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pignut    
n. 花生米,山胡桃类,山胡桃果

花生米,山胡桃类,山胡桃果

pignut
n 1: an American hickory tree having bitter nuts [synonym: {pignut},
{pignut hickory}, {brown hickory}, {black hickory}, {Carya
glabra}]


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  • Llama. cpp now supports distributed inference across multiple . . . - Reddit
    A few days ago, rgerganov's RPC code was merged into llama cpp and the old MPI code has been removed So llama cpp supports working distributed inference now You can run a model across more than 1 machine It's a work in progress and has limitations It currently is limited to FP16, no quant support yet Also, I couldn't get it to work with Vulkan But considering those limitations, it works
  • You can mix different brand GPUs for multi-GPU setups with llama. cpp . . .
    During a discussion in another topic, it seems many people don't know that you can mix GPUs in a multi-GPU setup with llama cpp They don't all have to be the same brand You can combine Nvidia, AMD, Intel and other GPUs together using Vulkan For someone like me who has a mish mash of GPUs from everyone, this is a big win
  • llamacpp - Reddit
    r llamacpp llama cpp is the Linux of LLM toolkits out there, it's kinda ugly, but it's fast, it's very flexible and you can do so much if you are willing to use it I'm curious why other's are using llama cpp
  • Anyone is using llamacpp for real? : r LocalLLaMA - Reddit
    The llama cpp project is crucial for providing an alternative, allowing us to access LLMs freely, not just in terms of cost but also in terms of accessibility, like free speech I help companies deploy their own infrastructure to host LLMs and so far they are happy with their investment
  • Llama 3 8B instruct with fixed BPE tokenizer uploaded
    The issue was technically not in the tokenizer itself, but in the pre-tokenizer, which is a pre-processing step that is a part of the inference portion of llama cpp The change in the conversion process is just to mark what pre-tokenizer should be used for the model, since llama cpp now supports multiple different pre-tokenizers
  • Memory Tests using Llama. cpp KV cache quantization
    Now that Llama cpp supports quantized KV cache, I wanted to see how much of a difference it makes when running some of my favorite models The short answer is a lot! Using "q4_0" for the KV cache, I was able to fit Command R (35B) onto a single 24GB Tesla P40 with a context of 8192, and run with the full 131072 context size on 3x P40's I tested using both split "row" and split "layer", using
  • Efficient Memory Optimizations for Llama. cpp : r LocalLLaMA - Reddit
    Hi, I have been using llama cpp for a while now and it has been awesome, but last week, after I updated with git pull I am getting out of memory…
  • Llama. cpp now supports BitNet! : r LocalLLaMA - Reddit
    So? Is there a way to finetune an existing model to BitNet? Like a finetuned BitNet version of command-r-plus or llama-3 would be nice
  • llama. cpp and thread count optimization [Revisited] : r LocalLLaMA - Reddit
    My experience (5950x, 4700U), and experience of others with high core count is that RAM speed is much more important than number of threads or even frequency For example, there is a guy with 7950x and DDR5 6000, and it is almost 2x compared to my system with DDR4 3000 Moreover, setting more than 8 threads in my case, decreases models performance Small models don't show improvements in speed
  • Extensive LLama. cpp benchmark more speed on CPU, 7b to 30b . . . - Reddit
    Extensive LLama cpp benchmark more speed on CPU, 7b to 30b, Q2_K, to Q6_K and FP16, X3D, DDR-4000 and DDR-6000





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