coderavi/VibeThinker-3B-mlx-fp16
TEXT GENERATIONConcurrency Cost:1Model Size:3.1BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:Jun 19, 2026License:mitArchitecture:Transformer Open Weights Cold
VibeThinker-3B-mlx-fp16 is a 3.1 billion parameter language model, converted by coderavi to the MLX format from the WeiboAI/VibeThinker-3B base model. This model is optimized for efficient deployment and inference on Apple Silicon, leveraging the MLX framework. Its primary utility lies in providing a compact yet capable language model for local execution on compatible hardware.
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VibeThinker-3B-mlx-fp16 Overview
VibeThinker-3B-mlx-fp16 is a 3.1 billion parameter language model, specifically adapted for the Apple MLX framework by coderavi. It originates from the WeiboAI/VibeThinker-3B base model and was converted using mlx-lm version 0.31.2. This conversion enables efficient local inference on Apple Silicon devices.
Key Characteristics
- Parameter Count: 3.1 billion parameters, offering a balance between performance and resource consumption.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing longer inputs and generating more coherent, extended outputs.
- MLX Optimization: Designed for optimal performance on Apple Silicon, making it suitable for developers working within the Apple ecosystem.
- Base Model: Derived from WeiboAI/VibeThinker-3B, indicating its foundational capabilities as a general-purpose language model.
Use Cases
- Local Development: Ideal for developers building applications that require on-device AI capabilities on Apple hardware.
- Experimentation: Provides a readily deployable model for experimenting with MLX and language model inference without cloud dependencies.
- Resource-Constrained Environments: Suitable for scenarios where a powerful yet efficient language model is needed on devices with limited computational resources, leveraging the MLX framework's efficiency.