Instructions to use disinfozone/kenosistron_GGUFs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use disinfozone/kenosistron_GGUFs with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="disinfozone/kenosistron_GGUFs",
	filename="Q2_K/kenosistron-Q2_K-00001-of-00002.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use disinfozone/kenosistron_GGUFs 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 disinfozone/kenosistron_GGUFs:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf disinfozone/kenosistron_GGUFs:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/disinfozone/kenosistron_GGUFs:Q4_K_M

LM Studio
Jan

vLLM

How to use disinfozone/kenosistron_GGUFs with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/disinfozone/kenosistron_GGUFs:Q4_K_M

Ollama
How to use disinfozone/kenosistron_GGUFs with Ollama:
```
ollama run hf.co/disinfozone/kenosistron_GGUFs:Q4_K_M
```

Unsloth Studio

How to use disinfozone/kenosistron_GGUFs 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 disinfozone/kenosistron_GGUFs 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 disinfozone/kenosistron_GGUFs to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for disinfozone/kenosistron_GGUFs to start chatting

How to use disinfozone/kenosistron_GGUFs with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf disinfozone/kenosistron_GGUFs: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": "disinfozone/kenosistron_GGUFs:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use disinfozone/kenosistron_GGUFs with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf disinfozone/kenosistron_GGUFs: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 disinfozone/kenosistron_GGUFs:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use disinfozone/kenosistron_GGUFs with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf disinfozone/kenosistron_GGUFs: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 "disinfozone/kenosistron_GGUFs: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 disinfozone/kenosistron_GGUFs with Docker Model Runner:
```
docker model run hf.co/disinfozone/kenosistron_GGUFs:Q4_K_M
```

Lemonade

How to use disinfozone/kenosistron_GGUFs with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull disinfozone/kenosistron_GGUFs:Q4_K_M

Run and chat with the model

lemonade run user.kenosistron_GGUFs-Q4_K_M

List all available models

lemonade list

kenosistron GGUFs

GGUF builds of disinfozone/kenosistron for llama.cpp and everything downstream of it (LM Studio, ollama, koboldcpp, and friends). Read the main model card for what this model is, its training story, and the sampler doctrine. The short version of the doctrine: run it hot.

Files

Each quant lives in its own folder, split into shards under HF's 50 GB file limit. Point llama.cpp at the first shard (-00001-of-...); it finds the rest automatically.

Quant	Size	Notes
`Q2_K/`	54 GB	The 64 GB tier. The roughest cut, made without imatrix calibration; expect some quality loss.
`Q3_K_M/`	67 GB	For 96 GB machines.
`Q4_K_M/`	86 GB	The mainstream pick for 128 GB machines.
`Q5_K_M/`	96 GB	Closest to the MLX oQ5e build.
`Q8_0/`	129 GB	For purists with the memory to spare (192 GB and up).

Running

llama-server -m Q4_K_M/kenosistron-Q4_K_M-00001-of-00002.gguf -c 32768 \
  --temp 1.3 --top-p 0.995 --min-p 0.03 \
  --xtc-probability 0.4 --xtc-threshold 0.1 \
  --frequency-penalty 0.5 --presence-penalty 0.5

Those flags are the "Explorer" preset (the server default on our own box). The full five-preset ladder (Grounded t1.1 through Feral t1.4) is on the main card; every parameter maps directly to llama.cpp flags as above. This model loops when run cold and runs clean when run hot; do not "play it safe" with temperature 0.7, you will get the worst of it. And do not lobotomize it with helpful-assistant system prompts. The intended system instruction (Character Card) is on the main card.

What the GGUFs do not have

The MLX build ships a retrained MTP (multi-token prediction) speculative head, realigned to the model's own distribution (greedy acceptance 61.9% to 72.1%). llama.cpp does not implement MTP speculative decoding for this architecture, so the converter skips those tensors. Text quality is identical; the speculative decode speedup is MLX/oMLX-only.

Siblings

disinfozone/kenosistron: the MLX build (oQ5e 5-bit, Apple Silicon).
disinfozone/kenosistron-lora: the raw LoRA adapter, retrained MTP head, and rebuild scripts (MIT).
disinfozone/kenosistron-bf16: the full-precision merge these quants come from.

Downloads last month: 126

GGUF

Model size

121B params

Architecture

nemotron_h_moe

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

8-bit

Model tree for disinfozone/kenosistron_GGUFs

Base model

nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16

Finetuned

disinfozone/kenosistron-bf16

Quantized

(1)

this model