MtgMatrix
llm-foundry
MtgMatrix | llm-foundry | |
---|---|---|
1 | 37 | |
3 | 3,753 | |
- | 4.6% | |
6.9 | 9.7 | |
4 months ago | 7 days ago | |
Python | Python | |
- | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
MtgMatrix
-
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!!
llm-foundry
-
Fine Tuning Mistral 7B on Magic the Gathering Draft
Related comment from gwern: https://news.ycombinator.com/item?id=38438859
Also - why qlora rather than a full finetune? Using LambdaLabs, It'd cost roughly the same as your quote. Cheaper I think if you're willing to gamble with fp8: https://github.com/mosaicml/llm-foundry/tree/main/scripts/tr.... And fewer hyperparameters to tune as well
-
Consortium launched to build the largest open LLM
Traditionally, training runs can "explode" and fail, but there are methods to incrementally back them up and resume when that happens, see https://www.mosaicml.com/blog/mpt-7b
-
Applying All Recent Innovations To Train a Code Model
MosaicML released the MPT-7B model, which has a context of 60k tokens, thanks to the ALiBi position encoding.
-
Fine Tuning Language Models
Most AI runners just ignore licensing and run LLaMA finetunes.
But if you want to avoid the non commercial LLaMA license, you have 3 good options for a base model.
- OpenLlama 13B
- MPT 30B
- Falcon 40B
Of these, Falcon 40B is very difficult to run (slow in 4 bit, basically requires a professional GPU, no good cpu offloading yet).
OpenLLaMA 13B only supports a context size of 2048 as of today... But that could change soon.
So you probably want MPT instruct 30B, specifically this one:
https://huggingface.co/TheBloke/mpt-30B-instruct-GGML
As the page says, you can try it out on a decent PC of your own with the OpenCL build of KoboldCPP. Change it to "instruct" mode, use the template on the page, offload as many layers as you can to your PC's dGPU, and run it in instruct mode. It may already work for your summarization needs.
If not, you can finetune it with MPT's code and summarization d
https://github.com/mosaicml/llm-foundry
Or train OpenLLaMA 13B with SuperHOT + summarization data using QLORA.
-
Finetune MPT-30B using QLORA
BTW. they finally merged a MPT patch to work with lora: https://github.com/mosaicml/llm-foundry/issues/304
- [N] Meet MPT-30B: A Fully OpenSouce LLM that Outperforms GPT-3 - Dr. Mandar Karhade, MD. PhD.
-
MPT-30B QLoRA on 24 GB VRAM
Did you run into this error while using qlora on MPT30b?: https://github.com/mosaicml/llm-foundry/issues/413
-
MosaicML Agrees to Join Databricks to Power Generative AI for All
Yes? Their github is under Apache, their base model is under apache, the training data is not theirs, and they provide scripts how to convert it for the pretrain step. They have scripts for pretraining and finetuning as well. Basically for everything.
-
Best model for commercial use?
mosaicml/llm-foundry: LLM training code for MosaicML foundation models (github.com)
-
MosaicML launches MPT-30B: A new open-source model that outperforms GPT-3
MosaicML, a company that provides a platform for training and deploying large language models (LLMs), has recently released its second open-source foundation model called MPT-30B. The model is part of the MosaicML Foundation Series and comes after the smaller MPT-7B model that was launched in May 2023.
What are some alternatives?
peft - 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
qlora - QLoRA: Efficient Finetuning of Quantized LLMs
basaran - Basaran is an open-source alternative to the OpenAI text completion API. It provides a compatible streaming API for your Hugging Face Transformers-based text generation models.
RasaGPT - 💬 RasaGPT is the first headless LLM chatbot platform built on top of Rasa and Langchain. Built w/ Rasa, FastAPI, Langchain, LlamaIndex, SQLModel, pgvector, ngrok, telegram
LMFlow - An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.
prompt-engineering - ChatGPT Prompt Engineering for Developers - deeplearning.ai
llm-numbers - Numbers every LLM developer should know
lion-pytorch - 🦁 Lion, new optimizer discovered by Google Brain using genetic algorithms that is purportedly better than Adam(w), in Pytorch
chatbot-ui - AI chat for every model.
laion.ai
chat-ui - Open source codebase powering the HuggingChat app
llm-jeopardy - Automated prompting and scoring framework to evaluate LLMs using updated human knowledge prompts