llm-foundry
LocalAI
llm-foundry | LocalAI | |
---|---|---|
37 | 83 | |
3,730 | 20,076 | |
4.0% | 9.3% | |
9.7 | 9.9 | |
4 days ago | 7 days ago | |
Python | C++ | |
Apache License 2.0 | MIT License |
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llm-foundry
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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
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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
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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.
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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.
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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.
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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
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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.
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Best model for commercial use?
mosaicml/llm-foundry: LLM training code for MosaicML foundation models (github.com)
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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.
LocalAI
- LocalAI: Self-hosted OpenAI alternative reaches 2.14.0
- Drop-In Replacement for ChatGPT API
- Voxos.ai – An Open-Source Desktop Voice Assistant
- Ask HN: Set Up Local LLM
- FLaNK Stack Weekly 11 Dec 2023
- Is there any open source app to load a model and expose API like OpenAI?
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What do you use to run your models?
If you're running this as a server, I would recommend LocalAI https://github.com/mudler/LocalAI
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OpenAI Switch Kit: Swap OpenAI with any open-source model
LocalAI can do that: https://github.com/mudler/LocalAI
https://localai.io/features/openai-functions/
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"ChatGPT romanesc"
De inspirație, LocalAI, un replacement la OpenAI. E deja hot pe GitHub.
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Local LLM's to run on old iMac / Hardware
Your hardware should be fine for inferencing, as long as you don't bother trying to get the GPU working.
My $0.02 would be to try getting LocalAI running on your machine with OpenCL/CLBlas acceleration for your CPU. If you're running other things, you could limit the inferencing process to 2 or 3 threads. That should get it working; I've been able to inference even 13b models on cheap Rockchip SOCs. Your CPU should be fine, even if it's a little outdated.
LocalAI: https://github.com/mudler/LocalAI
Some decent models to start with:
TinyLlama (extremely small/fast): https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v0.3-GGU...
Dolphin Mistral (larger size, better responses: https://huggingface.co/TheBloke/dolphin-2.1-mistral-7B-GGUF
What are some alternatives?
qlora - QLoRA: Efficient Finetuning of Quantized LLMs
gpt4all - gpt4all: run open-source LLMs anywhere
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.
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language 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
llama-cpp-python - Python bindings for llama.cpp
LMFlow - An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
prompt-engineering - ChatGPT Prompt Engineering for Developers - deeplearning.ai
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
llm-numbers - Numbers every LLM developer should know
FastChat - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.