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# Metal

Metal quantization performs affine quantization on Apple Silicon (MPS) devices using Metal kernels hosted on the Hugging Face Hub ([kernels-community/mlx-quantization-metal-kernels](https://huggingface.co/kernels-community/mlx-quantization-metal-kernels)). These kernels originate from the [MLX](https://github.com/ml-explore/mlx) framework and are compiled via the [`kernels`](https://github.com/huggingface/kernels) library.

Weights are packed into `uint32` tensors with per-group scales and biases, and the forward pass uses a fused dequantization + matmul Metal kernel (`affine_qmm_t`). This keeps memory usage low while running inference entirely on the GPU with no CPU round-trips.

Supported bit-widths are **2, 4, and 8**. Group size is configurable (default 64).

## Requirements

- Apple Silicon Mac (M1 / M2 / M3 / M4) with MPS support
- The `kernels` package:

```bash
pip install kernels
```

The Metal kernels are downloaded from the Hub automatically on first use — no manual compilation required.

## Quantize on-the-fly

Load any model and quantize it during loading by passing a [`MetalConfig`]. All eligible `nn.Linear` layers are replaced with quantized versions.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, MetalConfig

quantization_config = MetalConfig(bits=4, group_size=64)
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.2-1B",
    device_map="mps",
    quantization_config=quantization_config,
)

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B")
inputs = tokenizer("Apple Silicon is", return_tensors="pt").to("mps")
output = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

## Load a pre-quantized model

If a checkpoint already contains quantized weights (`weight` as packed uint32, `scales`, `qbiases`), they are loaded directly — no re-quantization needed.

```python
from transformers import AutoModelForCausalLM, MetalConfig

model = AutoModelForCausalLM.from_pretrained(
    "your-org/model-metal-4bit",
    device_map="mps",
)
```

## Dequantize

On machines without MPS, a pre-quantized checkpoint is automatically dequantized back to float so the model remains usable on CPU or CUDA. You can also force this behavior explicitly:

```python
from transformers import AutoModelForCausalLM, MetalConfig

config = MetalConfig(dequantize=True)
model = AutoModelForCausalLM.from_pretrained(
    "your-org/model-metal-4bit",
    quantization_config=config,
    device_map="cpu",
)
```

## Exclude layers

Certain layers (e.g., `lm_head`) can be excluded from quantization via `modules_to_not_convert`:

```python
config = MetalConfig(bits=4, group_size=64, modules_to_not_convert=["lm_head"])
```

## Configuration options

| Parameter | Default | Description |
|---|---|---|
| `bits` | `4` | Bit-width for weight quantization (2, 4, or 8) |
| `group_size` | `64` | Number of elements per quantization group |
| `modules_to_not_convert` | `None` | List of module names to keep in full precision |
| `dequantize` | `False` | Force dequantization to float (for non-MPS devices) |
