gemma 4 E4B Agentic Opus Reasoning GeminiCLI mlx 4bit

gemma

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released July 2026context N/A

A community-converted variant of Gemma 4 E4B, quantized to 4-bit precision using MLX for efficient local inference on Apple Silicon. The compression trade-off means reduced memory footprint at the potential cost of some output fidelity compared to the full-precision original. Its 'Agentic Opus Reasoning' label suggests tuning toward multi-step reasoning tasks, though specifics of any fine-tuning are not documented.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Creative Writing

Moderate

Coding

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

gemma 4 E4B Agentic Opus Reasoning GeminiCLI mlx 4bit

gemma

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released July 2026context N/A

A community-converted variant of Gemma 4 E4B, quantized to 4-bit precision using MLX for efficient local inference on Apple Silicon. The compression trade-off means reduced memory footprint at the potential cost of some output fidelity compared to the full-precision original. Its 'Agentic Opus Reasoning' label suggests tuning toward multi-step reasoning tasks, though specifics of any fine-tuning are not documented.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Creative Writing

Moderate

Coding

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Glossary

4-bit PrecisionA quantization level where model weights are stored using only 4 bits per value, significantly reducing model size at the cost of some accuracy.AgenticA model designed to act autonomously by making decisions, selecting actions, and using tools to accomplish multi-step tasks.Apple SiliconApple's custom-designed processors (like M1, M2, M3) optimized for running machine learning models on Mac computers.Bit PrecisionThe number of bits used to represent each number in a model; lower bit precision (like 3-bit) means smaller file size but potentially less accurate calculations.FidelityThe degree to which a quantized or compressed model preserves the quality and accuracy of the original full-precision model.Fine-TuningThe process of further training a pre-trained model on new data to adapt it for specific tasks or domains.Full-PrecisionA model using standard 32-bit floating-point numbers to represent weights, providing maximum accuracy but requiring more memory.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.Local InferenceRunning an AI model directly on your own computer rather than sending data to a remote server, keeping data private and reducing latency.MLXA machine learning framework optimized for running models efficiently on Apple Silicon chips.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.Multi-Step ReasoningThe ability to break down complex problems into smaller steps and solve them sequentially, rather than jumping directly to an answer.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.QuantizedA technique that reduces a model's size and memory usage by storing weights with lower precision (fewer bits), trading some accuracy for efficiency.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Reasoning TasksProblems that require a model to think through multiple steps logically to arrive at an answer, rather than just pattern-matching.

Capabilities

Use Case Fit

Capabilities

Use Case Fit

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Glossary