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safetensor_model_fine_tuning_project

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A newer version of this model is available: openai/gpt-oss-120b

T5 Command Description Generator

This project fine-tunes a T5 model (t5-small) to generate descriptions of terminal commands based on prompts in the format "Describe the command: {name} in {source}". The model is trained on a dataset (all_commands.csv) containing command names, descriptions, and sources (e.g., cmd, linux, macos, vbscript). After fine-tuning, the model can generate descriptions for commands, such as "List information about file(s)" for ls in linux.

Table of Contents

Overview

The T5 (Text-to-Text Transfer Transformer) model is fine-tuned to map prompts like "Describe the command: ls in linux" to descriptions like "List information about file(s)". The dataset used for training is all_commands.csv, which includes commands from various environments (cmd, linux, macos, vbscript). The fine-tuned model is saved to ./new_cmd_model and can be used to generate command descriptions interactively or programmatically.

Dataset

The dataset (all_commands.csv) contains the following columns:

  • name: The command name (e.g., ls, dir, chmod, MsgBox).
  • description: A brief description of what the command does (e.g., "List information about file(s)").
  • source: The environment the command belongs to (cmd, linux, macos, vbscript).

Example entries:

name,description,source
ls,List information about file(s),linux
dir,Display a list of files and folders,cmd
chmod,Change access permissions,macos
MsgBox,Display a dialogue box message,vbscript

The dataset is split into 80% training and 20% validation sets for fine-tuning.

Requirements

  • Python 3.8+
  • Libraries:
    • transformers
    • torch
    • sentencepiece
    • datasets
  • CUDA-enabled GPU (optional, for faster training; fp16=True in the script enables mixed precision if available)
  • Dataset file: all_commands.csv (place in the project directory)

Install dependencies:

pip install transformers torch sentencepiece datasets

Setup

  1. Activate the Environment: Ensure you're in a Python environment with the required libraries. For example, using Conda:

    conda activate safetensor_new

  2. Prepare the Dataset: Place all_commands.csv in the project directory (e.g., C:\app\dataset).

  3. Directory Structure:

    C:\app\dataset\
├── all_commands.csv
├── new_cmd_model\ (created after fine-tuning)
└── fine_tune_script.py

Fine-Tuning the Model

The fine-tuning script (fine_tune_script.py) trains a t5-small model on the all_commands.csv dataset to generate command descriptions.

Script Overview

  • Model: t5-small (can be upgraded to t5-base for better performance).
  • Input Prompt: "Describe the command: {name} in {source}" (e.g., "Describe the command: ls in linux").
  • Output: The command’s description (e.g., "List information about file(s)").
  • Training Parameters:
    • Epochs: 3
    • Learning rate: 5e-5
    • Batch size: 8
    • Output directory: ./new_cmd_model
    • Mixed precision training: Enabled if CUDA is available

Running the Script

Save the following script as fine_tune_script.py and run it:

from transformers import T5ForConditionalGeneration, T5Tokenizer, Trainer, TrainingArguments
from datasets import load_dataset
import torch

# Load model and tokenizer
model_name = "t5-small"
model = T5ForConditionalGeneration.from_pretrained(model_name)
tokenizer = T5Tokenizer.from_pretrained(model_name)

# Load dataset
dataset = load_dataset("csv", data_files={"train": "all_commands.csv"})
dataset = dataset["train"].train_test_split(test_size=0.2)
dataset["validation"] = dataset["test"]

# Preprocess function
def preprocess_function(examples):
    inputs = [f"Describe the command: {name} in {source}" for name, source in zip(examples["name"], examples["source"])]
    targets = examples["description"]
    model_inputs = tokenizer(inputs, max_length=128, truncation=True, padding="max_length")
    labels = tokenizer(targets, max_length=256, truncation=True, padding="max_length")
    model_inputs["labels"] = labels["input_ids"]
    return model_inputs

# Apply preprocessing
tokenized_dataset = dataset.map(preprocess_function, batched=True, remove_columns=dataset["train"].column_names)

# Training arguments
training_args = TrainingArguments(
    output_dir="./new_cmd_model",
    evaluation_strategy="epoch",
    learning_rate=5e-5,
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    num_train_epochs=3,
    weight_decay=0.01,
    save_strategy="epoch",
    load_best_model_at_end=True,
    metric_for_best_model="eval_loss",
    greater_is_better=False,
    fp16=torch.cuda.is_available(),
)

# Initialize Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset["train"],
    eval_dataset=tokenized_dataset["validation"],
)

# Train the model
trainer.train()

# Save the model and tokenizer
model.save_pretrained("./new_cmd_model")
tokenizer.save_pretrained("./new_cmd_model")
print("Fine-tuning complete. Model saved to './new_cmd_model'.")

Run the script:

python fine_tune_script.py

This will train the model and save it to ./new_cmd_model.

Using the Model

After fine-tuning, you can use the model to generate command descriptions with prompts like "Describe the command: {name} in {source}". Below is a script to load and use the model interactively or programmatically.

Usage Script

Save the following as use_t5_command_description.py:

import os
from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch
from datetime import datetime

# Define model path
model_path = "./new_cmd_model"

# Check if model directory exists
if not os.path.exists(model_path):
    raise FileNotFoundError(f"Model directory '{model_path}' not found.")

# Load the fine-tuned model and tokenizer
try:
    model = T5ForConditionalGeneration.from_pretrained(model_path)
    tokenizer = T5Tokenizer.from_pretrained(model_path, legacy=False)
    print(f"[{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] Model and tokenizer loaded successfully.")
except Exception as e:
    raise Exception(f"Error loading model or tokenizer: {str(e)}")

# Function to generate a command description
def generate_description(command, source, max_length=100):
    prompt = f"Describe the command: {command} in {source}"
    print(f"[{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] Input prompt: {prompt}")
    inputs = tokenizer(prompt, return_tensors="pt", max_length=128, truncation=True)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = {key: value.to(device) for key, value in inputs.items()}
    print(f"[{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] Using device: {device}")
    try:
        outputs = model.generate(
            inputs["input_ids"],
            max_length=max_length,
            num_beams=4,
            length_penalty=1.0,
            early_stopping=True
        )
        description = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
        if not description:
            return "Warning: No description generated. Check if the command and source are valid."
        return description
    except Exception as e:
        return f"Error generating description: {str(e)}"

# Example usage
test_commands = [
    ("ls", "linux"),
    ("dir", "cmd"),
    ("chmod", "macos"),
    ("MsgBox", "vbscript")
]

print("\nGenerated Descriptions:")
print("-" * 50)
for command, source in test_commands:
    description = generate_description(command, source)
    print(f"Command: {command} ({source})")
    print(f"Description: {description}")
    print("-" * 50)

# Interactive mode
print("\nInteractive Mode: Enter a command and source to get its description.")
print("Valid sources: cmd, linux, macos, vbscript")
print("Type 'exit' to quit.\n")
while True:
    command = input("Enter command name (or 'exit' to quit): ").strip()
    if command.lower() == "exit":
        break
    source = input("Enter source (e.g., cmd, linux, macos, vbscript): ").strip().lower()
    valid_sources = ["cmd", "linux", "macos", "vbscript"]
    if source not in valid_sources:
        print(f"Invalid source. Please use one of: {', '.join(valid_sources)}")
        continue
    description = generate_description(command, source)
    print(f"\nCommand: {command} ({source})")
    print(f"Description: {description}")
    print("-" * 50)

print("Exiting interactive mode.")

Run the script:

python use_t5_command_description.py

Example Output

After fine-tuning and running the usage script, you should see output like:

[2025-09-04 11:50:00] Model and tokenizer loaded successfully.

Generated Descriptions:
--------------------------------------------------
[2025-09-04 11:50:01] Input prompt: Describe the command: ls in linux
[2025-09-04 11:50:01] Using device: cuda
Command: ls (linux)
Description: List information about file(s)
--------------------------------------------------
[2025-09-04 11:50:02] Input prompt: Describe the command: dir in cmd
[2025-09-04 11:50:02] Using device: cuda
Command: dir (cmd)
Description: Display a list of files and folders
--------------------------------------------------
[2025-09-04 11:50:03] Input prompt: Describe the command: chmod in macos
[2025-09-04 11:50:03] Using device: cuda
Command: chmod (macos)
Description: Change access permissions
--------------------------------------------------
[2025-09-04 11:50:04] Input prompt: Describe the command: MsgBox in vbscript
[2025-09-04 11:50:04] Using device: cuda
Command: MsgBox (vbscript)
Description: Display a dialogue box message
--------------------------------------------------

Interactive Mode: Enter a command and source to get its description.
Valid sources: cmd, linux, macos, vbscript
Type 'exit' to quit.

Enter command name (or 'exit' to quit): ping
Enter source (e.g., cmd, linux, macos, vbscript): linux
[2025-09-04 11:50:05] Input prompt: Describe the command: ping in linux
[2025-09-04 11:50:05] Using device: cuda
Command: ping (linux)
Description: Test a network connection
--------------------------------------------------
Enter command name (or 'exit' to quit): exit
Exiting interactive mode.

Troubleshooting

  • Empty Descriptions:
    • Ensure all_commands.csv has valid entries with no missing descriptions.
    • Increase num_train_epochs to 5–10 or use t5-base for better performance.
    • Check training logs in ./new_cmd_model for high loss values.
  • Model Loading Issues:
    • Verify the model saved correctly in ./new_cmd_model.
    • Try loading a checkpoint (e.g., ./new_cmd_model/checkpoint-XXX) if issues persist.
  • Environment Errors:
    • Ensure dependencies are installed: pip install transformers torch sentencepiece datasets.
    • For CUDA errors, ensure your GPU drivers are up-to-date or set fp16=False in the training script.
  • Deprecation Warning:
    • The script uses evaluation_strategy, which is deprecated. Update to eval_strategy in newer transformers versions:
      training_args = TrainingArguments(
    output_dir="./new_cmd_model",
    eval_strategy="epoch",
    ...
)

Future Improvements

  • Augment Dataset: Add more command descriptions or variations to improve generalization.
  • Use Larger Model: Switch to t5-base for better accuracy (update model_name and retrain).
  • Extend Task: Modify to generate commands from task descriptions (e.g., "List files in linux" → ls) by retraining with swapped inputs/outputs.
  • Command Execution: Add functionality to execute generated commands (requires careful validation for security).

For questions about xAI’s API, visit https://x.ai/api. [2025-09-04

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