| | --- |
| | language: |
| | - en |
| | license: mit |
| | library_name: transformers |
| | pipeline_tag: image-to-text |
| | tags: |
| | - multimodal |
| | - vision |
| | - vision-language |
| | - reasoning |
| | - verification |
| | - inspection |
| | - enterprise |
| | - private-inference |
| | - nvidia |
| | - blackwell |
| | - b200 |
| | --- |
| | |
| | # 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) |
| |
|
| | ```python |
| | 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 |
| |
|
| | ``` |
| | |
