stanford_alpaca
FlexGen
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| stanford_alpaca | FlexGen | |
|---|---|---|
| 108 | 19 | |
| 28,723 | 5,350 | |
| 1.2% | - | |
| 2.0 | 10.0 | |
| about 1 month 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?
- OpenAI board in discussions with Sam Altman to return as CEO
- Are there any AI like ChatGPT without content restrictions?
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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)
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Shock tick up for wage growth to 7.3% in blow for Bank of England
I'm not talking about OpenAI ChatGPT I'm talking about things ALPACA, and where did they train these models? Off the existing models for a fraction of a fraction of a fraction of the cost: https://crfm.stanford.edu/2023/03/13/alpaca.html
- 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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Repos and tutorials for a full finetune (not LoRA)
AFAIK, the original alpaca repo was a full finetune. https://github.com/tatsu-lab/stanford_alpaca
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.
- Same as with Stable Diffusion, new AI based LAION, are coming up slowly but surely: 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
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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
- FlexGen - Run 175B Parameter Models on consumer hardware
- Running large language models like ChatGPT on a single GPU
- FlexGen: Running large language models like ChatGPT/GPT-3/OPT-175B on a single GPU
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.
ChatGLM-6B - ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型
CTranslate2 - Fast inference engine for Transformer models
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++
accelerate - 🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
rust-bert - Rust native ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)
Alpaca-Turbo - Web UI to run alpaca model locally
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.