Leanstral-RotorQuant-MLX-8bit

[!TIP] KV-cache quantization without any fork (recommended, 2026): upstream llama.cpp/Ollama now cover this natively — use -ctk q8_0 -ctv q8_0 (~half KV memory, negligible quality loss: perplexity +0.002–0.05) or -ctk q4_0 -ctv q4_0 (~quarter memory, ≈7.6% perplexity increase). In Ollama: OLLAMA_KV_CACHE_TYPE=q8_0 with OLLAMA_FLASH_ATTENTION=1. Keep K and V types symmetric to stay on the fast fused Flash-Attention path. Since April 2026, mainline llama.cpp also applies Hadamard rotation to KV activations (PR #21038), which greatly improves low-bit KV quality (opt-out: LLAMA_ATTN_ROT_DISABLE=1).

The RotorQuant/TurboQuant fork flow below is experimental/legacy: the TurboQuant llama.cpp PR was closed without merging (June 2026) and the fork is unmaintained relative to mainline. It is NOT required to use this model.

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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dual compression: 8-bit MLX weight quantization for reduced model size plus the legacy RotorQuant KV-cache fork (superseded by upstream llama.cpp KV options) for efficient long-context inference with KV-cache handling.

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.

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)

About the RotorQuant / TurboQuant labels

RotorQuant and TurboQuant are this project's release labels, not distinct quantization algorithms — for any given tier, both brand repos carry byte-identical weights produced with the standard MLX / llama.cpp quantizers. No brand-specific speedup is claimed or measured. The KV-cache fork these labels originally referred to is legacy; for KV-cache memory savings use the upstream options described above (-ctk/-ctv q8_0, OLLAMA_KV_CACHE_TYPE).

KV-Cache Quantization Comparison

Memory Estimates

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
• RotorQuant GitHub
• MLX Framework

Quant trade-off (MLX lane)

(Current variant — 8bit — is bolded.)

Variants in this family

(Showing 8 sibling variants under majentik/leanstral-*. The current variant — RotorQuant-MLX-8bit — is bolded.)