Instructions to use Mattimax/DAC6.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mattimax/DAC6.5 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Mattimax/DAC6.5")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("Mattimax/DAC6.5", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Mattimax/DAC6.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mattimax/DAC6.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mattimax/DAC6.5",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Mattimax/DAC6.5

SGLang

How to use Mattimax/DAC6.5 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Mattimax/DAC6.5" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mattimax/DAC6.5",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Mattimax/DAC6.5" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mattimax/DAC6.5",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Mattimax/DAC6.5 with Docker Model Runner:
```
docker model run hf.co/Mattimax/DAC6.5
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

DAC6.5 — M.INC. Architecture Series

DAC6 Logo

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.*, and projector.*).
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.

☕ Support my research

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.

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Safetensors

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F32

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