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Datablations Alternatives
Similar projects and alternatives to datablations
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transformers
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
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guidance
Discontinued A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance] (by microsoft)
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SuperAGI
<⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.
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tree-of-thoughts
Plug in and Play Implementation of Tree of Thoughts: Deliberate Problem Solving with Large Language Models that Elevates Model Reasoning by atleast 70%
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TinyLlama
Discontinued The TinyLlama project is an open endeavor to pretrain a 1.1B Llama model on 3 trillion tokens.
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chain-of-thought-hub
Benchmarking large language models' complex reasoning ability with chain-of-thought prompting
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TheVault
[EMNLP 2023] The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better datablations alternative or higher similarity.
datablations discussion
datablations reviews and mentions
Posts with mentions or reviews of datablations.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-09-05.
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OpenAI funded FrontierMath Benchmarks and had access to the set
I believe the paper being referenced is “Scaling Data-Constrained Language Models” (https://arxiv.org/abs/2305.16264).
For correctness, you can use a solver to verify generated data.
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IsoFLOP curves of large language models are flat
There is some good published research about doing multiple passes over the training data, and how quickly learning saturates. The TL:DR is that diminishing returns kicks in after about 4 epochs.
https://arxiv.org/abs/2305.16264
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Gemini is only 1x Chinchilla, so it undertrained for production
1x chinchilla means it's not really undertrained but that more could be squeezed without excessive difficulty https://arxiv.org/abs/2305.16264
- Can LLMs learn from a single example?
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Chinchilla’s Death
You might want to give a read to "Scaling Data-Constrained Language Models" [1]. They basically generalized the Chinchilla scaling law by investigating behavior on multi-epoch runs.
[1] https://arxiv.org/abs/2305.16264
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RWKV Pile+ seems to be training on far more tokens than any LLM ever has
I would imagine that there is a lot of overlap, yeah. That said, training on repeated data does seem to be effective at this level.
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(2/2) May 2023
Scaling Data-Constrained Language Models (https://arxiv.org/abs/2305.16264)
- How to Keep Scaling Large Language Models when Data Runs Out? A New AI Research Trains 400 Models with up to 9B Parameters and 900B Tokens to Create an Extension of Chinchilla Scaling Laws for Repeated Data
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Stats
Basic datablations repo stats
8
342
3.3
about 1 year ago
huggingface/datablations is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of datablations is Jupyter Notebook.
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