metadata

base_model: OpenGVLab/InternVL3-2B-Instruct
language:
  - multilingual
library_name: transformers
license: apache-2.0
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
pipeline_tag: image-text-to-text
tags:
  - internvl
  - custom_code
  - llama-cpp
  - gguf-my-repo
base_model_relation: finetune

Triangle104/InternVL3-2B-Instruct-Q5_K_M-GGUF

This model was converted to GGUF format from OpenGVLab/InternVL3-2B-Instruct using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

We introduce InternVL3, an advanced multimodal large language model (MLLM) series that demonstrates superior overall performance. Compared to InternVL 2.5, InternVL3 exhibits superior multimodal perception and reasoning capabilities, while further extending its multimodal capabilities to encompass tool usage, GUI agents, industrial image analysis, 3D vision perception, and more. Additionally, we compare InternVL3 with Qwen2.5 Chat models, whose corresponding pre-trained base models are employed as the initialization of the langauge component in InternVL3. Benefitting from Native Multimodal Pre-Training, the InternVL3 series achieves even better overall text performance than the Qwen2.5 series.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/InternVL3-2B-Instruct-Q5_K_M-GGUF --hf-file internvl3-2b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/InternVL3-2B-Instruct-Q5_K_M-GGUF --hf-file internvl3-2b-instruct-q5_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/InternVL3-2B-Instruct-Q5_K_M-GGUF --hf-file internvl3-2b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo Triangle104/InternVL3-2B-Instruct-Q5_K_M-GGUF --hf-file internvl3-2b-instruct-q5_k_m.gguf -c 2048