Instructions to use QuixiAI/Ina-v11.1-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use QuixiAI/Ina-v11.1-gguf 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 QuixiAI/Ina-v11.1-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuixiAI/Ina-v11.1-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuixiAI/Ina-v11.1-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuixiAI/Ina-v11.1-gguf: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 QuixiAI/Ina-v11.1-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuixiAI/Ina-v11.1-gguf: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 QuixiAI/Ina-v11.1-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuixiAI/Ina-v11.1-gguf:Q4_K_M

Use Docker

docker model run hf.co/QuixiAI/Ina-v11.1-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use QuixiAI/Ina-v11.1-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuixiAI/Ina-v11.1-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuixiAI/Ina-v11.1-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuixiAI/Ina-v11.1-gguf:Q4_K_M

Ollama
How to use QuixiAI/Ina-v11.1-gguf with Ollama:
```
ollama run hf.co/QuixiAI/Ina-v11.1-gguf:Q4_K_M
```

Unsloth Studio

How to use QuixiAI/Ina-v11.1-gguf 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 QuixiAI/Ina-v11.1-gguf 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 QuixiAI/Ina-v11.1-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuixiAI/Ina-v11.1-gguf to start chatting

How to use QuixiAI/Ina-v11.1-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuixiAI/Ina-v11.1-gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "QuixiAI/Ina-v11.1-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use QuixiAI/Ina-v11.1-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuixiAI/Ina-v11.1-gguf:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "QuixiAI/Ina-v11.1-gguf:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use QuixiAI/Ina-v11.1-gguf with Docker Model Runner:
```
docker model run hf.co/QuixiAI/Ina-v11.1-gguf:Q4_K_M
```

Lemonade

How to use QuixiAI/Ina-v11.1-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuixiAI/Ina-v11.1-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Ina-v11.1-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use QuixiAI/Ina-v11.1-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuixiAI/Ina-v11.1-gguf:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default QuixiAI/Ina-v11.1-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

Llamacpp Quantizations of Ina-v11.1

Ina interprets persona definitions as executable instructions. The model follows <<CHARACTER_DESCRIPTION>> blocks with extremely high fidelity even during 10k–15k token erotic or dark-fiction role-play sessions.

Fine-tuned by BaiAI and Eric Hartford (QuixiAI) using QLoRA + DPO on large volumes of RP logs, creator-voice datasets, and persona modules.

Contributor Credits:

Using llama.cpp

Original model: https://huggingface.co/QuixiAI/Ina-v11.1

Run them in LM Studio

Run them directly with llama.cpp, or any other llama.cpp based project

Download a file (not the whole branch) from below:

Filename	Quant type	File Size	Split	Description
Ina-v11.1-Q8_0.gguf	Q8_0	70GB	true	Extremely high quality, generally unneeded but max available quant.
Ina-v11.1-Q6_K.gguf	Q6_K	54GB	true	Very high quality, near perfect, recommended.
Ina-v11.1-Q5_K_M.gguf	Q5_K_M	47GB	true	High quality, recommended.
Ina-v11.1-Q5_K_S.gguf	Q5_K_S	38GB	true	High quality, recommended.
Ina-v11.1-Q4_K_M.gguf	Q4_K_M	40GB	true	Good quality, default size for most use cases, recommended.
Ina-v11.1-Q4_1.gguf	Q4_1	41GB	true	Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon.
Ina-v11.1-Q4_K_S.gguf	Q4_K_S	38GB	true	Slightly lower quality with more space savings, recommended.
Ina-v11.1-Q4_0.gguf	Q4_0	37GB	true	Legacy format, offers online repacking for ARM and AVX CPU inference.
Ina-v11.1-IQ4_NL.gguf	IQ4_NL	38GB	true	Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference.
Ina-v11.1-IQ4_XS.gguf	IQ4_XS	36GB	true	Decent quality, smaller than Q4_K_S with similar performance, recommended.
Ina-v11.1-Q3_K_L.gguf	Q3_K_L	35GB	true	Lower quality but usable, good for low RAM availability.
Ina-v11.1-Q3_K_M.gguf	Q3_K_M	32GB	true	Low quality.
Ina-v11.1-IQ3_M.gguf	IQ3_M	30GB	true	Medium-low quality, new method with decent performance comparable to Q3_K_M.
Ina-v11.1-Q3_K_S.gguf	Q3_K_S	29GB	true	Low quality, not recommended.
Ina-v11.1-IQ3_XS.gguf	IQ3_XS	27GB	false	Lower quality, new method with decent performance, slightly better than Q3_K_S.

Downloading using huggingface-cli

Click to view download instructions

First, make sure you have hugginface-cli installed:

pip install -U "huggingface_hub[cli]"

Then, you can target the specific file you want:

huggingface-cli download QuixiAI/Ina-v11.1-gguf --include "Ina-v11.1-Q4_K_M.gguf" --local-dir ./

If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:

huggingface-cli download QuixiAI/Ina-v11.1-gguf --include "Ina-v11.1-Q8_0/*" --local-dir ./

You can either specify a new local-dir (Ina-v11.1-Q8_0) or download them all in place (./)

Which file should I choose?

Click here for details

A great write up with charts showing various performances is provided by Artefact2 here

The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.

If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.

Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.

If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.

If you want to get more into the weeds, you can check out this extremely useful feature chart:

llama.cpp feature matrix

But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.

These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.

Credits

I copied Bartowski's model card and made it my own, cheers!

Downloads last month: 311

GGUF

Model size

71B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuixiAI/Ina-v11.1-gguf

Base model

NeverSleep/Lumimaid-v0.2-70B

Finetuned

QuixiAI/Ina-v11.1

Quantized

(8)

this model