LLaMA-Adapter
gpt4all
LLaMA-Adapter | gpt4all | |
---|---|---|
16 | 139 | |
4,021 | 65,076 | |
- | 3.3% | |
9.4 | 9.8 | |
11 months ago | 1 day ago | |
Python | C++ | |
GNU General Public License v3.0 only | MIT License |
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LLaMA-Adapter
- Are you selfhosting a ChatGPT alternative?
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Best general purpose model for commercial license?
Either LLaMA with Alpaca LoRA 65B, or LLaMA-Adapter-V2-65B chat demo. I haven't seen any tests of the 65B LLaMA-Adapter-V2, but they claim it's as good as ChatGPT when compared using GPT-4.
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LLaMA-Adapter V2: fine-tuned LLaMA 65B for visual instruction, and LLaMA Chat65B trained with ShareGPT data for chatting. Chat65B model has been released.
Chat65B: https://github.com/ZrrSkywalker/LLaMA-Adapter/tree/main/llama_adapter_v2_chat65b
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LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
How to efficiently transform large language models (LLMs) into instruction followers is recently a popular research direction, while training LLM for multi-modal reasoning remains less explored. Although the recent LLaMA-Adapter demonstrates the potential to handle visual inputs with LLMs, it still cannot generalize well to open-ended visual instructions and lags behind GPT-4. In this paper, we present LLaMA-Adapter V2, a parameter-efficient visual instruction model. Specifically, we first augment LLaMA-Adapter by unlocking more learnable parameters (e.g., norm, bias and scale), which distribute the instruction-following ability across the entire LLaMA model besides adapters. Secondly, we propose an early fusion strategy to feed visual tokens only into the early LLM layers, contributing to better visual knowledge incorporation. Thirdly, a joint training paradigm of image-text pairs and instruction-following data is introduced by optimizing disjoint groups of learnable parameters. This strategy effectively alleviates the interference between the two tasks of image-text alignment and instruction following and achieves strong multi-modal reasoning with only a small-scale image-text and instruction dataset. During inference, we incorporate additional expert models (e.g. captioning/OCR systems) into LLaMA-Adapter to further enhance its image understanding capability without incurring training costs. Compared to the original LLaMA-Adapter, our LLaMA-Adapter V2 can perform open-ended multi-modal instructions by merely introducing 14M parameters over LLaMA. The newly designed framework also exhibits stronger language-only instruction-following capabilities and even excels in chat interactions. Our code and models are available at https://github.com/ZrrSkywalker/LLaMA-Adapter.
- Surpasses ChatGPT on Some Tasks
- [News] This language model surpasses ChatGPT on some prompts
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Meet LLaMA-Adapter: A Lightweight Adaption Method For Fine-Tuning Instruction-Following LLaMA Models Using 52K Data Provided By Stanford Alpaca
Quick Read: https://www.marktechpost.com/2023/03/31/meet-llama-adapter-a-lightweight-adaption-method-for-fine-tuning-instruction-following-llama-models-using-52k-data-provided-by-stanford-alpaca/ Paper: https://arxiv.org/pdf/2303.16199.pdf Github: https://github.com/ZrrSkywalker/LLaMA-Adapter
- LLaMA-Adapter: Efficient Fine-Tuning of LLaMA
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[R] LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
Found relevant code at https://github.com/ZrrSkywalker/LLaMA-Adapter + all code implementations here
- You can now fine-tune LLaMA to follow instructions within ONE hour
gpt4all
- Show HN: I made an app to use local AI as daily driver
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Ollama Python and JavaScript Libraries
I don’t know if Ollama can do this but https://gpt4all.io/ can.
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Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023?
Gpt4all is a local desktop app with a Python API that can be trained on your documents: https://gpt4all.io/
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WyGPT: Minimal mature GPT model in C++
The readme page is cryptic. What does 'mature' mean in this context? What is the sample text a continuation of?
Hving a gif the thing in use would be great, similar to the gpt4all readme page. (https://github.com/nomic-ai/gpt4all)
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LibreChat
Check https://github.com/nomic-ai/gpt4all instead.
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OpenAI Negotiations to Reinstate Altman Hit Snag over Board Role
"I ran performance tests on two systems, here's the results of system 1, and heres the results of system 2. Summarize the results, and build a markdown table containing x,y,z rows."
"extract the reusable functions out of this bash script"
"write me a cfssl command to generate a intermediate CA"
"What is the regex for _____"
"Here are my accomplishments over the last 6 months, summarize them into a 1 page performance report."
etc etc etc
If you're not using GPT4 or some LLM as part of your daily flow you're working too hard.
Get GPT4All (https://gpt4all.io), log into OpenAI, drop $20 on your account, get a API key, and start using GPT4.
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Darbe uzdraude naudotis CHATGPT: ar cia normalu?
offline versija, nors ir ne tokia pažengus - https://github.com/nomic-ai/gpt4all ; https://gpt4all.io/index.html
- GPT4All: An ecosystem of open-source on-edge large language models - by Nomic AI
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Why use OpenAI's ChatGPT3.5 online service, if you can instead host your own local llama?
Take a look at https://gpt4all.io, their docs are pretty awesome
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Ask HN: Are you using a local LLM? If yes, what for?
I run one. I built an iMessage-like frontend to it using plain JS and a Python websocket backend. I mostly just use it for curiosity and playing with different prompts. I only have 16GB of RAM to dedicate to it, so I use an 8B parameter model which is enough for fun and chitchat, but I don't find it good enough to replace ChatGPT.
https://github.com/nomic-ai/gpt4all
What are some alternatives?
LoRA - Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
llama.cpp - LLM inference in C/C++
bench-warmers - DigThatData's Public Brainstorming space
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
chatgpt-telegram-bot - 🤖 A Telegram bot that integrates with OpenAI's official ChatGPT APIs to provide answers, written in Python
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
text-generation-webui-docker - Docker variants of oobabooga's text-generation-webui, including pre-built images.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
open_llama - OpenLLaMA, a permissively licensed open source reproduction of Meta AI’s LLaMA 7B trained on the RedPajama dataset
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
TavernAI - Atmospheric adventure chat for AI language models (KoboldAI, NovelAI, Pygmalion, OpenAI chatgpt, gpt-4)
AutoGPT - AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.