Irfanuruchi/Llama-3.2-3B-Computer-Engineering-LLM

🖥️ Llama-3.2-3B-Computer-Engineering-LLM

Specialized AI Assistant for Computer Engineering Fine-tuned Meta-Llama-3-3B with 4-bit quantization + LoRA adapters

<div align="center"> <a href="https://github.com/IrfanUruchi/Llama-3.2-3B-Computer-Engineering-LLM"> <img src="https://img.shields.io/badge/🔗GitHub-Repo-181717?style=for-the-badge&logo=github" alt="GitHub"> </a> <a href="https://huggingface.co/Irfanuruchi/Llama-3.2-3B-Computer-Engineering-LLM"> <img src="https://img.shields.io/badge/🤗HuggingFace-ModelRepo-FFD21F?style=for-the-badge" alt="HuggingFace"> </a> <br> <img src="https://img.shields.io/badge/ModelSize-3.2Bparameters-blue" alt="Model Size"> <img src="https://img.shields.io/badge/Quantization-4bit-green" alt="Quantization"> <img src="https://img.shields.io/badge/Adapter-LoRA-orange" alt="Adapter"> <img src="https://img.shields.io/badge/Context-8k-lightgrey" alt="Context"> <img src="https://img.shields.io/badge/License-Llama3.2-yellow" alt="License"> </div>

📜 License Compliance Notice

This model is derived from Meta's Llama 3.2 and is governed by the Llama 3.2 Community License. By using this model, you agree to:

• —Not use the model or its outputs to improve other LLMs
• —Not use the model for commercial purposes without separate agreement
• —Include attribution to Meta and this project
• —Accept the license's acceptable use policy

🛠️ Technical Specifications

Architecture

Training Data

• —Curated computer engineering corpus
• —Key domains covered:
• —Computer architecture
• —Embedded systems
• —VLSI design
• —Hardware description languages
• —Low-level programming

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Irfanuruchi/Llama-3.2-3B-Computer-Engineering-LLM"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto"
)

prompt = """You are a computer engineering expert. Explain concisely:
Q: What's the difference between RISC and CISC architectures?
A:"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(
    **inputs,
    max_new_tokens=150,
    temperature=0.7,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Responsible use

This model inherits all use restrictions from the Llama 3.2 license. Special considerations:

Not for production deployment without compliance review Outputs should be verified by domain experts Knowledge cutoff: July 2024

Citation

If using this model in research, please cite:

@misc{llama3.2-computer-eng,
  author = {Irfanuruchi},
  title = {Llama-3.2-3B-Computer-Engineering-LLM},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/Irfanuruchi/Llama-3.2-3B-Computer-Engineering-LLM}}
}