AMFORGE/gc_editor1

GearCut Editor (gc_editor)

gc_editor is a compact instruction-to-operations model that powers GearCut, an ultra-lightweight, FFmpeg-based video editor. It translates a plain-English editing instruction into a list of structured editing operations (JSON) that GearCut's project -> ffmpeg compiler then executes. It is designed to run locally, on CPU, so the editor needs no cloud service and no user video ever leaves the machine.

Developed by AMEFORGE. Built on the in-house SparseMind architecture (sparse attention + sparse FFN, dynamic neuron typing, and episodic memory).

What it does

• —Input: the current timeline state + a natural-language instruction.
• —Output: a JSON array of editing operations.

INPUT
clips: c1=intro.mp4(0.0-8.0) | remove the first 3 seconds of the clip =>

OUTPUT
[{"op":"trim","clip":"c1","in":3.0,"out":8.0}]

Supported operations (v1): trim, split, import, append, delete, reorder, export.

Model details

Evaluation

Model set with use diversity= false Measured on a held-out synthetic validation split. The meaningful metrics are not perplexity but whether the generated operations are usable:

Training data

Trained on 60,000 synthetically generated (timeline + instruction -> operations) examples for 3000 steps. The generator covers the v1 operation set with varied phrasings, clip references, file names, timestamps, and presets.

Intended use & scope

Intended as the natural-language command layer inside the GearCut editor. It is not a general-purpose assistant and only emits GearCut operations.

Limitations

• —Synthetic training data. The model is strongest on phrasings close to the generator's templates. Unusual real-world wording may be handled less reliably until the data is expanded with real examples.
• —English only (v1). A bilingual (EN/FR) version is planned.
• —Narrow operation set (v1). Transitions, multi-track, and effects are not yet covered.
• —Custom architecture. The HF inference widget is disabled; load and run the model with the snippet below.

How to use

# Download gc_editor.pt + the GearCut tokenizer from this repo, then rebuild the
# SparseMind model with the same config stored in the checkpoint and load weights.
import torch, sentencepiece as spm
ckpt = torch.load("gc_editor.pt", map_location="cpu")
cfg  = ckpt["config"]          # the exact training config
# model = SparseMind(Config(**cfg)); model.load_state_dict(ckpt["model"]); model.eval()
sp = spm.SentencePieceProcessor(); sp.Load("gearcut_tok.model")
prompt = 'clips: c1=intro.mp4(0.0-8.0) | remove the first 3 seconds of the clip =>'
# ids = sp.EncodeAsIds(prompt) ; generate ; stop at EOS ; json.loads the output

Citation

@misc{gearcut_editor,
  title  = {GearCut Editor: an instruction-to-operations model for lightweight video editing},
  author = {AMEFORGE},
  year   = {2026},
  note   = {Built on the SparseMind architecture}
}