Dot Loom Conductor 14B

--- license: apache-2.0 base_model: mistralai/Ministral-3-14B-Base-2512 base_model_relation: adapter library_name: peft pipeline_tag: text-generation language: - en datasets: - usedot/dot-loom-conductor-v2 thumbnail: https://huggingface.co/usedot/Dot-Loom-Conductor-14B/resolve/main/assets/dot-loom-conductor-cover.png tags: - lora - peft - mistral - structured-output - model-routing - inference-orchestration - multi-agent - dot-loom --- <p align="center"> <img src="assets/dot-mark.png" width="104" alt="Dot"> </p> <h1 align="center">Dot Loom Conductor 14B</h1> <p align="center"><strong>A learned control policy for budget-constrained multi-model inference.</strong></p> <p align="center"> <a href="https://huggingface.co/datasets/usedot/dot-loom-conductor-v2">Dataset</a> &nbsp;·&nbsp; <a href="https://huggingface.co/spaces/usedot/Dot-Loom-Conductor-Lab">Policy explorer</a> &nbsp;·&nbsp; <a href="TECHNICAL_REPORT.md">Technical report</a> &nbsp;·&nbsp; <a href="https://github.com/usedotai/dot-loom/tree/main/research/ministral-conductor">Source and receipts</a> </p>  Dot Loom Conductor receives a task, three candidate worker profiles, and hard call, credit, and latency budgets. It returns a schema-constrained **Lean**, **Balanced**, or **Strict** execution plan with role assignments, access rules, estimated receipts, and bounded reason codes. It does not answer the task. It decides how the task should be executed. | Release fact | Result | |---|---:| | Base checkpoint | `mistralai/Ministral-3-14B-Base-2512` | | Released artifact | Rank-32 PEFT LoRA adapter | | Training corpus | 9,000 synthetic routing traces | | Held-out evaluation | 1,200 family-disjoint cases | | Exact-plan match | 90.2% raw adapter, 90.8% with runtime guard | | Lift over deterministic Loom | +11.5 percentage points with guard | | Hard-budget violations after guard | 0 of 1,200 | | Training run | 52.6 minutes on 1x NVIDIA H200 SXM5 | No user prompts, production conversations, wallets, API keys, private code, or customer data were used in training. ## Why this model exists Most agent stacks either use one model for every request or hard-code a fixed writer and reviewer pipeline. Both approaches spend the same amount of inference on easy and difficult work. The conductor learns a different policy: - **Lean:** one w...