MtgMatrix
peft
| MtgMatrix | peft | |
|---|---|---|
| 1 | 26 | |
| 3 | 14,341 | |
| - | 7.2% | |
| 6.9 | 9.7 | |
| 4 months ago | 2 days ago | |
| Python | Python | |
| - | Apache License 2.0 |
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MtgMatrix
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Fine Tuning Mistral 7B on Magic the Gathering Draft
Excellent, thank you for posting this!
I was actually just looking into fine-tuning an LLM for Magic: The Gathering this week -- I've been building a small card-similarity browser using semantic embeddings of cards to find functionally or flavorfully similar cards.
I've just been using InstructorXL, but either Instructor doesn't have enough innate knowledge of the game, or else I need to work on better prompts, but so far I've tried 9 different prompts, and none of them seem to perform very well for generating embeddings:
https://github.com/HanClinto/MtgMatrix/blob/main/data/create...
So my next step was to try and download a dataset of similar cards (I have some ideas on this), and I was trying to see if I could use this to do triplet-loss training of a large embedding model or something.
Aaaaand, that's as far as I've gotten. I haven't actually figured out _how_ to hook all of that up, but your post is extremely inspirational for me. Thank you for posting this!!
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?
llm-foundry - LLM training code for Databricks foundation models
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
LoRA - Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
alpaca-lora - Instruct-tune LLaMA on consumer hardware
dalai - The simplest way to run LLaMA on your local machine
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
minLoRA - minLoRA: a minimal PyTorch library that allows you to apply LoRA to any PyTorch model.
lamini
simple-llm-finetuner - Simple UI for LLM Model Finetuning
alpaca_lora_4bit
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
SwapCudaVersionWindows - How to swap/switch CUDA versions on Windows