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Logic-v2

A practical multimodal reasoning engine for verification and inspection

Welcome

Logic-v2 is a multimodal model built for teams who need more than captions.
It is designed to help systems inspect inputs, reason about correctness, and produce conclusions you can automate.

If you are building an internal service, an engineering workflow, or a “gatekeeper” step in a pipeline (approve/reject/flag), this model is intended for that kind of work.

What it is good for

Logic-v2 is optimized for logic-first multimodal reasoning, especially when the question is:

Is something missing, inconsistent, or incorrect?
Does this violate an expected constraint or rule?
Can this be validated, or should it be rejected?
What evidence supports the decision?

Typical inputs include:

diagrams, dashboards, screenshots
infrastructure photos (racks, cabling, labels)
QA/inspection images
structured prompts that ask for validation, not creativity

What it is not

Logic-v2 is not intended for:

general-purpose chat
creative writing or storytelling
meme generation
consumer-grade low-latency experiences

If your goal is conversation or creativity, you will likely prefer a different model.

Design principles

Logic over fluency
Predictability over creativity
Systems over chat interfaces
Private inference over public endpoints

This model is meant to be a reliable component inside engineering and enterprise workflows.

Hardware and deployment intent

Logic-v2 was built and validated in a cluster-style environment and is intended for serious GPU infrastructure, particularly NVIDIA Blackwell-class systems (e.g., B200).

Recommended deployment patterns:

private inference service (internal API)
pipeline stage (validation/inspection gate)
controlled environments (security-boundary friendly)

Usage (Transformers)

from transformers import AutoModelForVision2Seq, AutoProcessor

model_id = "amihai4by/logic-v2"

model = AutoModelForVision2Seq.from_pretrained(
    model_id,
    trust_remote_code=True
)

processor = AutoProcessor.from_pretrained(model_id)

For production workloads, consider serving with vLLM or a dedicated inference stack that matches your latency and concurrency requirements.

Limitations and considerations

Model outputs can be sensitive to prompt structure. For decision workflows, prefer:
- explicit constraints
- requested output schema (JSON)
- “state assumptions” and “cite evidence from input” patterns
This model is not designed to replace domain experts. It is designed to assist and gate workflows with high signal.

Responsible use

Use Logic-v2 in contexts where:

automated decisions can be reviewed or audited
failure modes are understood and monitored
you have a fallback path for ambiguous or low-confidence cases

Avoid using it as the sole authority for high-stakes decisions without human oversight.

License

MIT

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Safetensors

Model size

8B params

Tensor type

BF16