InternVL2.5-MPO#

  • Context Length: 16384

  • Model Name: InternVL2.5-MPO

  • Languages: en, zh

  • Abilities: chat, vision

  • Description: InternVL 2.5 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, 1 Billion)#

  • Model Format: pytorch

  • Model Size (in billions): 1

  • Quantizations: 4-bit, 8-bit, none

  • Engines: vLLM, Transformers (vLLM only available for quantization none)

  • Model ID: OpenGVLab/InternVL2_5-MPO-1B

  • 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 InternVL2.5-MPO --size-in-billions 1 --model-format pytorch --quantization ${quantization}

Model Spec 2 (pytorch, 2 Billion)#

  • Model Format: pytorch

  • Model Size (in billions): 2

  • Quantizations: 4-bit, 8-bit, none

  • Engines: vLLM, Transformers (vLLM only available for quantization none)

  • Model ID: OpenGVLab/InternVL2_5-MPO-2B

  • 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 InternVL2.5-MPO --size-in-billions 2 --model-format pytorch --quantization ${quantization}

Model Spec 3 (pytorch, 4 Billion)#

  • Model Format: pytorch

  • Model Size (in billions): 4

  • Quantizations: 4-bit, 8-bit, none

  • Engines: vLLM, Transformers (vLLM only available for quantization none)

  • Model ID: OpenGVLab/InternVL2_5-MPO-4B

  • 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 InternVL2.5-MPO --size-in-billions 4 --model-format pytorch --quantization ${quantization}

Model Spec 4 (awq, 4 Billion)#

  • Model Format: awq

  • Model Size (in billions): 4

  • Quantizations: Int4

  • Engines: vLLM, Transformers

  • Model ID: OpenGVLab/InternVL2_5-4B-MPO-AWQ

  • 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 InternVL2.5-MPO --size-in-billions 4 --model-format awq --quantization ${quantization}

Model Spec 5 (pytorch, 8 Billion)#

  • Model Format: pytorch

  • Model Size (in billions): 8

  • Quantizations: 4-bit, 8-bit, none

  • Engines: vLLM, Transformers (vLLM only available for quantization none)

  • Model ID: OpenGVLab/InternVL2_5-MPO-8B

  • 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 InternVL2.5-MPO --size-in-billions 8 --model-format pytorch --quantization ${quantization}

Model Spec 6 (awq, 8 Billion)#

  • Model Format: awq

  • Model Size (in billions): 8

  • Quantizations: Int4

  • Engines: vLLM, Transformers

  • Model ID: OpenGVLab/InternVL2_5-MPO-8B-AWQ

  • 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 InternVL2.5-MPO --size-in-billions 8 --model-format awq --quantization ${quantization}

Model Spec 7 (pytorch, 26 Billion)#

  • Model Format: pytorch

  • Model Size (in billions): 26

  • Quantizations: 4-bit, 8-bit, none

  • Engines: vLLM, Transformers (vLLM only available for quantization none)

  • Model ID: OpenGVLab/InternVL2_5-MPO-26B

  • 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 InternVL2.5-MPO --size-in-billions 26 --model-format pytorch --quantization ${quantization}

Model Spec 8 (awq, 26 Billion)#

  • Model Format: awq

  • Model Size (in billions): 26

  • Quantizations: Int4

  • Engines: vLLM, Transformers

  • Model ID: OpenGVLab/InternVL2_5-MPO-26B-AWQ

  • 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 InternVL2.5-MPO --size-in-billions 26 --model-format awq --quantization ${quantization}

Model Spec 9 (pytorch, 38 Billion)#

  • Model Format: pytorch

  • Model Size (in billions): 38

  • Quantizations: 4-bit, 8-bit, none

  • Engines: vLLM, Transformers (vLLM only available for quantization none)

  • Model ID: OpenGVLab/InternVL2_5-MPO-38B

  • 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 InternVL2.5-MPO --size-in-billions 38 --model-format pytorch --quantization ${quantization}

Model Spec 10 (awq, 38 Billion)#

  • Model Format: awq

  • Model Size (in billions): 38

  • Quantizations: Int4

  • Engines: vLLM, Transformers

  • Model ID: OpenGVLab/InternVL2_5-MPO-38B-AWQ

  • 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 InternVL2.5-MPO --size-in-billions 38 --model-format awq --quantization ${quantization}

Model Spec 11 (pytorch, 78 Billion)#

  • Model Format: pytorch

  • Model Size (in billions): 78

  • Quantizations: 4-bit, 8-bit, none

  • Engines: vLLM, Transformers (vLLM only available for quantization none)

  • Model ID: OpenGVLab/InternVL2_5-MPO-78B

  • 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 InternVL2.5-MPO --size-in-billions 78 --model-format pytorch --quantization ${quantization}

Model Spec 12 (awq, 78 Billion)#

  • Model Format: awq

  • Model Size (in billions): 78

  • Quantizations: Int4

  • Engines: vLLM, Transformers

  • Model ID: OpenGVLab/InternVL2_5-MPO-78B-AWQ

  • 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 InternVL2.5-MPO --size-in-billions 78 --model-format awq --quantization ${quantization}