Instructions to use Lewdiculous/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lewdiculous/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Lewdiculous/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix", dtype="auto") - llama-cpp-python
How to use Lewdiculous/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Lewdiculous/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix", filename="Infinitely-Laydiculus-9b-F16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Lewdiculous/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Lewdiculous/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Lewdiculous/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Lewdiculous/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Lewdiculous/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix:Q4_K_M
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/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Lewdiculous/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix:Q4_K_M
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/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Lewdiculous/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix:Q4_K_M
Use Docker
docker model run hf.co/Lewdiculous/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Lewdiculous/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix with Ollama:
ollama run hf.co/Lewdiculous/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix:Q4_K_M
- Unsloth Studio
How to use Lewdiculous/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Lewdiculous/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Lewdiculous/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Lewdiculous/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix to start chatting
- Docker Model Runner
How to use Lewdiculous/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix with Docker Model Runner:
docker model run hf.co/Lewdiculous/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix:Q4_K_M
- Lemonade
How to use Lewdiculous/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Lewdiculous/Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix:Q4_K_M
Run and chat with the model
lemonade run user.Infinitely-Laydiculous-9B-GGUF-IQ-Imatrix-Q4_K_M
List all available models
lemonade list
This repository hosts GGUF-IQ-Imatrix quantizations for Nitral-AI/Infinitely-Laydiculous-9B.
Huge thanks to @Nitral-AI for merging this one.
Instruct format, context size, samplers:
Extended Alpaca (recommended) format, for more information check the main base model card here.
The expected --contextsize this model can handle is 8192.
SillyTavern - TextGen/Samplers.
What does "Imatrix" mean?
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. This was just to add a bit more diversity to the data.
Steps:
Base⇢ GGUF(F16)⇢ Imatrix-Data(F16)⇢ GGUF(Imatrix-Quants)
Using the latest llama.cpp at the time.
Quants:
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_XXS"
]
If you want anything that's not here or another model, feel free to request.
Original model information:
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: Endevor/InfinityRP-v1-7B
layer_range: [0, 20]
- sources:
- model: l3utterfly/mistral-7b-v0.1-layla-v4
layer_range: [12, 32]
merge_method: passthrough
dtype: float16
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