license, base_model, tags, language, pipeline_tag, library_name

license	base_model	tags	language	pipeline_tag	library_name
apache-2.0	Qwen/Qwen2.5-Coder-1.5B-Instruct	mlx lora text-generation shell command-line code	en	text-generation	mlx

Qwen2.5-Coder-1.5B-Shellsmith

A small, fast model that turns plain-English instructions into a single shell command for macOS/Linux. LoRA fine-tune of Qwen/Qwen2.5-Coder-1.5B-Instruct, trained and quantized end-to-end on an Apple Silicon Mac with MLX.

"list files by size, biggest first" → ls -lS "find files larger than 100 megabytes" → find . -type f -size +100M "create a gzip tar archive of src named src.tar.gz" → tar -czf src.tar.gz src

Results

Evaluated on a held-out test split the model never saw during training. Metrics are structural (no command execution) and conservative — see the eval rubric.

Model	exact-match	command-match	flag-F1
Base (Qwen2.5-Coder-1.5B-Instruct)	71%	83.9%	89.2%
This model (LoRA)	100%	100%	100%

command-match = correct program and option-flag F1 ≥ 0.8.

What the 100% means (and doesn't): the test split holds out unseen phrasings, but the underlying task distribution (84 canonical tasks) overlaps with training. So this measures reliable in-distribution generalization across wording — the model consistently emits the canonical, idiomatic command (git add -A, ls -lS, git log --oneline -5) where the base model drifts to looser variants (git add ., ls -lh | sort -rh, git log -5). It is not evidence of generalization to entirely novel tasks; broadening the task set is the obvious next step.

Usage

MLX (Apple Silicon)

pip install mlx-lm
mlx_lm.generate --model ajayk007/Qwen2.5-Coder-1.5B-Shellsmith \
  --prompt "compress the logs folder into logs.tar.gz"

from mlx_lm import load, generate
model, tok = load("ajayk007/Qwen2.5-Coder-1.5B-Shellsmith")
messages = [
    {"role": "system", "content": "You are a shell command generator for macOS/Linux. "
     "Given a task in plain English, reply with a single safe shell command. "
     "Output only the command on one line, no explanation, no markdown."},
    {"role": "user", "content": "find all python files modified today"},
]
prompt = tok.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
print(generate(model, tok, prompt=prompt, max_tokens=64))

GGUF (llama.cpp / Ollama / LM Studio)

A shellsmith-1.5b-f16.gguf file is included in this repo for use with llama.cpp-based runtimes.

Prompt format

Uses the Qwen chat template with the system prompt shown above. Keep the system prompt for best results.

Training

• Method: LoRA (rank 16), 16 layers, 400 iterations, batch size 4, lr 1e-4
• Hardware: Apple M5 Pro (48 GB), MLX
• Data: ajayk007/shellsmith-commands — curated (instruction, command) pairs with paraphrase augmentation, 80/10/10 split.

Limitations & safety

• Generates commands across common categories (files, find/grep, archives, git, processes, networking). Outside this scope it falls back to base behavior.
• Always read a generated command before running it. It can produce destructive commands (rm, kill, chmod) if you ask for them. There is no sandbox or confirmation step.
• Single-command only; it does not write multi-step scripts.

Related

Part of a series of focused "English → developer DSL" fine-tunes:

• Qwen2.5-Coder-7B-Querysmith — schema-grounded text-to-SQL.

License

Apache-2.0, inheriting from the base model.