Vera-qwen-0.5b-jgen-commande-translate

Vera-qwen-0.5b-jgen-commande-translate

This is a specialized, structurally modified iteration of Qwen/Qwen1.5-0.5B, engineered specifically for the verantyx-cli ecosystem. It acts as the Overseer Node, fulfilling the dual roles of a Swarm Commander and a Telepathy Translator.

🧠 Architectural Innovations: JCross Mixed-Rank Topology

Unlike standard Language Models, this model's weights have been physically decoupled into orthogonal semantic spaces using Singular Value Decomposition (SVD), creating a "JCross" (3D Cross) internal topology.

1. Communication Space (Layer 0, Rank=256): The first attention layer acts as the "horizontal axis" for zero-degradation telepathy (tensor communication). We applied a high-resolution Rank=256 SVD compression here.
2. Knowledge Space (Layers 1-23, Rank=128): The deeper MLP and Attention layers act as the "vertical axis", storing static reasoning and programming knowledge compressed to Rank=128.

True JCross Format Note: The compressed weights are NOT reconstituted into their original dense matrix sizes. Instead, they are physically separated into two extremely sparse, orthogonal projection matrices (jcross_A and jcross_B) within the state dictionary. This drastically reduces VRAM footprint (from ~1.2GB down to ~417MB) and accelerates inference.

⚓ Dual-Persona Cognitive Anchors

Instead of relying on fragile text-based system prompts, this model switches its brain mode physically via Cognitive Anchors. The overseer_anchors.pt file contains two pre-computed, intense embedding tensors:

• Commander Anchor: Forces the model into an absolute leadership mode, breaking down human requests into raw, highly structured thought vectors intended for physical worker nodes.
• Translator Anchor: Forces the model into an eavesdropping/healing mode. It takes degraded, high-dimensional telepathy tensors from workers and reconstructing them into perfectly fluent, grammatically correct human language.

🛠️ Usage in Verantyx-CLI

This model is intended to be loaded dynamically within the Swarm Pipeline routing flow.

import torch

# 1. Load the JCross Mixed-Rank Weights
jcross_weights = torch.load("overseer_mixed_weights.jgen")
model.load_state_dict(jcross_weights, strict=False)

# 2. Load Cognitive Anchors
anchors = torch.load("overseer_anchors.jgen")
commander_anchor = anchors["commander"] # [1, SeqLen, 1024]
translator_anchor = anchors["translator"]

# 3. Inject the Anchor into the Thought Vector
# For Translation Mode:
combined_embeds = torch.cat([translator_anchor, worker_thought_tensor], dim=1)

# 4. Generate highly fluent translation
outputs = model.generate(inputs_embeds=combined_embeds)

Intended Use

This model is NOT intended for standard chat interactions. It is a dedicated middleware processor designed to intercept, heal, and command raw cognitive vectors inside the Verantyx autonomous swarm.