brkichle/lora-llama3-archimate
0
ArchiMateGPT: Llama 3.1 8B Instruct + LoRA Adapter
Base model: meta-llama/Llama-3.1-8B-Instruct LoRA config: r=96, α=192, dropout=0.05, target\modules=\["q\proj","k\proj","v\proj","o\proj"], inference\mode=true
Intended Use
Fine-tuned to interpret and generate ArchiMate 3.1 architecture descriptions, diagrams, and modeling advice. Ideal for embedding into applications that need automated ArchiMate guidance.
Not for: personal data inference, non-architecture chat.
Quantitative Metrics
Example
<details> <summary>Click to expand</summary>
Input:
Design a high-level ArchiMate view for a cloud migration scenario.Output:
ArchiMate View:
- Application Component: Cloud Migration Service
- Business Role: Migration Lead
- Infrastructure Service: Virtual Network
...</details>
Inference
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from peft import PeftModel
# Load base + LoRA
tokenizer = AutoTokenizer.from_pretrained(
"meta-llama/Llama-3.1-8B-Instruct", use_fast=True
)
base = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-3.1-8B-Instruct",
device_map="auto", torch_dtype="auto"
)
model = PeftModel.from_pretrained(base, "brkichle/lora-llama3-archimate")
model.eval()
# Create generation pipeline
pipe = pipeline(
"text-generation", model=model, tokenizer=tokenizer,
device_map="auto", return_full_text=False,
max_new_tokens=256, temperature=0.7, top_p=0.9,
repetition_penalty=1.1, pad_token_id=tokenizer.eos_token_id
)
# Run
response = pipe("Show me an ArchiMate overview of a microservices architecture.")
print(response[0]["generated_text"])Limitations
- May hallucinate unsupported ArchiMate elements; always validate generated views with domain experts.
- Large prompts can degrade coherence.
License & Citation
MIT License. Please cite:
@misc{archimategpt2025,
title={ArchiMateGPT: LoRA‐fine‐tuned Llama 3.1 for ArchiMate 3.1},
author={Your Name},
year={2025},
publisher={Hugging Face}
}