qlora
system-design-primer
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qlora | system-design-primer | |
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80 | 380 | |
9,388 | 254,953 | |
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7.4 | 0.0 | |
7 months ago | 5 days ago | |
Jupyter Notebook | Python | |
MIT License | GNU General Public License v3.0 or later |
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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 ” } ]
system-design-primer
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10 GitHub repositories that every developer must follow
✅ donnemartin/system-design-primer: https://github.com/donnemartin/system-design-primer
- FAANG - Guia Descomplicado de Entrevistas - parte 2
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10 GitHub Repos to Become a Better Backend Developer
View on GitHub
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[Need Recommendation] System design concepts based repos that provide bird's-eye-view
I've been giving interviews for past couple of months and this github repo has helped me so much for system design perspective and I can see myself excelling at interviews. - https://github.com/donnemartin/system-design-primer
- GitHub – system-design-primer: Learn how to design large-scale systems
- FLaNK Stack Weekly for 30 Oct 2023
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Getting ACL surgery in two day and pretty nervous.
You'll be on opiod's probably the first 1-2 days, so sleeping should be fine. Everything will be allright, don't worry too much. Just use the time now to prepare for the time after, make sure you go through post-surgery-essentials thread. Once you are out of the OR you won't have the energy to think about those details, so make sure you take that prep serious.
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Tool decision - What architecture would you choose and why?
Tooling isn’t architecture. Figure out what you need to handle both personas and volume/throughput and then lay out the capabilities you’ll need. As you lay out points of ingress, egress, consumption you can start to lay out sequences(think in persona and sequence diagrams to express interactions between services). Lastly, evaluate tools that offer some of these capabilities and weigh the trade-offs (there are always trade-offs: https://github.com/donnemartin/system-design-primer).
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Is there an EU country which I might work there being an average non-EU developer
[1] https://github.com/donnemartin/system-design-primer [2] https://www.teamblind.com/post/My-Approach-to-System-Design-V4SJARdx
What are some alternatives?
alpaca-lora - Instruct-tune LLaMA on consumer hardware
Grokking-the-Coding-Interview-Patterns - This course categorizes coding interview problems into a set of 16 patterns. Each pattern will be a complete tool - consisting of data structures, algorithms, and analysis techniques - to solve a specific category of problems. The goal is to develop an understanding of the underlying pattern, so that, we can apply that pattern to solve other problems. [UnavailableForLegalReasons - Repository access blocked]
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
developer-roadmap - Interactive roadmaps, guides and other educational content to help developers grow in their careers.
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
machine-learning-interview - Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.
ggml - Tensor library for machine learning
interview - Everything you need to prepare for your technical interview
alpaca_lora_4bit
awesome-interview-questions - :octocat: A curated awesome list of lists of interview questions. Feel free to contribute! :mortar_board:
llm-foundry - LLM training code for Databricks foundation models
manim - Animation engine for explanatory math videos