User considers storing LLM tokens for specialized model training

By PulseAugur Editorial · [1 sources] · 2026-08-06 07:20

The user is considering storing all tokens processed by large language models (LLMs) to aid in training smaller, specialized models. This approach could significantly improve performance for specific tasks, potentially through retrieval and editing mechanisms. The user notes that while LLMs have become very inexpensive, this data strategy could still offer benefits. AI

IMPACT This strategy could enable more efficient and cost-effective fine-tuning of smaller, task-specific AI models.

RANK_REASON User's personal reflection on a potential AI strategy, not a formal announcement or research.

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User considers storing LLM tokens for specialized model training

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Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] · 2026-08-06 07:20

I think I should just store every single token I put into a LLM and get out of them. I ponder about training very tiny models (like 1B to 2B) for highly specifi

I think I should just store every single token I put into a LLM and get out of them. I ponder about training very tiny models (like 1B to 2B) for highly specific tasks and that data would help me big time. Something something like retrieval and edit models. Could make a lot of ta…

COVERAGE [1]

I think I should just store every single token I put into a LLM and get out of them. I ponder about training very tiny models (like 1B to 2B) for highly specifi

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