README.md

---
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.