MiniCPM V 4 OpenVINO INT4
This repository contains the OpenVINO IR format of the openbmb/MiniCPM-V-4 model, heavily optimized for CPU inference.
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 1,04 MB–1,74 GB
- Modelo base
- openbmb/MiniCPM-V-4
- Versión
- vRevision a4fe669
- Actualizado
- 12 oct 2026
- Descargas
- 8
- Fuente
- Hugging Face
Descripción de la fuente
MiniCPM-V-4 OpenVINO INT4 (Preserved FP16 Vision)
This repository contains the OpenVINO IR format of the openbmb/MiniCPM-V-4 model, heavily optimized for CPU inference.
Optimization Details
To ensure optimal performance without sacrificing multimodal capabilities, a selective quantization strategy was applied:
- Language Model (LLM): Quantized to INT4 (Asymmetric, Group Size 128) for maximum memory efficiency and CPU inference speed.
- Vision Encoder & Resampler: Preserved in FP16. This surgical approach ensures that the model's visual acuity (including text recognition, UI elements, and fine details) remains identical to the original unquantized model.
Hardware Targeting
This format is designed to run efficiently on standard CPUs, fully utilizing AVX-512 and VNNI instruction sets (e.g., Intel Ice Lake and newer) without requiring dedicated GPU hardware.
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 27 ago
Download files
Artefactos del modelo
6 artefactos
Artefactos del modelo
6 artefactosopenvino_text_embeddings_model.bin
bin · 359 MB · SHA-256 2a039d1d977b…7881 · Hugging Face
Download fileopenvino_vision_embeddings_model.bin
bin · 797 MB · SHA-256 0cabc71c2bb7…74ce · Hugging Face
Download fileExtractos de fuentes
2 extractos
Extractos de fuentes
2 extractos--- base_model: openbmb/MiniCPM-V-4 base_model_relation: quantized quantized_by: HyX3 license: apache-2.0 tags: - quantization - openvino - int4 - vision - multimodal - minicpm pipeline_tag: image-to-text --- # MiniCPM-V-4 OpenVINO INT4 (Preserved FP16 Vision) This repository contains the OpenVINO IR format of the `openbmb/MiniCPM-V-4` model, heavily optimized for CPU inference. ## Optimization Details To ensure optimal performance without sacrificing multimodal capabilities, a selective quantization strategy was applied: - **Language Model (LLM):** Quantized to **INT4** (Asymmetric, Group Size 128) for maximum memory efficiency and CPU inference speed. - **Vision Encoder & Resampler:** Preserved in **FP16**. This surgical approach ensures that the model's visual acuity (including text recognition, UI elements, and fine details) remains identical to the original unquantized model. ## Hardware Targeting This format is designed to run efficiently on standard CPUs, fully utilizing AVX-512 and VNNI instruction sets (e.g., Intel Ice Lake and newer) without requiring dedicated GPU hardware.
Source context: 8 downloads · 0 likes · Pipeline image-to-text · Repo HyX3/MiniCPM-V-4-OpenVINO-INT4
