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Model: ajayk007/Qwen2.5-Coder-1.5B-Shellsmith
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
+tags:
+  - mlx
+  - lora
+  - text-generation
+  - shell
+  - command-line
+  - code
+language:
+  - en
+pipeline_tag: text-generation
+library_name: 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`](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct),
+trained and quantized end-to-end on an Apple Silicon Mac with
+[MLX](https://github.com/ml-explore/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](https://github.com/saiajay1/shellsmith/blob/main/eval/rubric.md).
+
+| 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)
+
+```bash
+pip install mlx-lm
+mlx_lm.generate --model ajayk007/Qwen2.5-Coder-1.5B-Shellsmith \
+  --prompt "compress the logs folder into logs.tar.gz"
+```
+
+```python
+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`](https://huggingface.co/datasets/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](https://huggingface.co/ajayk007/Qwen2.5-Coder-7B-Querysmith) — schema-grounded text-to-SQL.
+
+## License
+
+Apache-2.0, inheriting from the base model.