llm-foundry
mlc-llm
llm-foundry | mlc-llm | |
---|---|---|
37 | 89 | |
3,730 | 17,150 | |
4.0% | 4.3% | |
9.7 | 9.9 | |
4 days ago | 3 days ago | |
Python | Python | |
Apache License 2.0 | 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.
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.
mlc-llm
- FLaNK 04 March 2024
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Ai on a android phone?
This one uses gpu, it doesn't support Mistral yet: https://github.com/mlc-ai/mlc-llm
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MLC vs llama.cpp
I have tried running mistral 7B with MLC on my m1 metal. And it kept crushing (git issue with description). Memory inefficiency problems.
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[Project] Scaling LLama2 70B with Multi NVIDIA and AMD GPUs under 3k budget
Project: https://github.com/mlc-ai/mlc-llm
- Scaling LLama2-70B with Multi Nvidia/AMD GPU
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AMD May Get Across the CUDA Moat
For LLM inference, a shoutout to MLC LLM, which runs LLM models on basically any API that's widely available: https://github.com/mlc-ai/mlc-llm
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ROCm Is AMD's #1 Priority, Executive Says
One of your problems might be that gfx1032 is not supported by AMD's ROCm packages, which has a laughably short list of supported hardware: https://rocm.docs.amd.com/en/latest/release/gpu_os_support.h...
The normal workaround is to assign the closest architecture, eg gfx1030, so `HSA_OVERRIDE_GFX_VERSION=10.3.0` might help
Also, it looks like some of your tested projects are OpenCL? For me, I do something like: `yay -S rocm-hip-sdk rocm-ml-sdk rocm-opencl-sdk` to cover all the bases.
My recent interest has been LLMs and this is my general step by step for those (llama.cpp, exllama) for those interested: https://llm-tracker.info/books/howto-guides/page/amd-gpus
I didn't port the docs back in, but also here's a step-by-step w/ my adventures getting TVM/MLC working w/ an APU: https://github.com/mlc-ai/mlc-llm/issues/787
From my experience, ROCm is improving, but there's a good reason that Nvidia has 90% market share even at big price premiums.
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Show HN: Ollama for Linux – Run LLMs on Linux with GPU Acceleration
Maybe they're talking about https://github.com/mlc-ai/mlc-llm which is used for web-llm (https://github.com/mlc-ai/web-llm)? Seems to be using TVM.
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Show HN: Fine-tune your own Llama 2 to replace GPT-3.5/4
you already have TVM for the cross platform stuff
see https://tvm.apache.org/docs/how_to/deploy/android.html
or https://octoml.ai/blog/using-swift-and-apache-tvm-to-develop...
or https://github.com/mlc-ai/mlc-llm
- Ask HN: Are you training and running custom LLMs and how are you doing it?
What are some alternatives?
qlora - QLoRA: Efficient Finetuning of Quantized LLMs
llama.cpp - LLM inference in C/C++
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.
ggml - Tensor library for machine learning
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
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
LMFlow - An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
prompt-engineering - ChatGPT Prompt Engineering for Developers - deeplearning.ai
llama-cpp-python - Python bindings for llama.cpp
llm-numbers - Numbers every LLM developer should know
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.