README.md

---
title: Linux Command Generator (Llama 3.2 1B)
tags:
  - text-generation
  - instruction-tuned
  - llama
  - unsloth
  - lora
  - linux
  - command-generation
license: other
language:
  - en
library_name: transformers
pipeline_tag: text-generation
datasets:
  - custom
base_model: unsloth/Llama-3.2-1B-Instruct
---

### mecha-org/linux-command-generator-llama3.2-1b

Natural language → Linux command. A compact Llama 3.2 1B Instruct model fine‑tuned (LoRA) to turn plain‑English requests into correct shell commands.

## Video Demonstration of the model running on the Mecha Comet
<video controls>
  <source src="https://web-assets.mecha.so/hugging-face/mecha-command-generator-aug-10-2025.mp4" type="video/mp4">
  Your browser does not support the video tag.
</video>
For more information of the Mecha Comet, our pocket little handheld computer - click <a href="https://mecha.so/comet">here</a>

### TL;DR
- Base: `unsloth/Llama-3.2-1B-Instruct`
- Method: LoRA (r=16, alpha=16, dropout=0)
- Context: 2048 tokens
- Data: 8,669 pairs across 11 categories
- Use cases: quick command lookup, learning CLI, automation

## Run with Ollama (baby steps)

1) Install Ollama: see `https://ollama.com/download`.

2) Verify install:
```bash
ollama --version
```

3) Run the model interactively:
```bash
ollama run mecha-org/linux-command-generator-llama3.2-1b
```
Then type a request, e.g.:
- "List all files in the current directory with detailed information"
- "Compress the file data.txt using bzip2"
- "Find all .py files in the current directory and subdirectories"

Press Ctrl+C to exit.

4) One‑off (non‑interactive):
```bash
ollama run mecha-org/linux-command-generator-llama3.2-1b -p "Display the first 5 lines of access.log"
# Expected: head -n 5 access.log
```

5) Get command‑only answers (when needed):
```bash
ollama run mecha-org/linux-command-generator-llama3.2-1b -p "Output only the command with no explanation. Show system information including kernel version"
# Expected: uname -a
```


### Use a local GGUF with Ollama (fallback)
If you have `model.gguf`, put it next to a `Modelfile`:

```
FROM ./model.gguf
PARAMETER temperature 0.2
PARAMETER top_p 0.9
PARAMETER num_ctx 2048
SYSTEM You are a Linux command generator. Output only the command with no explanation.
TEMPLATE {{ .Prompt }}
```

Create and run:
```bash
ollama create linux-cmd-gen -f Modelfile
ollama run linux-cmd-gen -p "Find all .py files recursively"
# Expected: find . -name "*.py"
```

## Other ways to use (optional)

### Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "mecha-org/linux-command-generator-llama3.2-1b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16 if torch.cuda.is_available() else None)

def generate_command(description: str) -> str:
    messages = [{"role": "user", "content": description}]
    inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
    if torch.cuda.is_available():
        inputs = inputs.to(model.device)
        model = model.to("cuda")
    outputs = model.generate(input_ids=inputs, max_new_tokens=64)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

print(generate_command("List all files in the current directory with detailed information"))
# -> ls -la
```

### Unsloth
```python
from unsloth import FastLanguageModel

model_id = "mecha-org/linux-command-generator-llama3.2-1b"
model, tokenizer = FastLanguageModel.from_pretrained(model_name=model_id, max_seq_length=2048)
FastLanguageModel.for_inference(model)

msgs = [{"role": "user", "content": "Compress the file data.txt using bzip2"}]
inputs = tokenizer.apply_chat_template(msgs, tokenize=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(input_ids=inputs, max_new_tokens=32)
print(tokenizer.decode(output[0], skip_special_tokens=True))
# -> bzip2 data.txt
```

## Example prompts → commands
- "Show system information including kernel version" → `uname -a`
- "Find all .py files in the current directory and subdirectories" → `find . -name "*.py"`
- "Display the first 5 lines of access.log" → `head -n 5 access.log`
- "Change permissions of script.sh to make it executable for owner" → `chmod +x script.sh`
- "Create a tar archive backup.tar containing all files in the documents folder" → `tar -cf backup.tar documents/`

## Dataset (overview)
8,669 input→command pairs across:
- Compression & Archiving: bzip2, gzip, tar, zip
- File & Directory: cd, cp, find, ls, mkdir, mv, pwd, rm, rmdir, touch
- Permissions & Ownership: chgrp, chmod, chown
- Viewing & Editing: cat, echo, head, less, tail, vim
- Networking: curl, dig, host, ifconfig, ip, netstat, ping, ssh, wget
- Package mgmt: apt, dpkg
- Process mgmt: kill, killall, nice, pkill, renice
- Search & Filter: awk, grep, locate, sed
- System info/monitoring: df, du, free, top, uname
- User/group: useradd, usermod, groupadd, passwd, sudo
- Misc/system control: cron, systemctl, tmux, screen, service

Format:
```json
{"input": "Describe what you want to do", "output": "linux_command_here"}
```

## Training details
- Base: `unsloth/Llama-3.2-1B-Instruct`
- LoRA on attention + MLP projections:
  - r=16, lora_alpha=16, lora_dropout=0
  - target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
- Max sequence length: 2048
- SFT on responses only (TRL SFTTrainer), Unsloth-optimized
- Example hparams: per‑device batch 2, grad accum 4, epochs 3, lr 2e‑4
- Reference: Tesla P100 16GB (~45 minutes), ~2.8GB VRAM (adapters)

## Safety and responsible use
- Always inspect commands before executing.
- Avoid destructive operations unless you fully understand consequences.
- For apps, add denylists and validations (e.g., block `rm -rf /`, `mkfs`, `dd`).

## Notes on GGUF
- Works with `llama.cpp` and Ollama.
- Typical memory (approx.): q4_k_s ~600MB, q4_k_m ~700MB, q8_0 ~1.1GB, f16 ~2.2GB.

## License
Derived from Meta Llama 3.2. Use must comply with the base model license. Check your deployment context for any additional constraints.

## Citation
```
@software{hrsvrn_linux_command_generator_llama32_1b,
  author  = {Harshvardhan Vatsa},
  title   = {Linux Command Generator (Llama 3.2 1B)},
  year    = {2025},
  url     = {https://huggingface.co/mecha-org/linux-command-generator-llama3.2-1b}
}
```

## Acknowledgements
- Base: `unsloth/Llama-3.2-1B-Instruct`
- Libraries: `unsloth`, `transformers`, `trl`, `accelerate`, `bitsandbytes`