stanford_alpaca
safetensors
stanford_alpaca | safetensors | |
---|---|---|
108 | 31 | |
28,967 | 2,516 | |
0.5% | 2.9% | |
2.0 | 7.9 | |
3 months ago | 23 days 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.
[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
safetensors
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Llamafile lets you distribute and run LLMs with a single file
The ML field is doing work in that area: https://github.com/huggingface/safetensors
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Hugging Face raises $235M from investors including Salesforce and Nvidia
FYI the file format, safetensors, was proposed, developed and maintained by HF, and involved people from groups such as Eleuther and Stability for external security audits.
https://github.com/huggingface/safetensors https://huggingface.co/blog/safetensors-security-audit
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I Made Stable Diffusion XL Smarter by Finetuning It on Bad AI-Generated Images
Thank you for note on this. I had not heard there were already trojan horse malware being slipped into tensor files as python scripts. Apparently torch pickle uses eval on the tensor file with no filter.
Heard surprisingly little commentary on this topic. The full explanation of how Safetensors are "Safe" can be found from the developer at: https://github.com/huggingface/safetensors/discussions/111
- Pickle safety in Python
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What makes .safetensors files safe?
Here the developer goes into some detail about what kinds of protections .safetensor files have : https://github.com/huggingface/safetensors/discussions/111
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Security PSA: huggingface models are code. not just data.
Use the safetensors format, which allows safe persistence and loading of models for common libraries - TensorFlow, PyTorch, JAX, etc. We went through external audits in the last few months (blog post). The current direction will be to have this as the default format.
- What's your favorite model. Right now I'm really enjoying dreamshaper.
- Lora, ggml, safetensors, hf, etc. Is there a glossary and guide on which model to choose?
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Stability AI Launches the First of Its StableLM Suite of Language Models
I've been diving in lately and while it's not efficient, the only way to do manage is to create a new conda/mamba environment, or a custom Docker image for all the conflicting packages.
For safety and speed, you should prefer the safetensor format: https://huggingface.co/docs/safetensors/speed
If you know what you are doing you can do your own conversions: https://github.com/huggingface/safetensors or for safety, https://huggingface.co/spaces/diffusers/convert
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CKPT to Safetensors
GitHub - huggingface/safetensors: Simple, safe way to store and distribute tensors
What are some alternatives?
alpaca-lora - Instruct-tune LLaMA on consumer hardware
stable-diffusion-webui - Stable Diffusion web UI
ChatGLM-6B - ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型
llama.cpp - LLM inference in C/C++
Open-Assistant - OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
Safe-and-Stable-Ckpt2Safetensors-Conversion-Tool-GUI - Convert your Stable Diffusion checkpoints quickly and easily.
InvokeAI - InvokeAI is a leading creative engine for Stable Diffusion models, empowering professionals, artists, and enthusiasts to generate and create visual media using the latest AI-driven technologies. The solution offers an industry leading WebUI, supports terminal use through a CLI, and serves as the foundation for multiple commercial products.
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
Stable-Diffusion-Pickle-Scanner-GUI - Pickle Scanner GUI
Alpaca-Turbo - Web UI to run alpaca model locally
stable-diffusion-webui-model-toolkit - A Multipurpose toolkit for managing, editing and creating models.