yal-discord-bot
llama.cpp
| yal-discord-bot | llama.cpp | |
|---|---|---|
| 5 | 780 | |
| 72 | 57,984 | |
| - | - | |
| 6.5 | 10.0 | |
| about 1 year ago | 5 days ago | |
| Python | C++ | |
| GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
yal-discord-bot
- I wrote a Discord bot to host your own ChatGPT-style chatbot with ALPACA finetuned LLaMA weights on consumer GPUs. 13b fits in a 3080, 30b fits in a 3090. No censorship, ask anything you want!
- [P] Discord Chatbot for LLaMA 4-bit quantized that runs 13b in <9 GiB VRAM
- Discord Chatbot for LLaMA 4-bit quantized that runs 13b in <9 GiB VRAM (r/MachineLearning)
llama.cpp
-
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
-
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
-
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
-
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
-
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?
signal-aichat - An AI chatbot for Signal powered by Google Bard, Bing Chat, ChatGPT, HuggingChat, and llama.cpp
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
pifs - πfs - the data-free filesystem!
gpt4all - gpt4all: run open-source LLMs anywhere
alpaca-lora - Instruct-tune LLaMA on consumer hardware
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
GPTQ-for-LLaMa
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
chat-llama-discord-bot - A Discord Bot for chatting with LLaMA, Vicuna, Alpaca, MPT, or any other Large Language Model (LLM) supported by text-generation-webui or llama.cpp.
ggml - Tensor library for machine learning
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM