Introducing the SQL Console on Datasets
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Caleb Fahlgren PRO
cfahlgren1
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reacted to
Muhammadreza's
post with โค๏ธ
7 days ago
Post
2518
Hey guys.
This is my first post here on huggingface. I'm glad to be a part of this amazing community!
This is my first post here on huggingface. I'm glad to be a part of this amazing community!
- 2 replies
reacted to
merve's
post with ๐ฅ๐
12 days ago
Post
4931
Hugging Face Hub Python library now comes with easy inference for vision language models! โจ
$ pip install huggingface_hub ๐ค
$ pip install huggingface_hub ๐ค
- 1 reply
posted
an
update
12 days ago
Post
1074
If you are like me, I like to find up and coming datasets and spaces before everyone else.
I made a trending repo space cfahlgren1/trending-repos where it shows:
- New up and coming Spaces in the last day
- New up and coming Datasets in the last 2 weeks
It's a really good way to find some new gems before they become popular. For example, someone is working on a way to dynamically create assets inside a video game here: gptcall/AI-Game-Creator
I made a trending repo space cfahlgren1/trending-repos where it shows:
- New up and coming Spaces in the last day
- New up and coming Datasets in the last 2 weeks
It's a really good way to find some new gems before they become popular. For example, someone is working on a way to dynamically create assets inside a video game here: gptcall/AI-Game-Creator
reacted to
reach-vb's
post with ๐
22 days ago
Post
2370
What a great day for Open Science!
@AIatMeta
released models, datasets, and code for many of its research artefacts! ๐ฅ
1. Meta Segment Anything Model 2.1: An updated checkpoint with improved results on visually similar objects, small objects and occlusion handling. A new developer suite will be added to make it easier for developers to build with SAM 2.
Model checkpoints: reach-vb/sam-21-6702d40defe7611a8bafa881
2. Layer Skip: Inference code and fine-tuned checkpoints demonstrating a new method for enhancing LLM performance.
Model checkpoints: facebook/layerskip-666b25c50c8ae90e1965727a
3. SALSA: New code enables researchers to benchmark AI-based attacks to validate security for post-quantum cryptography.
Repo: https://github.com/facebookresearch/LWE-benchmarking
4. Meta Lingua: A lightweight and self-contained codebase designed to train language models at scale.
Repo: https://github.com/facebookresearch/lingua
5. Meta Open Materials: New open source models and the largest dataset to accelerate AI-driven discovery of new inorganic materials.
Model checkpoints: fairchem/OMAT24
6. MEXMA: A new research paper and code for our novel pre-trained cross-lingual sentence encoder covering 80 languages.
Model checkpoint: facebook/MEXMA
7. Self-Taught Evaluator: a new method for generating synthetic preference data to train reward models without relying on human annotations.
Model checkpoint: facebook/Self-taught-evaluator-llama3.1-70B
8. Meta Spirit LM: An open-source language model for seamless speech and text integration.
Repo: https://github.com/facebookresearch/spiritlm
1. Meta Segment Anything Model 2.1: An updated checkpoint with improved results on visually similar objects, small objects and occlusion handling. A new developer suite will be added to make it easier for developers to build with SAM 2.
Model checkpoints: reach-vb/sam-21-6702d40defe7611a8bafa881
2. Layer Skip: Inference code and fine-tuned checkpoints demonstrating a new method for enhancing LLM performance.
Model checkpoints: facebook/layerskip-666b25c50c8ae90e1965727a
3. SALSA: New code enables researchers to benchmark AI-based attacks to validate security for post-quantum cryptography.
Repo: https://github.com/facebookresearch/LWE-benchmarking
4. Meta Lingua: A lightweight and self-contained codebase designed to train language models at scale.
Repo: https://github.com/facebookresearch/lingua
5. Meta Open Materials: New open source models and the largest dataset to accelerate AI-driven discovery of new inorganic materials.
Model checkpoints: fairchem/OMAT24
6. MEXMA: A new research paper and code for our novel pre-trained cross-lingual sentence encoder covering 80 languages.
Model checkpoint: facebook/MEXMA
7. Self-Taught Evaluator: a new method for generating synthetic preference data to train reward models without relying on human annotations.
Model checkpoint: facebook/Self-taught-evaluator-llama3.1-70B
8. Meta Spirit LM: An open-source language model for seamless speech and text integration.
Repo: https://github.com/facebookresearch/spiritlm
- 3 replies
reacted to
victor's
post with ๐ค๐ฅ
30 days ago
Post
2502
NEW - Inference Playground
Maybe like me you have always wanted a super easy way to compare llama3.2-1B vs. llama3.2-3B? or the same model with different temperatures?
Trying and comparing warm Inference API models has never been easier!
Just go to https://hf.co/playground, set your token and you're ready to go.
We'll keep improving, feedback welcome ๐
Maybe like me you have always wanted a super easy way to compare llama3.2-1B vs. llama3.2-3B? or the same model with different temperatures?
Trying and comparing warm Inference API models has never been easier!
Just go to https://hf.co/playground, set your token and you're ready to go.
We'll keep improving, feedback welcome ๐
- 2 replies
reacted to
reach-vb's
post with ๐๐ฅ
about 1 month ago
Post
2022
On-device AI framework ecosystem is blooming these days:
1. llama.cpp - All things Whisper, LLMs & VLMs - run across Metal, CUDA and other backends (AMD/ NPU etc)
https://github.com/ggerganov/llama.cpp
2. MLC - Deploy LLMs across platforms especially WebGPU (fastest WebGPU LLM implementation out there)
https://github.com/mlc-ai/web-llm
3. MLX - Arguably the fastest general purpose framework (Mac only) - Supports all major Image Generation (Flux, SDXL, etc), Transcription (Whisper), LLMs
https://github.com/ml-explore/mlx-examples
4. Candle - Cross-platform general purpose framework written in Rust - wide coverage across model categories
https://github.com/huggingface/candle
Honorable mentions:
1. Transformers.js - Javascript (WebGPU) implementation built on top of ONNXruntimeweb
https://github.com/xenova/transformers.js
2. Mistral rs - Rust implementation for LLMs & VLMs, built on top of Candle
https://github.com/EricLBuehler/mistral.rs
3. Ratchet - Cross platform, rust based WebGPU framework built for battle-tested deployments
https://github.com/huggingface/ratchet
4. Zml - Cross platform, Zig based ML framework
https://github.com/zml/zml
Looking forward to how the ecosystem would look 1 year from now - Quite bullish on the top 4 atm - but open source ecosystem changes quite a bit! ๐ค
Also, which frameworks did I miss?
1. llama.cpp - All things Whisper, LLMs & VLMs - run across Metal, CUDA and other backends (AMD/ NPU etc)
https://github.com/ggerganov/llama.cpp
2. MLC - Deploy LLMs across platforms especially WebGPU (fastest WebGPU LLM implementation out there)
https://github.com/mlc-ai/web-llm
3. MLX - Arguably the fastest general purpose framework (Mac only) - Supports all major Image Generation (Flux, SDXL, etc), Transcription (Whisper), LLMs
https://github.com/ml-explore/mlx-examples
4. Candle - Cross-platform general purpose framework written in Rust - wide coverage across model categories
https://github.com/huggingface/candle
Honorable mentions:
1. Transformers.js - Javascript (WebGPU) implementation built on top of ONNXruntimeweb
https://github.com/xenova/transformers.js
2. Mistral rs - Rust implementation for LLMs & VLMs, built on top of Candle
https://github.com/EricLBuehler/mistral.rs
3. Ratchet - Cross platform, rust based WebGPU framework built for battle-tested deployments
https://github.com/huggingface/ratchet
4. Zml - Cross platform, Zig based ML framework
https://github.com/zml/zml
Looking forward to how the ecosystem would look 1 year from now - Quite bullish on the top 4 atm - but open source ecosystem changes quite a bit! ๐ค
Also, which frameworks did I miss?
- 1 reply
onward ๐ค๐
reacted to
fdaudens's
post with ๐
about 1 month ago
Post
3329
๐ 1,000,000 public models milestone achieved on Hugging Face! ๐คฏ
This chart by @cfahlgren1 shows the explosive growth of open-source AI. It's not just about numbers - it's a thriving community combining cutting-edge ML with real-world applications. cfahlgren1/hub-stats
Can't wait to see what's next!
This chart by @cfahlgren1 shows the explosive growth of open-source AI. It's not just about numbers - it's a thriving community combining cutting-edge ML with real-world applications. cfahlgren1/hub-stats
Can't wait to see what's next!
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- 2 replies
replied to
their
post
about 2 months ago
You can find some interesting queries in this SQL Snippets Space. A really neat one is a sql snippet to convert an alpaca dataset to conversational.
posted
an
update
about 2 months ago
Post
1836
Have you tried the new SQL Console yet?
Would love to know any queries you've tried or general feedback! If you haven't go try it out and let us know ๐ค
If you have some interesting queries feel free to share the URLs as well!
Would love to know any queries you've tried or general feedback! If you haven't go try it out and let us know ๐ค
If you have some interesting queries feel free to share the URLs as well!
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- 1 reply
reacted to
charlesdedampierre's
post with ๐ฅ
about 2 months ago
Post
4141
Please check the Open Source AI Network: we mapped the top 500 HF users
based on their followers' profiles.
The map can be found here: bunkalab/mapping_the_OS_community
based on their followers' profiles.
The map can be found here: bunkalab/mapping_the_OS_community
- 1 reply
reacted to
m-ric's
post with ๐ฅ
about 2 months ago
Post
2173
๐๐ฟ๐ฐ๐ฒ๐ฒ ๐ฟ๐ฒ๐น๐ฒ๐ฎ๐๐ฒ๐ ๐ฆ๐๐ฝ๐ฒ๐ฟ๐ก๐ผ๐๐ฎ, ๐ฏ๐ฒ๐๐๐ฒ๐ฟ ๐ณ๐ถ๐ป๐ฒ-๐๐๐ป๐ฒ ๐ผ๐ณ ๐๐น๐ฎ๐บ๐ฎ-๐ฏ.๐ญ-๐ณ๐ฌ๐!
2๏ธโฃ versions: 70B and 8B
๐ง Trained by distilling logits from Llama-3.1-405B
๐ฅ Used a clever compression method to reduce dataset weight from 2.9 Petabytes down to 50GB (may share it in a paper)
โ๏ธ Not all benchmarks are improved: GPQA and MUSR go down a slight bit
๐ค 8B weights are available on HF (not the 70B)
Read their blog post ๐ https://blog.arcee.ai/arcee-supernova-training-pipeline-and-model-composition/
Model weights (8B) ๐ arcee-ai/Llama-3.1-SuperNova-Lite
2๏ธโฃ versions: 70B and 8B
๐ง Trained by distilling logits from Llama-3.1-405B
๐ฅ Used a clever compression method to reduce dataset weight from 2.9 Petabytes down to 50GB (may share it in a paper)
โ๏ธ Not all benchmarks are improved: GPQA and MUSR go down a slight bit
๐ค 8B weights are available on HF (not the 70B)
Read their blog post ๐ https://blog.arcee.ai/arcee-supernova-training-pipeline-and-model-composition/
Model weights (8B) ๐ arcee-ai/Llama-3.1-SuperNova-Lite
replied to
their
post
2 months ago
Disclaimer: This is being funded by some free credits on Together, so will only be up as supplies last ๐คฃ or someone decides to sponsor it. But here are some cool examples:
posted
an
update
2 months ago
Post
1129
Made a fun Space powered by Llama 405B for creating real, working react apps with the awesome plus that you can contribute to an open react dataset by upvoting or downvoting the response ๐ค.
cfahlgren1/llama-artifacts
cfahlgren1/react-code-instructions
cfahlgren1/llama-artifacts
cfahlgren1/react-code-instructions
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- 1 reply
reacted to
Xenova's
post with ๐ฅ๐
3 months ago
Post
12058
I can't believe this... Phi-3.5-mini (3.8B) running in-browser at ~90 tokens/second on WebGPU w/ Transformers.js and ONNX Runtime Web! ๐คฏ Since everything runs 100% locally, no messages are sent to a server โ a huge win for privacy!
- ๐ค Demo: webml-community/phi-3.5-webgpu
- ๐งโ๐ป Source code: https://github.com/huggingface/transformers.js-examples/tree/main/phi-3.5-webgpu
- ๐ค Demo: webml-community/phi-3.5-webgpu
- ๐งโ๐ป Source code: https://github.com/huggingface/transformers.js-examples/tree/main/phi-3.5-webgpu
- 11 replies
reacted to
victor's
post with ๐
3 months ago
Post
5343
๐ Calling all Hugging Face users! We want to hear from YOU!
What feature or improvement would make the biggest impact on Hugging Face?
Whether it's the Hub, better documentation, new integrations, or something completely different โ we're all ears!
Your feedback shapes the future of Hugging Face. Drop your ideas in the comments below! ๐
What feature or improvement would make the biggest impact on Hugging Face?
Whether it's the Hub, better documentation, new integrations, or something completely different โ we're all ears!
Your feedback shapes the future of Hugging Face. Drop your ideas in the comments below! ๐
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- 133 replies