🧠 Ozygram Neural 7B (Dream-RSI & Kev Engine) v2

A Local Neuro-Symbolic Model for Autonomous Code Self-Healing, AST Mutative Repair, and Transactional Integrity.

📌 Model Overview

Ozygram Neural 7B v2 is a specialized, open-weights code intelligence model engineered for autonomous local self-healing, automated bug localization, and high-concurrency transactional software engineering.

Trained natively on local GPU hardware via QLoRA over curated neuro-symbolic contracts, it introduces a dual-phase execution architecture:

1. Dream-RSI (Recursive Self-Improvement & MCTS): An internal reasoning chain enclosed within <|thought_start|> and <|thought_end|>. It mentally explores candidate AST trajectories, simulates execution graphs, evaluates blast radius across dependent files, detects concurrency deadlocks, and discards naive or destructive paths before committing code.
2. Kev Action Engine: A zero-prose, deterministic JSON action protocol (ast_mutation and apply_patch), delivering atomic target/replacement hunks directly to IDEs and autonomous runtime agents without conversational hallucinations or fragile line offsets.

🏗️ Architecture & Philosophy

               [ User Incident / Pytest Failure / Production Traceback ]
                                    │
                                    ▼
       ┌────────────────────────────────────────────────────────┐
       │               Ozygram Neural Orchestrator              │
       │                                                        │
       │  Phase 1: Dream-RSI (Internal Monte Carlo Tree Search) │
       │  <|thought_start|>                                     │
       │   - Parse Error Spectrum & Root Cause                  │
       │   - Validate AST Graph Dependency (Blast Radius)       │
       │   - Discard Naive / Destructive Branches (-1.0 score)  │
       │   - Simulate Transactional Rollback & Concurrency Paths│
       │  <|thought_end|>                                       │
       │                                                        │
       │  Phase 2: Kev Action Engine (Deterministic JSON)       │
       │  {                                                     │
       │    "action": "ast_mutation" | "apply_patch",           │
       │    "target_files": [...],                              │
       │    "blast_radius_validated": true,                     │
       │    "target": "faulty_code_snippet",                    │
       │    "replacement": "surgical_repaired_code"             │
       │  }                                                     │
       └────────────────────────────────────────────────────────┘
                                    │
                                    ▼
       [ IDE / Continue / VS Code / Kev AST Engine: Live Patch ]

📈 Training Metrics & Convergence

• Base Model: Qwen/Qwen2.5-7B-Instruct
• Training Method: 4-bit NormalFloat (NF4) QLoRA + Paged AdamW 8-bit + Gradient Checkpointing
• Total Epochs: 3.0 / 3.0 (450 optimization steps)
• Final Training Loss: 0.005904 (steady monotonic descent from 0.082 initial)
• Mean Token Accuracy: >99.4%
• Gradient Norm: 0.044 (zero gradient explosion, stable convergence)
• Hardware: NVIDIA Quadro RTX 4000 Max-Q (8 GB VRAM), Windows WDDM Desktop primary display.

📊 Benchmark & Real-World Validation

1. Master Gym Benchmark Suite (100% Pass)

• Gym 1: The Blast Radius Challenge — Native AST dependency graph traversal without brute-force file crawling (5/5 PASS - 100%).
• Gym 2: The Infinite Loop Simulation — Concurrency deadlocks, semaphore leaks, and transactional rollback (5/5 PASS - 100%).
• Gym 3: The Speculative Speedrun — High-throughput boilerplate delegation (5/5 PASS - 100%).

2. Live Local CRM Self-Healing Benchmark (crmgeofa-localdev)

Evaluated directly on an enterprise ERP/CRM codebase containing >40 modules, asynchronous export workers, and high-concurrency Excel report generation:

⚡ Prompt Contract & Prefix Caching

To guarantee 100% Prefix Caching (zero prefill latency on local GPUs), queries must adhere to the immutable contract:

<|im_start|>system
[CONTRATO OZYGRAM NEURAL V1]
- Arquitectura: Neuro-Simbólica Local.
- Salida del Razonamiento Interno: Entre <|thought_start|> y <|thought_end|>.
- Formato de Acción (Kev Engine): JSON estructural estricto sin prosa externa.
- Herramientas AST nativas mapeadas en memoria: [Engram_O1_Definitions]
<|im_end|>
<|im_start|>user
{error_traceback_and_code_context}
<|im_end|>
<|im_start|>assistant
<|thought_start|>
[MCTS Diagnóstico de Producción: ...]
  -> Verificando AST y ramas semánticas...
[MCTS Backtrack: Descartando soluciones ingenuas...]
[Conclusión Dream-RSI: Transmitiendo acción JSON de Kev Engine...]
<|thought_end|>
{
  "action": "ast_mutation",
  "target_files": ["app/workers/export_worker.py"],
  "blast_radius_validated": true,
  "target": "finally:\n            pass",
  "replacement": "finally:\n            self.semaphore.release()",
  "execution_status": "ready"
}
<|im_end|>

💻 How to Run

1. In Ollama (Recommended)

Download the quantized GGUF (ozygram-qwen7b-Q4_K_M.gguf) and create the model:

ollama create ozygram -f Modelfile
ollama run ozygram

Modelfile:

FROM ./ozygram-qwen7b-Q4_K_M.gguf

PARAMETER temperature 0.2
PARAMETER top_p 0.95
PARAMETER repeat_penalty 1.1
PARAMETER stop "<|im_end|>"

TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ else }}<|im_start|>system
[CONTRATO OZYGRAM NEURAL V1]
- Arquitectura: Neuro-Simbólica Local.
- Salida del Razonamiento Interno: Entre <|thought_start|> y <|thought_end|>.
- Formato de Acción (Kev Engine): JSON estructural estricto sin prosa externa.
- Herramientas AST nativas mapeadas en memoria: [Engram_O1_Definitions]
<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ .Response }}<|im_end|>"""

2. In Transformers (Python)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Xangel0s/ozyvler-Ozygram-Neural-7B-Coder-MCTS"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

⚙️ Hardware Profile & Quantization

📜 Citation

@misc{ozygram_neural_v2_2026,
  author = {Ozygram Research & Autonomous Engineering Team},
  title = {Ozygram Neural v2: Local Neuro-Symbolic MCTS Model for Autonomous Code Self-Healing},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Xangel0s/ozyvler-Ozygram-Neural-7B-Coder-MCTS}}
}