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Merge branch 'upstream' into concedo_experimental
# Conflicts: # .github/workflows/build.yml # .github/workflows/docker.yml # README.md # build-xcframework.sh # common/CMakeLists.txt # examples/CMakeLists.txt # ggml/src/ggml-cpu/CMakeLists.txt # ggml/src/ggml-cuda/CMakeLists.txt # ggml/src/ggml-metal/ggml-metal.m # ggml/src/ggml-metal/ggml-metal.metal # ggml/src/ggml-sycl/CMakeLists.txt # ggml/src/ggml-sycl/backend.hpp # ggml/src/ggml-sycl/common.hpp # ggml/src/ggml-sycl/ggml-sycl.cpp # ggml/src/ggml-sycl/mmvq.cpp # ggml/src/ggml-sycl/vecdotq.hpp # scripts/compare-llama-bench.py # src/CMakeLists.txt # src/llama-model.cpp # src/llama.cpp # tests/test-backend-ops.cpp # tests/test-opt.cpp # tools/llama-bench/README.md # tools/llama-bench/llama-bench.cpp # tools/mtmd/CMakeLists.txt # tools/mtmd/README.md # tools/mtmd/clip.cpp # tools/rpc/rpc-server.cpp # tools/server/CMakeLists.txt # tools/server/README.md
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# Multimodal
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llama.cpp supports multimodal input via `libmtmd`. Currently, there are 2 tools support this feature:
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- [llama-mtmd-cli](../tools/mtmd/README.md)
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- [llama-server](../tools/server/README.md) via OpenAI-compatible `/chat/completions` API
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To enable it, can use use one of the 2 methods below:
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- Use `-hf` option with a supported model (see a list of pre-quantized model below)
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- To load a model using `-hf` while disabling multimodal, use `--no-mmproj`
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- To load a model using `-hf` while using a custom mmproj file, use `--mmproj local_file.gguf`
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- Use `-m model.gguf` option with `--mmproj file.gguf` to specify text and multimodal projector respectively
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By default, multimodal projector will be offloaded to GPU. To disable this, add `--no-mmproj-offload`
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For example:
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```sh
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# simple usage with CLI
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llama-mtmd-cli -hf ggml-org/gemma-3-4b-it-GGUF
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# simple usage with server
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llama-server -hf ggml-org/gemma-3-4b-it-GGUF
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# using local file
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llama-server -m gemma-3-4b-it-Q4_K_M.gguf --mmproj mmproj-gemma-3-4b-it-Q4_K_M.gguf
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# no GPU offload
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llama-server -hf ggml-org/gemma-3-4b-it-GGUF --no-mmproj-offload
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```
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## Pre-quantized models
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These are ready-to-use models, most of them come with `Q4_K_M` quantization by default.
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Replaces the `(tool_name)` with the name of binary you want to use. For example, `llama-mtmd-cli` or `llama-server`
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NOTE: some models may require large context window, for example: `-c 8192`
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```sh
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# Gemma 3
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(tool_name) -hf ggml-org/gemma-3-4b-it-GGUF
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(tool_name) -hf ggml-org/gemma-3-12b-it-GGUF
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(tool_name) -hf ggml-org/gemma-3-27b-it-GGUF
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# SmolVLM
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(tool_name) -hf ggml-org/SmolVLM-Instruct-GGUF
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(tool_name) -hf ggml-org/SmolVLM-256M-Instruct-GGUF
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(tool_name) -hf ggml-org/SmolVLM-500M-Instruct-GGUF
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(tool_name) -hf ggml-org/SmolVLM2-2.2B-Instruct-GGUF
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(tool_name) -hf ggml-org/SmolVLM2-256M-Video-Instruct-GGUF
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(tool_name) -hf ggml-org/SmolVLM2-500M-Video-Instruct-GGUF
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# Pixtral 12B
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(tool_name) -hf ggml-org/pixtral-12b-GGUF
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# Qwen 2 VL
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(tool_name) -hf ggml-org/Qwen2-VL-2B-Instruct-GGUF
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(tool_name) -hf ggml-org/Qwen2-VL-7B-Instruct-GGUF
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# Qwen 2.5 VL
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(tool_name) -hf ggml-org/Qwen2.5-VL-3B-Instruct-GGUF
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(tool_name) -hf ggml-org/Qwen2.5-VL-7B-Instruct-GGUF
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(tool_name) -hf ggml-org/Qwen2.5-VL-32B-Instruct-GGUF
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(tool_name) -hf ggml-org/Qwen2.5-VL-72B-Instruct-GGUF
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# Mistral Small 3.1 24B (IQ2_M quantization)
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(tool_name) -hf ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF
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# InternVL 2.5 and 3
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(tool_name) -hf ggml-org/InternVL2_5-1B-GGUF
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(tool_name) -hf ggml-org/InternVL2_5-4B-GGUF
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(tool_name) -hf ggml-org/InternVL3-1B-Instruct-GGUF
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(tool_name) -hf ggml-org/InternVL3-2B-Instruct-GGUF
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(tool_name) -hf ggml-org/InternVL3-8B-Instruct-GGUF
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(tool_name) -hf ggml-org/InternVL3-14B-Instruct-GGUF
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```
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