axolotl
tiktoken
| axolotl | tiktoken | |
|---|---|---|
| 29 | 30 | |
| 5,811 | 9,884 | |
| 9.3% | 4.6% | |
| 9.8 | 6.7 | |
| 5 days ago | 21 days ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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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!
tiktoken
- FLaNK AI - 01 April 2024
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GPT-3.5 crashes when it thinks about useRalativeImagePath too much
Their tokenizer is open source: https://github.com/openai/tiktoken
Data files that contain vocabulary are listed here: https://github.com/openai/tiktoken/blob/9e79899bc248d5313c7d...
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How fast is JS tiktoken?
OpenAI's refference tokeniser - https://github.com/openai/tiktoken
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Anthropic announces Claude 2.1 – 200k context, less refusals
ChatGPT presumably adds them as special tokens to the cl100k_base tokenizer, as they demo in the tiktoken documentation: https://github.com/openai/tiktoken#extending-tiktoken
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What is the best way to get an approximate number of tokens for a piece of text?
I want to measure the approximate number of tokens in a piece of text to understand if I will need to modify it before passing it into the context of an OpenAI API call. Tiktoken can do this, but I'm not sure if it's overkill to use that library just for this simple task. I don't need to actually tokenize the text, I just need an approximate count (e.g. within like 1% of the text's actual token length for text that represents the visible text on a webpage).
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Show HN: LLaMA tokenizer that runs in browser
https://platform.openai.com/tokenizer or the official python library tiktoken https://github.com/openai/tiktoken or this JS port of tiktoken https://github.com/dqbd/tiktoken
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Made a GPT-3.5-Turbo and GPT-4 Tokenizer
It's built on top of the tiktoken library and is basically just a lambda function in the backend.
- AiPrice - an API for calculating OpenAI tokens and pricing
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Anyone able to explain what happened here?
"All" is a single token in OpenAI's tiktoken Tokenizer, unrelated to the token for capital "A". Even lowercase "all" is a distinct token from "All" or "ALL."
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Which lib is the tokenizer page using to calculate the tokens?
check tiktoken
What are some alternatives?
signal-cli - signal-cli provides an unofficial commandline, JSON-RPC and dbus interface for the Signal messenger.
tokenizer - Pure Go implementation of OpenAI's tiktoken tokenizer
gpt-llm-trainer
daath-ai-parser - Daath AI Parser is an open-source application that uses OpenAI to parse visible text of HTML elements.
LoRA - Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
skypilot - SkyPilot: Run LLMs, AI, and Batch jobs on any cloud. Get maximum savings, highest GPU availability, and managed execution—all with a simple interface.
LMFlow - An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.
bricks - Open-source natural language enrichments at your fingertips.
koboldcpp - A simple one-file way to run various GGML and GGUF models with KoboldAI's UI
terminal-copilot - A smart terminal assistant that helps you find the right command.