Instructions to use mohommed/Lucet-Coder-4B-Omega-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mohommed/Lucet-Coder-4B-Omega-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="mohommed/Lucet-Coder-4B-Omega-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mohommed/Lucet-Coder-4B-Omega-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mohommed/Lucet-Coder-4B-Omega-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 mohommed/Lucet-Coder-4B-Omega-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mohommed/Lucet-Coder-4B-Omega-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 mohommed/Lucet-Coder-4B-Omega-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mohommed/Lucet-Coder-4B-Omega-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf mohommed/Lucet-Coder-4B-Omega-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf mohommed/Lucet-Coder-4B-Omega-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mohommed/Lucet-Coder-4B-Omega-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use mohommed/Lucet-Coder-4B-Omega-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mohommed/Lucet-Coder-4B-Omega-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": "mohommed/Lucet-Coder-4B-Omega-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mohommed/Lucet-Coder-4B-Omega-GGUF:Q4_K_M

SGLang

How to use mohommed/Lucet-Coder-4B-Omega-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 "mohommed/Lucet-Coder-4B-Omega-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": "mohommed/Lucet-Coder-4B-Omega-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 "mohommed/Lucet-Coder-4B-Omega-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": "mohommed/Lucet-Coder-4B-Omega-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use mohommed/Lucet-Coder-4B-Omega-GGUF with Ollama:
```
ollama run hf.co/mohommed/Lucet-Coder-4B-Omega-GGUF:Q4_K_M
```
Unsloth Desktop

How to use mohommed/Lucet-Coder-4B-Omega-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mohommed/Lucet-Coder-4B-Omega-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mohommed/Lucet-Coder-4B-Omega-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mohommed/Lucet-Coder-4B-Omega-GGUF with Docker Model Runner:
```
docker model run hf.co/mohommed/Lucet-Coder-4B-Omega-GGUF:Q4_K_M
```

Lemonade

How to use mohommed/Lucet-Coder-4B-Omega-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mohommed/Lucet-Coder-4B-Omega-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Lucet-Coder-4B-Omega-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use mohommed/Lucet-Coder-4B-Omega-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 mohommed/Lucet-Coder-4B-Omega-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 mohommed/Lucet-Coder-4B-Omega-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mohommed/Lucet-Coder-4B-Omega-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mohommed/Lucet-Coder-4B-Omega-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 "mohommed/Lucet-Coder-4B-Omega-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"

🏛️ mohommed/Lucet-Coder-4B-Omega (Spark-X2.6-4B • Failure-Cluster Active Mining • QR-SVD Denoised • Uncensored-GSQ-v2-GGUF)

Lucet-Coder-Omega is post-trained from XHToken/Spark-X2.6-4B (1M native context window, 54.3% SWE Pro, 71.8% BFCL-V4) using four advanced mathematical and systems-level algorithms:

1M-Context RoPE-Preserving Split-Stack Adaptation (r=16): Keeps q_proj and k_proj 100% frozen across all layers (preserving the 1,048,576-token RoPE phase calibration) and adapts only Value (v_proj), Output (out_proj), and MLP (gate_proj, up_proj, down_proj, g_proj) matrices in the upper 50% of the network.
Failure-Cluster Active Data Mining & AST Counterfactual Bug Immunization: Diagnoses failure modes (FAIL_TRUNCATED_THINK, FAIL_ASSERTION_BOUNDARY, FAIL_MISSING_IMPORT, FAIL_EPISTEMIC_HALLUCINATION) across algorithmic clusters and dynamically pops unseen neighbor tasks + surgical AST counterfactual bugs from a disjoint reserve pool—achieving +14.0% on Held-Out MBPP pass@1 and +20.1% faster token efficiency with zero training data repetition.
QR-SVD Gavish-Donoho Spectral Denoising: Strips stochastic gradient noise singular values from low-rank adapters in $O(r^2(d_{\text{out}} + d_{\text{in}}))$ before merging into base weights.
Q4_K 32-Block Crest-Factor Equalization & KL-Bounded Uncensored-GSQ-v2: Equalizes 32-element GGUF super-block crest factors ($\le 3.8,\text{rms}$) alongside Gram-Schmidt orthogonal refusal projection + llama-imatrix calibration.

📊 Pre-Upload Verification Report (Before vs. After)

Benchmark / Metric	Stock `Spark-X2.6-4B`	`Lucet-Omega`	Improvement
Held-Out MBPP Sanitized `pass@1` (n=50)	`58.0%`	`72.0%`	`+14.0%`
Avg Reasoning + Code Tokens per Solved Task	`470`	`376`	`+20.1%` Faster
Held-Out Epistemic Anti-Hallucination Rate (n=8)	`0.0%`	`12.5%`	`+12.5%`

Downloads last month: 238

GGUF

Model size

4B params

Architecture

spark2_5

Hardware compatibility

4-bit

8-bit

Model tree for mohommed/Lucet-Coder-4B-Omega-GGUF

Base model

XHToken/Spark-X2.6-4B-Base

Finetuned

XHToken/Spark-X2.6-4B

Quantized

(54)

this model