LLM-As-Chatbot
peft
| LLM-As-Chatbot | peft | |
|---|---|---|
| 3 | 26 | |
| 3,242 | 13,962 | |
| - | 4.7% | |
| 9.0 | 9.7 | |
| 6 months ago | 6 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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LLM-As-Chatbot
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OpenAI's GPT-4 Red Teamer Nathan Labenz: the GPT-4 base model recommends assassinating humans, naming specific targets
The first one is from https://github.com/deep-diver/Alpaca-LoRA-Serve
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Show HN: ChatLLaMA – A ChatGPT style chatbot for Facebook's LLaMA
this is useless because it doesn't handle context:
Q: Name five genres of music.
A: Jazz, country, hip-hop, blues, classical.
Q: Name a famous artist from the third genre.
A: Salvador DalÃ.
Whereas this one actually supports context: https://github.com/deep-diver/Alpaca-LoRA-Serve
- Show HN: Finetune LLaMA-7B on commodity GPUs using your own text
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?
alpaca-lora - Instruct-tune LLaMA on consumer hardware
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
simple-llm-finetuner - Simple UI for LLM Model Finetuning
LoRA - Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
hh-rlhf - Human preference data for "Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback"
alpaca-7b-truss
dalai - The simplest way to run LLaMA on your local machine
stanford_alpaca - Code and documentation to train Stanford's Alpaca models, and generate the data.
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM (Android/Linux/Windows/Mac)
minLoRA - minLoRA: a minimal PyTorch library that allows you to apply LoRA to any PyTorch model.