Qwen2.5 VL 3B Instruct quantized.w4a16

Qwen2.5

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

Released February 2025128K context≈ 96,000 words3B params

A compact vision-language model that punches at a reasonable weight for its size, handling both text and image inputs within a generous 128k token context window. The w4a16 quantization keeps memory footprint lean by compressing weights to 4-bit while keeping activations at 16-bit, which means some precision is traded for efficiency. It's the kind of model you'd reach for when running multimodal workloads on constrained hardware without wanting to sacrifice too much capability.

Capabilities

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

Multilingual

Moderate

Reasoning & Logic

Qwen2.5 VL 3B Instruct quantized.w4a16

Qwen2.5

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

Released February 2025128K context≈ 96,000 words3B params

A compact vision-language model that punches at a reasonable weight for its size, handling both text and image inputs within a generous 128k token context window. The w4a16 quantization keeps memory footprint lean by compressing weights to 4-bit while keeping activations at 16-bit, which means some precision is traded for efficiency. It's the kind of model you'd reach for when running multimodal workloads on constrained hardware without wanting to sacrifice too much capability.

Capabilities

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

Multilingual

Moderate

Reasoning & Logic

Glossary

Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.Language ModelAn AI model trained to predict and generate text by learning patterns from large amounts of written data.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.MultimodalA model that can process and understand multiple types of input, such as both text and images.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.QuantizationReducing a model's numerical precision (e.g., from 16-bit to 4-bit) to shrink memory usage and speed up inference.TokenA small unit of text (a word, subword, or punctuation mark) that a language model breaks input into for processing.Vision-LanguageA model designed to understand and reason about both visual content (images) and natural language text together.Vision-Language ModelAn AI model that understands both images and text, allowing it to answer questions about images or describe what it sees.W4A16A quantization format where model weights are stored in 4-bit precision while calculations use 16-bit precision, balancing efficiency with accuracy.W4A16 QuantizationA specific quantization scheme where weights are stored in 4-bit precision while activations remain in 16-bit precision, balancing memory savings with accuracy.WeightsThe numerical parameters inside a neural network that determine how it processes input and generates output.

Capabilities

Capabilities

Use Case Fit

Glossary