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Notes on Llama 4: The Hits, the Misses, and the Disasters
- The Llama 4 family includes Scout, Maverick, and Behemoth models, with Behemoth still in training and reportedly outperforming current models.
- Despite having three models, the Llama license limitations remain unchanged, restricting use for companies with over 700 million monthly users and excluding Europeans.
- Meta shifted from dense models to Mixture of Experts in Llama 4, with the Scout having a 10M context length, outperforming past models.
- The Llama 4 models are praised for their natively multi-modal capabilities, understanding texts, images, audio, and videos.
- Teacher-student distillation from Llama 4 Behemoth to Maverick marks a significant quality improvement step.
- Concerns arise as Llama 4 models underperform peers in various benchmarks, including coding tasks, long-form writing, and multi-tool calls.
- Llama 4 faces criticism for confused positioning in the market, not being affordable yet lacking brilliance compared to rivals.
- Issues like exaggerated context length claims, benchmark discrepancies, and tokenization troubles plague the Llama 4 launch.
- While hope remains for Behemoth to redeem Meta's reputation, concerns over its performance compared to Grok 3 linger.
- The rushed release of Llama 4 has led to benchmark controversies and overall disappointment in model performance across various evaluations.
- Despite the setbacks, Meta aims to address and improve the issues faced with Llama 4, hoping to stabilize implementations and unlock the models' value.
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