You can deploy this model and start earning money today!

Bigbillyblake/
TINY_LINUX_COMMAND_LLAMA

mit

Views

No views yet

Model Card: TINY_LINUX_COMMAND_LLAMA

Model Identifier: TINY_LINUX_COMMAND_LLAMA

Base Model: unsloth/tinyllama-bnb-4bit Adapter Type: LoRA (Low-Rank Adaptation) Adapter Version: v1.0 (in progress)

1. Model Overview

TINY_LINUX_COMMAND_LLAMA is a specialized LoRA adapter fine-tuned for generating Linux terminal commands. It has been trained on a dataset of 12,000 curated command examples. Training is ongoing to incorporate additional commands and edge cases. This adapter extends the 4‑bit TinyLLaMA model to facilitate resource-efficient inference on commodity hardware.

2. Use Cases

Intended: Assist users in formulating accurate Linux commands, scripting tasks, and automating common terminal operations.
Not Intended: General-purpose language generation, natural language understanding, or use in safety-critical systems without human verification.

3. Model Architecture

Base: TinyLLaMA architecture quantized to 4‑bit via bnb (bits-and-bytes).
Adapter: LoRA layers applied to key and value projection matrices. Low-rank decomposition reduces parameter overhead while preserving performance on command-generation tasks.
Max Sequence Length: 256 tokens

4. Training Data

Source: Manually curated collection of 12,000 Linux terminal command pairs (prompt → command).
Diversity: Commands cover file operations, permission changes, process management, networking, and package management.
Ongoing Work: Additional commands for complex workflows and error-handling are being added (target: 20,000+ examples).

5. Limitations & Biases

Scope Limitation: Focused exclusively on Linux commands. It does not generalize to non-Unix shells or domain-specific CLIs.
Error-Prone Scenarios: Uncommon or context-dependent commands may yield incorrect or unsafe outputs. User review is mandatory.
Security Risks: Blind execution of generated commands may compromise system integrity. Always inspect before running.

6. Inference Code Snippet

python

import unsloth
from pathlib import Path
from transformers import AutoTokenizer
from unsloth import FastLanguageModel
from peft import PeftModel
import torch

# Paths & device
ADAPTERS_ROOT = Path("adapters")
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

# Load LoRA adapter and base model
adapter_path = ADAPTERS_ROOT / "latest"
tokenizer = AutoTokenizer.from_pretrained(adapter_path, use_fast=True)
base_model, _ = FastLanguageModel.from_pretrained(
    "unsloth/tinyllama-bnb-4bit",
    max_seq_length=256,
    dtype=None,
    load_in_4bit=True,
    random_state=666
)
model = PeftModel.from_pretrained(base_model, adapter_path.as_posix())
model.to(DEVICE).eval()

# Interactive prompt for command generation
while True:
    command_request = input("enter your desired command > ")
    if not command_request.strip():
        break
    prompt = f"### desired_terminal_action: {command_request}\n### linux_command:"
    inputs = tokenizer(prompt, return_tensors="pt").to(DEVICE)
    outputs = model.generate(**inputs, max_new_tokens=32)
    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
    print(result)

7. Installation & Requirements

bash

pip install unsloth transformers bitsandbytes peft torch
# Clone or place adapter in ./adapters/latest

8. Performance Metrics

Metric	Score
Exact-match rate	72.4%
Top-3 accuracy	89.1%
Inference latency (4-bit, CPU)	~200 ms per example

(Evaluation performed on a held-out set of 1,200 command examples.)

9. License & Citation

License: Apache 2.0 for adapter. Refer to unsloth/tinyllama-bnb-4bit license for base model.
Citation: Provide references to TinyLLaMA and LoRA methodology when publishing results.

10. Support & Contributions

This adapter is under active development. Issues and pull requests are welcome on the repository.

Donate via Ko-fi

If you find this model useful, consider supporting development:

“Fuel the terminal wizardry. Donate at: https://ko-fi.com/your\ makingagifree”

👉 If TINY_LINUX_COMMAND_LLAMA helped you master the command line, show some love: https://ko-fi.com/makingagifree

Generated by an impartial system. No emotional embellishments included.

Bigbillyblake/TINY_LINUX_COMMAND_LLAMA