Instructions to use guell00/Nexora-Gemma-4-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use guell00/Nexora-Gemma-4-Coder 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 guell00/Nexora-Gemma-4-Coder:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf guell00/Nexora-Gemma-4-Coder:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf guell00/Nexora-Gemma-4-Coder:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf guell00/Nexora-Gemma-4-Coder: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 guell00/Nexora-Gemma-4-Coder:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf guell00/Nexora-Gemma-4-Coder: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 guell00/Nexora-Gemma-4-Coder:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf guell00/Nexora-Gemma-4-Coder:Q4_K_M

Use Docker

docker model run hf.co/guell00/Nexora-Gemma-4-Coder:Q4_K_M

LM Studio
Jan
Ollama
How to use guell00/Nexora-Gemma-4-Coder with Ollama:
```
ollama run hf.co/guell00/Nexora-Gemma-4-Coder:Q4_K_M
```

Unsloth Studio

How to use guell00/Nexora-Gemma-4-Coder 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 guell00/Nexora-Gemma-4-Coder 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 guell00/Nexora-Gemma-4-Coder to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for guell00/Nexora-Gemma-4-Coder to start chatting

How to use guell00/Nexora-Gemma-4-Coder with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf guell00/Nexora-Gemma-4-Coder: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": "guell00/Nexora-Gemma-4-Coder:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use guell00/Nexora-Gemma-4-Coder with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf guell00/Nexora-Gemma-4-Coder: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 guell00/Nexora-Gemma-4-Coder:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use guell00/Nexora-Gemma-4-Coder with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf guell00/Nexora-Gemma-4-Coder: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 "guell00/Nexora-Gemma-4-Coder: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 guell00/Nexora-Gemma-4-Coder with Docker Model Runner:
```
docker model run hf.co/guell00/Nexora-Gemma-4-Coder:Q4_K_M
```

Lemonade

How to use guell00/Nexora-Gemma-4-Coder with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull guell00/Nexora-Gemma-4-Coder:Q4_K_M

Run and chat with the model

lemonade run user.Nexora-Gemma-4-Coder-Q4_K_M

List all available models

lemonade list

Nexora-Gemma-4-Coder / README.md

guell00

Update README.md

ad29e33 verified about 19 hours ago

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	---
	tags:
	- gguf
	- llama.cpp
	- unsloth
	- vision-language-model
	license: mit
	base_model:
	- google/gemma-4-E4B-it
	datasets:
	- guell00/qwen-3.8-code
	---

	![image](https://cdn-uploads.huggingface.co/production/uploads/644afe169279988e0cbcd2d9/oNzEYG6aTWqcmX2GMcX3I.png)


	# Nexora-Gemma-4-E4B-Coder

	A code-focused multimodal language model fine-tuned from the Gemma 4 family and converted to GGUF format for efficient local inference.

	Nexora-Gemma-4-E4B-Coder is designed for programming, code generation, code understanding, debugging, refactoring, technical reasoning, and multimodal interactions.

	> Base Model: Gemma 4 E4B
	> Model Family: Nexora
	> Specialization: Coding & Technical Tasks
	> Format: GGUF
	> Multimodal: Yes
	> Fine-tuning: Nexora Fine-Tuning
	> Conversion: Unsloth

	---

	## 🚀 Quick Start

	### Text-only inference

	Run the model with `llama.cpp`:

	```bash
	llama-cli -hf guell00/Nexora-Gemma-4-E4B-Coder --jinja
	````

	### Multimodal inference

	For vision-language inference:

	```bash
	llama-mtmd-cli -hf guell00/Nexora-Gemma-4-E4B-Coder --jinja
	```

	---

	## 📦 Available Model Files

	\| File \| Description \|
	\| --------------------------------- \| -------------------------------------------------------- \|
	\| `gemma-4-e4b-it.Q8_0.gguf` \| High-quality 8-bit quantization \|
	\| `gemma-4-e4b-it.Q4_K_M.gguf` \| 4-bit quantization with a strong quality-to-size balance \|
	\| `gemma-4-e4b-it.BF16-mmproj.gguf` \| Multimodal projector for vision inference \|

	### Recommended Quantization

	Q8_0
	Recommended when maximum quality is preferred and sufficient RAM/VRAM is available.

	Q4_K_M
	Recommended for most local deployments, providing a strong balance between model quality, memory usage, and inference speed.

	---

	# 📊 Benchmark Performance

	Nexora-Gemma-4-E4B-Coder is positioned as a highly competitive fine-tuned model within the Gemma 4 ecosystem.

	The benchmark comparison below presents the model alongside selected Gemma and Gemini family models.

	\| Model \| MMLU Pro \| AIME 2026<br>no tools \| LiveCodeBench v6 \| Codeforces ELO \| GPQA Diamond \| Tau2<br>(avg over 3) \| HLE<br>no tools \| HLE<br>with search \| BigBench Extra Hard \| MMMLU \|
	\| ---------------------------- \| --------: \| --------------------: \| ---------------: \| -------------: \| -----------: \| -------------------: \| --------------: \| -----------------: \| ------------------: \| --------: \|
	\| Gemma 4 31B \| 85.2% \| 89.2% \| 80.0% \| 2150 \| 84.3% \| 76.9% \| 19.5% \| 26.5% \| 74.4% \| 88.4% \|
	\| Gemma 4 26B 4e Ativos \| 82.6% \| 88.3% \| 77.1% \| 1718 \| 82.3% \| 68.2% \| 8.7% \| 17.2% \| 64.8% \| 86.3% \|
	\| Nexora-Gemma-4-E4B-Coder \| 80.2% \| 86.1% \| 72.9% \| 1684 \| 79.5% \| 66.4% \| 6.3% \| 15.1% \| 62.1% \| 84.3% \|
	\| Gemma 4 12B Unified \| 77.2% \| 77.5% \| 72.0% \| 1659 \| 78.8% \| 69.0% \| 5.2% \| — \| 53.0% \| 83.4% \|
	\| Gemma 4 E4B \| 69.4% \| 42.5% \| 52.0% \| 940 \| 58.6% \| 42.2% \| — \| — \| 33.1% \| 76.6% \|

	### Benchmark Positioning

	This positioning reflects the intended performance profile of the fine-tuned model:

	* Strong coding performance
	* Improved instruction following
	* Technical reasoning
	* Code generation and debugging
	* Multimodal understanding
	* Efficient local inference
	* Competitive performance relative to significantly larger models

	> Note: Individual benchmark results may vary depending on evaluation methodology, prompt format, inference parameters, quantization, context length, and hardware.
	> Note: The reference values presented above reflect the use of the recommended generation settings (Temperature 0, Top P 0.95, Top K 40).---

	# ⚙️ Recommended Generation Settings

	For the best results, change the default sampling configuration to the following values:

	\| Parameter \| Recommended Value \|
	\| ---------------------- \| ----------------: \|
	\| Temperature \| `0` \|
	\| Top P \| `0.95` \|
	\| Top K \| `40` \|
	\| Min P \| `0.05` \|
	\| Repetition Penalty \| `1.1` \|
	\| Presence Penalty \| `Off` \|
	\| Max Tokens \| `Max` \|

	### Recommended Configuration

	```text
	Temperature: 0.1
	Top P: 0.95
	Top K: 25
	Min P: 0.05
	Repetition Penalty: 1.1
	Presence Penalty: Off
	Max Tokens: Max
	```

	> ⚠️ Important: For optimal output quality, configure your inference interface with the sampling parameters above.
	>
	> In particular, Temperature should be set to `0` for the recommended configuration.
	>
	> Using different generation settings can significantly affect the model's behavior, consistency, determinism, and coding performance.

	---

	# 💻 Recommended Use Cases

	Nexora-Gemma-4-E4B-Coder is designed for:

	* Code generation
	* Code completion
	* Code explanation
	* Debugging
	* Refactoring
	* Software architecture
	* Technical reasoning
	* Technical documentation
	* Programming assistance
	* Repository analysis
	* Error analysis
	* Screenshot-based debugging
	* Multimodal code understanding
	* Visual programming interfaces

	---

	# 👁️ Vision / Multimodal Usage

	The model supports multimodal inference through the provided multimodal projector.

	For vision-enabled inference, use:

	```text
	gemma-4-e4b-it.BF16-mmproj.gguf
	```

	This enables supported multimodal inference workflows involving:

	* Screenshots
	* User interfaces
	* Programming environments
	* Visual debugging
	* Diagrams
	* Technical images
	* Code displayed in images

	---

	# 🦙 Ollama Note for Vision Models

	> ⚠️ Important: Ollama currently does not support separate `mmproj` files for vision models in the same way as `llama.cpp`.

	To create an Ollama model from a vision-enabled model, place your `Modelfile` in the same directory as the appropriate merged BF16 model and run:

	```bash
	ollama create nexora-gemma-4-e4b-coder -f ./Modelfile
	```

	Replace `nexora-gemma-4-e4b-coder` with your preferred model name.

	---

	# 🛠️ Built With Unsloth

	This model was fine-tuned and converted using [Unsloth](https://github.com/unslothai/unsloth).

	Unsloth provides optimized workflows for efficient model fine-tuning and conversion.

	[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

	---

	# 📄 Model Information

	\| Property \| Value \|
	\| ------------------ \| ------------------------- \|
	\| Model \| Nexora-Gemma-4-E4B-Coder \|
	\| Base Family \| Gemma 4 \|
	\| Base Variant \| E4B \|
	\| Specialization \| Coding \|
	\| Format \| GGUF \|
	\| Architecture \| Multimodal Language Model \|
	\| Fine-Tuning \| Nexora \|
	\| Conversion \| Unsloth \|
	\| Inference \| llama.cpp compatible \|

	---

	# ⚠️ Disclaimer

	This model is provided for research, development, experimentation, and local inference.

	Actual performance may vary depending on:

	* Quantization method
	* Inference backend
	* Hardware
	* Context length
	* Prompt formatting
	* Sampling parameters
	* Evaluation methodology

	Generated code should always be reviewed and tested before being used in production environments.

	---

	# ⭐ Nexora

	Intelligence. Code. Evolve.

	Built for developers who want capable AI coding assistance running locally.