Qwen1.5 MoE A2.7B Chat

Name: Qwen1.5 MoE A2.7B Chat
Author: Qwen (Alibaba)

by Qwen (Alibaba)Qwen

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

Released March 202433K context≈ 24,576 words14.3B params

Qwen1.5 MoE A2.7B Chat runs a mixture-of-experts architecture, activating only 2.7B parameters per forward pass despite housing 14.3B total. This makes it computationally lean at inference time — it thinks with a small crew even though a larger team is on standby. It handles conversational tasks capably but sits in a mid-tier capability range, reflecting its modest active parameter count.

Capabilities

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

Multilingual

Moderate

Long Context

Qwen1.5 MoE A2.7B Chat

by Qwen (Alibaba)Qwen

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

Released March 202433K context≈ 24,576 words14.3B params

Qwen1.5 MoE A2.7B Chat runs a mixture-of-experts architecture, activating only 2.7B parameters per forward pass despite housing 14.3B total. This makes it computationally lean at inference time — it thinks with a small crew even though a larger team is on standby. It handles conversational tasks capably but sits in a mid-tier capability range, reflecting its modest active parameter count.

Capabilities

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

Multilingual

Moderate

Long Context

Glossary

Active Parameter CountThe number of model parameters that are actually used during inference for a given input, as opposed to the total parameters available.ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.Forward PassA single computation cycle where input data flows through the model's layers to produce an output prediction.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.Inference TimeThe amount of time it takes for a model to process input and generate output after it has been trained.Parameter CountThe total number of adjustable weights in a model; more parameters generally mean more capacity to learn, but also require more computing power.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.

Qwen1.5 MoE A2.7B Chat

Capabilities

Qwen1.5 MoE A2.7B Chat

Capabilities

Benchmark Scores

Use Case Fit

Glossary