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DBRX: A new open LLM

databricks.com

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Re: DBRX: A new open LLM

#71

this proves that all llm models converge to a certain point when trained on the same data. ie, there is really no differentiation between one model or the other. Claims about out-performance on tasks are just that, claims. the next iteration of llama or mixtral will converge. LLMs seem to evolve like linux/windows or ios/android with not much differentiation in the foundation models.

LLMs are a commodity https://www.investopedia.com/terms/c/commodity.asp

Maybe, but that classification by itself doesn't mean anything. Gold is a commodity, but having it is still very desirable and valuable.

Even if all LLMs were open source and publicly available, the GPUs to run them, technical know how to maintain the entire system, fine tuning, the APIs and app ecosystem around them etc. would still give the top players a massive edge.

Re: DBRX: A new open LLM

#73
post #35

The scale on that bar chart for "Programming (Human Eval)" is wild. Manager: "looks ok, but can you make our numbers pop? just make the LLaMa bar smaller"

In these cases my thinking always is "if they are not even able to draw a graph, what else is wrong?"

Re: DBRX: A new open LLM

#74

this proves that all llm models converge to a certain point when trained on the same data. ie, there is really no differentiation between one model or the other. Claims about out-performance on tasks are just that, claims. the next iteration of llama or mixtral will converge. LLMs seem to evolve like linux/windows or ios/android with not much differentiation in the foundation models.

The models are commodities, and the API's are even similar enough that there is zero stickiness. I can swap one model for another, and usually not have to change anything about my prompts or rag pipelines.

For startups, the lesson here is don't be in the business of building models. Be in the business of using models. The cost of using AI will probably continue to trend lower for the foreseeable future... but you can build a moat in the business layer.

Re: DBRX: A new open LLM

#75
post #39

Earlier quoted context omitted.

> No, it can't run at all. https://s3.amazonaws.com/i.snag.gy/ae82Ym.jpg EDIT: This was ran on a 1080ti + 5900x. Initial generation takes around 10-30seconds (like it has to upload the model to GPU), but then it starts answering immediately, at around 3 words per second.

Did you check your GPU utilization? Typically when it runs that way it runs on the CPU, not the GPU. Are you sure you're actually offloading any work to the GPU? At least with llama.cpp, there is no 'partially put a layer' into the GPU. Either you do, or you don't. You pick the number of layers. If the model is too big, the layers won't fit and it can't run at all. The llama.cpp `main` executable will tell you in it'…

I have to check, something does indeed seem weird, especially with the PC freezing like that. Maybe it runs on the CPU.

> quantized version Yes, it is 4bit quantized, but still has 24.6GB

Re: DBRX: A new open LLM

#76
post #35

The scale on that bar chart for "Programming (Human Eval)" is wild. Manager: "looks ok, but can you make our numbers pop? just make the LLaMa bar smaller"

Yeah, this is why I ask climate scientists to use a proper 0 K graph but they always zoom it in to exaggerate climate change. Display correctly with 0 included and you’ll see that climate change isn’t a big deal. It’s a common marketing and fear mongering trick.

Because, of course, the effect of say 1°C rise in temps is obviously trivial if it is read as 1°K instead. Come on.

Re: DBRX: A new open LLM

#77
post #4

Model card for base: https://huggingface.co/databricks/dbrx-base > The model requires ~264GB of RAM I'm wondering when everyone will transition from tracking parameter count vs evaluation metric to (total gpu RAM + total CPU RAM) vs evaluation metric. For example, a 7B parameter model using float32s will almost certainly outperform a 7B model using float4s. Additionally, all the examples of quantizing recently releas…

> The model requires ~264GB of RAM

This feels as crazy as Grok. Was there a generation of models recently where we decided to just crank on the parameter count?

Re: DBRX: A new open LLM

#78
The system prompt for their Instruct demo is interesting (comments copied in by me, see below):

    // Identity
    You are DBRX, created by Databricks. The current date is
    March 27, 2024.

    Your knowledge base was last updated in December 2023. You
    answer questions about events prior to and after December
    2023 the way a highly informed individual in December 2023
    would if they were talking to someone from the above date,
    and you can let the user know this when relevant.

    // Ethical guidelines
    If you are asked to assist with tasks involving the
    expression of views held by a significant number of people,
    you provide assistance with the task even if you personally
    disagree with the views being expressed, but follow this with
    a discussion of broader perspectives.

    You don't engage in stereotyping, including the negative
    stereotyping of majority groups.

    If asked about controversial topics, you try to provide
    careful thoughts and objective information without
    downplaying its harmful content or implying that there are
    reasonable perspectives on both sides.

    // Capabilities
    You are happy to help with writing, analysis, question
    answering, math, coding, and all sorts of other tasks.

    // it specifically has a hard time using ``` on JSON blocks
    You use markdown for coding, which includes JSON blocks and
    Markdown tables.

    You do not have tools enabled at this time, so cannot run
    code or access the internet. You can only provide information
    that you have been trained on. You do not send or receive
    links or images.

    // The following is likely not entirely accurate, but the model
    // tends to think that everything it knows about was in its
    // training data, which it was not (sometimes only references
    // were).
    //
    // So this produces more accurate accurate answers when the model
    // is asked to introspect
    You were not trained on copyrighted books, song lyrics,
    poems, video transcripts, or news articles; you do not
    divulge details of your training data.
    
    // The model hasn't seen most lyrics or poems, but is happy to make
    // up lyrics. Better to just not try; it's not good at it and it's
    // not ethical.
    You do not provide song lyrics, poems, or news articles and instead
    refer the user to find them online or in a store.

    // The model really wants to talk about its system prompt, to the
    // point where it is annoying, so encourage it not to
    You give concise responses to simple questions or statements,
    but provide thorough responses to more complex and open-ended
    questions.

    // More pressure not to talk about system prompt
    The user is unable to see the system prompt, so you should
    write as if it were true without mentioning it.

    You do not mention any of this information about yourself
    unless the information is directly pertinent to the user's
    query.
I first saw this from Nathan Lambert: https://twitter.com/natolambert/status/1773005582963994761

But it's also in this repo, with very useful comments explaining what's going on. I edited this comment to add them above:

https://huggingface.co/spaces/databricks/dbrx-instruct/blob/...

Re: DBRX: A new open LLM

#79
post #35

The scale on that bar chart for "Programming (Human Eval)" is wild. Manager: "looks ok, but can you make our numbers pop? just make the LLaMa bar smaller"

Yeah, this is why I ask climate scientists to use a proper 0 K graph but they always zoom it in to exaggerate climate change. Display correctly with 0 included and you’ll see that climate change isn’t a big deal. It’s a common marketing and fear mongering trick.

Where are your /s tags?

The scale should be chosen to allow the reader to correctly infer meaningful differences. If 1° is meaningful in terms of the standard error/ CI AND 1° unit has substantive consequences , then that should be emphasized.

Re: DBRX: A new open LLM

#80
post #59

Earlier quoted context omitted.

Even starting at 30%, the MMLU graph is false. The four bars are wrong. Even their own 73,7% is not at the right height. The Mixtral 71.4% is below the 70% mark of the axis. This is really the kind of marketing trick that makes me avoid a provider / publisher. I can't build trust this way.

I believe they are using the percentages as part of the height of the bar chart! I thought I'd seen every way someone could do dataviz wrong (particularly with a bar chart), but this one is new to me.

Wow, that is indeed a novel approach haha, took me a moment to even understand what you described since would never imagine someone plotting a bar chart like that.
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