llama-mps
transformers
llama-mps | transformers | |
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4 | 181 | |
83 | 127,531 | |
- | 2.0% | |
3.8 | 10.0 | |
10 months ago | about 14 hours ago | |
Python | Python | |
GNU General Public License v3.0 only | Apache License 2.0 |
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llama-mps
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llama.cpp now officially supports GPU acceleration.
There are currently at least 3 ways to run llama on m1 with GPU acceleration. - mlc-llm (pre-built, only 1 model has been ported) - tinygrad (very memory efficient, not that easy to integrate into other projects) - llama-mps (original llama codebase + llama adapter support)
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LLaMA-7B in Pure C++ with full Apple Silicon support
There is also a gpu-acelerated fork of the original repo
https://github.com/remixer-dec/llama-mps
- Llama-CPU: Fork of Facebooks LLaMa model to run on CPU
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[D] Tutorial: Run LLaMA on 8gb vram on windows (thanks to bitsandbytes 8bit quantization)
I tried to port the llama-cpu version to a gpu-accelerated mps version for macs, it runs, but the outputs are not as good as expected and it often gives "-1" tokens. Any help and contributions on fixing it are welcome!
transformers
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How to count tokens in frontend for Popular LLM Models: GPT, Claude, and Llama
Thanks to transformers.js, we can run the tokenizer and model locally in the browser. Transformers.js is designed to be functionally equivalent to Hugging Face's transformers python library, meaning you can run the same pretrained models using a very similar API.
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Reading list to join AI field from Hugging Face cofounder
Not sure what you are implying. Thomas Wolf has the second highest number of commits on HuggingFace/transformers. He is clearly competent & deeply technical
https://github.com/huggingface/transformers/
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Llama3.np: pure NumPy implementation of Llama3
Sure, knowing the basics of LLM math is necessary. But it's also _enough_ to know this math to fully grasp the code. There are only 4 concepts - attention, feed-forward net, RMS-normalization and rotary embeddings - organized into a clear structure.
Now compare it to the Hugginface implementation [1]. In addition to the aforementioned concepts, you need to understand the hierarchy of `PreTrainedModel`s, 3 types of attention, 3 types of rotary embeddings, HF's definition of attention mask (which is not the same as mask you read about in transformer tutorials), several types of cache class, dozens of flags to control things like output format or serialization, etc.
It's not that Meta's implementation is good and HF's implementation is bad - they pursue different goals in their own optimal way. But if you just want to learn how the model works, Meta's code base is great.
[1]: https://github.com/huggingface/transformers/blob/main/src/tr...
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XLSTM: Extended Long Short-Term Memory
Fascinating work, very promising.
Can you summarise how the model in your paper differs from this one ?
https://github.com/huggingface/transformers/issues/27011
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AI enthusiasm #9 - A multilingual chatbot📣🈸
transformers is a package by Hugging Face, that helps you interact with models on HF Hub (GitHub)
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Maxtext: A simple, performant and scalable Jax LLM
Is t5x an encoder/decoder architecture?
Some more general options.
The Flax ecosystem
https://github.com/google/flax?tab=readme-ov-file
or dm-haiku
https://github.com/google-deepmind/dm-haiku
were some of the best developed communities in the Jax AI field
Perhaps the “trax” repo? https://github.com/google/trax
Some HF examples https://github.com/huggingface/transformers/tree/main/exampl...
Sadly it seems much of the work is proprietary these days, but one example could be Grok-1, if you customize the details. https://github.com/xai-org/grok-1/blob/main/run.py
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Lossless Acceleration of LLM via Adaptive N-Gram Parallel Decoding
The HuggingFace transformers library already has support for a similar method called prompt lookup decoding that uses the existing context to generate an ngram model: https://github.com/huggingface/transformers/issues/27722
I don't think it would be that hard to switch it out for a pretrained ngram model.
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AI enthusiasm #6 - Finetune any LLM you want💡
Most of this tutorial is based on Hugging Face course about Transformers and on Niels Rogge's Transformers tutorials: make sure to check their work and give them a star on GitHub, if you please ❤️
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Schedule-Free Learning – A New Way to Train
* Superconvergence + LR range finder + Fast AI's Ranger21 optimizer was the goto optimizer for CNNs, and worked fabulously well, but on transformers, the learning rate range finder sadi 1e-3 was the best, whilst 1e-5 was better. However, the 1 cycle learning rate stuck. https://github.com/huggingface/transformers/issues/16013
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Gemma doesn't suck anymore – 8 bug fixes
Thanks! :) I'm pushing them into transformers, pytorch-gemma and collabing with the Gemma team to resolve all the issues :)
The RoPE fix should already be in transformers 4.38.2: https://github.com/huggingface/transformers/pull/29285
My main PR for transformers which fixes most of the issues (some still left): https://github.com/huggingface/transformers/pull/29402
What are some alternatives?
llama - Inference code for Llama models
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
awesome-ml - Curated list of useful LLM / Analytics / Datascience resources
llama - Inference code for LLaMA models
transformer-pytorch - Transformer: PyTorch Implementation of "Attention Is All You Need"
LLaMA_MPS - Run LLaMA inference on Apple Silicon GPUs.
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️
huggingface_hub - The official Python client for the Huggingface Hub.