---
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
library_name: gguf
language:
- en
tags:
- lora
- gguf
- llama.cpp
- wllama
- ffmpeg
- git
- imagemagick
- modelxare
---

# Command add-ons for Qwen2.5 0.5B

Three small LoRA adapters that turn a plain-language request into a command line, one tool each.
They run in the browser with [wllama](https://github.com/ngxson/wllama) or natively with llama.cpp,
on top of one shared base model, and are published as signed releases in
[modelXare](https://modelxare.com) 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).

## Base model

They apply only to
[`qwen2.5-0.5b-instruct-q4_k_m.gguf`](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct-GGUF/blob/9217f5db79a29953eb74d5343926648285ec7e67/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.

## Prompt

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.

```sh
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."
```

## Training

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.

## Evaluation

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) |

## Limits

- Commands are suggestions: read them before running them. They can be wrong, and some (`git reset
  --hard`, `git push --force`, overwriting output files) destroy data.
- English requests only. One tool per adapter; requests that need several tools are out of scope.
- Unusual flags, filters and long multi-step requests are the weakest cases, ffmpeg most of all.
  Known misses: "discard my changes to README.md" gave `git checkout -- . README.md` (which discards
  every file's changes), and trimming or GIF requests often get the wrong ffmpeg flags.
- ImageMagick answers sometimes write over the input file (`magick photo.jpg -rotate 90 photo.jpg`).

## License

Apache-2.0, like the base model. Qwen2.5-0.5B-Instruct is by the Qwen Team, Alibaba Cloud.