petals
llama.cpp
petals | llama.cpp | |
---|---|---|
98 | 773 | |
8,684 | 57,463 | |
1.5% | - | |
8.3 | 10.0 | |
5 days ago | about 16 hours ago | |
Python | C++ | |
MIT License | MIT License |
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petals
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Mistral Large
So how long until we can do an open source Mistral Large?
We could make a start on Petals or some other open source distributed training network cluster possibly?
[0] https://petals.dev/
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Distributed Inference and Fine-Tuning of Large Language Models over the Internet
Can check out their project at https://github.com/bigscience-workshop/petals
- Make no mistake—AI is owned by Big Tech
- Would you donate computation and storage to help build an open source LLM?
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Run 70B LLM Inference on a Single 4GB GPU with This New Technique
There is already an implementation along the same line using the torrent architecture.
https://petals.dev/
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Run LLMs in bittorrent style
Check it out at Petals.dev. Chatbot
- Is distributed computing dying, or just fading into the background?
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Ask HN: Are there any projects currently exploring distributed AI training?
https://github.com/bigscience-workshop/petals
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Mistral 7B,The complete Guide of the Best 7B model
https://github.com/bigscience-workshop/petals
Inference only: https://lite.koboldai.net/
- Run LLMs at home, BitTorrent‑style
llama.cpp
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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
- Mixtral 8x22B
- Llama.cpp: Improve CPU prompt eval speed
What are some alternatives?
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
llama - Inference code for Llama models
gpt4all - gpt4all: run open-source LLMs anywhere
alpaca-lora - Instruct-tune LLaMA on consumer hardware
GLM-130B - GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023)
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/Auto-GPT]
ggml - Tensor library for machine learning
Open-Assistant - OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM