alexgusevski/Qwen2.5-7B-Instruct-1M-Thinking-Claude-Gemini-GPT5.2-DISTILL-mlx-fp16

Model Overview

The alexgusevski/Qwen2.5-7B-Instruct-1M-Thinking-Claude-Gemini-GPT5.2-DISTILL-mlx-fp16 is a 7.6 billion parameter instruction-tuned language model. It is a specialized variant of the Qwen2.5-7B-Instruct model, distinguished by its distillation process which incorporates 'thinking' data derived from advanced models like Claude, Gemini, and GPT-5.2. This unique training approach aims to enhance its reasoning and instructional following capabilities.

Key Characteristics

• Architecture: Based on the Qwen2.5-7B-Instruct family.
• Parameter Count: 7.6 billion parameters.
• Context Length: Supports a substantial context length of 131,072 tokens.
• Distillation: Fine-tuned using a dataset that includes 'thinking' processes from Claude, Gemini, and GPT-5.2, potentially improving its ability to generate coherent and logical responses.
• MLX Format: Converted to the MLX format, making it optimized for efficient inference on Apple Silicon devices.

Intended Use Cases

This model is particularly well-suited for:

• Local Inference: Ideal for developers and users seeking to run powerful language models directly on Apple Silicon hardware.
• Instruction Following: Its instruction-tuned nature and distillation from advanced models suggest strong performance in following complex instructions.
• Reasoning Tasks: The 'thinking' data integration may benefit applications requiring more sophisticated reasoning or problem-solving.
• Prototyping and Development: Provides a robust base for developing AI applications that leverage the Qwen2.5 architecture with enhanced reasoning capabilities.