armnn
llama.cpp
| armnn | llama.cpp | |
|---|---|---|
| 2 | 780 | |
| 1,128 | 58,425 | |
| 2.3% | - | |
| 9.2 | 10.0 | |
| 7 days ago | 3 days ago | |
| C++ | C++ | |
| MIT License | MIT License |
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armnn
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LeCun: Qualcomm working with Meta to run Llama-2 on mobile devices
Like ARM? https://github.com/ARM-software/armnn
Optimization for this workload has arguably been in-progress for decades. Modern AVX instructions can be found in laptops that are a decade old now, and most big inferencing projects are built around SIMD or GPU shaders. Unless your computer ships with onboard Nvidia hardware, there's usually not much difference in inferencing performance.
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Apple previews Live Speech, Personal Voice, and more new accessibility features
Yes, and generic multicore ARM CPUs can run ARM's standard compute library regardless of their hardware: https://github.com/ARM-software/armnn
Plus, the benchmark you've linked to is comparing CPU accelerated code to the notoriously crippled MKL execution. A more appropriate comparison would test Apple's AMX units against the Ryzen's SIMD-optimized inferencing.
llama.cpp
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IBM Granite: A Family of Open Foundation Models for Code Intelligence
if you can compile stuff, then looking at llama.cpp (what ollama uses) is also interesting: https://github.com/ggerganov/llama.cpp
the server is here: https://github.com/ggerganov/llama.cpp/tree/master/examples/...
And you can search for any GGUF on huggingface
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Ask HN: Affordable hardware for running local large language models?
Yes, Metal seems to allow a maximum of 1/2 of the RAM for one process, and 3/4 of the RAM allocated to the GPU overall. There’s a kernel hack to fix it, but that comes with the usual system integrity caveats. https://github.com/ggerganov/llama.cpp/discussions/2182
- Xmake: A modern C/C++ build tool
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Better and Faster Large Language Models via Multi-Token Prediction
For anyone interested in exploring this, llama.cpp has an example implementation here:
https://github.com/ggerganov/llama.cpp/tree/master/examples/...
- Llama.cpp Bfloat16 Support
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Fine-tune your first large language model (LLM) with LoRA, llama.cpp, and KitOps in 5 easy steps
Getting started with LLMs can be intimidating. In this tutorial we will show you how to fine-tune a large language model using LoRA, facilitated by tools like llama.cpp and KitOps.
- GGML Flash Attention support merged into llama.cpp
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Phi-3 Weights Released
well https://github.com/ggerganov/llama.cpp/issues/6849
- Lossless Acceleration of LLM via Adaptive N-Gram Parallel Decoding
- Llama.cpp Working on Support for Llama3