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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE",
	filename="gguf/MiniMax-2.7-LegalReapV2Adaptive-BASE.Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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 Studio

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

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ProprietaryLegal/MiniMax-2.7-LegalReapV2Adaptive-BASE to start chatting

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 @mariozechner/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

Hermes Agent new

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 new

OpenClaw new

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"

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

MiniMax-2.7-LegalReapV2Adaptive-BASE

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.

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Safetensors

Model size

150B params

Tensor type

BF16

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