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:

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

Multimodal inference

For vision-language inference:

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 no tools	LiveCodeBench v6	Codeforces ELO	GPQA Diamond	Tau2 (avg over 3)	HLE no tools	HLE 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:

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:

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.

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

📄 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.

guell00/Nexora-Gemma-4-Coder

Nexora-Gemma-4-E4B-Coder