Leanstral-TurboQuant-MLX-4bit
4-bit MLX weight-quantized Leanstral-2603 with TurboQuant 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: 4-bit MLX weight quantization for reduced model size plus TurboQuant KV-cache quantization for efficient long-context inference.
Overview
This repository provides a dual-compressed configuration: MLX 4-bit weight quantization reduces the static memory footprint, while TurboQuant compresses the KV cache at runtime. 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 | 4-bit (MLX) |
| KV-cache quantization | TurboQuant |
| Weight memory | ~60 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-TurboQuant-MLX-4bit")
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 TurboQuant?
TurboQuant (arXiv: 2504.19874) is a KV-cache quantization method that compresses the key-value cache used during autoregressive generation. By quantizing the KV cache to lower precision, TurboQuant reduces memory consumption proportionally to context length. Combined with MLX 4-bit weight quantization, this dual compression approach makes it feasible to run Leanstral's ~119B parameter model on Apple Silicon hardware.
Memory Estimates
| Component | Estimate |
|---|---|
| Model weights (4-bit) | ~60 GB |
| KV-cache | Reduced via TurboQuant |
| 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-TurboQuant -- Full-precision weights + TurboQuant KV cache
- majentik/Leanstral-TurboQuant-MLX-2bit -- MLX 2-bit + TurboQuant
- majentik/Leanstral-TurboQuant-MLX-1bit -- MLX 1-bit + TurboQuant
- majentik/Leanstral-RotorQuant-MLX-4bit -- MLX 4-bit + RotorQuant (faster prefill/decode)
- TurboQuant paper
- Downloads last month
- 29
4-bit
Model tree for majentik/Leanstral-TurboQuant-MLX-4bit
Base model
mistralai/Leanstral-2603