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Ayush Thakur

ayush-thakur02
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https://bio.link/ayush_thakur02

AI & ML interests

LLM, NLP, RAG, Distributed Computing

Organizations

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ayush-thakur02's activity

reacted to Wauplin's post with ā¤ļø 8 months ago
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3379
šŸš€ I'm excited to announce that huggingface_hub's InferenceClient now supports OpenAI's Python client syntax! For developers integrating AI into their codebases, this means you can switch to open-source models with just three lines of code. Here's a quick example of how easy it is.

Why use the InferenceClient?
šŸ”„ Seamless transition: keep your existing code structure while leveraging LLMs hosted on the Hugging Face Hub.
šŸ¤— Direct integration: easily launch a model to run inference using our Inference Endpoint service.
šŸš€ Stay Updated: always be in sync with the latest Text-Generation-Inference (TGI) updates.

More details in https://huggingface.co/docs/huggingface_hub/main/en/guides/inference#openai-compatibility
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reacted to Niansuh's post with šŸ‘ 9 months ago
reacted to their post with šŸ‘ 10 months ago
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2920
Enhancing Distributed Systems with Self-Healing Nodes and Adaptive Data Sharding

Paper: Self-healing Nodes with Adaptive Data-Sharding (2405.00004)

The paper introduces an innovative approach to improve distributed systems by integrating self-healing nodes with adaptive data sharding. This method leverages advanced concepts like self-replication, fractal regeneration, and predictive sharding to enhance scalability, performance, fault tolerance, and adaptability.

Key Concepts:
- Self-Replication: Nodes can create copies of themselves or their data to aid in recovery and load balancing.
- Fractal Regeneration: Nodes can reconfigure and restore their functionality after partial damage, inspired by natural fractals.
- Predictive Sharding: Nodes can anticipate future data trends and proactively adjust data distribution to optimize performance.

Methodology:
The approach consists of four main steps:
- Temporal data sharding based on data's temporal characteristics.
- Self-replicating nodes to enhance data availability and reliability.
- Fractal regeneration for robust recovery mechanisms.
- Predictive sharding using consistent hashing to anticipate and adapt to future data trends.

Results and Analysis:
Experimental evaluations show that this approach outperforms existing data sharding techniques in scalability, performance, fault tolerance, and adaptability. The use of synthetic data and workload generators created realistic scenarios for testing.

Applications:
The methodology can be applied to various domains such as distributed database systems, blockchain networks, IoT, and cloud computing, offering improvements in data distribution efficiency and system resilience.
posted an update 10 months ago
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2920
Enhancing Distributed Systems with Self-Healing Nodes and Adaptive Data Sharding

Paper: Self-healing Nodes with Adaptive Data-Sharding (2405.00004)

The paper introduces an innovative approach to improve distributed systems by integrating self-healing nodes with adaptive data sharding. This method leverages advanced concepts like self-replication, fractal regeneration, and predictive sharding to enhance scalability, performance, fault tolerance, and adaptability.

Key Concepts:
- Self-Replication: Nodes can create copies of themselves or their data to aid in recovery and load balancing.
- Fractal Regeneration: Nodes can reconfigure and restore their functionality after partial damage, inspired by natural fractals.
- Predictive Sharding: Nodes can anticipate future data trends and proactively adjust data distribution to optimize performance.

Methodology:
The approach consists of four main steps:
- Temporal data sharding based on data's temporal characteristics.
- Self-replicating nodes to enhance data availability and reliability.
- Fractal regeneration for robust recovery mechanisms.
- Predictive sharding using consistent hashing to anticipate and adapt to future data trends.

Results and Analysis:
Experimental evaluations show that this approach outperforms existing data sharding techniques in scalability, performance, fault tolerance, and adaptability. The use of synthetic data and workload generators created realistic scenarios for testing.

Applications:
The methodology can be applied to various domains such as distributed database systems, blockchain networks, IoT, and cloud computing, offering improvements in data distribution efficiency and system resilience.
posted an update 11 months ago
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2645
Super RAGs in Mistral 8x7B-v1 -
The recent paper on arXiv introduces Super Retrieval-Augmented Generation (Super RAGs), a groundbreaking approach to improve Large Language Models (LLMs) by integrating external knowledge sources. This integration into the Mistral 8x7B v1 LLM has shown notable improvements in accuracy, speed, and user satisfaction.

What are your thoughts on the potential of Super RAGs to transform the future of AI and LLMs?

Introducing Super RAGs in Mistral 8x7B-v1 (2404.08940)