DAC6.5 — M.INC. Architecture Series
M.INC. is proud to present DAC6.5, the flagship release introducing our custom, proprietary multimodal architecture. Designed to bring high-performance vision-language capabilities to small-footprint and edge compute environments, DAC6.5 seamlessly unifies perception and reasoning into a single cohesive ecosystem.
DAC6.5 couples the LFM2.5-230M language backbone with the high-resolution SigLIP2 vision encoder through a custom-trained projection module, delivering strong zero-shot image understanding, rapid inference, and minimal memory overhead.
Model Architecture & Technical Overview
Unlike fragmented pipelines requiring complex multi-stage orchestration, DAC6.5 is engineered as an integrated, end-to-end multimodal system:
- Language Backbone (
LFM2.5-230M): Ultra-lightweight text engine fine-tuned for high throughput and low-latency response generation. - Vision Encoder (
SigLIP2): State-of-the-art visual feature extraction, capturing deep spatial and semantic details from input images. - Proprietary Projector: A dedicated cross-modal projection layer trained from scratch by M.INC. to map visual representation spaces directly into the language model's embedding space.
Repository Structure & Core Artifacts
This repository contains the complete unified weights, configuration schemas, and quantization variants required for deployment:
Weights & Configurations
model.safetensors— Unified model weights stored with isolated, clean namespaces (language_model.*,vision_encoder.*, andprojector.*).config.json— Core DAC6.5 architecture specifications and inter-component routing.tokenizer.json,tokenizer_config.json,chat_template.jinja— Complete tokenizer configuration and standard chat templates for the LFM engine.vision_config.json,processor_config.json— Preprocessing pipelines and parameter definitions for the SigLIP2 vision encoder.
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About M.INC.
M.INC. focuses on research and development of custom neural architectures, efficient language models, and accessible multimodal systems. DAC6.5 represents the first milestone in our custom architecture series, establishing a new baseline for compact, locally deployable artificial intelligence.