internvl2#

  • Context Length: 32768

  • Model Name: internvl2

  • Languages: en, zh

  • Abilities: chat, vision

  • Description: InternVL 2 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/InternVL2-1B

  • 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 --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/InternVL2-2B

  • 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 --size-in-billions 2 --model-format pytorch --quantization ${quantization}

Model Spec 3 (awq, 2 Billion)#

  • Model Format: awq

  • Model Size (in billions): 2

  • Quantizations: Int4

  • Engines:

  • Model ID: OpenGVLab/InternVL2-2B-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 --size-in-billions 2 --model-format awq --quantization ${quantization}

Model Spec 4 (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/InternVL2-4B

  • 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 --size-in-billions 4 --model-format pytorch --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/InternVL2-8B

  • 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 --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:

  • Model ID: OpenGVLab/InternVL2-8B-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 --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/InternVL2-26B

  • 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 --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:

  • Model ID: OpenGVLab/InternVL2-26B-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 --size-in-billions 26 --model-format awq --quantization ${quantization}

Model Spec 9 (pytorch, 40 Billion)#

  • Model Format: pytorch

  • Model Size (in billions): 40

  • Quantizations: 4-bit, 8-bit, none

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

  • Model ID: OpenGVLab/InternVL2-40B

  • 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 --size-in-billions 40 --model-format pytorch --quantization ${quantization}

Model Spec 10 (awq, 40 Billion)#

  • Model Format: awq

  • Model Size (in billions): 40

  • Quantizations: Int4

  • Engines:

  • Model ID: OpenGVLab/InternVL2-40B-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 --size-in-billions 40 --model-format awq --quantization ${quantization}

Model Spec 11 (pytorch, 76 Billion)#

  • Model Format: pytorch

  • Model Size (in billions): 76

  • Quantizations: 4-bit, 8-bit, none

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

  • Model ID: OpenGVLab/InternVL2-Llama3-76B

  • 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 --size-in-billions 76 --model-format pytorch --quantization ${quantization}

Model Spec 12 (awq, 76 Billion)#

  • Model Format: awq

  • Model Size (in billions): 76

  • Quantizations: Int4

  • Engines:

  • Model ID: OpenGVLab/InternVL2-Llama3-76B-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 --size-in-billions 76 --model-format awq --quantization ${quantization}