Qwen3-VL-2B — CrispEmbed GGUF

Models

Features

• DeepStack vision fusion: intermediate vision-encoder features injected into LLM decoder layers
• Fused flash attention: uses ggml_flash_attn_ext for efficient inference
• Backend KV cache: decode stays on GPU (Metal/CUDA), no per-token CPU transfer
• Interleaved mRoPE: improved position encoding vs Qwen2.5-VL
• QK RMSNorm: per-head query/key normalization

Usage

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Provenance and EU AI Act Art. 53 note

• Upstream model: Qwen/Qwen3-VL-2B-Instruct — published by Qwen.
• Upstream licence: apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
• What was done here: format conversion and/or quantisation only (GGUF). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
• Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository. No training-content summary was found on the upstream model card at the time of writing; that documentation gap is upstream's and is not filled here.
• Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.