glm-4.1v-thinking#

  • Context Length: 65536

  • Model Name: glm-4.1v-thinking

  • Languages: en, zh

  • Abilities: chat, vision, reasoning

  • Description: GLM-4.1V-9B-Thinking, designed to explore the upper limits of reasoning in vision-language models.

Specifications#

Model Spec 1 (pytorch, 9 Billion)#

  • Model Format: pytorch

  • Model Size (in billions): 9

  • Quantizations: none

  • Engines: vLLM, Transformers

  • Model ID: THUDM/GLM-4.1V-9B-Thinking

  • 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 glm-4.1v-thinking --size-in-billions 9 --model-format pytorch --quantization ${quantization}

Model Spec 2 (awq, 9 Billion)#

  • Model Format: awq

  • Model Size (in billions): 9

  • Quantizations: Int4

  • Engines: vLLM, Transformers

  • Model ID: dengcao/GLM-4.1V-9B-Thinking-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 glm-4.1v-thinking --size-in-billions 9 --model-format awq --quantization ${quantization}

Model Spec 3 (gptq, 9 Billion)#

  • Model Format: gptq

  • Model Size (in billions): 9

  • Quantizations: Int4-Int8Mix

  • Engines: vLLM, Transformers

  • Model ID: dengcao/GLM-4.1V-9B-Thinking-GPTQ-Int4-Int8Mix

  • 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 glm-4.1v-thinking --size-in-billions 9 --model-format gptq --quantization ${quantization}