prompt-tuning
EasyLM
| prompt-tuning | EasyLM | |
|---|---|---|
| 1 | 8 | |
| 623 | 2,241 | |
| 1.1% | - | |
| 5.7 | 7.7 | |
| 5 months ago | 4 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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prompt-tuning
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Need advice on learning to apply techniques from research papers.
For instance, right now I want to try applying the prompt-tuning technique on simple classification tasks. The github page looks as follows: https://github.com/google-research/prompt-tuning.
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]
What are some alternatives?
flaxmodels - Pretrained deep learning models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet, etc.
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
camel - 🐫 CAMEL: Communicative Agents for “Mind” Exploration of Large Language Model Society (NeruIPS'2023) https://www.camel-ai.org
Open-Llama - The complete training code of the open-source high-performance Llama model, including the full process from pre-training to RLHF.
brev-cli - Connect your laptop to cloud computers. Follow to stay updated about our product
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.
modal-examples - Examples of programs built using Modal
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
h2o-llmstudio - H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs. Documentation: https://h2oai.github.io/h2o-llmstudio/
GradCache - Run Effective Large Batch Contrastive Learning Beyond GPU/TPU Memory Constraint
Macaw-LLM - Macaw-LLM: Multi-Modal Language Modeling with Image, Video, Audio, and Text Integration