caveman-ai / trim-1

# trim-1
trim-1 is a small model that decides which lines of a context an AI agent
needs for its current ask, and drops the rest.
Give it a context (a tool result, a file, a log, a long chat) and the current
query. It returns the context's own lines, in their original order, with a
short marker wherever lines were removed. It never rewrites or summarizes a
line, so identifiers, paths and error text come through exactly as they were.
- **1.2 MB.** 712 gradient-boosted trees, not a neural network.
- **Milliseconds on one CPU core.** No GPU, no network call.
- **Deterministic.** No sampling and no randomness: the same context and query
 give the same output.
- **No dependencies.** Pure Go, standard library only. The model is embedded.
The model card, with benchmark results and limits, is on Hugging Face:
caveman-so/trim-1.
## Install
Needs Go 1.23 or newer.
 [code] 
## Use
From the command line:
 [code] 
 [code] 
The trimmed context goes to stdout. A one-line summary goes to stderr:
 [code] 
From Go:
 [code] 
`trim.Score(context, query)` returns every line with its keep probability, if
you want to make the selection yourself.
## Which setting to use
`--point` decides how hard trim-1 cuts: `conservative`, `balanced` (the
default) or `aggressive`. Cutting harder costs quality, because every removed
line is one the model can no longer read.
- **Long-document retrieval** is what trim-1 is for. Use `balanced`.
- **Coding-agent tool outputs** are short and dense. Stay at `balanced` or
 `conservative` and expect small savings.
The model card
has the numbers behind this.
## When it does nothing
trim-1 fails closed. It returns the context unchanged, and says why on stderr,
when:
- the query is blank,
- the context is under 2,000 bytes,
- the context is over 8 MiB or 200,000 lines,
- the context already has a removal marker line, such as trim-1's own output,
- the cut would remove less than 10% of the bytes.
The `trim` command exits with status 2 on a missing `--query` or an unknown
`--point`. Flags go before the file name.
## Test
 [code] 
`TestPredictionParity` checks the Go tree evaluation against the training
library's own predictions, bit for bit. `TestGolden` pins the full output on a
sample log.
## License
Apache-2.0. `model.go` includes a copy of Go's `math.Exp`, under the BSD license
in `LICENSE-go`. trim-1 is the model inside
Caveman.