and physical computing.
+
+Engineered with Multi-Head Latent Attention (MLA), 24 routed experts plus 1 shared expert (Top-2 routing, ~1.63B active compute per token), and Multi-Token Prediction (MTP, Depth 1).
+
+> **IMPORTANT DISCLOSURE: TRAINED BUT UNTESTED (TREINADO MAS NÃO TESTADO)**  
+> Mark-Engineer-V2-8B has completed its pre-training curriculum (convergence loss: 1.9047). However, this checkpoint has **not yet undergone live human qualitative evaluation, automated red-teaming, or standardized bench testing**. All stated capabilities represent architectural design specifications and curriculum objectives. Users are advised to run preliminary evaluations in sandboxed environments.
+
+---
+
+## Architectural Specifications
+
+| Parameter | Value | Description |
+| :--- | :--- | :--- |
+| **Total Physical Parameters** | **8,005,905,408 (~8.01B)** | Total neural weights across dense and expert layers |
+| **Active Parameters per Token** | **~1,626,500,000 (~1.63B)** | Compute footprint per token during forward pass |
+| **Layers** | **32** | 2 Initial Dense Blocks + 30 DeepSeekMoE Blocks |
+| **Hidden Dimension ($d_{model}$)** | **2,560** | Base model embedding dimension |
+| **Attention Mechanism** | **MLA (Multi-Head Latent Attention)** | Decoupled RoPE with low-rank KV & Q compression |
+| **Query Compression ($q_{lora}$)** | **1,024** | Compresses query states into latent representation |
+| **Key-Value Compression ($kv_{lora}$)** | **512** | Compresses KV states into 512-dim latent vector |
+| **RoPE Decoupled Dimension** | **32** | Positional embedding applied only to decoupled key/query |
+| **MoE Routing Topology** | **1 Shared + 24 Routed Experts** | Top-2 routed experts + 1 universal shared expert |
+| **Expert Dimension ($d_{expert}$)** | **1,254** | Intermediate dimension per expert block |
+| **Multi-Token Prediction (MTP)** | **Depth 1 ($t+1, t+2$)** | Parallel dual-head prediction for fast speculative decoding |
+| **Vocabulary Size** | **49,152 tokens** | Byte-Pair Encoding (BPE) tuned for code, math, and technical prose |
+| **Context Window** | **8,192 tokens** | Native rotary positional context span |
+
+---
+
+## Expert Domain Specialization Matrix
+
+The 24 routed experts are partitioned across specialized computational domains:
+
+| Expert ID | Domain Descriptor | Focus Area |
+| :---: | :--- | :--- |
+| **E00** | `linux_kernel_core_mm` | Linux kernel memory management, page tables, slab/slub allocators, eBPF |
+| **E01** | `embedded_systems_firmware_rtos` | Bare-metal C, FreeRTOS, Zephyr, ARM Cortex-M/R, memory-mapped I/O |
+| **E02** | `electrical_engineering_circuits_pcb` | Circuit theory, power converters, analog/digital filter design, KiCad schematics |
+| **E03** | `mechanical_engineering_physics_cad` | Statics, dynamics, stress analysis, thermodynamics, mechanical tolerances |
+| **E04** | `robotics_kinematics_dynamics_ros2` | Forward/inverse kinematics, Denavit-Hartenberg, PID controllers, ROS 2, odometry |
+| **E05** | `hardware_fpga_verilog_riscv` | Digital logic, RTL design, Verilog/VHDL, RISC-V ISA extensions |
+| **E06** | `c_cpp_systems_memory_models` | C99/C11, C++20/23, RAII, concurrency atomics, lock-free rings, memory orderings |
+| **E07** | `rust_systems_concurrency` | Unsafe Rust, pointer provenance, Tokio runtime, crossbeam channels, borrow checker |
+| **E08** | `go_distributed_backends` | Goroutines, Go scheduler internals, sync pools, low-latency microservices |
+| **E09** | `asm_binary_reverse_engineering` | x86_64, ARM64, RISC-V disassembly, ELF/PE parsing, Ghidra decompilation |
+| **E10** | `cybersec_red_team_pwn` | Heap exploitation, tcache poisoning, ROP/JOP chains, ASLR/DEP mitigations |
+| **E11** | `cybersec_appsec_crypto` | Constant-time cryptography, AES-GCM, TLS 1.3 protocol verification, threat models |
+| **E12** | `software_architecture_full_system` | C4 blueprints, Domain-Driven Design (DDD), clean architecture, event sourcing |
+| **E13** | `distributed_systems_consensus` | Raft, Multi-Paxos, Kafka partitions, 2PC/Saga distributed transactions |
+| **E14** | `database_internals_storage` | LSM-trees, B+Trees, Write-Ahead Logs (WAL), MVCC engines, query planners |
+| **E15** | `networking_protocols_kernel` | TCP/IP stack tuning, QUIC, HTTP/3, AF_XDP, zero-copy packet processing |
+| **E16** | `compilers_interpreters_jit` | LLVM IR passes, AST transformations, register allocation, SSA form |
+| **E17** | `enterprise_polyglot_tier1` | Modern Python, Java/Kotlin, C#/.NET, TypeScript, PostgreSQL/MySQL |
+| **E18** | `cloud_native_devops_infra` | Kubernetes operators, Docker/OCI runtimes, Terraform, NixOS, Prometheus |
+| **E19** | `vague_prompt_intent_disambiguation` | Implied context deduction, sensible engineering defaults, interactive clarification |
+| **E20** | `epistemic_calibration_research` | Uncertainty assessment, zero-hallucination verification, fact-checking citations |
+| **E21** | `top20_polyglot_linguistics` | Technical vocabulary and syntax across top 20 global languages (EN, PT, ES, etc.) |
+| **E22** | `high_eq_senior_tech_lead_persona` | Constructive, articulate, and patient technical communication persona |
+| **E23** | `formal_verification_debugging` | TLA+ specifications, Model Checking, Valgrind, AddressSanitizer, Linux perf profiling |
+
+---
+
+## Training Convergence
+
+The model was trained on the consolidated Mark Ultra-Master Curriculum with multi-objective optimization:
+
+$$\mathcal{L}_{\text{Total}} = \mathcal{L}_{\text{NTP}} + 0.3 \cdot \mathcal{L}_{\text{MTP}} + 10^{-4} \cdot \mathcal{L}_{\text{Router\_Z}}$$
+
+- **Initial Loss:** 11.4500
+- **Final Pre-training Loss:** **1.9047**
+  - Next-Token Prediction (NTP) Loss: **1.2952**
+  - Knowledge Distillation (KD) Loss: **0.4190**
+  - Multi-Token Prediction (MTP) Loss: **0.1905**
+  - Router Z-Loss: Stable ($< 1.2 \times 10^{-4}$)
+- **Learning Rate Schedule:** Cosine decay with linear warmup ($1.2 \times 10^{-4} \rightarrow 1.0 \times 10^{-5}$)
+
+---
+
+## Quickstart: Plug and Play Deployment
+
+### 1. Ollama (Recommended)
+
+1. Download the quantized model file `mark_engineer_v2_8b_iq4_nl.gguf` from the Releases section.
+2. Create a `Modelfile`:
+
+```dockerfile
+FROM ./mark_engineer_v2_8b_iq4_nl.gguf
+
+TEMPLATE """<|im_start|>system
+{{ .System }}<|im_end|>
+<|im_start|>user
+{{ .Prompt }}<|im_end|>
+<|im_start|>assistant
+<think>
+"""
+
+PARAMETER stop "<|im_end|>"
+PARAMETER stop "<|endoftext|>"
+PARAMETER temperature 0.2
+PARAMETER top_p 0.95
+
+SYSTEM """Voce e o Mark Engineer V2 8B, um modelo de sistemas e engenharia de software de altissima capacidade."""
+```
+
+3. Register and run the model:
+
+```bash
+ollama create mark-engineer-v2-8b -f Modelfile
+ollama run mark-engineer-v2-8b
+```
+
+---
+
+### 2. LM Studio / llama.cpp
+
+Load `mark_engineer_v2_8b_iq4_nl.gguf` directly into LM Studio or llama.cpp:
+
+```bash
+./llama-cli -m mark_engineer_v2_8b_iq4_nl.gguf -p "Implement a lock-free ring buffer in Rust using atomic pointers." -n 1024 --temp 0.2
+```
+
+---
+
+### 3. PyTorch / Transformers
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "miceworks/Mark-Engineer-V2-8B",
+    trust_remote_code=True,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("miceworks/Mark-Engineer-V2-8B")
+
+inputs = tokenizer("<|im_start|>user\nExplain the slab allocator in the Linux kernel.<|im_end|>\n<|im_start|>assistant\n", return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.2)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+---
+
+## License
+
+GNU Affero General Public License v3.0 (AGPL-3.0). Free for open-source research and community deployment.