Instructions to use Lewdiculous/Eris_PrimeV4-Vision-7B-GGUF-IQ-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-Vision-7B-GGUF-IQ-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-Vision-7B-GGUF-IQ-Imatrix:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Lewdiculous/Eris_PrimeV4-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 serve -hf Lewdiculous/Eris_PrimeV4-Vision-7B-GGUF-IQ-Imatrix:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Lewdiculous/Eris_PrimeV4-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/Eris_PrimeV4-Vision-7B-GGUF-IQ-Imatrix:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Lewdiculous/Eris_PrimeV4-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/Eris_PrimeV4-Vision-7B-GGUF-IQ-Imatrix:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Lewdiculous/Eris_PrimeV4-Vision-7B-GGUF-IQ-Imatrix:Q4_K_M

Use Docker

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

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

Lemonade

How to use Lewdiculous/Eris_PrimeV4-Vision-7B-GGUF-IQ-Imatrix with Lemonade:

Pull the model

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

Run and chat with the model

lemonade run user.Eris_PrimeV4-Vision-7B-GGUF-IQ-Imatrix-Q4_K_M

List all available models

lemonade list

Atomic Chat

These are quants for an experimental model.

     "Q4_K_M", "Q4_K_S", "IQ4_XS", "Q5_K_M", "Q5_K_S",
     "Q6_K", "Q8_0", "IQ3_M", "IQ3_S", "IQ3_XXS"

Original model weights:
https://huggingface.co/Nitral-AI/Eris_PrimeV4-Vision-7B

Vision/multimodal capabilities:

Click here to see how this would work in practice in a roleplay chat.

Click here to see what your SillyTavern Image Captions extension settings should look like.

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, it's also hosted in this repository inside the mmproj folder.

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.

Downloads last month: 483

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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