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Re:I'm making LLMs work on small and old devices by Alpha3031 on Thursday November 12, 2026 @08:26PM (#50476916)
[delayed]
Re:I'm making LLMs work on small and old devices by nico on Friday November 13, 2026 @12:48AM (#50478872)
Trained entirely from scratch. Which gets it pretty far in terms of basic conversational skills, especially when also trained on chat conversations

The training sources are:

1. Google Synthetic PersonaChat

2. DailyDialog

3. SODA conversational data

4. Hand-written model-personality conversations, repeated during training

5. Identity-corrected conversations—rewritten names, human-life claims, and hobby responses

6. 960 synthetic recovery conversations, teaching it to admit ignorance after user push-back

The final set contains 23,270 conversations

However, the chain reasoning and math problem solving skills, I was only able to add recently, by grafting certain parts from a 1B model, then doing space-weight programming, and finally finetuning/training to get the model to "get used to" reasoning

The full model has

* Frozen base model to interpret questions

* Additional trained heads to select question types, evidence, and follow-up behavior

* A library tool for retrieving facts

* Working memory and internal script language execution for arithmetic, conversions, and comparisons across answers. For example, it can retrieve a building’s height, remember that number, then execute “half of that”

* Identity extraction and physical simulation, so you can do thing like: "Ben has the ball", "Who has the ball", Model: "Ben", "Ben gave the ball to Sally", "Who has the ball now?", "Sally"

It's relatively easy adding skills either via its internal scripting language, grafting+weight programming, fine-tuning and/or training it to use a new skill. The main limitation right now seems to be just quality data and proper supervision, ie. someone to talk to the model flag issues, fix them, then re-test. This is kinda tedious, but I've found that even the best frontier model is kinda lacking in quality when producing synthetic conversational data, and most datasets out there have a lot of cruft. So I get better results just manually producing the data and iterating, than feeding it GBs of datasets

Creating and training this model has been a lot of fun. Since it's so tiny, it's easy to experiment with and try a lot of different things. Most training I can just do in a few minutes locally

Re:I'm making LLMs work on small and old devices by mrashad74 on Friday November 13, 2026 @12:58AM (#50478929)
actually your methodology inspired me to try smth related to small models and harnesses I've been thinking about - and seems you got the main pieces (the idea was trying to see what custom harnesses can extract more accurate reasoning from small models)
Re:I'm making LLMs work on small and old devices by nico on Friday November 13, 2026 @01:16AM (#50479077)
Awesome, happy to hear that. Hopefully you can post about it here again when you have some results :)