.. _models_llm_internvl-chat: ======================================== internvl-chat ======================================== - **Context Length:** 32768 - **Model Name:** internvl-chat - **Languages:** en, zh - **Abilities:** chat, vision - **Description:** InternVL 1.5 is an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. Specifications ^^^^^^^^^^^^^^ Model Spec 1 (pytorch, 2 Billion) ++++++++++++++++++++++++++++++++++++++++ - **Model Format:** pytorch - **Model Size (in billions):** 2 - **Quantizations:** 4-bit, 8-bit, none - **Engines**: Transformers - **Model ID:** OpenGVLab/Mini-InternVL-Chat-2B-V1-5 - **Model Hubs**: `Hugging Face `__ Execute the following command to launch the model, remember to replace ``${quantization}`` with your chosen quantization method from the options listed above:: xinference launch --model-engine ${engine} --model-name internvl-chat --size-in-billions 2 --model-format pytorch --quantization ${quantization} Model Spec 2 (pytorch, 4 Billion) ++++++++++++++++++++++++++++++++++++++++ - **Model Format:** pytorch - **Model Size (in billions):** 4 - **Quantizations:** 4-bit, 8-bit, none - **Engines**: Transformers - **Model ID:** OpenGVLab/Mini-InternVL-Chat-4B-V1-5 - **Model Hubs**: `Hugging Face `__ Execute the following command to launch the model, remember to replace ``${quantization}`` with your chosen quantization method from the options listed above:: xinference launch --model-engine ${engine} --model-name internvl-chat --size-in-billions 4 --model-format pytorch --quantization ${quantization} Model Spec 3 (pytorch, 26 Billion) ++++++++++++++++++++++++++++++++++++++++ - **Model Format:** pytorch - **Model Size (in billions):** 26 - **Quantizations:** 4-bit, 8-bit, none - **Engines**: Transformers - **Model ID:** OpenGVLab/InternVL-Chat-V1-5 - **Model Hubs**: `Hugging Face `__, `ModelScope `__ Execute the following command to launch the model, remember to replace ``${quantization}`` with your chosen quantization method from the options listed above:: xinference launch --model-engine ${engine} --model-name internvl-chat --size-in-billions 26 --model-format pytorch --quantization ${quantization}