How to use from
llama.cpp
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Lewdiculous/Copium-Cola-9B-GGUF-IQ-Imatrix:
# Run inference directly in the terminal:
llama cli -hf Lewdiculous/Copium-Cola-9B-GGUF-IQ-Imatrix:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Lewdiculous/Copium-Cola-9B-GGUF-IQ-Imatrix:
# Run inference directly in the terminal:
llama cli -hf Lewdiculous/Copium-Cola-9B-GGUF-IQ-Imatrix:
Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Lewdiculous/Copium-Cola-9B-GGUF-IQ-Imatrix:
# Run inference directly in the terminal:
./llama-cli -hf Lewdiculous/Copium-Cola-9B-GGUF-IQ-Imatrix:
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Lewdiculous/Copium-Cola-9B-GGUF-IQ-Imatrix:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Lewdiculous/Copium-Cola-9B-GGUF-IQ-Imatrix:
Use Docker
docker model run hf.co/Lewdiculous/Copium-Cola-9B-GGUF-IQ-Imatrix:
Quick Links
    quantization_options = [
        "Q4_K_M", "Q4_K_S", "IQ4_NL", "IQ4_XS", "Q5_K_M", 
        "Q5_K_S", "Q6_K", "Q8_0", "IQ3_M", "IQ3_S", "IQ3_XS", "IQ3_XXS"
    ]

GGUF-Imatrix quantizations for Test157t/Copium-Cola-9B.

All credits belong to the author.

If you liked these, check out the work with FantasiaFoundry's GGUF-IQ-Imatrix-Quantization-Script.

What does "Imatrix" mean?

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-b2343:

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.

Original model information:

image/png

This model was merged using the passthrough merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: ChaoticNeutrals/Eris_7B
        layer_range: [0, 20]
  - sources:
      - model: ChaoticNeutrals/Eris_7B
        layer_range: [12, 32]
merge_method: passthrough
dtype: float16
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