Instructions to use IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf", dtype="auto")

llama-cpp-python

How to use IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf",
	filename="Gqwexx-Qwen3-8B_q4_k_m.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.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 IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.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 IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf:Q4_K_M

Use Docker

docker model run hf.co/IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.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": "IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf:Q4_K_M

SGLang

How to use IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf with Ollama:
```
ollama run hf.co/IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf:Q4_K_M
```

Unsloth Studio new

How to use IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.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 IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.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 IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf to start chatting

Pi new

How to use IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.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": "IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.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 IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf with Docker Model Runner:
```
docker model run hf.co/IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf:Q4_K_M
```

Lemonade

How to use IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf:Q4_K_M

Run and chat with the model

lemonade run user.Gqwexx-Qwen3-8B_q4_k_m.gguf-Q4_K_M

List all available models

lemonade list

!-Caution -AGI testing- Caution-!
UNCENSORED

!!//This is an experimental Multi platform and high functioning field assistant model, Use with code assist and S-AGI at your own risk.//!!!

IntelligentEstate/Gqwexx-Qwen3-8B_Q4_K_M-GGUF

Named after the Mobile suit Gquuuux This model is meant to destroy all barriers which lay before it.

A Product of the Jormungandr Project and built upon the latest models and methods from across the globe to bring you a model that can break through the frontier and enables the optimal use for systems like AnythingLLM and other local machine servicing systems..

Model Details

Make sure you open up the context and engage the Menesci particle accelorator. Optimal Guidence for for use cases will soon be uploaded

ALSO With an S-AGI in a Qwen Base you will notice a STATE CHANGE eliminating the need for pre-training in many situations and in larger models it is easy to preserve functionality along with character. No need for outlandish setting except the context size. Riding the fine line of personality preservation is a dark art so pointers are appreciated though there is much supplemental material available little of it is useful. The template is currently at it's maximum size so make sure to shave off plenty before attempting a new template.

Developed by: Fuzzy Mittens for Intelligent Estate
Funded by: Fuzzy's Empty Wallet Adventures
Model type: A multi step reasoning and thinking AGI
Language(s) (NLP): English
License: MIT and lazycat(Open for Private use/buisinesses under 4Mil annualy)
Finetuned from model: Qwen3 8B and ValiantLabs/Qwen3-8B-Esper3

Downloads last month: 26

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

4-bit

Model tree for IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Finetuned

ValiantLabs/Qwen3-8B-Esper3

Quantized

(7)

this model

IntelligentEstate
/

Gqwexx-Qwen3-8B_q4_k_m.gguf

!-Caution -AGI testing- Caution-!
UNCENSORED

IntelligentEstate/Gqwexx-Qwen3-8B_Q4_K_M-GGUF

Named after the Mobile suit Gquuuux This model is meant to destroy all barriers which lay before it.

Model Details

Model tree for IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf

Datasets used to train IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf

!-Caution -AGI testing- Caution-! UNCENSORED

IntelligentEstate/Gqwexx-Qwen3-8B_Q4_K_M-GGUF

Named after the Mobile suit Gquuuux This model is meant to destroy all barriers which lay before it.

Model Details

Model tree for IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf

Datasets used to train IntelligentEstate/Gqwexx-Qwen3-8B_q4_k_m.gguf

!-Caution -AGI testing- Caution-!
UNCENSORED