Instructions to use ProprietaryLegal/minimax-m3-legal-reap-0.22 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ProprietaryLegal/minimax-m3-legal-reap-0.22 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ProprietaryLegal/minimax-m3-legal-reap-0.22", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ProprietaryLegal/minimax-m3-legal-reap-0.22", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("ProprietaryLegal/minimax-m3-legal-reap-0.22", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ProprietaryLegal/minimax-m3-legal-reap-0.22 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ProprietaryLegal/minimax-m3-legal-reap-0.22"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ProprietaryLegal/minimax-m3-legal-reap-0.22",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ProprietaryLegal/minimax-m3-legal-reap-0.22

SGLang

How to use ProprietaryLegal/minimax-m3-legal-reap-0.22 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ProprietaryLegal/minimax-m3-legal-reap-0.22" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ProprietaryLegal/minimax-m3-legal-reap-0.22",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ProprietaryLegal/minimax-m3-legal-reap-0.22" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ProprietaryLegal/minimax-m3-legal-reap-0.22",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ProprietaryLegal/minimax-m3-legal-reap-0.22 with Docker Model Runner:
```
docker model run hf.co/ProprietaryLegal/minimax-m3-legal-reap-0.22
```

MiniMax-M3 Legal REAP 0.22

MiniMax-M3 Legal REAP 0.22 is a PLI Labs research artifact: a text-only, expert-pruned derivative of MiniMax-M3, produced to test whether a very large sparse mixture-of-experts model can be compressed around legal work (drafting, source-grounded synthesis, adversarial review, abstention, and long-context document reasoning) rather than around coding benchmarks.

This is an early, unhealed research checkpoint. It has not been recovery-tuned, it carries no published evaluation numbers, and it is held under review. It is not a legal assistant, and its outputs may be degraded relative to the base model. All legal use requires attorney supervision and independent source verification.

Built with MiniMax M3.

Release Status


Status	Research artifact — unhealed raw prune, under review
Evaluation	None published. No aggregate benchmark tables exist yet
Healing	Recovery fine-tune (heal) is the next pipeline stage — not yet applied
Verification	Held-out KL / faithfulness verification is pending
Recommended use	Research and internal evaluation only

The public claim is deliberately narrow: this is a structurally complete, legal-calibrated expert-prune of MiniMax-M3, released as a research artifact so the method and the checkpoint can be inspected. It is not a benchmark-backed release and should not be treated as one until healing and verification complete and aggregate results are published.

Model Details

Field	Value
Model family	MiniMax-M3 (native multimodal MoE; this derivative is text-only)
Publisher	PLI Labs / Proprietary Legal Intelligence
Base model	`MiniMaxAI/MiniMax-M3`
Pruning method	REAP-style routed-expert pruning (observation-driven saliency)
Prune ratio	0.22 (floor-enforced, per-layer adaptive)
Working precision	bf16
Format	Transformers / safetensors checkpoint (original split-expert layout)
Total parameters	~330B after prune (base ~428B)
Active parameters	~23B (top-4 routing preserved — active count essentially unchanged)
Intended domain	Lawyer-supervised legal drafting, review, and analysis
Contact	nick@proprietarylegal.com

What changed structurally

The base MiniMax-M3 text backbone has 60 decoder layers: layers 0–2 are dense (no routed experts) and layers 3–59 are MoE, with 128 routed experts plus 1 always-on shared expert per MoE layer and top-4 sigmoid routing.

This derivative:

prunes only the routed experts on the 57 MoE layers, using a per-layer adaptive schedule at an overall 0.22 ratio. Retained routed-expert counts vary by layer between 89 and 111 of 128 (deeper, more separable layers give up more experts; capability-carrying layers are protected by floors);
leaves layers 0–2 (dense) untouched;
preserves the shared expert on every MoE layer;
preserves top-4 routing, so the per-token active-parameter budget is essentially unchanged; the reduction is in total stored parameters;
drops the vision tower and multimodal projector — this is a text-only checkpoint of the MiniMaxM3SparseForCausalLM text stack. MiniMax Sparse Attention (MSA), the learned router, routing bias, layernorms, embeddings, and the MTP modules are copied unchanged. The router weight and routing-bias rows for dropped experts are removed by row-slicing (no numeric renormalization).

Intended Use

This artifact is intended for:

research on legal-domain MoE compression;
private, local evaluation by legal teams and model researchers;
lawyer-supervised legal drafting and revision assistance in an evaluation setting;
long-form legal and factual synthesis, source-grounded summarization, and retrieval-sensitive analysis under review.

The intended deployment pattern is local or on-premises evaluation on hardware controlled by the lawyer, firm, or legal organization.

Out-of-Scope Use

Do not use this artifact for:

unsupervised legal advice;
final court filings without attorney review;
citation generation without independent verification;
client-specific work where the user cannot verify the source record;
any production deployment (this is an unhealed, unevaluated research checkpoint);
non-legal capability claims (coding, general chat, multimodal — the vision path is removed and no capability is claimed).

Method Summary

The prune direction is legal, not code. Routed-expert saliency was measured by running a private legal-domain calibration corpus through an observer that records, per expert, the REAP saliency signal (router gate value combined with activation magnitude over the active-token set). The calibration corpus spans legal drafting, summarization, adversarial review, abstention, and long-context trial-transcript roles (975 rows across 13 length-bucketed components); its contents are private and not distributed. Per-role observations were combined with a role-weighted merge that emphasizes legal drafting.

A per-layer adaptive schedule then set each layer's retained-expert count under capability floors (protecting under-covered layers and low-confidence carriers), and the lowest-saliency experts were dropped per layer. The checkpoint was written by a streaming split-to-split serializer that rewrites the on-disk shards directly and never loads the full model, touching roughly one shard buffer plus one tensor of peak memory. See the accompanying GitHub documentation for method detail.

The saliency criterion follows REAP (Lasby et al., "REAP the Experts: Why Pruning Prevails for One-Shot MoE Compression", arXiv:2510.13999).

Loading

The checkpoint stores routed experts in split per-expert form with per-layer nonuniform expert counts (89-111 of the original 128 per MoE layer). A small modeling_minimax_m3_vl_split.py ships in the repo: it subclasses the native transformers MiniMax-M3 implementation, replacing only the routed-experts module with per-expert linear layers (memory-lazy sparse-attention masks included), and is wired via auto_map — load with trust_remote_code=True:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "ProprietaryLegal/minimax-m3-legal-reap-0.22",
    trust_remote_code=True,
    torch_dtype="bfloat16",
    device_map="auto",
)

The split-linear expert layout is deliberately compatible with bitsandbytes 4-bit quantization (BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4")), which quantizes every per-expert linear; the packed native layout cannot be NF4-quantized. Forward equivalence of the split implementation against the native packed implementation is test-pinned (bit-identical in bf16, identical top-k routing).

Evaluation Status

No evaluation numbers are published. This checkpoint is the raw, unhealed prune. Field evidence and internal research indicate that MiniMax-M3's experts are near-orthogonal (little merge headroom) and that the honest cold-drop safe band for legal work is roughly 0.16–0.25, with the losses concentrated in knowledge, faithfulness, refusal, and long-context behavior rather than surface fluency — axes that coding-style benchmarks do not see. A recovery heal and held-out KL / faithfulness verification are the required next gates before any quality claim.

Provenance

Item	Value
Base model	`MiniMaxAI/MiniMax-M3`
Base revision	`MiniMaxAI/MiniMax-M3` (local bf16 conversion of the official release; no upstream revision pin recorded)
Prune ratio	0.22 (floor-enforced, per-layer adaptive)
Selection metric	REAP saliency (canonical `reap` metric)
Seed	42
Cut-plan hash	`2e71105f68e30c81073623dc613448c24e94c06356d82414b9db86a78dd020c8`
Retained experts/layer	89–111 of 128 (per-layer adaptive)
Dense layers preserved	0, 1, 2
Shard total size	670,639,198,956 bytes (~624.6 GiB, 126 shards)
Transformers baseline	4.52.4 (base config); load path fuses split experts at load

A machine-readable provenance sidecar (reap_prune_provenance.json) and an expert-count census accompany the checkpoint and record the base index hash, the observation hash, the full per-layer cut plan, and the cut-plan hash.

Privacy Boundary

This release does not include client names, client files, closed-client materials, attorney-client communications, privileged work product, private prompts, calibration row contents, internal hostnames or IP addresses, local file paths, credentials, or tokens. The calibration corpus is described only generically.

License Boundary

This is a derivative work of MiniMax-M3 and remains subject to the MiniMax Community License (license_name: minimax-community); the upstream LICENSE is redistributed with this checkpoint. In summary, and without replacing the license text:

The license permits use, modification, and redistribution of the software and derivative works, including publishing this pruned checkpoint.
Commercial use carries additional obligations: you must prominently display "Built with MiniMax M3", and you must send a notice to MiniMax (or, above a revenue threshold, obtain prior written authorization) per the license terms.
The license includes a prohibited-uses appendix (no unlawful content, no military use, no exploitation of minors, no harmful disinformation, no discriminatory content).

Review the upstream license in full before use, especially for any commercial deployment.

Citation

PLI Labs. MiniMax-M3 Legal REAP 0.22 model card. Proprietary Legal Intelligence, 2026. Derived from MiniMaxAI/MiniMax-M3. Pruning method after Lasby et al., arXiv:2510.13999.

Model tree for ProprietaryLegal/minimax-m3-legal-reap-0.22

Base model

MiniMaxAI/MiniMax-M3

Finetuned

(13)

this model

Paper for ProprietaryLegal/minimax-m3-legal-reap-0.22

REAP the Experts: Why Pruning Prevails for One-Shot MoE compression

Paper • 2510.13999 • Published Oct 15, 2025 • 21

ProprietaryLegal
/

minimax-m3-legal-reap-0.22