Loading…
Loading…
Three small LoRA adapters that turn a plain-language request into a command line, one tool each. They run in the browser with wllama or natively with llama.cpp, on top of one shared base model, and are published as signed releases in modelXare by weXare.
| File | Tool | Example request | Answer |
|---|---|---|---|
git.gguf |
git | undo my last commit but keep the changes | git reset HEAD~1 |
ffmpeg.gguf |
ffmpeg | extract the audio from talk.mp4 as an mp3 | ffmpeg -i talk.mp4 -vn talk.mp3 |
imagemagick.gguf |
ImageMagick | make a 200px thumbnail of photo.jpg | magick photo.jpg -thumbnail 200x output.jpg |
Each file is 5.9 MB (f16, rank 8, 16 layers).
They apply only to
qwen2.5-0.5b-instruct-q4_k_m.gguf
(491 MB, SHA-256 74a4da8c9fdbcd15bd1f6d01d621410d31c6fc00986f5eb687824e7b93d7a9db). One copy of the
base serves all three, and switching between them needs no reload.
Each adapter was trained with one fixed system prompt and the request as the user message, using the
base model's own chat template. Use the same system prompt; the modelXare releases carry it in their
command-generation operation. ffmpeg and ImageMagick answer UNSUPPORTED / unsupported_request
when the tool can't do what was asked.
llama-cli -m qwen2.5-0.5b-instruct-q4_k_m.gguf --lora git.gguf -cnv \
-sys "You are a Git command translator. Read the user's natural-language request and reply with ONLY the exact git CLI command(s) that fulfill it. Use a single line when one command is enough; use newlines for a short sequence. Do not wrap commands in markdown, quotes, or explanation."
LoRA (QLoRA on mlx-community/Qwen2.5-0.5B-Instruct-4bit) with mlx-lm: rank 8, 16 layers, learning
rate 1e-4, batch 4, completion-only loss, early stopping on validation loss. Converted to PEFT layout
and then to GGUF with llama.cpp's convert_lora_to_gguf.py.
| Adapter | Training rows | Best checkpoint (val loss) |
|---|---|---|
| git | 4,592 | 0.047 |
| ffmpeg | 2,504 | 0.169 (iter 300) |
| ImageMagick | 2,505 | 0.100 |
The git data comes from templates with slot pools and typing noise. The ffmpeg and ImageMagick data was generated by a larger language model from a task description, then checked by script.
211 held-out requests per tool, greedy decoding, through native llama.cpp (node-llama-cpp) with the q4_K_M base. Many test rows give an output file name the request never mentioned, so the second column also accepts a different output file name when the request names none.
| Adapter | Exact match | Exact match, any unnamed output name | Base model alone (exact) |
|---|---|---|---|
| git | 83% | 86% | 30% (first 60 rows) |
| ImageMagick | 65% | 80% | 2% (first 60 rows) |
| ffmpeg | 35% | 68% | 0% (first 60 rows) |
git reset --hard, git push --force, overwriting output files) destroy data.git checkout -- . README.md (which discards
every file's changes), and trimming or GIF requests often get the wrong ffmpeg flags.magick photo.jpg -rotate 90 photo.jpg).Apache-2.0, like the base model. Qwen2.5-0.5B-Instruct is by the Qwen Team, Alibaba Cloud.
Qwen/Qwen2.5-0.5B-Instruct