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quantization_options = [
"Q4_K_M", "Q4_K_S", "IQ4_XS", "Q5_K_M",
"Q5_K_S", "Q6_K", "Q8_0", "IQ3_M", "IQ3_S", "IQ3_XS", "IQ3_XXS"
]
All credits belong to the author.
If you liked these, check out the work with FantasiaFoundry's GGUF-IQ-Imatrix-Quantization-Script.
Personal note:
This model should give you less refusals, given it's merged with the unhinged Layla-V4.
It stands for Importance Matrix, a technique used to improve the quality of quantized models.
[1]
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 and lead to better quality preservation, especially when the calibration data is diverse.
[2]
For --imatrix data, included imatrix.dat was used.
Using llama.cpp-b2350:
Base⇢ GGUF(F16)⇢ Imatrix-Data(F16)⇢ GGUF(Imatrix-Quants)
The new IQ3_S quant-option has shown to be better than the old Q3_K_S, so I added that instead of the later. Only supported in koboldcpp-1.59.1 or higher.
If you want any specific quantization to be added, feel free to ask.
This is a merge of pre-trained language models created using mergekit.
This model was merged using the passthrough merge method.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
slices:
- sources:
- model: ChaoticNeutrals/Eris_Remix_7B
layer_range: [0, 20]
- sources:
- model: l3utterfly/mistral-7b-v0.1-layla-v4
layer_range: [12, 32]
merge_method: passthrough
dtype: float16