trlx
stanford_alpaca
trlx | stanford_alpaca | |
---|---|---|
6 | 108 | |
4,332 | 28,856 | |
1.2% | 0.9% | |
7.9 | 2.0 | |
4 months ago | about 2 months ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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trlx
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Recapping the AI, Machine Learning and Data Science Meetup — May 2, 2024
Transformer Reinforcement Learning X on GitHub
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Why did Stability not copy Midjourney's RLHF process? And what's the future of Stable Diffusion?
We drove and released the top RLHF framework TRLX for example from our Carper AI lab used by some of the biggest companies in the world: https://github.com/CarperAI/trlx
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[R] Stanford-Alpaca 7B model (an instruction tuned version of LLaMA) performs as well as text-davinci-003
If you checkout the trlx repo they have some examples and they have an example of how they trained sft and ppo on the hh dataset. So it’s basically that but with llama. https://github.com/CarperAI/trlx/blob/main/examples/hh/sft_hh.py
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Sam Altman: OpenAI’s GPT-4 will launch only when they can do it safely & responsibly. “In general we are going to release technology much more slowly than people would like. We're going to sit on it for much longer…”. Also confirmed video model in the works.
We’ll release our first trained model with Stability AI soon. If you want to start tinkering with RLHF now, we’re also helping develop TRLX: https://github.com/CarperAI/trlx — the open source library for reinforcement learning with transformers.
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[P] RLHF Learning to Summarize: Implementation by CarperAI with trlX
trlX library here: https://github.com/CarperAI/trlx
- Will we ever see an open source alternative to ChatGPT?
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.
[0] https://www.nltk.org/nltk_data/
[1] https://crfm.stanford.edu/2023/03/13/alpaca.html
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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
What are some alternatives?
alpaca-lora - Instruct-tune LLaMA on consumer hardware
PaLM-rlhf-pytorch - Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM
ChatGLM-6B - ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型
trl - Train transformer language models with reinforcement 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.
RL4LMs - A modular RL library to fine-tune language models to human preferences
llama.cpp - LLM inference in C/C++
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
summarize-from-feedback - Code for "Learning to summarize from human feedback"
Alpaca-Turbo - Web UI to run alpaca model locally