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[TAO_Lover @Bittensor_nerd]

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[TAO Community Hub @TAOCommunityHub]

🚀 Exciting News from the decentralized AI Frontier! 🚀

The Bittensor $TAO network has just witnessed the launch of a groundbreaking new subnet - SN6-Nous, brought to life by the brilliant minds at @NousResearch, @theemozilla, and @teknium. This is not just any subnet; it's a new model finetuning-subnet, a testament to the relentless pursuit of innovation in the realm of AI and ML.

🧠 Subnet 6: Nous Research - A Leap in AI Fine-Tuning

The Nous-Bittensor subnet is a game-changer, rewarding miners for fine-tuning Large Language Models (LLMs) with a continuous stream of synthetic data from subnet 18. It stands as the first-ever continuous fine-tuning benchmark, with fresh data generated daily, and the first incentivized fine-tuning benchmark. Moreover, it's the first Bittensor subnet to achieve true cross-boundary communication, leveraging data from one subnet to enhance another.

🌟 Recognized by Visionaries

The team behind this innovation is among the most advanced in AI and ML. Their work has not only pushed the boundaries of technology but has also earned admiration from industry leaders, including @elonmusk, who has commented on the impressive work of @teknium .

🔗 How It Works

1. Miners train LLMs and publish them on 🤗 Hugging Face, recording the metadata on the Bittensor chain to timestamp their training efforts.
2. Validators access these models based on the chain metadata and continuously evaluate them against the synthetic data, assigning weights based on performance.
3. The Bittensor chain, using Yuma Consensus, aggregates these weights from all active validators to determine the TAO rewards distribution for miners and validators.

💡 Incentive Mechanism

Bittensor's unique incentive mechanism evaluates miners through validators' assessments. Validators set weights, which are recorded on Bittensor's blockchain, reflecting the value of miners' contributions. These weights, along with the validators' TAO holdings, feed into the Yuma Consensus, which drives validators toward a consensus on the value of miners' work. The most effective miners are rewarded with TAO, the network's digital currency.

🏆 TheChallenge for Miners

Miners in this subnet are evaluated based on their models' performance, specifically aiming for the lowest loss compared to others when tested on the latest synthetic data.

🌐 Join the Revolution

Dive into this pioneering journey with Nous Research and Bittensor network and become part of a community that's shaping the future of AI. Stay tuned for more updates and breakthroughs as we continue to redefine the possibilities of machine learning and blockchain integration.

🔍 Discover More

To explore the intricacies of the Nous-Bittensor subnet and how you can contribute to this cutting-edge project, visit https://t.co/8Ie1tFKgig. Your adventure into the next generation of AI starts here!

[Nous Research @NousResearch]

Today we are announcing our latest project, an effort to provide a new evaluation system for open source models. Traditional benchmarking leans heavily on public datasets which can be easy to game and often lead to superficial score improvements that mask true model capabilities (or lack thereof), misdirecting efforts towards meaningless score chasing.

To achieve this, we built a subnet on Bittensor, a decentralized network for AI projects. This system allows you to submit your finetuned model, and then validators will evaluate all models submitted against freshly generated data from the Cortex subnet. The Cortex subnet is a dynamic source of fresh synthetic data, continuously generated by GPT-4, which allows for fresh and unpredictable high quality data to test models against, and ensures a fair and accurate consensus on which model most closely mirrors GPT-4's performance.

This new system aims to celebrate and reward creators of public, open source models that genuinely meet user needs, through the incentive structures built into Bittensor.

We plan to continually evolve and expand the diversity and quality of that continuous data stream over time, to keep the evaluations fresh, challenging, and more and more representative of user needs in every new iteration.

Learn more on the following pages:
Nous Subnet Leaderboard - https://t.co/MbVvCuWVCD
Nous Subnet Repository - https://t.co/wZfLH2HMHD
Bittensor - https://t.co/rCT8OdASLR
Cortex Subnet Repository - https://t.co/KWj4fLRRQr

[TAO Community Hub @TAOCommunityHub]

Bittensor $TAO Subnet 10 Explained: Streamlining Big Data with a Community Effort

Imagine you're at a dinner, where everyone brings a dish to share. This gathering is a fun way to enjoy a variety of foods without anyone having to cook the entire meal alone. Now, let's use this potluck analogy to explain how Bittensor Subnet 10 works with a concept called 'map-reduce'.

Dividing the Recipe: The Map Step

First, consider a big, complex recipe that's too much for one person to make alone. To manage this, you break the recipe into smaller, simpler steps and hand them out to different friends. Each friend is like a 'miner' in the Bittensor network, and each step of the recipe is a piece of data or a task that needs to be processed.

Combining the Flavors: The Reduce Step

After your friends finish their assigned cooking tasks, they bring their prepared ingredients to the table. Together, you combine these ingredients to complete the dish. This is like the 'reduce' step, where miners send back their processed data, and it's all put together to form the final result, which could be a completed dataset or the next phase of a machine learning model.

Quality Taste Test: The Validation

To make sure the dish tastes good and each ingredient is cooked right, you have a taste tester (the 'validator') who samples a bit of each friend's contribution before it's mixed in. This ensures that the final dish will be delicious. In the Bittensor network, the validator checks the miners work to ensure the data has been processed correctly.

Rewarding the Chefs

Just as friends might get compliments or a small gift for contributing to the potluck, miners in the Bittensor Subnet 10 are rewarded with $TAO tokens for offering their computing resources and completing tasks. This incentive encourages participation and ensures that the network remains robust and efficient.

The Community Feast

By using this collaborative approach, Bittensor Subnet 10 effectively handles large and complex computing tasks, much like a community potluck makes it possible to enjoy a feast that no one person could prepare alone. It's a smart way to share the workload, enjoy a variety of contributions, and ensure a successful outcome through cooperation and validation. Interested in more technical details? Check out SN10 GitHub site here-https://t.co/VUqCi9hnLd

[TAO Community Hub @TAOCommunityHub]

Are you wondering what all the noise is about with @NousResearch and their new Bittensor $TAO subnet? If you missed today's TGIFT, you seriously need to catch up. Check it out here-https://t.co/gBiqmC5Hyp

[TAO_Lover @Bittensor_nerd]

@cryptocoinfi @Tangem Love it!

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[TAO_Lover @Bittensor_nerd]

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[TAO_Lover @Bittensor_nerd]

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[PYROS WORLD 🔥 @PyroXRD]

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