ISQ.md

In situ quantization

In situ quantization works by quantizing non GGUF or GGML models in-place. This allows you to take advantage of flash attention, and reduces memory footprint when running the model. Currently, all layers which would be Linear are able to be quantized.

An API is exposed on the Python and Rust APIs which provide the ability to dynamically re-ISQ models at runtime.

To set the ISQ type for individual layers, use a model topology.

ISQ quantization types

• Q4_0
• Q4_1
• Q5_0
• Q5_1
• Q8_0
• Q8_1 (not available on CUDA)
• Q2K
• Q3K
• Q4K
• Q5K
• Q6K
• Q8K (not available on CUDA)
• HQQ4
• HQQ8

When using ISQ, it will automatically load ISQ-able weights into CPU memory before applying ISQ. The ISQ application process moves the weights to device memory. This process is implemented to avoid memory spikes from loading the model in full precision.

For Mixture of Expert models, a method called MoQE can be applied to only quantize MoE layers. This is configured via the ISQ organization parameter in all APIs.

Python Example

runner = Runner(
    which=Which.GGUF(
        tok_model_id="mistralai/Mistral-7B-Instruct-v0.1",
        quantized_model_id="TheBloke/Mistral-7B-Instruct-v0.1-GGUF",
        quantized_filename="mistral-7b-instruct-v0.1.Q4_K_M.gguf",
    ),
    in_situ_quant="Q4K",
)

Rust Example

You can find this example here.

let model = TextModelBuilder::new("microsoft/Phi-3.5-mini-instruct")
    .with_isq(IsqType::Q8_0)
    .with_logging()
    .with_paged_attn(|| PagedAttentionMetaBuilder::default().build())?
    .build()
    .await?;

Server example

cargo run --release --features "cuda flash-attn" -- --port 1234 --log output.txt --isq Q2K plain -m mistralai/Mistral-7B-Instruct-v0.1 -a mistral