211–220 of 695 posts
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Personally I’d prefer that LLMs did not refer to themselves as “I”. It’s software, not an “I”.
If I start a prompt with "Can you...", what do you suggest the LLM to respond? Or do you think I'm doing it wrong?
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Llama 4 Scout, Maximum context length: 10M tokens. This is a nice development.
Is the recall and reasoning equally good across the entirety of the 10M token window? Cause from what I've seen many of those window claims equate to more like a functional 1/10th or less context length.
It will be fun to see what we get here, but I have no doubt the extra tokens will be useful - lots of use cases can do almost as well with summary-level accuracy memory.
It seems to be comparable to other top models. Good, but nothing ground breaking.
It means you can run it on a high-ram apple silicon and it's going to be insanely fast on groq (thousands of tokens per second). Time to first token will bottleneck the generation.
"It’s well-known that all leading LLMs have had issues with bias—specifically, they historically have leaned left when it comes to debated political and social topics. This is due to the types of training data available on the internet." Perhaps. Or, maybe, "leaning left" by the standards of Zuck et al. is more in alignment with the global population. It's a simpler explanation.
Worldwide centrist and conservative groups account for 60%+ of the population. The training data bias is due to the traditional structure of Internet media which reflects the underlying population very poorly. See also for example recent USAID gutting and reasons behind it.
Source?
>See also for example recent USAID gutting and reasons behind it.
A very politically motivated act does not prove anything about the “traditional structure of Internet media which reflects the underlying population very poorly”.
From model cards, suggested system prompt: > You are Llama 4. Your knowledge cutoff date is August 2024. You speak Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese. Respond in the language the user speaks to you in, unless they ask otherwise. It's interesting that there's no single one of CJK languages mentioned. I'm tempted to call this a racist model ev…
The (smaller) Scout model is really attractive for Apple Silicon. It is 109B big but split up into 16 experts. This means that the actual processing happens in 17B. Which means responses will be as fast as current 17B models. I just asked a local 7B model (qwen 2.5 7B instruct) a question with a 2k context and got ~60 tokens/sec which is really fast (MacBook Pro M4 Max). So this could hit 30 token/sec. Time to first…
> the actual processing happens in 17B This is a common misconception of how MoE models work. To be clear, 17B parameters are activated for each token generated . In practice you will almost certainly be pulling the full 109B parameters though the CPU/GPU cache hierarchy to generate non-trivial output, or at least a significant fraction of that.
The (smaller) Scout model is really attractive for Apple Silicon. It is 109B big but split up into 16 experts. This means that the actual processing happens in 17B. Which means responses will be as fast as current 17B models. I just asked a local 7B model (qwen 2.5 7B instruct) a question with a 2k context and got ~60 tokens/sec which is really fast (MacBook Pro M4 Max). So this could hit 30 token/sec. Time to first…
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Castrated, if you're trying way too hard (and not well) to avoid getting called on that overly emotive metaphor: a capon is a gelded rooster.
There is a key distinction and context: caponation has a productive purpose from the pov of farmers and their desired profits.
"It’s well-known that all leading LLMs have had issues with bias—specifically, they historically have leaned left when it comes to debated political and social topics. This is due to the types of training data available on the internet." Perhaps. Or, maybe, "leaning left" by the standards of Zuck et al. is more in alignment with the global population. It's a simpler explanation.
The truth has a well known liberal bias -- Stephen Colbert
"It’s well-known that all leading LLMs have had issues with bias—specifically, they historically have leaned left when it comes to debated political and social topics. This is due to the types of training data available on the internet." Perhaps. Or, maybe, "leaning left" by the standards of Zuck et al. is more in alignment with the global population. It's a simpler explanation.
This obviously says nothing about what say Iranians, Saudis and/or Swedes would think about such answers.