# For llama.cpp / Ollama usage
# llama-cli -m asr-to-bash-q4_k_m.gguf -p 'Convert: list all files'

# Or with Python:
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("marksverdhai/asr-to-bash")
tokenizer = AutoTokenizer.from_pretrained("marksverdhai/asr-to-bash")

messages = [
    {"role": "system", "content": "You are a helpful assistant that converts spoken commands into bash commands."},
    {"role": "user", "content": "Convert this spoken command to bash: list all files including hidden ones"}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0]))
# Output: ls -la

Examples

ASR Transcription	Bash Command
"list all files"	`ls -la`
"git status"	`git status`
"change directory to home"	`cd ~`
"kill process one two three four"	`kill 1234`
"show running containers"	`docker ps`

Training

Fine-tuned using Unsloth with LoRA on a custom dataset of ~100 ASR transcription to bash command pairs.

Base model: google/functiongemma-270m-it
LoRA rank: 16
Training epochs: 3

marksverdhai/asr-to-bash-gguf

ASR-to-Bash (GGUF)

Usage

Examples

Training

marksverdhai/
asr-to-bash-gguf