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Hello OLMo: A truly open LLM

blog.allenai.org

41–50 of 76 posts

Re: Hello OLMo: A truly open LLM

#41

This is the only LLM that is exciting to me. Clearly, LLMs are powerful tools that may end up replacing search and even go much further than simple searches by performing the research for you and producing final answers. Closed models like those from Open AI (ironically) or Anthropic cannot be audited. When most users will end up blindly hitting Microsoft’s Copilot button, which they are forcing OEMs to adopt, who’s…

> Open weight models like Llama keep repeatedly catching up to the best closed models from OpenAI or Anthropic or others.

Since when? I’ve had the complete opposite experience.

Re: Hello OLMo: A truly open LLM

#43

This is the only LLM that is exciting to me. Clearly, LLMs are powerful tools that may end up replacing search and even go much further than simple searches by performing the research for you and producing final answers. Closed models like those from Open AI (ironically) or Anthropic cannot be audited. When most users will end up blindly hitting Microsoft’s Copilot button, which they are forcing OEMs to adopt, who’s…

> For example, Google’s Gemini had secret meta prompts that biased it towards certain types of answers and also caused it to produce hallucinated images that were funny but also dystopian ( https://arstechnica.com/information-technology/2024/02/googl ...). Such a bizarre take to call this "dystopian". The model happened to create some out-there pictures. I mean, it's no more outlandish then giant dragons and snakes a…

Right, "who cares" about the truth in our dystopian world? 1984 is apparently too long ago for people to remember the ministry of truth...

Re: Hello OLMo: A truly open LLM

#44
post #13

This is the only LLM that is exciting to me. Clearly, LLMs are powerful tools that may end up replacing search and even go much further than simple searches by performing the research for you and producing final answers. Closed models like those from Open AI (ironically) or Anthropic cannot be audited. When most users will end up blindly hitting Microsoft’s Copilot button, which they are forcing OEMs to adopt, who’s…

Pet peeve: Google's Gemini LLM model was not to blame for the image generation weirdness. That would be like blaming DALL-E weirdness on GPT-4. Unfortunately, Google marketing decided to slap the "Gemini" brand on both the end-user interface used to interact with the model AND the actual model itself, hence people constantly calling out Gemini-the-model for weird decisions made as part of Gemini-the-user-interface.

> That would be like blaming DALL-E weirdness on GPT-4.

Actually when you trigger DALL-E through GPT-4 (i.e. with the LLM generating the prompt to give the diffusion model then returning the resulting image to the user) the LLM's system instructions [1] say "7. Diversify depictions of ALL images with people to always include always DESCENT and GENDER for EACH person using direct terms." and a bunch of stuff along those lines.

In OpenAI's system this doesn't always trigger; if the user asks for an image of trash being collected, the user hasn't explicitly asked for any people to be depicted, so the LLM doesn't find anything in the prompt that needs diversity added. The trash-being-collected prompt gets passed to DALL-E unmodified, and the resulting image has all male workers.

[1] https://raw.githubusercontent.com/spdustin/ChatGPT-AutoExper...

Re: Hello OLMo: A truly open LLM

#45
The hype around LLMs won't last past 2030 I suppose. LLMs - we have statistical inference soup that gets outdated like stagnant pond water and by each passing day, becoming less accurate.

I am curious how long the hype wave lasts. Ones I have recently seen was K8S. It settled down and won TBH.

Re: Hello OLMo: A truly open LLM

#46

It's odd. Running inference on this (and other models in its class) and I keep running into a "repeating token" situation with moderate-to-long context windows. It feels almost as if, during inference, the model hits some format of local minimum that it careens around, and while temperature seems to affect this - it doesn't really fix it. at temp 0.2: > [{'generated_text': 'What follows is a transcript of a talk betw…

... this can get a little goofy even with do_sample=False and no temp: | [{'generated_text': "DAUGHTER: tell me a story FATHER: but it's late DAUGHTER: please? FATHER: okay, once upon a time there was a little girl who lived in a little house with her mother and father and her brother and sister and her dog and her cat and her hamster and her fish and her bird and her rabbit and her horse and her cow and her sheep an…

That's seems a perfect story to put a little child to bed :D.

I have used a similar recursive story in the past. My son still jokes about it.

Re: Hello OLMo: A truly open LLM

#47
post #19
post #9

Is this one of the first LLMs of note that was successfully trained on AMD GPUs? I wonder how seamless the process was and if they faced any issues there.

Databricks (who also participated in OLMo, it's probably the same codebase) trained on AMD before, see 2023 post https://www.databricks.com/blog/amd-mi250 . It was probably seamless, as any issues were fixed by Databricks in 2023.

[deleted]

Re: Hello OLMo: A truly open LLM

#49
post #9

Is this one of the first LLMs of note that was successfully trained on AMD GPUs? I wonder how seamless the process was and if they faced any issues there.

https://huggingface.co/LumiOpen/Poro-34B Also fully trained on LUMI.

(more models here: https://huggingface.co/LumiOpen)

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