Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
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2504.01990
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242
Mohammed Mohammed Ali
MohammedEltoum
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Google published a 69-page whitepaper on Prompt Engineering and its best practices, a must-read if you are using LLMs in production:
> zero-shot, one-shot, few-shot
> system prompting
> chain-of-thought (CoT)
> ReAct
LINK: https://www.kaggle.com/whitepaper-prompt-engineering
> code prompting
> best practices
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SmolVLM: Redefining small and efficient multimodal models
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hesamation's
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Google published a 69-page whitepaper on Prompt Engineering and its best practices, a must-read if you are using LLMs in production:
> zero-shot, one-shot, few-shot
> system prompting
> chain-of-thought (CoT)
> ReAct
LINK: https://www.kaggle.com/whitepaper-prompt-engineering
> code prompting
> best practices
> zero-shot, one-shot, few-shot
> system prompting
> chain-of-thought (CoT)
> ReAct
LINK: https://www.kaggle.com/whitepaper-prompt-engineering
> code prompting
> best practices
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Dropping Downstream tasks using newly initialized parameters and weights ([classifier.bias & weights]) support domain-specific ๐ถ๐บ๐ฎ๐ด๐ฒ ๐ฐ๐น๐ฎ๐๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป. Based on siglip2-base-patch16-224 and DomainNet (single-domain, multi-source adaptation), with Fashion-MNIST & More for experimental testing. ๐งคโ๏ธ
Fashion-Mnist : prithivMLmods/Fashion-Mnist-SigLIP2
Mnist-Digits : prithivMLmods/Mnist-Digits-SigLIP2
Multisource-121 : prithivMLmods/Multisource-121-DomainNet
Painting-126 : prithivMLmods/Painting-126-DomainNet
Sketch-126 : prithivMLmods/Sketch-126-DomainNet
Clipart-126 : prithivMLmods/Clipart-126-DomainNet
Models are trained with different parameter settings for experimental purposes only, with the intent of further development. Refer to the model page below for instructions on running it with Transformers ๐ค.
Collection : prithivMLmods/domainnet-0324-67e0e3c934c03cc40c6c8782
Citations : SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features https://arxiv.org/pdf/2502.14786 & Moment Matching for Multi-Source Domain Adaptation : https://arxiv.org/pdf/1812.01754
Fashion-Mnist : prithivMLmods/Fashion-Mnist-SigLIP2
Mnist-Digits : prithivMLmods/Mnist-Digits-SigLIP2
Multisource-121 : prithivMLmods/Multisource-121-DomainNet
Painting-126 : prithivMLmods/Painting-126-DomainNet
Sketch-126 : prithivMLmods/Sketch-126-DomainNet
Clipart-126 : prithivMLmods/Clipart-126-DomainNet
Models are trained with different parameter settings for experimental purposes only, with the intent of further development. Refer to the model page below for instructions on running it with Transformers ๐ค.
Collection : prithivMLmods/domainnet-0324-67e0e3c934c03cc40c6c8782
Citations : SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features https://arxiv.org/pdf/2502.14786 & Moment Matching for Multi-Source Domain Adaptation : https://arxiv.org/pdf/1812.01754
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Hi community,
Few days back, I posted about my ongoing research on making reasoning mamba models and I found great insights from the community.
Today, I am announcing an update to the model weights. With newer checkpoints, the Falcon3 Mamba R1 model now outperforms very large transformer based LLMs (including Gemini) for Formal Logic questions of MMLU. It scores 60% on formal logic which is considered a tough subset of questions in MMLU.
I would highly appreciate your insights and suggestions on this new checkpoint.
Model Repo: hanzla/Falcon3-Mamba-R1-v0
Chat space: hanzla/Falcon3MambaReasoner
Few days back, I posted about my ongoing research on making reasoning mamba models and I found great insights from the community.
Today, I am announcing an update to the model weights. With newer checkpoints, the Falcon3 Mamba R1 model now outperforms very large transformer based LLMs (including Gemini) for Formal Logic questions of MMLU. It scores 60% on formal logic which is considered a tough subset of questions in MMLU.
I would highly appreciate your insights and suggestions on this new checkpoint.
Model Repo: hanzla/Falcon3-Mamba-R1-v0
Chat space: hanzla/Falcon3MambaReasoner
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Comparing reconstruction quality of various VAEs with an interactive demo
rizavelioglu/vae-comparison
rizavelioglu/vae-comparison
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Datasets Convertor ๐
openfree/Datasets-Convertor
Welcome to Datasets Convertor, the cutting-edge solution engineered for seamless and efficient data format conversion. Designed with both data professionals and enthusiasts in mind, our tool simplifies the transformation process between CSV, Parquet, and JSONL, XLS file formats, ensuring that your data is always in the right shape for your next analytical or development challenge. ๐ปโจ
Why Choose Datasets Convertor?
In todayโs data-driven world, managing and converting large datasets can be a daunting task. Our converter is built on top of robust technologies like Pandas and Gradio, delivering reliable performance with a modern, intuitive interface. Whether youโre a data scientist, analyst, or developer, Datasets Convertor empowers you to effortlessly switch between formats while maintaining data integrity and optimizing storage.
Key Features and Capabilities:
CSV โ Parquet Conversion:
Easily transform your CSV files into the highly efficient Parquet format and vice versa. Parquetโs columnar storage not only reduces file size but also accelerates query performanceโa critical advantage for big data analytics. ๐๐
CSV to JSONL Conversion:
Convert CSV files to JSONL (newline-delimited JSON) to facilitate efficient, line-by-line data processing. This format is particularly useful for streaming data applications, logging systems, and scenarios where incremental data processing is required. Each CSV row is meticulously converted into an individual JSON record, preserving all the metadata and ensuring compatibility with modern data pipelines. ๐โก๏ธ๐
Parquet to JSONL Conversion:
For those working with Parquet files, our tool offers a streamlined conversion to JSONL.
Parquet to XLS Conversion.
openfree/Datasets-Convertor
Welcome to Datasets Convertor, the cutting-edge solution engineered for seamless and efficient data format conversion. Designed with both data professionals and enthusiasts in mind, our tool simplifies the transformation process between CSV, Parquet, and JSONL, XLS file formats, ensuring that your data is always in the right shape for your next analytical or development challenge. ๐ปโจ
Why Choose Datasets Convertor?
In todayโs data-driven world, managing and converting large datasets can be a daunting task. Our converter is built on top of robust technologies like Pandas and Gradio, delivering reliable performance with a modern, intuitive interface. Whether youโre a data scientist, analyst, or developer, Datasets Convertor empowers you to effortlessly switch between formats while maintaining data integrity and optimizing storage.
Key Features and Capabilities:
CSV โ Parquet Conversion:
Easily transform your CSV files into the highly efficient Parquet format and vice versa. Parquetโs columnar storage not only reduces file size but also accelerates query performanceโa critical advantage for big data analytics. ๐๐
CSV to JSONL Conversion:
Convert CSV files to JSONL (newline-delimited JSON) to facilitate efficient, line-by-line data processing. This format is particularly useful for streaming data applications, logging systems, and scenarios where incremental data processing is required. Each CSV row is meticulously converted into an individual JSON record, preserving all the metadata and ensuring compatibility with modern data pipelines. ๐โก๏ธ๐
Parquet to JSONL Conversion:
For those working with Parquet files, our tool offers a streamlined conversion to JSONL.
Parquet to XLS Conversion.
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2 months ago
ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features
Paper
โข
2502.04320
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Published
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37
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
Paper
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2502.02737
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Published
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224
AIN: The Arabic INclusive Large Multimodal Model
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2502.00094
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Published
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17
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