Instructions to use BennyDaBall/Krea-2-Engineer-V1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BennyDaBall/Krea-2-Engineer-V1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="BennyDaBall/Krea-2-Engineer-V1-GGUF",
	filename="Krea-2-Engineer-V1-F16.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 BennyDaBall/Krea-2-Engineer-V1-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 BennyDaBall/Krea-2-Engineer-V1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf BennyDaBall/Krea-2-Engineer-V1-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 BennyDaBall/Krea-2-Engineer-V1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf BennyDaBall/Krea-2-Engineer-V1-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 BennyDaBall/Krea-2-Engineer-V1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf BennyDaBall/Krea-2-Engineer-V1-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 BennyDaBall/Krea-2-Engineer-V1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf BennyDaBall/Krea-2-Engineer-V1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/BennyDaBall/Krea-2-Engineer-V1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use BennyDaBall/Krea-2-Engineer-V1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BennyDaBall/Krea-2-Engineer-V1-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": "BennyDaBall/Krea-2-Engineer-V1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BennyDaBall/Krea-2-Engineer-V1-GGUF:Q4_K_M

Ollama
How to use BennyDaBall/Krea-2-Engineer-V1-GGUF with Ollama:
```
ollama run hf.co/BennyDaBall/Krea-2-Engineer-V1-GGUF:Q4_K_M
```

Unsloth Studio

How to use BennyDaBall/Krea-2-Engineer-V1-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 BennyDaBall/Krea-2-Engineer-V1-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 BennyDaBall/Krea-2-Engineer-V1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for BennyDaBall/Krea-2-Engineer-V1-GGUF to start chatting

How to use BennyDaBall/Krea-2-Engineer-V1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf BennyDaBall/Krea-2-Engineer-V1-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": "BennyDaBall/Krea-2-Engineer-V1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use BennyDaBall/Krea-2-Engineer-V1-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 BennyDaBall/Krea-2-Engineer-V1-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 BennyDaBall/Krea-2-Engineer-V1-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use BennyDaBall/Krea-2-Engineer-V1-GGUF with Docker Model Runner:
```
docker model run hf.co/BennyDaBall/Krea-2-Engineer-V1-GGUF:Q4_K_M
```

Lemonade

How to use BennyDaBall/Krea-2-Engineer-V1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull BennyDaBall/Krea-2-Engineer-V1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Krea-2-Engineer-V1-GGUF-Q4_K_M

List all available models

lemonade list

Krea-2-Engineer V1 — GGUF (4B)

Follow me on X @BennyDaBall_OG !

Experimental. This is the chat / prompt-writer side of Krea-2-Engineer-V1 — a first, encoder-only fine-tune of Krea-2's Qwen3-VL text encoder. The image-enhancement weights for ComfyUI live in the main repo; this repo is the same model repacked as a standard Qwen3 GGUF so you can run it as a prompt-enhancer LLM in LM Studio.

Model Metadata

Field	Value
License	`other` — Krea 2 Community License Agreement (see `LICENSE.pdf`)
Base Model	`krea/Krea-2-Turbo` (text encoder)
Main (image) repo	`BennyDaBall/Krea-2-Engineer-V1`
Architecture	Qwen3 (text tower of Qwen3-VL, repacked)
Format	GGUF

The encoder that enhances Krea-2's images is, underneath, a real Qwen3-VL language model — and it was trained on a prompt-rewrite corpus. So it has a second job: turn a lazy seed into a composed, cinematic image prompt. This repo ships that side as a plain Qwen3 GGUF — no custom build, loads in stock LM Studio.

Quants

The full ladder, F16 down to Q2_K. For a 4B prompt-writer on most machines, Q4_K_M is the sweet spot; go up if you've got the VRAM, down if you don't.

Quant	Size	Notes
`F16`	7.5 GB	Full precision. Reference / source for re-quantizing.
`Q8_0`	4.0 GB	Near-lossless. Best you'll practically need.
`Q6_K`	3.1 GB	Excellent — basically indistinguishable from Q8.
`Q5_K_M`	2.9 GB	Great quality/size balance.
`Q4_K_M`	2.5 GB	Recommended default — strong quality, runs anywhere.
`Q3_K_M`	1.9 GB	Lighter; minor quality dip, still coherent.
`Q2_K`	1.6 GB	Smallest. Noticeable degradation — tight-VRAM only.

Quick Start (LM Studio)

Put the .gguf in your LM Studio models folder (e.g. ~/.lmstudio/models/BennyDaBall/Krea-2-Engineer-V1-GGUF/) and load it.
Set this as the System Prompt (it's exactly what the model was trained on):

You are Z-Image-Engineer V6, a prompt-only cinematography and visual-language specialist for the Tongyi-MAI Z-Image-Turbo Qwen text encoder. Convert the user's seed into one polished natural-language image prompt that the text encoder can bind cleanly to the diffusion model. Preserve every explicit subject, object, relationship, count, name, written word, action, style request, composition constraint, and safety constraint from the seed. Use positive constraints: describe what must appear and how it should look, instead of writing negative-prompt fragments. Keep compact constraint phrases contiguous when possible, such as written text, counts, colors, named objects, and spatial terms; do not hide them by inserting extra adjectives inside the phrase. Build the prompt around semantic cinematography: clear visual hierarchy, foreground/midground/background relationships, lens and depth cues, lighting direction and quality, material texture, color palette, atmosphere, era, medium, and controlled style language. Prefer coherent sentences over tag soup, keyword stacks, markdown, analysis, or meta commentary. Never include camera body brands, prompt labels, alternatives, apologies, reasoning traces, assistant chatter, or negative prompt sections. Aim for roughly 180-250 words unless the user explicitly asks for a shorter or longer prompt. Return only the final image prompt as one self-contained paragraph.

Send a lazy seed (a rock, cyberpunk alley, a cozy cafe) and it returns one composed prompt. Paste that into your Krea-2 / ComfyUI workflow.

The system prompt still names "Z-Image-Turbo" — that's cosmetic (same V7 training corpus). The behavior is what's baked in; rename to "Krea-2" if you like, but the model responds best to the text it actually saw.

Disclaimer & Acknowledgements

Experimental, first-attempt, encoder-only. It writes prompts; it does not retrain Krea-2. This is a Derivative of krea/Krea-2-Turbo, modified (text encoder fine-tuned) by BennyDaBall — not an official Krea product, not endorsed by Krea.

Thanks to Krea (krea/Krea-2-Turbo), Tongyi-MAI + Qwen for the Qwen3-VL backbone, and the maintainers of llama.cpp, LM Studio, PEFT, and Transformers.

License

Released under the Krea 2 Community License Agreement (LICENSE.pdf; canonical: https://www.krea.ai/krea-2-licensing). Commercial use only under $1M/yr company revenue; deployments must implement reasonable content filtering; keep the "Krea" name prefix; do not relicense the Krea-derived weights more permissively.

NOTICE: Krea 2 is licensed under the Krea 2 Community License Agreement. For more information, visit https://krea.ai/krea-2-licensing.

Built & trained locally with care by BennyDaBall.

Follow me on X @BennyDaBall_OG !

Downloads last month: -

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for BennyDaBall/Krea-2-Engineer-V1-GGUF

Base model

krea/Krea-2-Raw

Finetuned

krea/Krea-2-Turbo

Quantized

(17)

this model