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This public link is valid for 7 days and shares a thread, including any personal information you added. This link or copies made by others cannot be deleted. If you share with third parties, their policies apply. Can’t copy the link right now. Try again later.
Leaked benchmarks show the Raven Exclusive scoring 85% on GSM8K (math reasoning). For context, Llama 3 8B scores around 78%. How does a model 40x smaller do this? completetinymodelraven exclusive
: Sometimes it's easier to write the core arguments before tackling the introduction and conclusion. This prevents "writer's block" on the first paragraph.
Due to the 32k context window, you can load a 500-chunk vector database into memory. The handles the cross-attention without OOM errors, a feat tiny models rarely achieve. This public link is valid for 7 days
But what exactly is the ? Why is it gaining traction in edge-computing circles, and how can you leverage its power?
The numbers show a clear value proposition: for edge applications where latency and privacy are paramount, the Exclusive version delivers superior performance without any increase in physical memory. Can’t copy the link right now
The model comprises 12 transformer blocks, each with:
Have you integrated the CompleteTinyModelRaven Exclusive into your stack? Join the Raven Discord community to share benchmarks and custom fine-tunes.
