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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: image-text-to-text |
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tags: |
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- multimodal |
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- llama-cpp |
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- gguf-my-repo |
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library_name: transformers |
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base_model: Qwen/Qwen2-VL-7B-Instruct |
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--- |
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# Triangle104/Qwen2-VL-7B-Instruct-Q5_K_M-GGUF |
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This model was converted to GGUF format from [`Qwen/Qwen2-VL-7B-Instruct`](https://huggingface.co./Qwen/Qwen2-VL-7B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co./spaces/ggml-org/gguf-my-repo) space. |
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Refer to the [original model card](https://huggingface.co./Qwen/Qwen2-VL-7B-Instruct) for more details on the model. |
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--- |
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Model details: |
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- |
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We're excited to unveil Qwen2-VL, the latest iteration of our Qwen-VL model, representing nearly a year of innovation. |
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What’s New in Qwen2-VL? |
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Key Enhancements: |
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SoTA understanding of images of various resolution & ratio: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc. |
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Understanding videos of 20min+: Qwen2-VL can understand videos over 20 minutes for high-quality video-based question answering, dialog, content creation, etc. |
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Agent that can operate your mobiles, robots, etc.: with the abilities of complex reasoning and decision making, Qwen2-VL can be integrated with devices like mobile phones, robots, etc., for automatic operation based on visual environment and text instructions. |
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Multilingual Support: to serve global users, besides English and Chinese, Qwen2-VL now supports the understanding of texts in different languages inside images, including most European languages, Japanese, Korean, Arabic, Vietnamese, etc. |
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Model Architecture Updates: |
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Naive Dynamic Resolution: Unlike before, Qwen2-VL can handle arbitrary image resolutions, mapping them into a dynamic number of visual tokens, offering a more human-like visual processing experience. |
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Multimodal Rotary Position Embedding (M-ROPE): Decomposes positional embedding into parts to capture 1D textual, 2D visual, and 3D video positional information, enhancing its multimodal processing capabilities. |
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We have three models with 2, 7 and 72 billion parameters. This repo contains the instruction-tuned 7B Qwen2-VL model. For more information, visit our Blog and GitHub. |
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--- |
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## Use with llama.cpp |
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Install llama.cpp through brew (works on Mac and Linux) |
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```bash |
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brew install llama.cpp |
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``` |
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Invoke the llama.cpp server or the CLI. |
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### CLI: |
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```bash |
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llama-cli --hf-repo Triangle104/Qwen2-VL-7B-Instruct-Q5_K_M-GGUF --hf-file qwen2-vl-7b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is" |
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``` |
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### Server: |
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```bash |
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llama-server --hf-repo Triangle104/Qwen2-VL-7B-Instruct-Q5_K_M-GGUF --hf-file qwen2-vl-7b-instruct-q5_k_m.gguf -c 2048 |
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``` |
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Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. |
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Step 1: Clone llama.cpp from GitHub. |
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``` |
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git clone https://github.com/ggerganov/llama.cpp |
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``` |
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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). |
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``` |
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cd llama.cpp && LLAMA_CURL=1 make |
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``` |
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Step 3: Run inference through the main binary. |
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``` |
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./llama-cli --hf-repo Triangle104/Qwen2-VL-7B-Instruct-Q5_K_M-GGUF --hf-file qwen2-vl-7b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is" |
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``` |
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or |
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``` |
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./llama-server --hf-repo Triangle104/Qwen2-VL-7B-Instruct-Q5_K_M-GGUF --hf-file qwen2-vl-7b-instruct-q5_k_m.gguf -c 2048 |
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``` |
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