EasyLM
GradCache
| EasyLM | GradCache | |
|---|---|---|
| 8 | 1 | |
| 2,247 | 310 | |
| - | - | |
| 7.7 | 4.5 | |
| 4 months ago | about 2 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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EasyLM
- Maxtext: A simple, performant and scalable Jax LLM
- How To Fine-Tune LLaMA, OpenLLaMA, And XGen, With JAX On A GPU Or A TPU
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Open-sourced LLMs are adept at mimicking ChatGPT’s style but not its factuality. There exists a substantial capabilities gap, which requires better base LM.
Title: The False Promise of Imitating Proprietary LLLs Authors: Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, Dawn Song Word Count: 3400 Average Reading Time: 18-20 minutes Source Code: https://github.com/young-geng/EasyLM Additional Links: https://huggingface.co/young-geng/koala-eval, https://huggingface.co/young-geng/koala
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Paid dev gig: develop a basic LLM PEFT finetuning utility
Check out easyLM https://github.com/young-geng/EasyLM
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OpenLLaMA Releases 7B/3B Checkpoints with 700B/600B Tokens
We release the weights in two formats: an EasyLM format to be use with our EasyLM framework, and a PyTorch format to be used with the Hugging Face transformers library.
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OpenLLaMA: An Open Reproduction of LLaMA
I am quite new to this, I would like to get it running. Would the process roughly be:
1. Get a machine with decent GPU, probably rent cloud GPU.
2. On that machine download the weights/model/vocab files from https://huggingface.co/openlm-research/open_llama_7b_preview...
3. Install Anaconda. Clone https://github.com/young-geng/EasyLM/.
4. Install EasyLM:
conda env create -f scripts/gpu_environment.yml - Koala: A Dialogue Model for Academic Research [Finetuned Llama-13B on a dataset generated by ChatGPT]
GradCache
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[D] How to handle absurd batch sizes in SimCLR / OpenAI's CLIP?
Maybe you should try this thing: https://github.com/luyug/GradCache
What are some alternatives?
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
h-former - H-Former is a VAE for generating in-between fonts (or combining fonts). Its encoder uses a Point net and transformer to compute a code vector of glyph. Its decoder is composed of multiple independent decoders which act on a code vector to reconstruct a point cloud representing a glpyh.
camel - 🐫 CAMEL: Communicative Agents for “Mind” Exploration of Large Language Model Society (NeruIPS'2023) https://www.camel-ai.org
jax-models - Unofficial JAX implementations of deep learning research papers
Open-Llama - The complete training code of the open-source high-performance Llama model, including the full process from pre-training to RLHF.
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
brev-cli - Connect your laptop to cloud computers. Follow to stay updated about our product
long-range-arena - Long Range Arena for Benchmarking Efficient Transformers
RWKV-LM - RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
fortuna - A Library for Uncertainty Quantification.
modal-examples - Examples of programs built using Modal
flaxmodels - Pretrained deep learning models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet, etc.