HuggingFaceTB/SmolVLM-Instruct
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Francisco Javier Arceo
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Introducing smolagents: simple agents that write actions in code.
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๐๏ธ Today I'm introducing a method to train static embedding models that run 100x to 400x faster on CPU than common embedding models, while retaining 85%+ of the quality! Including 2 fully open models: training scripts, datasets, metrics.
We apply our recipe to train 2 Static Embedding models that we release today! We release:
2๏ธโฃ an English Retrieval model and a general-purpose Multilingual similarity model (e.g. classification, clustering, etc.), both Apache 2.0
๐ง my modern training strategy: ideation -> dataset choice -> implementation -> evaluation
๐ my training scripts, using the Sentence Transformers library
๐ my Weights & Biases reports with losses & metrics
๐ my list of 30 training and 13 evaluation datasets
The 2 Static Embedding models have the following properties:
๐๏ธ Extremely fast, e.g. 107500 sentences per second on a consumer CPU, compared to 270 for 'all-mpnet-base-v2' and 56 for 'gte-large-en-v1.5'
0๏ธโฃ Zero active parameters: No Transformer blocks, no attention, not even a matrix multiplication. Super speed!
๐ No maximum sequence length! Embed texts at any length (note: longer texts may embed worse)
๐ Linear instead of exponential complexity: 2x longer text takes 2x longer, instead of 2.5x or more.
๐ช Matryoshka support: allow you to truncate embeddings with minimal performance loss (e.g. 4x smaller with a 0.56% perf. decrease for English Similarity tasks)
Check out the full blogpost if you'd like to 1) use these lightning-fast models or 2) learn how to train them with consumer-level hardware: https://huggingface.co/blog/static-embeddings
The blogpost contains a lengthy list of possible advancements; I'm very confident that our 2 models are only the tip of the iceberg, and we may be able to get even better performance.
Alternatively, check out the models:
* sentence-transformers/static-retrieval-mrl-en-v1
* sentence-transformers/static-similarity-mrl-multilingual-v1
We apply our recipe to train 2 Static Embedding models that we release today! We release:
2๏ธโฃ an English Retrieval model and a general-purpose Multilingual similarity model (e.g. classification, clustering, etc.), both Apache 2.0
๐ง my modern training strategy: ideation -> dataset choice -> implementation -> evaluation
๐ my training scripts, using the Sentence Transformers library
๐ my Weights & Biases reports with losses & metrics
๐ my list of 30 training and 13 evaluation datasets
The 2 Static Embedding models have the following properties:
๐๏ธ Extremely fast, e.g. 107500 sentences per second on a consumer CPU, compared to 270 for 'all-mpnet-base-v2' and 56 for 'gte-large-en-v1.5'
0๏ธโฃ Zero active parameters: No Transformer blocks, no attention, not even a matrix multiplication. Super speed!
๐ No maximum sequence length! Embed texts at any length (note: longer texts may embed worse)
๐ Linear instead of exponential complexity: 2x longer text takes 2x longer, instead of 2.5x or more.
๐ช Matryoshka support: allow you to truncate embeddings with minimal performance loss (e.g. 4x smaller with a 0.56% perf. decrease for English Similarity tasks)
Check out the full blogpost if you'd like to 1) use these lightning-fast models or 2) learn how to train them with consumer-level hardware: https://huggingface.co/blog/static-embeddings
The blogpost contains a lengthy list of possible advancements; I'm very confident that our 2 models are only the tip of the iceberg, and we may be able to get even better performance.
Alternatively, check out the models:
* sentence-transformers/static-retrieval-mrl-en-v1
* sentence-transformers/static-similarity-mrl-multilingual-v1
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upvoted
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about 1 month ago
Article
Introducing smolagents: simple agents that write actions in code.
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Since I published it on GitHub a few days ago,
Hugging Face's new agentic library ๐๐บ๐ผ๐น๐ฎ๐ด๐ฒ๐ป๐๐ has gathered nearly 4k stars ๐คฏ
โก๏ธ But we are just getting started on agents: so we are hiring an ML Engineer to join me and double down on this effort!
The plan is to build GUI agents: agents that can act on your computer with mouse & keyboard, like Claude Computer Use.
We will make it work better, and fully open. โจ
Sounds like something you'd like to do? Apply here ๐ https://apply.workable.com/huggingface/j/AF1D4E3FEB/
Hugging Face's new agentic library ๐๐บ๐ผ๐น๐ฎ๐ด๐ฒ๐ป๐๐ has gathered nearly 4k stars ๐คฏ
โก๏ธ But we are just getting started on agents: so we are hiring an ML Engineer to join me and double down on this effort!
The plan is to build GUI agents: agents that can act on your computer with mouse & keyboard, like Claude Computer Use.
We will make it work better, and fully open. โจ
Sounds like something you'd like to do? Apply here ๐ https://apply.workable.com/huggingface/j/AF1D4E3FEB/
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7 months ago