Initial release of mlx-agent-tools (v1.0.0)

4606829 verified 15 days ago

4.05 kB

# MLX Agent Tools

**Reusable acceleration, quantization, conversion, and serving utilities for Apple Silicon AI agents.**

Developed by **Chengyi Xu and Nexora** — **Nexora Research**

[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)
[![Tests](https://img.shields.io/badge/tests-24%20passed-brightgreen.svg)](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.

	# MLX Agent Tools

	Reusable acceleration, quantization, conversion, and serving utilities for Apple Silicon AI agents.

	Developed by Chengyi Xu and Nexora — Nexora Research

	[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)
	[![Tests](https://img.shields.io/badge/tests-24%20passed-brightgreen.svg)](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.