aurora-m/books-generation
Viewer
•
Updated
•
713k
•
83
•
2
Feynman Innovations
ajibawa-2023
AI & ML interests
LLM, RL, DL, ML, AGI. Developing LLMs (preferably fully fine tuned ) for various use cases.
Recent Activity
liked
a dataset
4 days ago
aurora-m/books-generation
new activity
6 days ago
ajibawa-2023/Code-290k-ShareGPT:How is this dataset created?
new activity
6 days ago
ajibawa-2023/Python-Code-23k-ShareGPT:Origin
Organizations
ajibawa-2023's activity
liked
a
dataset
4 days ago
How is this dataset created?
1
#3 opened 4 months ago
by
oo22010
Data generation process and LLM used
1
#2 opened 8 days ago
by
Chintan-Shah
reacted to
singhsidhukuldeep's
post with 🔥
about 1 month ago
Post
3600
Exciting Research Alert: Revolutionizing Complex Information Retrieval!
A groundbreaking paper from researchers at MIT, AWS AI, and UPenn introduces ARM (Alignment-Oriented LLM-based Retrieval Method), a novel approach to tackle complex information retrieval challenges.
>> Key Innovations
Information Alignment
The method first decomposes queries into keywords and aligns them with available data using both BM25 and embedding similarity, ensuring comprehensive coverage of information needs.
Structure Alignment
ARM employs a sophisticated mixed-integer programming solver to identify connections between data objects, exploring relationships beyond simple semantic matching.
Self-Verification
The system includes a unique self-verification mechanism where the LLM evaluates and aggregates results from multiple retrieval paths, ensuring accuracy and completeness.
>> Performance Highlights
The results are impressive:
- Outperforms standard RAG by up to 5.2 points in execution accuracy on Bird dataset
- Achieves 19.3 points higher F1 scores compared to existing approaches on OTT-QA
- Reduces the number of required LLM calls while maintaining superior retrieval quality
>> Technical Implementation
The system uses a three-step process:
1. N-gram indexing and embedding computation for all data objects
2. Constrained beam decoding for information alignment
3. Mixed-integer programming optimization for structure exploration
This research represents a significant step forward in making complex information retrieval more efficient and accurate. The team's work demonstrates how combining traditional optimization techniques with modern LLM capabilities can solve challenging retrieval problems.
A groundbreaking paper from researchers at MIT, AWS AI, and UPenn introduces ARM (Alignment-Oriented LLM-based Retrieval Method), a novel approach to tackle complex information retrieval challenges.
>> Key Innovations
Information Alignment
The method first decomposes queries into keywords and aligns them with available data using both BM25 and embedding similarity, ensuring comprehensive coverage of information needs.
Structure Alignment
ARM employs a sophisticated mixed-integer programming solver to identify connections between data objects, exploring relationships beyond simple semantic matching.
Self-Verification
The system includes a unique self-verification mechanism where the LLM evaluates and aggregates results from multiple retrieval paths, ensuring accuracy and completeness.
>> Performance Highlights
The results are impressive:
- Outperforms standard RAG by up to 5.2 points in execution accuracy on Bird dataset
- Achieves 19.3 points higher F1 scores compared to existing approaches on OTT-QA
- Reduces the number of required LLM calls while maintaining superior retrieval quality
>> Technical Implementation
The system uses a three-step process:
1. N-gram indexing and embedding computation for all data objects
2. Constrained beam decoding for information alignment
3. Mixed-integer programming optimization for structure exploration
This research represents a significant step forward in making complex information retrieval more efficient and accurate. The team's work demonstrates how combining traditional optimization techniques with modern LLM capabilities can solve challenging retrieval problems.
reacted to
Tonic's
post with 🔥
about 1 month ago
Post
2348
🙋🏻♂️hey there folks ,
Goedel's Theorem Prover is now being demo'ed on huggingface : Tonic/Math
give it a try !
Goedel's Theorem Prover is now being demo'ed on huggingface : Tonic/Math
give it a try !
reacted to
hba123's
post with 🔥
about 1 month ago
Post
1755
We developed a method that ensures almost-sure safety (i.e., safety with probability approaching 1). We proved this result. We then, present a practical implementation which we call InferenceGuard. InferenceGuard has impressive practical results: 91.04% on Alpaca-7B and 100% safety results on Beaver 7B-v3.
Now, it is easy to get high safety results like those if we want a dumb model, e.g., just don't answer or answer with EOS and so on. However, our goal is not to only have safe results, but also to make sure that the rewards are high - we want a good trade-off between safety and rewards! That's exactly, what we show. InferenceGuard achieves that!
Check it out: Almost Surely Safe Alignment of Large Language Models at Inference-Time (2502.01208)
Now, it is easy to get high safety results like those if we want a dumb model, e.g., just don't answer or answer with EOS and so on. However, our goal is not to only have safe results, but also to make sure that the rewards are high - we want a good trade-off between safety and rewards! That's exactly, what we show. InferenceGuard achieves that!
Check it out: Almost Surely Safe Alignment of Large Language Models at Inference-Time (2502.01208)
upvoted
a
collection
about 1 month ago
reacted to
davanstrien's
post with 👍
about 1 month ago
Post
1932
Updated the ColPali Query Generator Space
davanstrien/ColPali-Query-Generator to use
Qwen/Qwen2.5-VL-7B-Instruct.
Given an input image, it generates several queries along with explanations to justify them. This approach can generate synthetic data for fine-tuning ColPali models.
Given an input image, it generates several queries along with explanations to justify them. This approach can generate synthetic data for fine-tuning ColPali models.
reacted to
DawnC's
post with ❤️
2 months ago
Post
2299
🌟 PawMatchAI: Making Breed Selection More Intuitive! 🐕
Excited to share the latest update to this AI-powered companion for finding your perfect furry friend! I've made significant architectural improvements to enhance breed recognition accuracy and feature detection.
✨ What's New?
Enhanced breed recognition through advanced morphological feature analysis:
- Implemented a sophisticated feature extraction system that analyzes specific characteristics like body proportions, head features, tail structure, fur texture, and color patterns
- Added an intelligent attention mechanism that dynamically focuses on the most relevant features for each image
- Improved multi-dog detection capabilities through enhanced spatial feature analysis
- Achieved better precision in distinguishing subtle breed characteristics
🎯 Key Features:
Smart breed recognition powered by advanced AI architecture
Visual matching scores with intuitive color indicators
Detailed breed comparisons with interactive tooltips
Lifestyle-based recommendations tailored to your needs
💭 Project Vision
Combining my passion for AI and pets, this project represents another step toward creating meaningful AI applications. Each update aims to make the breed selection process more accessible while improving the underlying technology.
👉 Try it now: DawnC/PawMatchAI
Your likes ❤️ on this space fuel this project's growth!
#AI #MachineLearning #DeepLearning #Pytorch #ComputerVision #TechForLife
Excited to share the latest update to this AI-powered companion for finding your perfect furry friend! I've made significant architectural improvements to enhance breed recognition accuracy and feature detection.
✨ What's New?
Enhanced breed recognition through advanced morphological feature analysis:
- Implemented a sophisticated feature extraction system that analyzes specific characteristics like body proportions, head features, tail structure, fur texture, and color patterns
- Added an intelligent attention mechanism that dynamically focuses on the most relevant features for each image
- Improved multi-dog detection capabilities through enhanced spatial feature analysis
- Achieved better precision in distinguishing subtle breed characteristics
🎯 Key Features:
Smart breed recognition powered by advanced AI architecture
Visual matching scores with intuitive color indicators
Detailed breed comparisons with interactive tooltips
Lifestyle-based recommendations tailored to your needs
💭 Project Vision
Combining my passion for AI and pets, this project represents another step toward creating meaningful AI applications. Each update aims to make the breed selection process more accessible while improving the underlying technology.
👉 Try it now: DawnC/PawMatchAI
Your likes ❤️ on this space fuel this project's growth!
#AI #MachineLearning #DeepLearning #Pytorch #ComputerVision #TechForLife
-
- 2 replies
Good work! Keep it up!!
reacted to
clem's
post with ❤️
2 months ago
Post
4350
Cool to see
@ylecun
joining the top 10 of most followed on HF!
(and leaderboard by @mvaloatto is here: mvaloatto/TCTF)
(and leaderboard by @mvaloatto is here: mvaloatto/TCTF)
- 2 replies
reacted to
AkimfromParis's
post with 👀
2 months ago
Post
1722
💵 Polymarket is leveraging “Chatbot Arena LLM Leaderboard” on HuggingFace for online gambling on the “Top AI model on January 31?”. 🤗
As of January 3rd, 2025:
-1./ Gemini (83%) -2./ ChatGPT (13%) -3./ Other (2%) -4./ Claude (2%) -5./ Grok (1%) -6./ Llama (<1%)
🇺🇸 The market opinion is following historical data. It's clearly bias towards US historical AI giants, yet Polymarket is forbidden in the USA and for US citizens.
🇨🇳 In the “Other”, you might have Chinese AI labs that are probably the future AI leaders (Qwen, DeepSeek, Yi).
⚖️ In the market resolution, if two models are tied in the evaluation, they will take the alphabetical order. (e.g. if both were tied, “Google” would resolve to “Yes”, and “xAI” would resolve to “No”). 🙃
That might be illegal usage of the Chatbot Arena policy? And maybe HuggingFace? @clem
Or maybe authors and contributors should get a cut each month as “market markers”. @weichiang @angelopoulos
As of January 3rd, 2025:
-1./ Gemini (83%) -2./ ChatGPT (13%) -3./ Other (2%) -4./ Claude (2%) -5./ Grok (1%) -6./ Llama (<1%)
🇺🇸 The market opinion is following historical data. It's clearly bias towards US historical AI giants, yet Polymarket is forbidden in the USA and for US citizens.
🇨🇳 In the “Other”, you might have Chinese AI labs that are probably the future AI leaders (Qwen, DeepSeek, Yi).
⚖️ In the market resolution, if two models are tied in the evaluation, they will take the alphabetical order. (e.g. if both were tied, “Google” would resolve to “Yes”, and “xAI” would resolve to “No”). 🙃
That might be illegal usage of the Chatbot Arena policy? And maybe HuggingFace? @clem
Or maybe authors and contributors should get a cut each month as “market markers”. @weichiang @angelopoulos
- 1 reply
reacted to
cfahlgren1's
post with 👍
2 months ago
Post
2249
You'll notice the AI in the SQL Console is much better at working with chatml conversations:
Here's example of unnesting the cfahlgren1/react-code-instructions in less than 10 seconds by asking it. Check it out here: cfahlgren1/react-code-instructions
- "show me the average assistant response length"
- "extract user, system, and assistant messages into separate columns"
It's super easy to work with conversational datasets now with natural language 🗣️
Here's example of unnesting the cfahlgren1/react-code-instructions in less than 10 seconds by asking it. Check it out here: cfahlgren1/react-code-instructions
- "show me the average assistant response length"
- "extract user, system, and assistant messages into separate columns"
It's super easy to work with conversational datasets now with natural language 🗣️
- 2 replies
upvoted
6
papers
3 months ago
AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions
Paper
•
2410.20424
•
Published
•
40
Bel Esprit: Multi-Agent Framework for Building AI Model Pipelines
Paper
•
2412.14684
•
Published
•
1
Generative World Explorer
Paper
•
2411.11844
•
Published
•
76
PRefLexOR: Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning and Agentic Thinking
Paper
•
2410.12375
•
Published
•
4
DynaSaur: Large Language Agents Beyond Predefined Actions
Paper
•
2411.01747
•
Published
•
30
Generative Agent Simulations of 1,000 People
Paper
•
2411.10109
•
Published
•
4