SirSahOl/Qwen2.5-7B-Instruct-chat-mlx-16bit

TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 14, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

SirSahOl/Qwen2.5-7B-Instruct-chat-mlx-16bit is a 7.61 billion parameter Qwen2ForCausalLM architecture model, converted by SirSahOl for native inference on Apple Silicon GPUs using the MLX framework. This 16-bit quantization variant offers full unquantized bfloat16 precision, making it ideal for research, benchmarking, and high-fidelity reference output on M2/M3/M4 Max/Ultra Macs with 32GB+ unified memory. It supports a 32,768 token context length and is optimized for zero perplexity loss in precision-critical applications.

Loading preview...

Model Overview

This model, SirSahOl/Qwen2.5-7B-Instruct-chat-mlx-16bit, is a 16-bit MLX conversion of the original Qwen/Qwen2.5-7B-Instruct model, specifically optimized for native GPU inference on Apple Silicon (M1/M2/M3/M4 series) using Apple's MLX framework. With 7.61 billion parameters and a substantial 32,768 token context length, it provides full unquantized bfloat16 precision.

Key Characteristics

  • Apple Silicon Optimization: Engineered for efficient performance on Apple's unified memory architecture, leveraging the MLX framework.
  • 16-bit Precision: Offers the highest fidelity among its MLX quantized variants (4-bit, 8-bit, 16-bit), ensuring zero perplexity loss for maximum accuracy.
  • High Context Length: Supports a native context window of 32,768 tokens, extensible up to 131,072 with YaRN.
  • Hardware Requirements: Requires a minimum of 24GB unified memory, with 32GB+ recommended for optimal performance on M2/M3/M4 Max/Ultra chips.

Recommended Use Cases

  • Research and Benchmarking: Ideal for evaluating model performance and generating reference outputs where precision is paramount.
  • High-Fidelity Applications: Suitable for tasks demanding the highest accuracy and minimal quality degradation.
  • Workstation Deployment: Best for high-end Apple Silicon setups (M2/M3/M4 Max/Ultra with 36GB+ unified memory) for uncompromised inference.

For users with less unified memory, 4-bit and 8-bit MLX variants are available, offering different trade-offs between speed, memory footprint, and precision.

Finetunes
1 models