mlx-vlm
MLX-VLM is a package for inference and fine-tuning of Vision Language Models (VLMs) on your Mac using MLX.
Troubleshoot common MLX-VLM errors with this complete guide. Resolve runtime issues by validating inputs, flags, and model configurations for smooth operation.
How to Integrate MLX-VLM into Custom Applications: A Complete Developer's GuideIntegrate MLX-VLM into custom applications with this developer's guide. Learn to load models, prepare inputs, and generate outputs for Vision-Language inference on Apple Silicon using Python.
Which Models Are Supported by MLX-VLM? Complete List and Usage GuideDiscover which models MLX-VLM supports including Qwen2-VL, Qwen2.5-VL, Idefics3, and LLaVA. Get the complete list and usage guide for vision-language tasks on Apple Silicon.
TurboQuant Performance Benefits: Memory Reduction and Speed Optimization in MLX-VLMDiscover TurboQuant's performance benefits, reducing MLX-VLM memory usage by up to 76% and accelerating attention computation with custom Metal kernels.
How to Configure TurboQuant for the MLX-VLM Server: Complete Setup GuideLearn how to configure TurboQuant for the MLX-VLM server. Set non-integer bit-width and quantization scheme for optimal performance. Complete setup guide.
How to Use TurboQuant with the MLX-VLM Python API: A Complete GuideLearn how to use TurboQuant with the MLX-VLM Python API to compress KV-caches to 2-4 bits per dimension. This guide shows you how to leverage fractional kv_bits or kv_quant_scheme turboquant for efficient model performance.
How to Use TurboQuant with the MLX-VLM CLI: KV-Cache Compression GuideLearn to use TurboQuant with the MLX-VLM CLI using --kv-bits or --kv-quant-scheme turboquant. Reduce memory by up to 76% without compromising quality.
Supported Bit Widths for TurboQuant in MLX-VLM: Integer and Half-Integer Values ExplainedExplore TurboQuant's supported bit widths in MLX-VLM discover integer and half-integer options. Learn about the minimum accepted value and how fractional values are handled to optimize your models.
How TurboQuant Reduces Memory Usage for KV Cache in mlx‑vlmDiscover how TurboQuant slashes KV cache memory usage with low-bit integer quantization, packing keys and values into compact codebook indices to save significant memory.
TurboQuant KV Cache Quantization in MLX-VLM: Low-Bit Compression for Efficient GenerationDiscover TurboQuant KV Cache Quantization in MLX-VLM. This low-bit compression scheme efficiently reduces memory usage during generation, maintaining attention score fidelity.
How VisionFeatureCache Speeds Up Multi-Turn Conversations in MLX-VLMDiscover how VisionFeatureCache accelerates multi-turn conversations in MLX-VLM by caching image features and avoiding repeated vision encoder passes.
Vision Feature Caching in MLX-VLM: How It Speeds Up Multi-Turn Image ConversationsDiscover Vision Feature Caching in MLX-VLM. This LRU cache stores pre-computed vision embeddings to speed up multi-turn conversations by preventing redundant image processing.
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