WebChatRWKVstic
peft
WebChatRWKVstic | peft | |
---|---|---|
1 | 26 | |
93 | 14,083 | |
- | 4.7% | |
10.0 | 9.7 | |
about 1 year ago | about 18 hours ago | |
Python | Python | |
- | Apache License 2.0 |
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WebChatRWKVstic
peft
- LoftQ: LoRA-fine-tuning-aware Quantization
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Fine Tuning Mistral 7B on Magic the Gathering Draft
There is not a lot of great content out there making this clear, but basically all that matters for basic fine tuning is how much VRAM you have -- since the 3090 / 4090 have 24GB VRAM they're both pretty decent fine tuning chips. I think you could probably fine-tune a model up to ~13B parameters on one of them with PEFT (https://github.com/huggingface/peft)
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Whisper prompt tuning
Hi everyone. Recently I've been looking into the PEFT library (https://github.com/huggingface/peft) and I was wondering if it would be possible to do prompt tuning with OpenAI's Whisper model. They have an example notebook for tuning Whisper with LoRA (https://colab.research.google.com/drive/1vhF8yueFqha3Y3CpTHN6q9EVcII9EYzs?usp=sharing) but I'm not sure how to go about changing it to use prompt tuning instead.
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Code Llama - The Hugging Face Edition
In the coming days, we'll work on sharing scripts to train models, optimizations for on-device inference, even nicer demos (and for more powerful models), and more. Feel free to like our GitHub repos (transformers, peft, accelerate). Enjoy!
- PEFT 0.5 supports fine-tuning GPTQ models
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Exploding loss when trying to train OpenOrca-Platypus2-13B
image
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[D] Is there a difference between p-tuning and prefix tuning ?
I discussed part of this here: https://github.com/huggingface/peft/issues/123
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How does using QLoRAs when running Llama on CPU work?
It seems like the merge_and_unload function in this PEFT script might be what they are referring to: https://github.com/huggingface/peft/blob/main/src/peft/tuners/lora.py
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How to merge the two weights into a single weight?
To obtain the original llama model, one may refer to this doc. To merge a lora model with a base model, one may refer to PEFT or use the merge script provided by LMFlow.
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[D] [LoRA + weight merge every N step] for pre-training?
you could use a callback, like show here, https://github.com/huggingface/peft/issues/286 and call code to merge them here.
What are some alternatives?
simple-llm-finetuner - Simple UI for LLM Model Finetuning
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
FlexGen - Running large language models on a single GPU for throughput-oriented scenarios.
LoRA - Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
whisper.cpp - Port of OpenAI's Whisper model in C/C++
alpaca-lora - Instruct-tune LLaMA on consumer hardware
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
dalai - The simplest way to run LLaMA on your local machine
minimal-llama
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
OpenChatKit
minLoRA - minLoRA: a minimal PyTorch library that allows you to apply LoRA to any PyTorch model.