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4606829 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | # MLX Agent Tools
**Reusable acceleration, quantization, conversion, and serving utilities for Apple Silicon AI agents.**
Developed by **Chengyi Xu and Nexora** — **Nexora Research**
[](LICENSE)
[](tests/)
---
## Overview
Deploying large language and multimodal models (e.g. Qwen, Coder390, Fable) on Apple Silicon unified memory faces strict memory and architectural constraints. Standard tooling often causes out-of-memory kernel panics during quantization, corrupts speculative weights during format translation, or fails under background swap.
`mlx-agent-tools` provides five verified, zero-regression utility modules to overcome these hurdles:
| Module | Purpose | Key Property |
|---|---|---|
| `stream_convert.py` | Bounded CPU streaming affine 4-bit quantizer | Converts 27B+ checkpoints in 128-row chunks; peak RSS ≤ 3.08 GB. |
| `separate_mtp.py` | MTP sidecar extractor & repackager | Separates embedded MTP tensors from trunk weights to fix RMSNorm double-gain corruption. |
| `inference_window.py` | Exclusive inference window harness | Manages single-large-model transitions, process locks, and multi-turn TTFT measurements. |
| `cpu_checkpoint.py` | Bounded checkpoint row reader | Reads partial tensor slices directly from disk into CPU memory without loading the full model. |
| `redacting_log.py` | Zero-leakage credential log stream | Redacts API keys and secrets across split chunks before stdout/log persistence. |
---
## Installation
```bash
pip install -e .
```
Requires Python ≥ 3.10 and `mlx` ≥ 0.22.0.
---
## Usage
### 1. Bounded Streaming Quantization (`stream_convert.py`)
Quantize large checkpoints to MLX affine 4-bit format entirely on CPU with bounded memory:
```python
from mlx_agent_tools import stream_convert
# Converts shard by shard in bounded row chunks
stream_convert(
source_dir="path/to/bf16_model",
output_dir="path/to/mlx_q4_model",
hashes={"model-00001-of-00004.safetensors": {"sha256": "...", "size": 12345678}},
row_chunk=128
)
```
### 2. MTP Sidecar Separation (`separate_mtp.py`)
When MLX models embed `mtp.*` weights in primary trunk shards, `mlx-lm` erroneously applies an extra +1.0 shift to already-normalized RMSNorm weights. `separate_mtp` extracts MTP weights into a dedicated `mtp.safetensors` container while preserving exact trunk bytes:
```python
from mlx_agent_tools import separate_mtp
separate_mtp(
source_dir="path/to/embedded_mtp_model",
output_dir="path/to/clean_model"
)
```
### 3. CPU-Bounded Checkpoint Inspection (`cpu_checkpoint.py`)
Read exact weight rows from large safetensors files without allocating GPU memory:
```python
from mlx_agent_tools import rows
# Read rows 0..128 of an embedding table
tensor_slice = rows("path/to/model", "model.language_model.embed_tokens.weight", start=0, end=128)
```
### 4. Zero-Leakage Credential Redaction (`redacting_log.py`)
Wrap model server process output streams to guarantee API tokens never reach logs:
```python
import sys
from mlx_agent_tools import SecretLogStream
# Safe streaming wrapper
safe_stdout = SecretLogStream(sys.stdout, secret="sk-live-secret-token")
safe_stdout.write("Serving model with key sk-live-secret-token initialized.\n")
# Output: Serving model with key [redacted] initialized.
```
---
## Test Suite
All tools are accompanied by a comprehensive test suite (24 unit tests) covering bounded allocation, byte-exactness, SHA-256 verification, and error handling:
```bash
python3 -m unittest discover -s tests -p "test_*.py"
```
---
## Citation
```bibtex
@software{xu2026mlxagenttools,
title = {MLX Agent Tools: Reusable Acceleration, Quantization, and Serving Utilities for Apple Silicon},
author = {Xu, Chengyi and {{Nexora}}},
year = {2026},
month = oct,
url = {https://github.com/chengyixu/mlx-agent-tools}
}
```
---
## License
MIT License. Copyright (c) 2026 Chengyi Xu and Nexora.
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