coriander
llama.cpp
| coriander | llama.cpp | |
|---|---|---|
| 3 | 775 | |
| 832 | 57,463 | |
| - | - | |
| 0.0 | 10.0 | |
| 3 months ago | 2 days ago | |
| LLVM | C++ | |
| Apache License 2.0 | MIT License |
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coriander
- How to run Llama 13B with a 6GB graphics card
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Is it possible to virtualize a CUDA processor?
It’s not a full implementation of CUDA and requires some contortions to use but https://github.com/hughperkins/coriander is as good as anything else I’ve tried. It has been a few years though.
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EVGA will no longer make NVIDIA GPUs due to “disrespectful treatment” - Dexerto
It’s possible to run cuda on anything . There have been attempts to do this. https://github.com/hughperkins/coriander Unfortunately it seems development stalled.
llama.cpp
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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
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Embeddings are a good starting point for the AI curious app developer
Have just done this recently for local chat with pdf feature in https://recurse.chat. (It's a macOS app that has built-in llama.cpp server and local vector database)
Running an embedding server locally is pretty straightforward:
- Get llama.cpp release binary: https://github.com/ggerganov/llama.cpp/releases
What are some alternatives?
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
gptq - Code for the ICLR 2023 paper "GPTQ: Accurate Post-training Quantization of Generative Pretrained Transformers".
gpt4all - gpt4all: run open-source LLMs anywhere
intel-extension-for-pytorch - A Python package for extending the official PyTorch that can easily obtain performance on Intel platform
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
RadeonClockEnforcer - AHK script that forces maximum clocks while important applications are open. Automates OverdriveNTool's clock/voltage switching functionality for GPU and VRAM, with the purpose of enforcing maximum clocks while whitelisted applications are in focus.
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
HIPIFY - HIPIFY: Convert CUDA to Portable C++ Code [Moved to: https://github.com/ROCm/HIPIFY]
ggml - Tensor library for machine learning
sparsegpt - Code for the ICML 2023 paper "SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot".
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM