Instructions to use Lewdiculous/RP_Vision_7B-GGUF-IQ-Imatrix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Lewdiculous/RP_Vision_7B-GGUF-IQ-Imatrix with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Lewdiculous/RP_Vision_7B-GGUF-IQ-Imatrix", filename="RP_Vision_7B-F16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Lewdiculous/RP_Vision_7B-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/RP_Vision_7B-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Lewdiculous/RP_Vision_7B-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/RP_Vision_7B-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Lewdiculous/RP_Vision_7B-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/RP_Vision_7B-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Lewdiculous/RP_Vision_7B-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/RP_Vision_7B-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Lewdiculous/RP_Vision_7B-GGUF-IQ-Imatrix:Q4_K_M
Use Docker
docker model run hf.co/Lewdiculous/RP_Vision_7B-GGUF-IQ-Imatrix:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Lewdiculous/RP_Vision_7B-GGUF-IQ-Imatrix with Ollama:
ollama run hf.co/Lewdiculous/RP_Vision_7B-GGUF-IQ-Imatrix:Q4_K_M
- Unsloth Studio new
How to use Lewdiculous/RP_Vision_7B-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/RP_Vision_7B-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/RP_Vision_7B-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/RP_Vision_7B-GGUF-IQ-Imatrix to start chatting
- Docker Model Runner
How to use Lewdiculous/RP_Vision_7B-GGUF-IQ-Imatrix with Docker Model Runner:
docker model run hf.co/Lewdiculous/RP_Vision_7B-GGUF-IQ-Imatrix:Q4_K_M
- Lemonade
How to use Lewdiculous/RP_Vision_7B-GGUF-IQ-Imatrix with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Lewdiculous/RP_Vision_7B-GGUF-IQ-Imatrix:Q4_K_M
Run and chat with the model
lemonade run user.RP_Vision_7B-GGUF-IQ-Imatrix-Q4_K_M
List all available models
lemonade list
This repository hosts GGUF-IQ-Imatrix quants for ChaoticNeutrals/RP_Vision_7B.
This is a #multimodal model that also has #vision capabilities. Read the full card information if that is your use case.
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"
]
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.
Vision/multimodal capabilities:
If you want to use vision functionality:
- Make sure you are using the latest version of KoboldCpp.
To use the multimodal capabilities of this model, such as vision, you also need to load the specified mmproj file, you can get it here or as uploaded in the repository.
- You can load the mmproj by using the corresponding section in the interface:
- For CLI users, you can load the mmproj file by adding the respective flag to your usual command:
--mmproj your-mmproj-file.gguf
Quantization information:
Steps performed:
Base⇢ GGUF(F16)⇢ Imatrix-Data(F16)⇢ GGUF(Imatrix-Quants)
Using the latest llama.cpp at the time.
Original model information:
RP Vision
RP Vision aims to be a smart RP model capable of providing a pretty, pliable and perfectly pleasant experience for the user. This model is vision capable using the mmproj file included in the mmproj folder. Vision is only compatible with Koboldcpp at this time.
Vision/multimodal capabilities:
If you want to use vision functionality:
You must use the latest versions of Koboldcpp. To use the multimodal capabilities of this model and use vision you need to load the specified mmproj file, this can be found inside this model repo.
You can load the mmproj by using the corresponding section in the interface:
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