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internvl2-1b-crispembed-GGUF
cstr/internvl2-1b-crispembed-GGUF
internvl2-1b-crispembed-GGUF is a image-text-to-text model from cstr. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for gguf. The card lists the license as mit.
GGUF conversions of OpenGVLab/InternVL2-1B for use with CrispEmbed.
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Repo size
4.2 GB
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Repo makeup
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.gguf4.2 GB · 100%
At a glance
- Task
- Image-Text-to-Text
- Library
- gguf
- License
- mit
- Access
- Public
- Created
- Jun 12, 2026
- Updated
- Aug 2, 2026
- SHA
- 3c5ec35f
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Base models
Model card
From the Hugging Face model README
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.
- Task
- Image-Text-to-Text
- Library
- gguf
- License
- mit
- Languages
- en, de, zh, ja, ko
- Created
- Jun 12, 2026
- Updated
- Aug 2, 2026