Instructions to use Lewdiculous/Eris_PrimeV4.69-Vision-32k-7B-GGUF-Imatrix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Lewdiculous/Eris_PrimeV4.69-Vision-32k-7B-GGUF-Imatrix with 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/Eris_PrimeV4.69-Vision-32k-7B-GGUF-Imatrix:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Lewdiculous/Eris_PrimeV4.69-Vision-32k-7B-GGUF-Imatrix:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Lewdiculous/Eris_PrimeV4.69-Vision-32k-7B-GGUF-Imatrix:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Lewdiculous/Eris_PrimeV4.69-Vision-32k-7B-GGUF-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/Eris_PrimeV4.69-Vision-32k-7B-GGUF-Imatrix:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Lewdiculous/Eris_PrimeV4.69-Vision-32k-7B-GGUF-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/Eris_PrimeV4.69-Vision-32k-7B-GGUF-Imatrix:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Lewdiculous/Eris_PrimeV4.69-Vision-32k-7B-GGUF-Imatrix:Q4_K_M

Use Docker

docker model run hf.co/Lewdiculous/Eris_PrimeV4.69-Vision-32k-7B-GGUF-Imatrix:Q4_K_M

LM Studio
Jan
Ollama
How to use Lewdiculous/Eris_PrimeV4.69-Vision-32k-7B-GGUF-Imatrix with Ollama:
```
ollama run hf.co/Lewdiculous/Eris_PrimeV4.69-Vision-32k-7B-GGUF-Imatrix:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use Lewdiculous/Eris_PrimeV4.69-Vision-32k-7B-GGUF-Imatrix with Docker Model Runner:
```
docker model run hf.co/Lewdiculous/Eris_PrimeV4.69-Vision-32k-7B-GGUF-Imatrix:Q4_K_M
```

Lemonade

How to use Lewdiculous/Eris_PrimeV4.69-Vision-32k-7B-GGUF-Imatrix with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Lewdiculous/Eris_PrimeV4.69-Vision-32k-7B-GGUF-Imatrix:Q4_K_M

Run and chat with the model

lemonade run user.Eris_PrimeV4.69-Vision-32k-7B-GGUF-Imatrix-Q4_K_M

List all available models

lemonade list

Atomic Chat

#Roleplay #Multimodal #Vision

These are quants for an experimental model.

quantization_options = [
    "Q4_K_M", "Q4_K_S", "IQ4_XS",
    "Q5_K_M", "Q5_K_S",
    "Q6_K", "Q8_0"
]

Original model weights and information:
https://huggingface.co/Nitral-AI/Eris_PrimeV4.69-Vision-32k-7B

MMPROJ:
./mmproj/mmproj-model-f16.gguf

Vision/multimodal capabilities:

If you want to use vision functionality:

You must use the latest version 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:

Downloads last month: 1,177

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

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