openapi-devtools
qlora
openapi-devtools | qlora | |
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9 | 80 | |
3,795 | 9,472 | |
- | - | |
7.6 | 7.4 | |
30 days ago | 7 months ago | |
TypeScript | Jupyter Notebook | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
openapi-devtools
- U.S. National Park Service API
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Ask HN: Working with CPG Retail Data
Not specific to the CPG realm, but I've had some very good luck working with the OpenAPI-devtools Chrome Extension[1] (previous discussion here on hackernews[2]) to discover the underlying APIs of various sites that I want to scrape data from.
[1] https://github.com/AndrewWalsh/openapi-devtools
[2] https://news.ycombinator.com/item?id=38012032
- OpenAPI DevTools: Chrome extension that generates API specs for any app or website
- OpenAPI DevTools - Chrome extension that generates API specs for any app or website
- FLaNK Stack Weekly for 30 Oct 2023
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Show HN: OpenAPI DevTools – Chrome ext. that generates an API spec as you browse
As a follow up, the algorithm that powers this makes use of the chrome.devtools.network API. Specifically it passes the Request object that is in the HAR 1.2 archive format.
So if you can pass the equivalent of that in Firefox/other browsers to the insert method and switch things up a bit, it should be relatively straightforward. I will think about pulling out the core logic into its own lib.
https://developer.chrome.com/docs/extensions/reference/devto...
https://developer.chrome.com/docs/extensions/reference/devto...
https://github.com/AndrewWalsh/openapi-devtools/blob/main/sr...
qlora
- FLaNK Stack Weekly for 30 Oct 2023
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I released Marx 3B V3.
Marx 3B V3 is StableLM 3B 4E1T instruction tuned on EverythingLM Data V3(ShareGPT Format) for 2 epochs using QLoRA.
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Tuning and Testing Llama 2, Flan-T5, and GPT-J with LoRA, Sematic, and Gradio
https://github.com/artidoro/qlora
The tools and mechanisms to get a model to do what you want is ever so changing, ever so quickly. Build and understand a notebook yourself, and reduce dependencies. You will need to switch them.
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Yet another QLoRA tutorial
My own project right now is still in raw generated form, and this now makes me think about trying qlora's scripts since this gives me some confidence I should be able to get it to turn out now that someone else has carved a path and charted the map. I was going to target llamatune which was mentioned here the other day.
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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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[R] LaVIN-lite: Training your own Multimodal Large Language Models on one single GPU with competitive performance! (Technical Details)
4-bit quantization training mainly refers to qlora. Simply put, qlora quantizes the weights of the LLM into 4-bit for storage, while dequantizing them into 16-bit during the training process to ensure training precision. This method significantly reduces GPU memory overhead during training (the training speed should not vary much). This approach is highly suitable to be combined with parameter-efficient methods. However, the original paper was designed for single-modal LLMs and the code has already been wrapped in HuggingFace's library. Therefore, we extracted the core code from HuggingFace's library and migrated it into LaVIN's code. The main principle is to replace all linear layers in LLM with 4-bit quantized layers. Those interested can refer to our implementation in quantization.py and mm_adaptation.py, which is roughly a dozen lines of code.
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[D] To all the machine learning engineers: most difficult model task/type you’ve ever had to work with?
There have been some new development like QLora which help fine-tune LLMs without updating all the weights.
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Finetune MPT-30B using QLORA
This might be helpful: https://github.com/artidoro/qlora/issues/10
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is lora fine-tuning on 13B/33B/65B comparable to full fine-tuning?
curious, since qlora paper only reports lora/qlora comparison for full fine-tuning for small 7B models.for 13B/33B/65B, it does not do so (table 4 in paper)it would be helpful if anyone can please provide links where I can read more on efficacy of lora or disadvantages of lora?
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Need a detailed tutorial on how to create and use a dataset for QLoRA fine-tuning.
This might not be appropriate answer but did you take a look at this repository? https://github.com/artidoro/qlora With artidoro's repository it's pretty easy to train qlora. You just prepare your own dataset and run the following command: python qlora.py --model_name_or_path --dataset="path/to/your/dataset" --dataset_format="self-instruct" This is only available for several dataset formats. But every dataset format has to have input-output pairs. So the dataset json format has to be like this [ { “input”: “something ”, “output”:“something ” }, { “input”: “something ”, “output”:“something ” } ]
What are some alternatives?
mitmproxy
alpaca-lora - Instruct-tune LLaMA on consumer hardware
mockoon - Mockoon is the easiest and quickest way to run mock APIs locally. No remote deployment, no account required, open source.
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
elements - Build beautiful, interactive API Docs with embeddable React or Web Components, powered by OpenAPI and Markdown.
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
scalar - Beautiful API references from OpenAPI/Swagger files ✨
ggml - Tensor library for machine learning
apiclient-pydantic-generator - This code generator creates APIClient app from an openapi file.
alpaca_lora_4bit
api2ai - Create API agents from OpenAPI Specs
llm-foundry - LLM training code for Databricks foundation models