---

license: mit

language:

- en

- de

- zh

- ja

- ko

tags:

- ocr

- document-understanding

- vision-language-model

- gguf

- crispembed

base_model: OpenGVLab/InternVL2-1B

library_name: gguf

pipeline_tag: image-text-to-text

---

InternVL2-1B — CrispEmbed GGUF

GGUF conversions of OpenGVLab/InternVL2-1B for use with CrispEmbed.

Smallest competitive VLM for OCR — ideal for edge, mobile, and WASM deployment.

Model Details

| Property | Value |

|----------|-------|

| Architecture | InternVL2 (InternViT-300M + Qwen2-0.5B) |

| Total Parameters | ~0.9B |

| Vision Encoder | InternViT-300M-448px (24L, 1024d, identical to InternVL2.5-2B) |

| Projector | Pixel unshuffle (4:1) + LayerNorm + Linear + GELU + Linear |

| LLM Decoder | Qwen2-0.5B-Instruct (24L, 896d, GQA 14/2, SwiGLU, RMSNorm) |

| Input Resolution | 448x448 per tile, dynamic tiling (1-12 tiles) |

| License | MIT |

| OCRBench | 779 |

Available Quantizations

| File | Size | Compression | Notes |

|------|------|-------------|-------|

| internvl2-1b-f16.gguf | 2.3 GB | 1x | Full precision |

| internvl2-1b-q8_0.gguf | 955 MB | 2.4x | Good quality |

| internvl2-1b-q4_k.gguf | ~600 MB | ~4x | Smallest, vision Q8_0 floor |

Parity Verification

All components verified against Python reference (cos=1.000000):

• Vision encoder: 4/4 layers PASS
• Projector: PASS
• LLM decoder (Qwen2): 2/2 layers PASS

Credits

• Original model: OpenGVLab/InternVL2-1B (MIT)
• GGUF conversion: CrispEmbed

Provenance and EU AI Act Art. 53 note

• Upstream model: OpenGVLab/InternVL2-1B — published by OpenGVLab.
• Upstream licence: mit. 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.
• 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.