Instructions to use ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE:Q8_0
# Run inference directly in the terminal:
llama cli -hf ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE:Q8_0
# Run inference directly in the terminal:
llama cli -hf ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE:Q8_0

Use Docker

docker model run hf.co/ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE:Q8_0

LM Studio
Jan
Ollama
How to use ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE with Ollama:
```
ollama run hf.co/ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE:Q8_0
```
Unsloth Desktop

How to use ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE with Docker Model Runner:
```
docker model run hf.co/ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE:Q8_0
```

Lemonade

How to use ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE:Q8_0

Run and chat with the model

lemonade run user.MiniMax-2.7-LegalReapV2Adaptive-BASE-Q8_0

List all available models

lemonade list

Hermes Agent

How to use ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE:Q8_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE:Q8_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MiniMax-2.7-LegalReapV2Adaptive-BASE

Published by PLI Labs, the open research arm of Proprietary Legal Intelligence. PLI builds AI systems around a legal organization's own work product, combining retrieval, staged workflows, validation, and attorney review. Visit proprietarylegal.ai to explore our work and discuss what your organization needs.

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.

About PLI Labs

“Your proprietary work product is your proprietary intelligence.” PLI Labs is the open research arm of Proprietary Legal Intelligence. We publish legal model prunes, serving recipes for owned hardware, and open-source tools so readers can inspect our methods and test our claims before hiring us.

Founded by a practicing trial lawyer, PLI builds AI systems around a legal organization's own work product: retrieval, staged workflows, validation, and attorney review. The approach grew from systems running on live matters in a litigation practice. Our consulting work supports deployment through approved frontier APIs, private cloud, or open-weight models on hardware you own. Explore the research or contact us about your organization's work.

Website · PLI Labs · Research · GitHub · Hugging Face · Contact

Proprietary Legal Intelligence LLC is a technology consultancy, not a law firm, and does not provide legal services or legal advice.