llama3-8b-linux-assistant-lora

Model Card for Model ID

Model Card — LLaMA-3 8B Linux & Programming Assistant (LoRA)

Model Details

Model Description

• Developed by: Abishek
• Model type: Large Language Model (Instruction-tuned, LoRA adapter)
• Language(s) (NLP): English
• License: Meta LLaMA 3 License (base model)
• Finetuned from model [optional]: meta-llama/Meta-Llama-3-8B-Instruct

Model Sources [optional]

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Repository: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct Paper: https://ai.meta.com/research/publications/llama-3/ Demo: Not provided

Direct Use

*Linux command explanations *Beginner-friendly programming help (Python, Git) *Educational and learning assistance *CLI and scripting guidance

Downstream Use

Can be integrated into chatbots or developer assistants

Suitable for educational tools or internal documentation assistants

Out-of-Scope Use

Medical, legal, or financial advice

Safety-critical or production-grade decision systems

Recommendations

Users should:

Verify generated commands before execution

Avoid using the model for sensitive or high-risk tasks

Treat outputs as guidance, not ground truth

Topics:

Linux commands (chmod, ls, permissions)

Python basics and errors

Git workflows

Data sourced from documentation-style examples and manually curated Q&A

Training Procedure

Preprocessing

Alpaca-style prompt formatting

Max sequence length: 2048 tokens

Instruction + response concatenation

Training Hyperparameters

Training regime: QLoRA (4-bit)

Optimizer: AdamW

Learning rate: 2e-4

Batch size: 1

Gradient accumulation: 8

Epochs: 1

LoRA rank (r): 16

Evaluation

Testing Data

Manual prompt-based evaluation

Linux and programming queries not seen during training

Factors

Clarity

Correctness

Domain relevance

Hallucination reduction

Metrics

Qualitative comparison against base model

Results

Improved clarity and structure for Linux explanations

Reduced generic responses

Better beginner-friendly outputs

Summary

This LoRA adapter demonstrates how QLoRA enables efficient fine-tuning of large language models on limited hardware, producing meaningful domain adaptation with minimal compute.

Environmental Impact

Hardware Type: NVIDIA T4 GPU

Hours used: ~1–1.5 hours

Cloud Provider: Google Colab (Free Tier)

Compute Region: Unknown

Carbon Emitted: Low (small-scale experiment)

Technical Specifications Model Architecture and Objective

Decoder-only transformer (LLaMA-3)

Instruction-tuned via supervised fine-tuning (SFT)

Compute Infrastructure

Google Colab (Free Tier)

Hardware

NVIDIA T4 GPU

~16 GB VRAM

Software

PyTorch

Hugging Face Transformers

PEFT

BitsAndBytes

Unsloth

Citation BibTeX @misc{llama3_linux_lora, author = {Abishek}, title = {LLaMA-3 8B Linux & Programming Assistant (LoRA)}, year = {2025}, url = {https://huggingface.co/Master-Abi/llama3-8b-linux-assistant-lora} }

APA

Your Name. (2025). LLaMA-3 8B Linux & Programming Assistant (LoRA). Hugging Face.

Model Card Authors

Abishek / Master-Abi

Model Card Contact

Hugging Face: https://huggingface.co/Master-Abi

• PEFT 0.18.0