An adaptively expert-pruned, legal-domain-calibrated derivative of MiniMax-M2.7 — 35% of experts removed with per-layer precision, servable on stock llama.cpp, including pre-Ampere (Volta) hardware.

This is the BASE (pre-recovery) release of the LegalReap V2 line: the structural prune itself, published with full provenance before any post-training. Healed and optimized variants build on this artifact.

What makes this release interesting

Adaptive, not uniform. Most published MoE prunes cut every layer by the same ratio. This model's per-layer retained-expert counts (116–198 of the original 256) were chosen from measured expert saliency on real legal workloads — layers that concentrate importance keep more experts; redundant layers give up more. Overall: 10,317 of 15,872 experts retained (65.0%) across 62 MoE layers, top-8 routing preserved.
Calibrated on real professional work. Saliency observation ran over a curated, 16-bucket calibration corpus drawn from a practicing attorney's private legal workload — drafting, document analysis, financial tracing, RAG planning/judging, routing, summarization, and abstention roles across multiple context lengths. (The corpus itself is private and is not part of this release; no client material is embedded in, or recoverable from, a structural prune.)
A novel GGUF export path for adaptive MoE. Non-uniform expert counts don't fit GGUF's uniform stacked-expert format, so this release ships via a pad-to-uniform export: each layer is padded to 198 experts with never-selectable dummy experts (finite large-negative selection bias), and the true per-layer retained counts are recorded in the GGUF metadata. The result loads and serves on stock llama.cpp — no fork required. The conversion patch (against upstream convert_hf_to_gguf) is included in this repo for reproducibility.
Runs where big MoEs usually don't. The Q8_0 GGUF (189 GB, 809 tensors) serves comfortably on a 10× Tesla V100-32GB (sm_70) node with layer split — hardware a generation older than what most 200B-class MoEs assume — and is proportionally lighter everywhere else: ~22% smaller than the equivalent unpruned M2.7 quantization at every precision.

Files

File	What it is
`*.safetensors` + index	The pruned model, bf16, HF Transformers format (adaptive metadata in `config.json`)
`gguf/MiniMax-2.7-LegalReapV2Adaptive-BASE.Q8_0.gguf`	Q8_0 quantization, stock-llama.cpp servable
`llama-cpp-minimax-adaptive-gguf-export.patch`	Conversion patch (pad-to-uniform adaptive export)
`artifact_integrity.json`	Machine-checked integrity manifest (metadata consistency, shard coverage, tokenizer equivalence, tensor counts)
`LICENSE`	MiniMax non-commercial license (inherited from the base model)

config.json carries the full per-layer map in reap_adaptive_retained_experts_by_layer (62 entries, 116–198), with reap_adaptive_nonuniform_experts: true.

Method in brief

Expert saliency was observed layerwise (REAP-style weighted activation statistics) over the legal calibration corpus on V100 hardware, per-layer retention ratios were derived from the measured saliency distribution rather than a global constant, and the prune was executed with router renormalization (seed 42, target ratio 0.35). Integrity gates run at every step — metadata completeness, shard coverage, semantic tokenizer equivalence against the base model, and tensor-count verification of both exports (809/809).

Status and roadmap

This is a research preview and the deliberate starting point of a documented recovery pipeline: diagnostics battery → targeted recovery (router recalibration, router-KD, adapter healing) → verified, evaluated releases. Formal held-out evaluations accompany the healed variants rather than this structural BASE — that ordering is intentional, so every published number lands on a clean, reproducible footing.

Provenance

F16 GGUF sha256: 3994ce00d215e1aee63056e64e2e54ff07b3cb0a82fc71f5d82a417c79e5892c
Q8_0 GGUF sha256: 2d89f5dd56a5198276d5c5d947b59ca9d94ba586f856cf7884e2ab6e76b99da3
Built 2026-07-07 by Proprietary Legal Intelligence (PLI Labs).

License

Inherits the MiniMax non-commercial license of the base model (see LICENSE): non-commercial use, modification, and redistribution permitted with notice; commercial use requires prior written authorization from MiniMax.

ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE

MiniMax-2.7-LegalReapV2Adaptive-BASE