llama-mps
llama.cpp
| llama-mps | llama.cpp | |
|---|---|---|
| 4 | 780 | |
| 83 | 57,984 | |
| - | - | |
| 3.8 | 10.0 | |
| 9 months ago | 5 days ago | |
| Python | C++ | |
| GNU General Public License v3.0 only | MIT License |
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llama-mps
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llama.cpp now officially supports GPU acceleration.
There are currently at least 3 ways to run llama on m1 with GPU acceleration. - mlc-llm (pre-built, only 1 model has been ported) - tinygrad (very memory efficient, not that easy to integrate into other projects) - llama-mps (original llama codebase + llama adapter support)
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LLaMA-7B in Pure C++ with full Apple Silicon support
There is also a gpu-acelerated fork of the original repo
https://github.com/remixer-dec/llama-mps
- Llama-CPU: Fork of Facebooks LLaMa model to run on CPU
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[D] Tutorial: Run LLaMA on 8gb vram on windows (thanks to bitsandbytes 8bit quantization)
I tried to port the llama-cpu version to a gpu-accelerated mps version for macs, it runs, but the outputs are not as good as expected and it often gives "-1" tokens. Any help and contributions on fixing it are welcome!
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
What are some alternatives?
llama - Inference code for Llama models
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
gpt4all - gpt4all: run open-source LLMs anywhere
awesome-ml - Curated list of useful LLM / Analytics / Datascience resources
llama - Inference code for LLaMA models
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
LLaMA_MPS - Run LLaMA inference on Apple Silicon GPUs.
ggml - Tensor library for machine learning
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM