Instructions to use Lewdiculous/InfinityNexus_9B-GGUF-IQ-Imatrix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lewdiculous/InfinityNexus_9B-GGUF-IQ-Imatrix with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Lewdiculous/InfinityNexus_9B-GGUF-IQ-Imatrix", dtype="auto")

llama-cpp-python

How to use Lewdiculous/InfinityNexus_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/InfinityNexus_9B-GGUF-IQ-Imatrix",
	filename="InfinityNexus_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/InfinityNexus_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/InfinityNexus_9B-GGUF-IQ-Imatrix:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Lewdiculous/InfinityNexus_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/InfinityNexus_9B-GGUF-IQ-Imatrix:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Lewdiculous/InfinityNexus_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/InfinityNexus_9B-GGUF-IQ-Imatrix:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Lewdiculous/InfinityNexus_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/InfinityNexus_9B-GGUF-IQ-Imatrix:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Lewdiculous/InfinityNexus_9B-GGUF-IQ-Imatrix:Q4_K_M

Use Docker

docker model run hf.co/Lewdiculous/InfinityNexus_9B-GGUF-IQ-Imatrix:Q4_K_M

LM Studio
Jan
Ollama
How to use Lewdiculous/InfinityNexus_9B-GGUF-IQ-Imatrix with Ollama:
```
ollama run hf.co/Lewdiculous/InfinityNexus_9B-GGUF-IQ-Imatrix:Q4_K_M
```

Unsloth Studio

How to use Lewdiculous/InfinityNexus_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/InfinityNexus_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/InfinityNexus_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/InfinityNexus_9B-GGUF-IQ-Imatrix to start chatting

Atomic Chat new
Docker Model Runner
How to use Lewdiculous/InfinityNexus_9B-GGUF-IQ-Imatrix with Docker Model Runner:
```
docker model run hf.co/Lewdiculous/InfinityNexus_9B-GGUF-IQ-Imatrix:Q4_K_M
```

Lemonade

How to use Lewdiculous/InfinityNexus_9B-GGUF-IQ-Imatrix with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Lewdiculous/InfinityNexus_9B-GGUF-IQ-Imatrix:Q4_K_M

Run and chat with the model

lemonade run user.InfinityNexus_9B-GGUF-IQ-Imatrix-Q4_K_M

List all available models

lemonade list

This repository hosts GGUF-IQ-Imatrix quantizations for ChaoticNeutrals/InfinityNexus_9B.

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.

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:

InfinityNexus

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

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: jeiku/NarrativeNexus_7B
        layer_range: [12, 32]
merge_method: passthrough
dtype: float16

Downloads last month: 27

GGUF

Model size

9B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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Collection including Lewdiculous/InfinityNexus_9B-GGUF-IQ-Imatrix

Quantized Models (GGUF, IQ, Imatrix)

Collection

Various GGUF quantizations of small models. Models with a "checkmark" are personal favorites. An "orange arrow" means it's being uploaded. • 97 items • Updated Mar 2 • 71