Leanstral-RotorQuant-MLX-8bit

8-bit MLX weight-quantized Leanstral-2603 with RotorQuant KV-cache quantization for Lean 4 formal proof generation on Apple Silicon.

Leanstral is the first open-source AI agent purpose-built for Lean 4 formal proofs -- generating both executable code and machine-checkable mathematical proofs. This variant combines dual compression: 8-bit MLX weight quantization for reduced model size plus RotorQuant KV-cache quantization for efficient long-context inference with faster prefill and decode.

Approximate model size: ~120 GB

Overview

This repository provides a dual-compressed configuration: MLX 8-bit weight quantization reduces the static memory footprint, while RotorQuant compresses the KV cache at runtime with superior throughput. Together, they enable running Leanstral on high-memory Apple Silicon machines.

Spec	Value
Base model	mistralai/Leanstral-2603
Architecture	Mistral MoE (~119B parameters, 7 consolidated shards)
Weight quantization	8-bit (MLX)
KV-cache quantization	RotorQuant
Weight memory	~120 GB
Runtime	MLX (Apple Silicon)
License	Apache 2.0
Use case	Lean 4 formal verification, theorem proving, mathematical proofs

Quickstart

from mlx_lm import load, generate

model, tokenizer = load("majentik/Leanstral-RotorQuant-MLX-8bit")

prompt = "Prove that for all natural numbers n, n + 0 = n in Lean 4:"
response = generate(
    model,
    tokenizer,
    prompt=prompt,
    max_tokens=512,
)
print(response)

What is RotorQuant?

RotorQuant is a rotation-based KV cache quantization method that applies learned Clifford algebra rotations before quantizing the key-value cache. Key results:

• 5.3x faster prefill compared to TurboQuant baseline
• 28% faster decode throughput
• Perplexity: 6.91 vs 7.07 for TurboQuant (lower is better)

Combined with MLX 8-bit weight quantization, this dual compression approach makes it feasible to run Leanstral's ~119B parameter model on Apple Silicon hardware with excellent throughput.

KV-Cache Quantization Comparison

Method	Prefill Speed	Decode Speed	Memory Savings	Reference
TurboQuant	Baseline	Baseline	High	arXiv: 2504.19874
RotorQuant	5.3x faster	28% faster	High	GitHub

Memory Estimates

Component	Estimate
Model weights (8-bit)	~120 GB
KV-cache	Reduced via RotorQuant
Recommended hardware	Mac Studio M2/M3/M4 Ultra (192 GB+) or Mac Pro

Lean 4 Use Case

Leanstral excels at:

• Formal verification -- generating machine-checkable proofs of mathematical theorems
• Theorem proving -- interactive and automated proof search in Lean 4
• Code generation -- writing verified Lean 4 programs with correctness guarantees
• Proof repair -- fixing incomplete or broken proof scripts

See Also

• mistralai/Leanstral-2603 -- Base model
• majentik/Leanstral-RotorQuant -- Full-precision weights + RotorQuant KV cache
• majentik/Leanstral-RotorQuant-MLX-4bit -- MLX 4-bit + RotorQuant
• majentik/Leanstral-RotorQuant-MLX-2bit -- MLX 2-bit + RotorQuant
• majentik/Leanstral-TurboQuant-MLX-8bit -- MLX 8-bit + TurboQuant
• RotorQuant GitHub
• MLX Framework