qlora
transformers
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qlora | transformers | |
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80 | 173 | |
9,344 | 124,557 | |
- | 2.7% | |
7.4 | 10.0 | |
7 months ago | 1 day ago | |
Jupyter Notebook | Python | |
MIT License | Apache License 2.0 |
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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 ” } ]
transformers
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AI enthusiasm #6 - Finetune any LLM you wantđź’ˇ
Most of this tutorial is based on Hugging Face course about Transformers and on Niels Rogge's Transformers tutorials: make sure to check their work and give them a star on GitHub, if you please ❤️
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Schedule-Free Learning – A New Way to Train
* Superconvergence + LR range finder + Fast AI's Ranger21 optimizer was the goto optimizer for CNNs, and worked fabulously well, but on transformers, the learning rate range finder sadi 1e-3 was the best, whilst 1e-5 was better. However, the 1 cycle learning rate stuck. https://github.com/huggingface/transformers/issues/16013
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Gemma doesn't suck anymore – 8 bug fixes
Thanks! :) I'm pushing them into transformers, pytorch-gemma and collabing with the Gemma team to resolve all the issues :)
The RoPE fix should already be in transformers 4.38.2: https://github.com/huggingface/transformers/pull/29285
My main PR for transformers which fixes most of the issues (some still left): https://github.com/huggingface/transformers/pull/29402
- HuggingFace Transformers: Qwen2
- HuggingFace Transformers Release v4.36: Mixtral, Llava/BakLlava, SeamlessM4T v2
- HuggingFace: Support for the Mixtral Moe
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Paris-Based Startup and OpenAI Competitor Mistral AI Valued at $2B
If you want to tinker with the architecture Hugging Face has a FOSS implementation in transformers: https://github.com/huggingface/transformers/blob/main/src/tr...
If you want to reproduce the training pipeline, you couldn't do that even if you wanted to because you don't have access to thousands of A100s.
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Fail to reproduce the same evaluation metrics score during inference.
I am aware that using mixed precision reduces the stability of weight and there will be little consistency but don't expect it to be this much. I have attached the graph of evaluation metrics. If someone can give me some insight into this issue, that would be great.
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[D] What is a good way to maintain code readability and code quality while scaling up complexity in libraries like Hugging Face?
In transformers, they tried really hard to have a single function or method to deal with both self and cross attention mechanisms, masking, positional and relative encodings, interpolation etc. While it allows a user to use the same function/method for any model, it has led to severe parameter bloat. Just compare the original implementation of llama by FAIR with the implementation by HF to get an idea.
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Mixtral-7b-8expert working in Oobabooga (unquantized multi-gpu)
pip install git+https://github.com/huggingface/transformers.git@main
What are some alternatives?
alpaca-lora - Instruct-tune LLaMA on consumer hardware
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
llama - Inference code for Llama models
ggml - Tensor library for machine learning
transformer-pytorch - Transformer: PyTorch Implementation of "Attention Is All You Need"
alpaca_lora_4bit
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
llm-foundry - LLM training code for MosaicML foundation models
huggingface_hub - The official Python client for the Huggingface Hub.