It stands for Importance Matrix, a technique used to improve the quality of quantized models. The Imatrix is calculated based on calibration data, and it helps determine the importance of different model activations during the quantization process. The idea is to preserve the most important information during quantization, which can help reduce the loss of model performance, especially when the calibration data is diverse. [1] [2]

For imatrix data generation, kalomaze's groups_merged.txt with added roleplay chats was used, you can find it here.

Steps:

Base⇢ GGUF(F16)⇢ Imatrix-Data(F16)⇢ GGUF(Imatrix-Quants)

Quants:

python

    quantization_options = [
        "Q4_K_M", "IQ4_XS", "Q5_K_M", "Q5_K_S", "Q6_K",
        "Q8_0", "IQ3_M", "IQ3_S", "IQ3_XXS"
    ]

If you want anything that's not here or another model, feel free to request.

Original model information:

Merged on request of Lewdiculus.

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

yaml

slices:
  - sources:
      - model: Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context
        layer_range: [0, 32]
      - model: Endevor/InfinityRP-v1-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

Lewdiculous/InfinityNoodleRP-7b-GGUF-IQ-Imatrix

This model is highly experimental!

Models Merged

Configuration

Lewdiculous/
InfinityNoodleRP-7b-GGUF-IQ-Imatrix