stanford_alpaca
FlexGen
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stanford_alpaca | FlexGen | |
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108 | 19 | |
28,602 | 5,350 | |
1.4% | - | |
2.0 | 10.0 | |
16 days ago | about 1 year ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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stanford_alpaca
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How Open is Generative AI? Part 2
Alpaca is an instruction-oriented LLM derived from LLaMA, enhanced by Stanford researchers with a dataset of 52,000 examples of following instructions, sourced from OpenAI’s InstructGPT through the self-instruct method. The extensive self-instruct dataset, details of data generation, and the model refinement code were publicly disclosed. This model complies with the licensing requirements of its base model. Due to the utilization of InstructGPT for data generation, it also adheres to OpenAI’s usage terms, which prohibit the creation of models competing with OpenAI. This illustrates how dataset restrictions can indirectly affect the resulting fine-tuned model.
- Ask HN: AI/ML papers to catch up with current state of AI?
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Fine-tuning LLMs with LoRA: A Gentle Introduction
In this article, we're going to experiment with LoRA and fine-tune Llama Alpaca using commercial hardware.
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Creating a new Finetuned model
Most papers I did read showed at least a thousand, even 10000 at several cases, so I assumed that to be the trend in the case of Low rank adapter(PEFT) training.(source: [2305.14314] QLoRA: Efficient Finetuning of Quantized LLMs (arxiv.org) , Stanford CRFM (Alpaca) and the minimum being openchat/openchat · Hugging Face ; There are a lot more examples)
- Bye bye Bing
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The idea maze for AI startups (2015)
I think there's a new approach for “How do you get the data?” that wasn't available when this article was written in 2015. The new text and image generative models can now be used to synthesize training datasets.
I was working on an typing autocorrect project and needed a corpus of "text messages". Most of the traditional NLP corpuses like those available through NLTK [0] aren't suitable. But it was easy to script ChatGPT to generate thousands of believable text messages by throwing random topics at it.
Similarly, you can synthesize a training dataset by giving GPT the outputs/labels and asking it to generate a variety of inputs. For sentiment analysis... "Give me 1000 negative movie reviews" and "Now give me 1000 positive movie reviews".
The Alpaca folks used GPT-3 to generate high-quality instruction-following datasets [1] based on a small set of human samples.
Etc.
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[D] High-quality, open-source implementations of LLMs
Alpaca [GitHub]
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please 0.1.0 released: let GPT-4 remember CLI args
Now if only this could be used offline, eg. with alpaca https://github.com/tatsu-lab/stanford_alpaca
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Is there a Chatgpt (or other LLMs) powered application in the field of cybersecurity/privacy for end users/b2c?
If you have a strong enough computer, there is Alpaca and llama.cpp which are both open-source. They also have the best privacy feature of all: to be able to be ran locally offline on your computer. I believe there are more foss LLMs out there too but idr.
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Does ChatGPT suck at programming for everyone or just for me?
Are you aware that you can run a pretrained LLM on just 8gb of ram with a single x86 cpu?
FlexGen
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Training LLaMA-65B with Stanford Code
#1: Progress Update | 4 comments #2: the default UI on the pinned Google Colab is buggy so I made my own frontend - YAFFOA. | 18 comments #3: Paper reduces resource requirement of a 175B model down to 16GB GPU | 19 comments
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Replika users fell in love with their AI chatbot companions. Then they lost them
It's really just a gpu vram limitation: affordable GPUs are rather memory starved.
Fortunately people have started writing implementations for pipelining across multiple gpus.
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When, how and why will this Stable Diffusion spring stop?
Actually there's a solution : read this paper https://github.com/Ying1123/FlexGen/blob/main/docs/paper.pdf
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Exciting new shit.
Flexgen - Run big models on your small GPU https://github.com/Ying1123/FlexGen
- Paper reduces resource requirement of a 175B model down to 16GB GPU
- And Here..We..Go: Running large language models like ChatGPTon a single GPU. Up to 100x faster than other offloading systems
- Running large language models like ChatGPT on a single GPU
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[D] Large Language Models feasible to run on 32GB RAM / 8 GB VRAM / 24GB VRAM
Could try something like this: https://github.com/Ying1123/FlexGen
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Looks like Google is safe. Microsoft has lobotomized Bing AI Chat
Ppl are working on it https://github.com/Ying1123/FlexGen
What are some alternatives?
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.
CTranslate2 - Fast inference engine for Transformer models
ChatGLM-6B - ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型
Open-Assistant - OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
ggml - Tensor library for machine learning
llama.cpp - LLM inference in C/C++
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
Alpaca-Turbo - Web UI to run alpaca model locally
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/Auto-GPT]
accelerate - 🚀 A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision