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MarziehFadaee  authored a paper 2 days ago
Towards Best Practices for Open Datasets for LLM Training
shayekh  updated a Space 3 days ago
C4AI-Community/m-rewardbench
shayekh  updated a collection 5 days ago
Multilingual RewardBench
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prithivMLmods 
posted an update 1 day ago
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1421
ChemQwen-vL [ Qwen for Chem Vision ] 🧑🏻‍🔬

🧪Model : prithivMLmods/ChemQwen-vL

📝ChemQwen-vL is a vision-language model fine-tuned based on the Qwen2VL-2B Instruct model. It has been trained using the International Chemical Identifier (InChI) format for chemical compounds and is optimized for chemical compound identification. The model excels at generating the InChI and providing descriptions of chemical compounds based on their images. Its architecture operates within a multi-modal framework, combining image-text-text capabilities. It has been fine-tuned using datasets from: https://iupac.org/projects/

📒Colab Demo: https://tinyurl.com/2pn8x6u7, Collection : https://tinyurl.com/2mt5bjju

Inference with the documentation is possible with the help of the ReportLab library. https://pypi.org/project/reportlab/

🤗: @prithivMLmods
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singhsidhukuldeep 
posted an update 2 days ago
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923
Breaking News: LinkedIn's Content Search Engine Gets a Powerful Semantic Upgrade!

Excited to share insights about LinkedIn's innovative approach to content search, recently detailed in a groundbreaking paper by their Mountain View team. This advancement represents a significant shift from traditional keyword-based search to semantic understanding.

>> Technical Architecture

The new search engine employs a sophisticated two-layer architecture:

Retrieval Layer
- Token Based Retriever (TBR) for exact keyword matching
- Embedding Based Retriever (EBR) using a two-tower model with multilingual-e5 embeddings
- Pre-computed post embeddings stored in a dedicated embedding store for efficient retrieval

Multi-Stage Ranking
- L1 Stage: Initial filtering using a lightweight model
- L2 Stage: Advanced ranking with complex features including:
- Query-post semantic matching
- Author reputation analysis
- User engagement metrics
- Content freshness evaluation

>> Performance Improvements

The system has achieved remarkable results:
- 10%+ improvement in both on-topic rate and long-dwell metrics
- Enhanced ability to handle complex natural language queries
- Significant boost in sitewide engagement

This advancement enables LinkedIn to better serve complex queries like "how to ask for a raise?" while maintaining high performance at scale. The system intelligently balances between exact keyword matching and semantic understanding, ensuring optimal results for both navigational and conceptual searches.

What impresses me most is how the team solved the scale challenge - processing billions of posts efficiently using pre-computed embeddings and approximate nearest neighbor search. This is enterprise-scale AI at its finest.
Tonic 
posted an update 2 days ago
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1156
🙋🏻‍♂️ Hey there folks ,

Facebook AI just released JASCO models that make music stems .

you can try it out here : Tonic/audiocraft

hope you like it
shayekh 
updated a Space 3 days ago
singhsidhukuldeep 
posted an update 4 days ago
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498
Just read a fascinating survey paper on Query Optimization in Large Language Models by researchers at Tencent's Machine Learning Platform Department.

The paper deep dives into how we can enhance LLMs' ability to understand and answer complex queries, particularly in Retrieval-Augmented Generation (RAG) systems. Here's what caught my attention:

>> Key Technical Innovations

Core Operations:
- Query Expansion: Both internal (using LLM's knowledge) and external (web/knowledge base) expansion
- Query Disambiguation: Handling ambiguous queries through intent clarification
- Query Decomposition: Breaking complex queries into manageable sub-queries
- Query Abstraction: Stepping back to understand high-level principles

Under the Hood:
The system employs sophisticated techniques like GENREAD for contextual document generation, Query2Doc for pseudo-document creation, and FLARE's iterative anticipation mechanism for enhanced retrieval.

>> Real-World Applications

The framework addresses critical challenges in:
- Domain-specific tasks
- Knowledge-intensive operations
- Multi-hop reasoning
- Complex information retrieval

What's particularly impressive is how this approach significantly reduces hallucinations in LLMs while maintaining cost-effectiveness. The researchers have meticulously categorized query difficulties into four types, ranging from single-piece explicit evidence to multiple-piece implicit evidence requirements
Tonic 
posted an update 4 days ago
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2207
🙋🏻‍♂️Hey there folks , Open LLM Europe just released Lucie 7B-Instruct model , a billingual instruct model trained on open data ! You can check out my unofficial demo here while we wait for the official inference api from the group : Tonic/Lucie-7B hope you like it 🚀
shayekh 
updated a collection 5 days ago
Cartinoe5930 
authored a paper 5 days ago
singhsidhukuldeep 
posted an update 5 days ago
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606
Excited to share a groundbreaking development in recommendation systems - Legommenders, a comprehensive content-based recommendation library that revolutionizes how we approach personalized content delivery.

>> Key Innovations

End-to-End Training
The library enables joint training of content encoders alongside behavior and interaction modules, making it the first of its kind to offer truly integrated content understanding in recommendation pipelines.

Massive Scale
- Supports creation and analysis of over 1,000 distinct models
- Compatible with 15 diverse datasets
- Features 15 content operators, 8 behavior operators, and 9 click predictors

Advanced LLM Integration
Legommenders pioneers LLM integration in two crucial ways:
- As feature encoders for enhanced content understanding
- As data generators for high-quality training data augmentation

Superior Architecture
The system comprises four core components:
- Dataset processor for unified data handling
- Content operator for embedding generation
- Behavior operator for user sequence fusion
- Click predictor for probability calculations

Performance Optimization
The library introduces an innovative caching pipeline that achieves up to 50x speedup in evaluation compared to traditional approaches.

Developed by researchers from The Hong Kong Polytechnic University, this open-source project represents a significant leap forward in recommendation system technology.

For those interested in content-based recommendation systems, this is a must-explore tool. The library is available on GitHub for implementation and experimentation.
singhsidhukuldeep 
posted an update 7 days ago
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1752
Groundbreaking Survey on Large Language Models in Recommendation Systems!

Just read a comprehensive survey that maps out how LLMs are revolutionizing recommender systems. The authors have meticulously categorized existing approaches into two major paradigms:

Discriminative LLMs for Recommendation:
- Leverages BERT-like models for understanding user-item interactions
- Uses fine-tuning and prompt tuning to adapt pre-trained models
- Excels at tasks like user representation learning and ranking

Generative LLMs for Recommendation:
- Employs GPT-style models to directly generate recommendations
- Implements innovative techniques like in-context learning and zero-shot recommendation
- Supports natural language interaction and explanation generation

Key Technical Insights:
- Novel taxonomy of modeling paradigms: LLM Embeddings + RS, LLM Tokens + RS, and LLM as RS
- Integration methods spanning from simple prompting to sophisticated instruction tuning
- Hybrid approaches combining collaborative filtering with LLM capabilities
- Advanced prompt engineering techniques for controlled recommendation generation

Critical Challenges Identified:
- Position and popularity bias in LLM recommendations
- Limited context length affecting user history processing
- Need for better evaluation metrics for generative recommendations
- Controlled output generation and personalization challenges

This work opens exciting possibilities for next-gen recommendation systems while highlighting crucial areas for future research.
  • 1 reply
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Aryan8912 
updated a collection 8 days ago
Cartinoe5930 
authored a paper 8 days ago
Sri-Vigneshwar-DJ 
posted an update 8 days ago
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608
Checkout phi-4 from Microsoft, dropped a day ago... If you ❤️ the Phi series, then here is the GGUF - Sri-Vigneshwar-DJ/phi-4-GGUF. phi-4 is a 14B highly efficient open LLM that beats much larger models at math and reasoning - check out evaluations on the Open LLM.

Technical paper - https://arxiv.org/pdf/2412.08905 ; The Data Synthesis approach is interesting
yihaopeng 
authored a paper 9 days ago
prithivMLmods 
posted an update 9 days ago
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2965
200+ f{🤗} on Stranger Zone! [ https://huggingface.co/strangerzonehf ]

❤️‍🔥Stranger Zone's MidJourney Mix Model Adapter is trending on the Very Model Page, with over 45,000+ downloads. Additionally, the Super Realism Model Adapter has over 52,000+ downloads, remains the top two adapter on Stranger Zone!
strangerzonehf/Flux-Midjourney-Mix2-LoRA, strangerzonehf/Flux-Super-Realism-LoRA

👽Try Demo: prithivMLmods/FLUX-LoRA-DLC

📦Most Recent Adapters to Check Out :
+ Ctoon : strangerzonehf/Ctoon-Plus-Plus
+ Cardboard : strangerzonehf/Flux-Cardboard-Art-LoRA
+ Claude Art : strangerzonehf/Flux-Claude-Art
+ Flay Lay : strangerzonehf/Flux-FlatLay-LoRA
+ Smiley Portrait : strangerzonehf/Flux-Smiley-Portrait-LoRA

🤗Thanks for Community & OPEN SOURCEEE !!
  • 6 replies
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singhsidhukuldeep 
posted an update 10 days ago
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1425
Groundbreaking Research Alert: Correctness ≠ Faithfulness in RAG Systems

Fascinating new research from L3S Research Center, University of Amsterdam, and TU Delft reveals a critical insight into Retrieval Augmented Generation (RAG) systems. The study exposes that up to 57% of citations in RAG systems could be unfaithful, despite being technically correct.

>> Key Technical Insights:

Post-rationalization Problem
The researchers discovered that RAG systems often engage in "post-rationalization" - where models first generate answers from their parametric memory and then search for supporting evidence afterward. This means that while citations may be correct, they don't reflect the actual reasoning process.

Experimental Design
The team used Command-R+ (104B parameters) with 4-bit quantization on NVIDIA A100 GPU, testing on the NaturalQuestions dataset. They employed BM25 for initial retrieval and ColBERT v2 for reranking.

Attribution Framework
The research introduces a comprehensive framework for evaluating RAG systems across multiple dimensions:
- Citation Correctness: Whether cited documents support the claims
- Citation Faithfulness: Whether citations reflect actual model reasoning
- Citation Appropriateness: Relevance and meaningfulness of citations
- Citation Comprehensiveness: Coverage of key points

Under the Hood
The system processes involve:
1. Document relevance prediction
2. Citation prediction
3. Answer generation without citations
4. Answer generation with citations

This work fundamentally challenges our understanding of RAG systems and highlights the need for more robust evaluation metrics in AI systems that claim to provide verifiable information.
  • 2 replies
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Tonic 
posted an update 10 days ago
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1624
microsoft just released Phi-4 , check it out here : Tonic/Phi-4

hope you like it :-)
alielfilali01 
posted an update 11 days ago
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1791
3C3H AraGen Leaderboard welcomes today deepseek-ai/DeepSeek-V3 and 12 other models (including the late gpt-3.5 💀) to the ranking of best LLMs in Arabic !


Observations:
- DeepSeek-v3 ranked 3rd and only Open model among the top 5 !

- A 14B open model ( Qwen/Qwen2.5-14B-Instruct) outperforms gpt-3.5-turbo-0125 (from last year). This shows how much we came in advancing and supporting Arabic presence within the LLM ecosystem !

- Contrary to what observed in likelihood-acc leaderboards (like OALL/Open-Arabic-LLM-Leaderboard) further finetuned models like maldv/Qwentile2.5-32B-Instruct actually decreased the performance compared to the original model Qwen/Qwen2.5-32B-Instruct.
It's worth to note that the decrease is statiscally insignificant which imply that at best, the out-domain finetuning do not really hurts the model original capabilities acquired during pretraining.
Previous work addressed this (finetuning VS pretraining) but more investigation in this regard is required (any PhDs here ? This could be your question ...)


Check out the latest rankings: inceptionai/AraGen-Leaderboard
Sri-Vigneshwar-DJ 
posted an update 11 days ago
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2035
Just sharing a thought: I started using DeepSeek V3 a lot, and an idea struck me about agents "orchestrating during inference" on a test-time compute model like DeepSeek V3 or the O1 series.

Agents (Instruction + Function Calls + Memory) execute during inference, and based on the output decision, a decision is made to scale the time to reason or perform other tasks.
prithivMLmods 
posted an update 12 days ago
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5852
Reasoning SmolLM2 🚀

🎯Fine-tuning SmolLM2 on a lightweight synthetic reasoning dataset for reasoning-specific tasks. Future updates will focus on lightweight, blazing-fast reasoning models. Until then, check out the blog for fine-tuning details.

🔥Blog : https://huggingface.co/blog/prithivMLmods/smollm2-ft

🔼 Models :
+ SmolLM2-CoT-360M : prithivMLmods/SmolLM2-CoT-360M
+ Reasoning-SmolLM2-135M : prithivMLmods/Reasoning-SmolLM2-135M
+ SmolLM2-CoT-360M-GGUF : prithivMLmods/SmolLM2-CoT-360M-GGUF

🤠 Other Details :
+ Demo : prithivMLmods/SmolLM2-CoT-360M
+ Fine-tune nB : prithivMLmods/SmolLM2-CoT-360M