Qwen Text-to-SQL Demo
๐ฆ
1
Generate and compare SQL queries from natural language
Tuana Celik
Tuana
AI & ML interests
๐ฅ DevRel @ LlamaIndex
Recent Activity
updated a Space about 15 hours ago
Tuana/qwen-text2sql-demo published a Space about 15 hours ago
Tuana/qwen-text2sql-demo updated a model 1 day ago
Tuana/qwen35-08b-text2sqlOrganizations
updated a
Space about 15 hours ago
published a
Space about 15 hours ago
updated a
model 1 day ago
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liked a
Space 4 months ago
Congrats! Plus some resources
1
#1 opened 10 months ago
by Tuana
Question about project
2
#1 opened 11 months ago
by Tuana
liked a
Space 11 months ago
updated a
dataset about 1 year ago
liked a
dataset about 1 year ago
liked a
model almost 2 years ago
upvoted an article almost 2 years ago
Article
Mixedbread ๐ค deepset: Announcing our New German/English Embedding Model
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liked a
Space almost 2 years ago
reacted to merve's post with ๐ almost 2 years ago
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4268
I love Depth Anything V2 ๐
Itโs Depth Anything, but scaled with both larger teacher model and a gigantic dataset!
Here's a small TLDR of paper with a lot of findings, experiments and more.
I have also created a collection that has the models, the dataset, the demo and CoreML converted model ๐ merve/depth-anything-v2-release-6671902e798cd404513ffbf5
The authors have analyzed Marigold, a diffusion based model against Depth Anything and found out whatโs up with using synthetic images vs real images for MDE:
๐ Real data has a lot of label noise, inaccurate depth maps (caused by depth sensors missing transparent objects etc) and there are many details overlooked
๐ Synthetic data have more precise and detailed depth labels and they are truly ground-truth, but thereโs a distribution shift between real and synthetic images, and they have restricted scene coverage
The authors train different image encoders only on synthetic images and find out unless the encoder is very large the model canโt generalize well (but large models generalize inherently anyway) ๐ง
But they still fail encountering real images that have wide distribution in labels (e.g. diverse instances of objects) ๐ฅฒ
Depth Anything v2 framework is to..
๐ฆ Train a teacher model based on DINOv2-G based on 595K synthetic images
๐ท๏ธ Label 62M real images using teacher model
๐ฆ Train a student model using the real images labelled by teacher
Result: 10x faster and more accurate than Marigold!
The authors also construct a new benchmark called DA-2K that is less noisy, highly detailed and more diverse!
Itโs Depth Anything, but scaled with both larger teacher model and a gigantic dataset!
Here's a small TLDR of paper with a lot of findings, experiments and more.
I have also created a collection that has the models, the dataset, the demo and CoreML converted model ๐ merve/depth-anything-v2-release-6671902e798cd404513ffbf5
The authors have analyzed Marigold, a diffusion based model against Depth Anything and found out whatโs up with using synthetic images vs real images for MDE:
๐ Real data has a lot of label noise, inaccurate depth maps (caused by depth sensors missing transparent objects etc) and there are many details overlooked
๐ Synthetic data have more precise and detailed depth labels and they are truly ground-truth, but thereโs a distribution shift between real and synthetic images, and they have restricted scene coverage
The authors train different image encoders only on synthetic images and find out unless the encoder is very large the model canโt generalize well (but large models generalize inherently anyway) ๐ง
But they still fail encountering real images that have wide distribution in labels (e.g. diverse instances of objects) ๐ฅฒ
Depth Anything v2 framework is to..
๐ฆ Train a teacher model based on DINOv2-G based on 595K synthetic images
๐ท๏ธ Label 62M real images using teacher model
๐ฆ Train a student model using the real images labelled by teacher
Result: 10x faster and more accurate than Marigold!
The authors also construct a new benchmark called DA-2K that is less noisy, highly detailed and more diverse!
liked a
dataset almost 2 years ago
reacted to anakin87's post with ๐ฅ almost 2 years ago
Post
1282
Do you want to play a game against Llama 3? ๐ฆ๐ฆ๐ฆ
Meet ๐งโ๐ซ ๐๐ฎ๐ญ๐จ๐๐ฎ๐ข๐ณ๐ณ๐๐ซ, a new LLM application that you can use for learning or just for fun.
Try it out on Hugging Face Spaces ๐ค deepset/autoquizzer
๐๐จ๐ฐ ๐ข๐ญ ๐ฐ๐จ๐ซ๐ค๐ฌ
You provide an URL -> A multiple-choice quiz is instantly generated.
๐น You can play the quiz yourself.
๐น You can let the LLM play in two different ways
๐ Closed book: the LLM responds only by knowing the general topic and using its parametric knowledge and reasoning abilities.
๐๐ Web RAG: for each question, a Google search is done and the top 3 snippets are included in the prompt for the LLM.
๐๐ญ๐๐๐ค
๐๏ธ Haystack LLM framework https://haystack.deepset.ai/
๐ฆ Llama 3 8B Instruct
โก Groq
Original idea: @Tuana
Meet ๐งโ๐ซ ๐๐ฎ๐ญ๐จ๐๐ฎ๐ข๐ณ๐ณ๐๐ซ, a new LLM application that you can use for learning or just for fun.
Try it out on Hugging Face Spaces ๐ค deepset/autoquizzer
๐๐จ๐ฐ ๐ข๐ญ ๐ฐ๐จ๐ซ๐ค๐ฌ
You provide an URL -> A multiple-choice quiz is instantly generated.
๐น You can play the quiz yourself.
๐น You can let the LLM play in two different ways
๐ Closed book: the LLM responds only by knowing the general topic and using its parametric knowledge and reasoning abilities.
๐๐ Web RAG: for each question, a Google search is done and the top 3 snippets are included in the prompt for the LLM.
๐๐ญ๐๐๐ค
๐๏ธ Haystack LLM framework https://haystack.deepset.ai/
๐ฆ Llama 3 8B Instruct
โก Groq
Original idea: @Tuana
- 1 reply
Thanks for sharing @anakin87 โค๏ธ Really cool demo!
reacted to anakin87's post with โค๏ธ almost 2 years ago
Post
1282
Do you want to play a game against Llama 3? ๐ฆ๐ฆ๐ฆ
Meet ๐งโ๐ซ ๐๐ฎ๐ญ๐จ๐๐ฎ๐ข๐ณ๐ณ๐๐ซ, a new LLM application that you can use for learning or just for fun.
Try it out on Hugging Face Spaces ๐ค deepset/autoquizzer
๐๐จ๐ฐ ๐ข๐ญ ๐ฐ๐จ๐ซ๐ค๐ฌ
You provide an URL -> A multiple-choice quiz is instantly generated.
๐น You can play the quiz yourself.
๐น You can let the LLM play in two different ways
๐ Closed book: the LLM responds only by knowing the general topic and using its parametric knowledge and reasoning abilities.
๐๐ Web RAG: for each question, a Google search is done and the top 3 snippets are included in the prompt for the LLM.
๐๐ญ๐๐๐ค
๐๏ธ Haystack LLM framework https://haystack.deepset.ai/
๐ฆ Llama 3 8B Instruct
โก Groq
Original idea: @Tuana
Meet ๐งโ๐ซ ๐๐ฎ๐ญ๐จ๐๐ฎ๐ข๐ณ๐ณ๐๐ซ, a new LLM application that you can use for learning or just for fun.
Try it out on Hugging Face Spaces ๐ค deepset/autoquizzer
๐๐จ๐ฐ ๐ข๐ญ ๐ฐ๐จ๐ซ๐ค๐ฌ
You provide an URL -> A multiple-choice quiz is instantly generated.
๐น You can play the quiz yourself.
๐น You can let the LLM play in two different ways
๐ Closed book: the LLM responds only by knowing the general topic and using its parametric knowledge and reasoning abilities.
๐๐ Web RAG: for each question, a Google search is done and the top 3 snippets are included in the prompt for the LLM.
๐๐ญ๐๐๐ค
๐๏ธ Haystack LLM framework https://haystack.deepset.ai/
๐ฆ Llama 3 8B Instruct
โก Groq
Original idea: @Tuana
- 1 reply
updated a
Space about 2 years ago
updated a
Space over 2 years ago