Is llama 2 a good fit considering its small context window?
If you have the compute, might as well use the better model :)
The 3.2 series wasn't the kind of leap that 3.0 -> 3.1 was in terms of intelligence; it was just:
1. Meta releasing multimodal vision models for the first time (11B and 90B), and
2. Meta releasing much smaller models than the 3.1 series (1B and 3B).
I presume you want information of some value to you otherwise you wouldn't bother reading an article. Then you feed it to a probabilistic algorithm and so you can not have any idea what the output has to do with the input. Like https://i.imgur.com/n6hFwVv.png you can somewhat decipher what this slop wants to be but what if the summary leaves out or invents or inverts some crucial piece of info?
This is theoretically true, but to me at least, practically irrelevant. In all cases, for most values of the word "all", the summary does tell you what the article contains.
For me at least, the usefulness is not that the summary replaces reading the article. Instead, it's a signal telling me whether I should read it in the first place.
Also note
https://hachyderm.io/@inthehands/112006855076082650
> You might be surprised to learn that I actually think LLMs have the potential to be not only fun but genuinely useful. “Show me some bullshit that would be typical in this context” can be a genuinely helpful question to have answered, in code and in natural language — for brainstorming, for seeing common conventions in an unfamiliar context, for having something crappy to react to.
> Alas, that does not remotely resemble how people are pitching this technology.
Although I don't think this particular summarizer works for videos. And I don't think Ollama API supports audio ingestion for transcription. There are some summarizers that work with YouTube specifically (using automatic subtitles).
Now you can’t possibly get through all of them and have to decide which of those could be worth your time. And in that case, the tradeoff makes sense.
- # Changelog
## [1.1] - 2024-03-19
### Added - New `model_tokens.json` file containing token limits for various Ollama models. - Dynamic token limit updating based on selected model in options. - Automatic loading of model-specific token limits from `model_tokens.json`. - Chunking and recursive summary for long pages - Better handling of markdown returns
### Changed - Updated `manifest.json` to include `model_tokens.json` as a web accessible resource. - Modified `options.js` to handle dynamic token limit updates: - Added `loadModelTokens()` function to fetch model token data. - Added `updateTokenLimit()` function to update token limit based on selected model. - Updated `restoreOptions()` function to incorporate dynamic token limit updating. - Added event listener for model selection changes.
### Improved - User experience in options page with automatic token limit updates. - Flexibility in handling different models and their respective token limits.
### Fixed - Potential issues with incorrect token limits for different models.
