flan-alpaca
stanford_alpaca
flan-alpaca | stanford_alpaca | |
---|---|---|
5 | 108 | |
337 | 28,929 | |
-0.3% | 1.1% | |
5.7 | 2.0 | |
11 months ago | 2 months ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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flan-alpaca
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Is it feasible to develop multiple specialised language models that are small in size and expertise-specific, which can be merged to achieve comparable results to those obtained from a single large language model?
If you have enough task or domain specific training data, the model size becomes less important. For example, you can take an instruction tuned smaller model like FlanT5 and fine tune for your specific case: https://github.com/declare-lab/flan-alpaca
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Best Instruct-Trained Alternative to Alpaca/Vicuna?
Hi, you can try Flan-Alpaca here which does not have such restrictions: https://github.com/declare-lab/flan-alpaca
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Cerebras-GPT: A Family of Open, Compute-Efficient, Large Language Models
I've been following open source LLMs for a while and at first glance this doesn't seem too powerful compared to other open models, Flan-Alpaca[0] is licensed under Apache 2.0, and it seems to perform much better. Although I'm not sure about the legalities about that licensing, since it's basically Flan-T5 fine-tuned using the Alpaca dataset (which is under a Non-Commercial license).
Nonetheless, it's exciting to see all these open models popping up, and I hope that a LLM equivalent to Stable Diffusion comes sooner than later.
[0]: https://github.com/declare-lab/flan-alpaca
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[D] What is the best open source chatbot AI to do transfer learning on?
Someone's already taking care of that - Flan-Alpaca
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[P] ChatLLaMA - A ChatGPT style chatbot for Facebook's LLaMA
I think this might be exactly what you're looking for https://github.com/declare-lab/flan-alpaca
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-electron - The simplest way to run Alpaca (and other LLaMA-based local LLMs) on your own computer
alpaca-lora - Instruct-tune LLaMA on consumer hardware
agents - An Open-source Framework for Autonomous Language Agents
ChatGLM-6B - ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型
llama - Inference code for Llama 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.
stable-diffusion-ui - Easiest 1-click way to install and use Stable Diffusion on your computer. Provides a browser UI for generating images from text prompts and images. Just enter your text prompt, and see the generated image. [Moved to: https://github.com/easydiffusion/easydiffusion]
llama.cpp - LLM inference in C/C++
codealpaca
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
Alpaca-Turbo - Web UI to run alpaca model locally
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.