README.md

---
tags:
- computer-networking
- networking
- llama-3
- llama-3.1
- networking-protocols
- network-security
- tcp-ip
- ospf
- bgp
- research
license: llama3.1
base_model:
- meta-llama/Llama-3.1-8B-Instruct
---

# Llama-3.1-8B-Computer-Networks-LLM

[![GitHub Repo](https://img.shields.io/badge/GitHub-Repo-181717?style=for-the-badge&logo=github)](https://github.com/IrfanUruchi/Llama-3.1-8B-Computer-Networks-LLM)
[![Model Weights](https://img.shields.io/badge/🤗-Model_Weights-FFD21F?style=for-the-badge)](https://huggingface.co/Irfanuruchi/Llama-3.1-8B-Computer-Networks-LLM)
[![License](https://img.shields.io/badge/License-LLaMA_3.1-blue.svg?style=for-the-badge)](https://github.com/meta-llama/llama3/blob/main/LICENSE)

---

## 🔍 Model Description

**Fine-tuned from**: [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)

**Domain specialization**: Computer networking with enhanced capabilities in:
- Network protocol explanations (OSPF, BGP, TCP/IP stack)
- Configuration template generation
- Troubleshooting scenarios
- Security best practices
- RFC interpretation

---

## Installation & Usage

### Using Hugging Face Directly (Recommended)

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
from transformers import BitsAndBytesConfig

quant_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16
)

model = AutoModelForCausalLM.from_pretrained(
    "Irfanuruchi/Llama-3.1-8B-Computer-Networks-LLM",
    quantization_config=quant_config,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)

tokenizer = AutoTokenizer.from_pretrained("Irfanuruchi/Llama-3.1-8B-Computer-Networks-LLM")

prompt = """You are a network engineering expert. Answer concisely:
Q: What's the difference between TCP and UDP protocols?
A:"""


inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Local installation (GitHub):

```bash

git clone https://github.com/IrfanUruchi/Llama-3.1-8B-Computer-Networks-LLM.git
cd Llama-3.1-8B-Computer-Networks-LLM
```

The large safetensor model shards are not stored in the Github repository. Instead i have hosted them in MEGA , there are 6 files totalling around 11GB :

- [model-00001-of-00006.safetensors:](https://mega.nz/file/rppWmDpS#X5utsf27-npdkFQVCQzz_gFi-s5a4oCuUSUYtJDw6p4)
  
- [model-00002-of-00006.safetensors:](https://mega.nz/file/jkRDVapZ#QhG5Pl8mu-DORIqCvaOfEcHspcVV79Xu-nxiaSa8pmA)
  
- [model-00003-of-00006.safetensors:](https://mega.nz/file/fsQBjQ6D#MI9gi1L9BDycxGh8qE9D92Q1IiJIMkujFwGeel60rk0)
  
- [model-00004-of-00006.safetensors:](https://mega.nz/file/7lB3GQZT#va8qP_X-ADHwtmgyxNcGhRklZ6TKFMg9JuNT7Xbl0js)

- [model-00005-of-00006.safetensors:](https://mega.nz/file/n0oS2IoQ#toljZ9fC2pG1r7WTHO_rHhBYC1qv2lGI6Jg_UgwKWS8)
  
- [model-00006-of-00006.safetensors:](https://mega.nz/file/2xQQBbKL#QMpL6l8bymBtAJnJPzZibcd8U3vv9b4BeQY7D4vcr0U)

After downloading , place all the safetensors files into the folder with the other configuration file in your local copy of the repository. **Ensure that the model loading scripts point to the correct directory**.


Run inference localy (follow tutorial on GitHub for more details)

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

MODEL_PATH = "./model"  # Path to downloaded model files

tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_PATH,
    device_map="auto",
    trust_remote_code=True
)

prompt = (
    "As a network specialist, explain in detail:\n\n"
    "Q: How does BGP path selection work in large-scale networks?\n"
    "A:"
)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=200,
    temperature=0.7,
    do_sample=True
)

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

---

## Licence compliance

This model inherits Meta's LLaMA 3.1 License. Users must:

Accept Meta's license terms
Use only for non-commercial research
Provide attribution to both Meta and this project

---

Contributions are welcome!