baa-ai/MiniMax-M2.7-RAM-100GB-MLX

MiniMax-M2.7 — 100 GB (MLX)

Mixed-precision MLX build of MiniMaxAI/MiniMax-M2.7, prepared by baa.ai.

Metrics

Metric	Value
Size on disk	100.1 GB (20 shards)
Group size	64
Framework	MLX (Apple Silicon)

Benchmarks

Benchmark	Score	Notes
HumanEval pass@1 (single-shot)	87.2% (143/164)	164/164 completed, 0 skipped
HumanEval pass@1 (best-of-2)	94.5% (155/164)	Retry of the 21 single-shot failures recovered 12
Decode throughput (Apple Silicon)	36.4 tok/s (wall-gen) / 36.8 tok/s (task-mean)	296,683 tokens generated over 136.1 min

Settings for both runs match the Recommended inference settings below.

Recommended inference settings

sampler_params = {
    "temperature": 1.0,
    "top_p": 0.95,
    "top_k": 40,
    "repetition_penalty": 1.1,
    "max_tokens": 8192,
}

Chat template — thinking mode

MiniMax-M2.7 uses a <think>…</think> reasoning block. Important: the base chat template injects <think>\n at the end of the prompt before generation, so the model's output begins inside the reasoning block with no opening tag. Strip everything up to and including the first </think>:

def strip_thinking(text: str) -> str:
    if "</think>" in text:
        return text.split("</think>", 1)[1].strip()
    return text.strip()

Give the model enough token budget that it can finish reasoning and emit the </think> closing tag — we recommend at least 4096, and 8192 for harder problems.

Usage

from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler, make_logits_processors

model, tokenizer = load("baa-ai/MiniMax-M2.7-RAM-100GB-MLX")

sampler = make_sampler(temp=1.0, top_p=0.95, top_k=40)
logits_processors = make_logits_processors(repetition_penalty=1.1)

prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": "Write a Python function that reverses a string."}],
    tokenize=False,
    add_generation_prompt=True,
)

response = generate(
    model,
    tokenizer,
    prompt=prompt,
    max_tokens=8192,
    sampler=sampler,
    logits_processors=logits_processors,
)

if "</think>" in response:
    response = response.split("</think>", 1)[1].strip()
print(response)

Hardware

• Apple Silicon Mac with ~112 GB unified memory recommended for comfortable inference.
• Runs on less with swap, at substantially reduced throughput.

Variants

Variant	Size	Link
91 GB	96.4 GB	baa-ai/MiniMax-M2.7-RAM-91GB-MLX
100 GB	100.1 GB	baa-ai/MiniMax-M2.7-RAM-100GB-MLX
111 GB	110.9 GB	baa-ai/MiniMax-M2.7-RAM-111GB-MLX
116 GB	116.0 GB	baa-ai/MiniMax-M2.7-RAM-116GB-MLX
120 GB	120.1 GB	baa-ai/MiniMax-M2.7-RAM-120GB-MLX

Black Sheep AI Products

Shepherd — Private AI deployment platform that shrinks frontier models by 50-60% through RAM compression, enabling enterprises to run sophisticated AI on single GPU instances or Apple Silicon hardware. Deploy in your VPC with zero data leaving your infrastructure. Includes CI/CD pipeline integration, fleet deployment across Apple Silicon clusters, air-gapped and sovereign deployment support, and multi-format export (MLX, GGUF). Annual cloud costs from ~$2,700 — or run on a Mac Studio for electricity only.

Watchman — Capability audit and governance platform for compressed AI models. Know exactly what your quantized model can do before it goes live. Watchman predicts which capabilities survive compression in minutes — replacing weeks of benchmarking. Includes compliance-ready reporting for regulated industries, quality valley warnings for counterproductive memory allocations, instant regression diagnosis tracing issues to specific tensors, and 22 adversarial security probes scanning for injection, leakage, hallucination, and code vulnerabilities.

Learn more at baa.ai — Sovereign AI.

License

Inherited from the upstream MiniMax-M2.7 license: non-commercial use permitted; commercial use requires written authorization from MiniMax.

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Quantized by baa.ai