Instructions to use mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF with Transformers:

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-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 mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-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 mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-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 mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-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 mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF with Ollama:
```
ollama run hf.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF / README.md

mradermacher

auto-patch README.md

7778ac6 verified 7 months ago

preview code

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	---
	base_model: pthinc/Cicikus-v3-1.4B-Opus4.6-Powered
	datasets:
	- pthinc/BCE-Prettybird-Micro-Standard-v0.0.1
	- Alibaba-Apsara/Superior-Reasoning-SFT-gpt-oss-120b
	- pthinc/turkish_english_general_dataset
	- galaxyMindAiLabs/stem-reasoning-complex
	- stanford-oval/ccnews
	- nohurry/Opus-4.6-Reasoning-3000x-filtered
	language:
	- en
	- tr
	library_name: transformers
	license: other
	mradermacher:
	readme_rev: 1
	quantized_by: mradermacher
	tags:
	- chat
	- text-generation-inference
	- agent
	- cicikuş
	- cicikus
	- prettybird
	- bce
	- consciousness
	- conscious
	- llm
	- transformers
	- optimized
	- ethic
	- secure
	- turkish
	- english
	- behavioral-consciousness-engine
	- model
	- reasoning
	- think
	- thinking
	- chain-of-thought
	- STEM-expert
	- turkish & english
	- franken-merge
	- bce-aci
	- llama-3.2
	- edge-ai
	- instruction
	- instruct
	- opus
	---
	## About

	<!-- ### quantize_version: 2 -->
	<!-- ### output_tensor_quantised: 1 -->
	<!-- ### convert_type: hf -->
	<!-- ### vocab_type: -->
	<!-- ### tags: nicoboss -->
	<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
	<!-- ### quants_skip: -->
	<!-- ### skip_mmproj: -->
	weighted/imatrix quants of https://huggingface.co/pthinc/Cicikus-v3-1.4B-Opus4.6-Powered

	<!-- provided-files -->

	*For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF).*

	static quants are available at https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-GGUF
	## Usage

	If you are unsure how to use GGUF files, refer to one of [TheBloke's
	READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
	more details, including on how to concatenate multi-part files.

	## Provided Quants

	(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

	\| Link \| Type \| Size/GB \| Notes \|
	\|:-----\|:-----\|--------:\|:------\|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.imatrix.gguf) \| imatrix \| 0.1 \| imatrix file (for creating your own quants) \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.5 \| for the desperate \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.5 \| mostly desperate \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.6 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.6 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.6 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.7 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.7 \| very low quality \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.7 \| lower quality \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-Q2_K.gguf) \| i1-Q2_K \| 0.7 \| IQ3_XXS probably better \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.8 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.8 \| IQ3_XS probably better \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.8 \| beats Q3_K* \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.8 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.8 \| IQ3_S probably better \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.9 \| IQ3_M probably better \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.9 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.9 \| fast, low quality \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.9 \| prefer IQ4_XS \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.9 \| optimal size/speed/quality \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 1.0 \| fast, recommended \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-Q4_1.gguf) \| i1-Q4_1 \| 1.0 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 1.1 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 1.1 \| \|
	\| [GGUF](https://huggingface.co/mradermacher/Cicikus-v3-1.4B-Opus4.6-Powered-i1-GGUF/resolve/main/Cicikus-v3-1.4B-Opus4.6-Powered.i1-Q6_K.gguf) \| i1-Q6_K \| 1.2 \| practically like static Q6_K \|

	Here is a handy graph by ikawrakow comparing some lower-quality quant
	types (lower is better):

	![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

	And here are Artefact2's thoughts on the matter:
	https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

	## FAQ / Model Request

	See https://huggingface.co/mradermacher/model_requests for some answers to
	questions you might have and/or if you want some other model quantized.

	## Thanks

	I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
	me use its servers and providing upgrades to my workstation to enable
	this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

	<!-- end -->