Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model
Paper
•
2211.02001
•
Published
Rainforest Connection
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sasha
authored
3
papers
23 days ago
sasha
authored
a
paper
7 months ago
lhoestq
authored
a
paper
11 months ago
sasha
updated
a
dataset
11 months ago
Post
2786
Made a HF Dataset editor a la gg sheets here:
lhoestq/dataset-spreadsheets
With Dataset Spreadsheets:
✏️ Edit datasets in the UI
🔗 Share link with collaborators
🐍 Use locally in DuckDB or Python
Available for the 100,000+ parquet datasets on HF :)
With Dataset Spreadsheets:
✏️ Edit datasets in the UI
🔗 Share link with collaborators
🐍 Use locally in DuckDB or Python
Available for the 100,000+ parquet datasets on HF :)
antonyharfield
updated
a
Space
11 months ago
Post
4199
Hey ! I'm working on a 100% synthetic Dataset Hub here (you can search for any kind of datasets an the app invents them). The link is here:
infinite-dataset-hub/infinite-dataset-hub
Question for the Community:
Which models should I use to generate images and audio samples for those datasets ? 🤗
Question for the Community:
Which models should I use to generate images and audio samples for those datasets ? 🤗
-
- 4 replies
Post
3087
✨ Easy Synthetic Dataset File Generation using LLM DataGen ! Link: https://huggingface.co/spaces/lhoestq/LLM_DataGen
features + how it works:
✍️ Generate the dataset content you want just by entering a file name
💡 Optionally specify the column names you need
💨 The dataset is streamed and generated on-the-fly in JSON Lines format
✅ Generation is constrained to always output valid JSON
How does this work ?
1/ Enter a file name
2/ The model generates column names for such a file. Using structured generation, it can generate 2 to 5 column names using lower case characters and underscores. I use a prompt that asks to generate column names for a realistic dataset and low temperature.
3/ The columns are used to update the Finite State Machine for the dataset content structured generation, so that it is used to generate JSON objects using those columns
4/ The model generates JSON objects using structured generation again, using the updated Finite State Machine. I use a prompt that asks for realistic data and a temperature of 1.
> Why update a Finite State Machine instead of re-creating one ?
Creating one can take up to 30sec, while updating one takes 0.1s (though it requires to manipulate a graph which is not easy to implement)
> Batched generation is faster, why not use it ?
Generate in batches is faster but tends to generate duplicates for this demo.
Further work can be to provide different prompts (one per sequence in the batch) to end up with a different distribution of sequences in each batch. Or implement a custom sampler that would forbid generating the same data in sequences of the same batch.
> How does structured generation work ?
I used the
Let me know what you think ! And feel free to duplicate/modify it to try other models/prompts or sampling methods :)
features + how it works:
✍️ Generate the dataset content you want just by entering a file name
💡 Optionally specify the column names you need
💨 The dataset is streamed and generated on-the-fly in JSON Lines format
✅ Generation is constrained to always output valid JSON
How does this work ?
1/ Enter a file name
2/ The model generates column names for such a file. Using structured generation, it can generate 2 to 5 column names using lower case characters and underscores. I use a prompt that asks to generate column names for a realistic dataset and low temperature.
3/ The columns are used to update the Finite State Machine for the dataset content structured generation, so that it is used to generate JSON objects using those columns
4/ The model generates JSON objects using structured generation again, using the updated Finite State Machine. I use a prompt that asks for realistic data and a temperature of 1.
> Why update a Finite State Machine instead of re-creating one ?
Creating one can take up to 30sec, while updating one takes 0.1s (though it requires to manipulate a graph which is not easy to implement)
> Batched generation is faster, why not use it ?
Generate in batches is faster but tends to generate duplicates for this demo.
Further work can be to provide different prompts (one per sequence in the batch) to end up with a different distribution of sequences in each batch. Or implement a custom sampler that would forbid generating the same data in sequences of the same batch.
> How does structured generation work ?
I used the
outlines library with transformers to to define a JSON schema that the generation has to follow. It uses a Finite State Machine with token_id as transitions.Let me know what you think ! And feel free to duplicate/modify it to try other models/prompts or sampling methods :)
lhoestq
authored
a
paper
almost 2 years ago
sasha
authored
2
papers
almost 2 years ago
lhoestq
authored
a
paper
almost 2 years ago
lhoestq
authored
a
paper
over 2 years ago
sasha
authored
5
papers
over 2 years ago
Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness
Paper
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2302.10893
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Published
•
6
Evaluating the Social Impact of Generative AI Systems in Systems and Society
Paper
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2306.05949
•
Published
•
9
Quantifying the Carbon Emissions of Machine Learning
Paper
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1910.09700
•
Published
•
20
ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods
Paper
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2110.02871
•
Published
Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurements
Paper
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2210.01970
•
Published
•
13