merge from llama.cpp #33
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l3utterfly
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Aug 12, 2024
l3utterfly
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`ggml/src/llamafile/sgemm.o` was not deleted on `make clean`
* gguf-py : use classes for quants * convert_hf : simplify internal quantization type selection * gguf-py : fix flake8 lint * gguf-py : fix BF16 numpy view type * gguf-py : remove LlamaFileTypeMap Too specific to 'llama.cpp', and would be a maintenance burden to keep up to date. * gguf-py : add generic quantize and dequantize functions The quant classes no longer need to be known, only the target or the source type, for 'quantize' and 'dequantize', respectively.
* llama : avoid useless copies in dummy session writer * llama : avoid double tensor copy when saving session to buffer
This commit adds the `--pooling` option to the README.md file in the `examples/embedding` directory. The motivation for adding this options is that currently if the model used does not specify a pooling type the embedding example will fail with the following error message: ```console main: error: pooling type NONE not supported ``` This commit also updates the name of the executable in the examples section.
* ggml: use vulkan as gpu backend when available Signed-off-by: Matt Stephenson <mstephenson6@users.noreply.github.com> * whisper: enable using vk as default buffer type Signed-off-by: Matt Stephenson <mstephenson6@users.noreply.github.com> --------- Signed-off-by: Matt Stephenson <mstephenson6@users.noreply.github.com>
* init * rename * add run android for termux in readme * add android readme * add instructions in readme * change name in readme * Update README.md * fixed line * add result in readme * random pos_embed * add positions index * change for ollama * change for ollama * better pos_embed in clip * support ollama * updata cmakelist * updata cmakelist * rename wrapper * clear code * replace and organize code * add link * sync master * fix warnings * fix warnings * fix bug in bicubic resize when need resize iamge smaller * receive review comments and modify * receive review comments and modify * put all code into llava dir * fix quality problem in pr code * change n_layer * add space in "-1" * imitate reshape bug of python code * fix bug in clip * fix issues for merging * fix llama-minicpmv-cli in cmake file * change pr readme * fix code review * remove in line 33 directory in the /cmakelists.txt (not in example, in the main dir * fix cmakefile * add warn * fix KEY_HAS_MINICPMV_PROJ * remove load_image_size into clip_ctx * remove the extern "C", MINICPMV_API * fix uhd code for review comment * delete minicpmv-wrapper in pr * remove uhd_image_embed * Modify 2 notes * clip : style changes * del common.h in clip * fix Type-Check error * fix Type-Check error * fix Type-Check error * fix Type-Check error * fix makefile error * fix ubuntu-make error * try fix clip * try fix 1 --------- Co-authored-by: Hongji Zhu <fireyoucan@gmail.com> Co-authored-by: harvestingmoon <leewenyeong@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* llama : better replace_all (cont) ggml-ci * code : deduplicate replace_all ggml-ci
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Signed-off-by: tarilabs <matteo.mortari@gmail.com>
* gguf-py : add T5ENCODER model architecture * common : call llama_decode() during warmup only if the model has decoder * convert-hf : add T5EncoderModel * llama : add llama_model_has_decoder() API function * llama : split build_t5() into build_t5_encoder() and build_t5_decoder() * llama : add support for LLM_ARCH_T5ENCODER * llama-embedding : add support for LLAMA_POOLING_TYPE_NONE * llama-embedding : add support for encoder-only models --------- Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* default n_swa for phi-3 * fix * double check swa
…ronization overhead. (ggerganov#8943) * Optimize Vulkan backend for better CPU performance and less GPU synchronization overhead. - Allocation overhead for the temporary std::vectors was easily detectable with a sampling profiler and simple to remove. - ggml_vk_sync_buffer introduce a full pipeline sync which has a significant cost on the GPU side, sometimes larger than the actual kernel execution. Adding only barriers for shader read/writes and transfers seems to be sufficient looking at the code which either launches compute kernels or copies tensors. * Fix small typo --------- Co-authored-by: 0cc4m <picard12@live.de>
…gerganov#8956) Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Neo Zhang <>
* gguf-py : Numpy dequantization for most types * gguf-py : Numpy dequantization for grid-based i-quants
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