| blog | llama.cpp | |
|---|---|---|
| 5 | 775 | |
| 2,025 | 57,463 | |
| 5.0% | - | |
| 9.8 | 10.0 | |
| 2 days ago | 4 days ago | |
| Jupyter Notebook | C++ | |
| - | MIT License |
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blog
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Refact LLM: New 1.6B code model reaches 32% HumanEval and is SOTA for the size
[4] https://github.com/huggingface/blog/blob/main/starcoder.md
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A comprehensive guide to running Llama 2 locally
If you just want to do inference/mess around with the model and have a 16GB GPU, then this[0] is enough to paste into a notebook. You need to have access to the HF models though.
0. https://github.com/huggingface/blog/blob/main/llama2.md#usin...
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Let’s train your first Offline Decision Transformer model from scratch 🤖
The hands-on 👉https://github.com/huggingface/blog/blob/main/notebooks/101_train-decision-transformers.ipynb
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How to switch to half precision fp16?
I'm also running the optimized script but it doesn't run with 512x512 on my RTX3050 Ti mobile. On this website, they recommend to switch to fp16 for GPUs with less than 10gb of vram.
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Are people hiding their deep learning code?
Here's a notebook illustrating how to train a language model from scratch: https://github.com/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb
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?
text-generation-inference - Large Language Model Text Generation Inference
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
gpt4all - gpt4all: run open-source LLMs anywhere
awesome-notebooks - A powerful data & AI notebook templates catalog: prompts, plugins, models, workflow automation, analytics, code snippets - following the IMO framework to be searchable and reusable in any context.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
QuantumKatas - Tutorials and programming exercises for learning Q# and quantum computing
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
stable-diffusion - Optimized Stable Diffusion modified to run on lower GPU VRAM
ggml - Tensor library for machine learning
Practical_RL - A course in reinforcement learning in the wild
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM