lapdev
axolotl
| lapdev | axolotl | |
|---|---|---|
| 3 | 29 | |
| 1,337 | 5,899 | |
| 13.2% | 10.7% | |
| 5.9 | 9.8 | |
| 24 days ago | 5 days ago | |
| Rust | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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lapdev
axolotl
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Ask HN: Most efficient way to fine-tune an LLM in 2024?
The approach I see used is axolotl with QLoRA using cloud GPUs which can be quite cheap.
https://github.com/OpenAccess-AI-Collective/axolotl
- FLaNK AI - 01 April 2024
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LoRA from Scratch implementation for LLM finetuning
https://github.com/OpenAccess-AI-Collective/axolotl
- Optimized Triton Kernels for full fine tunes
- Axolotl
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Let’s Collaborate to Build a High-Quality, Open-Source Dataset for LLMs!
One option is to look at what Axolotl uses. They have a list of different dataset formats that they support. They're mostly in JSON with specific field names, so you could start putting a dataset together with a text editor or a JSON editor.
- Axolotl: Streamline fine-tuning of AI models
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Dataset Creation Tools?
You can save that overall set into a json file and load it up as training data in whatever you're using. I'm using axolotl for it at the moment. Though a GUI based option is probably best for the first couple of tries until you get a feel for the options.
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Progress on Reproducing Phi-1/1.5
Looking forward to the results! If it turns out the dataset is reproducible, then it might be a good candidate for ReLora training on axolotl!
What are some alternatives?
spec - Development Containers: Use a container as a full-featured development environment.
signal-cli - signal-cli provides an unofficial commandline, JSON-RPC and dbus interface for the Signal messenger.
devpod - Codespaces but open-source, client-only and unopinionated: Works with any IDE and lets you use any cloud, kubernetes or just localhost docker.
gpt-llm-trainer
coder - Coder provisions software development environments via Terraform on Linux, macOS, Windows, X86, ARM, and of course, Kubernetes.
LoRA - Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
cli - A reference implementation for the specification that can create and configure a dev container from a devcontainer.json.
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
synthetic-data-generator - 🦄 Use GPT to generate and label data
LMFlow - An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.
FLaNK-python-processors - Many processors
koboldcpp - A simple one-file way to run various GGML and GGUF models with KoboldAI's UI