Has Llama 3.1 405B Been Leaked? Where to Download Llama 3.1 405B Now

The artificial intelligence community is buzzing with excitement over the alleged leak of Meta's highly anticipated Llama 3.1 405B language model. This massive 405 billion parameter model represents a significant leap forward in AI capabilities, promising unprecedented performance across a wide range of natural language processing tasks. In this article, we'll explore the current situation surrounding Llama-3-405B, its technical specifications, and where you can potentially download it right now.

The Llama 3.1 405B Leak: Fact or Fiction?

Rumors of a LlamaLlama 3.1 405B leak began circulating on various online forums and social media platforms in recent days. While Meta has not officially confirmed or denied these claims, several sources claim to have access to the model weights and are sharing download links.

Origins of the Leak

The alleged leak appears to have originated on an anonymous imageboard, with users sharing magnet links and torrent files for a massive 764 GiB (approximately 820 GB) download purported to be the Llama 3.1 405B base model. This file size is consistent with what one would expect for a model of this scale, lending some credibility to the claims.

Where to Download Llama 3.1 405B

If you're eager to get your hands on Llama 3.1 405B, there are several potential avenues to explore. However, it's important to note that downloading and using leaked models may violate terms of service or legal agreements.

The most widely circulated method for obtaining Llama 3.1 405B is through torrent downloads. A magnet link has been shared on various platforms, allowing users to download the model using BitTorrent clients.

Llama 3.1 405B Torrent Download Link:

Magnet: magnet:?xt=urn:btih:c0e342ae5677582f92c52d8019cc32e1f86f1d83&dn=miqu-2&tr=udp%3A%2F%http://2Ftracker.openbittorrent.com%3A80

You can also try to download Llama 3.1 405B leak this link from miqu-2.

Hugging Face Repositories (Already Deleted):

Some users claim to have uploaded the model weights to Hugging Face, a popular platform for sharing machine learning models. However, these uploads may be quickly taken down due to potential copyright issues.

Here is the Now Disable Hugging Face Link: https://huggingface.co/cloud-district/miqu-2

Llama 3.1 405B vs GPT-4 vs Claude 3.5 Benchmark Comparison

When comparing Llama 3.1 405B to GPT-4 and Claude 3.5 Sonnet, we see a competitive landscape:

• BoolQ: Llama 3.1 405B (0.921) outperforms GPT-4 (0.905)
• GSM8K: Llama 3.1 405B (0.968) surpasses GPT-4 (0.942)
• HumanEval: GPT-4 (0.921) leads, with Llama 3.1 405B (0.854) following
• MMLU: Llama 3.1 405B shows strong performance, potentially rivaling GPT-4 and Claude 3.5 Sonnet

While specific benchmark scores for Claude 3.5 Sonnet are not provided, Anthropic claims it sets new industry benchmarks for graduate-level reasoning (GPQA), undergraduate-level knowledge (MMLU), and coding proficiency (HumanEval). This suggests that Claude 3.5 Sonnet is likely competitive with, if not superior to, both Llama 3.1 405B and GPT-4 in these areas.

Key Observations

1. Open-source Breakthrough: If these benchmarks hold true, Llama 3.1 405B could represent a significant milestone as an open-source model competing with top closed-source alternatives.
2. Specialized Strengths: Each model shows particular strengths in different areas. For instance, GPT-4 excels in coding tasks (HumanEval), while Llama 3.1 405B shows exceptional performance in mathematical reasoning (GSM8K).
3. Rapid Progress: The quick advancement from Llama 3 to Llama 3.1, with substantial improvements in performance, highlights the fast-paced nature of AI development.
4. Potential for Fine-tuning: It's important to note that these benchmarks represent base model performance. With further fine-tuning, each model's capabilities could be enhanced for specific tasks or domains.While these benchmarks provide valuable insights into the relative strengths of Llama 3.1 405B, GPT-4, and Claude 3.5 Sonnet, it's crucial to remember that real-world performance can vary. Factors such as specific use cases, fine-tuning, and ongoing model updates can significantly impact a model's effectiveness in practical applications. As the AI field continues to evolve, we can expect further advancements and shifts in the competitive landscape of large language models.

As we await the official release and comprehensive benchmarks of Llama-3-405B, the AI community remains abuzz with speculation and excitement. Whether it lives up to the hype or not, this model represents another significant step in the rapid evolution of large language models, promising to push the boundaries of what's possible in artificial intelligence.