axolotl
FLiPStackWeekly
| axolotl | FLiPStackWeekly | |
|---|---|---|
| 29 | 80 | |
| 5,811 | 14 | |
| 9.3% | - | |
| 9.8 | 9.9 | |
| 6 days ago | 5 days ago | |
| 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.
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!
FLiPStackWeekly
What are some alternatives?
signal-cli - signal-cli provides an unofficial commandline, JSON-RPC and dbus interface for the Signal messenger.
gorilla-cli - LLMs for your CLI
gpt-llm-trainer
awk-raycaster - Pseudo-3D shooter written completely in gawk using raycasting technique
LoRA - Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
litellm - Call all LLM APIs using the OpenAI format. Use Bedrock, Azure, OpenAI, Cohere, Anthropic, Ollama, Sagemaker, HuggingFace, Replicate (100+ LLMs)
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
modelscope - ModelScope: bring the notion of Model-as-a-Service to life.
LMFlow - An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.
create-nifi-pulsar-flink-apps - How to create a real-time scalable streaming app using Apache NiFi, Apache Pulsar and Apache Flink SQL
koboldcpp - A simple one-file way to run various GGML and GGUF models with KoboldAI's UI
FLiP-PulsarSummit2022Asia - FLiP-PulsarSummit2022Asia: Pulsar Summit Asia 2022