SoftwareArchitecture-Instruct-v1
Domain: Software Architecture (for technical professionals)
Type: Instruction-tuned LLM
Base: LiquidAI/LFM2-1.2B (1.2 B parameter hybrid edge-optimized model) :contentReference[oaicite:1]{index=1}
Fine-tuned on: ajibawa-2023/Software-Architecture dataset
Author: Mohamed Yasser (yasserrmd)
Model Description
SoftwareArchitecture-Instruct-v1 is an instruction-tuned adaptation of LiquidAI’s lightweight and efficient LFM2-1.2B model. It’s specifically tailored to deliver high-quality, accurate, and technically rich responses to questions about software architecture—designed with engineers and architects in mind.
The base model, LFM2-1.2B, features a 16-layer hybrid design (10 convolutional + 6 grouped query attention layers), supports a 32,768 token context, and offers fast inference on CPU, GPU, and NPU platforms—ideal for both cloud and edge deployments :contentReference[oaicite:2]{index=2}.
Benchmark Summary
We performed a 50-prompt benchmark across diverse software architecture topics:
| Metric | Value |
|---|---|
| Average Words per Response | ~144 |
| Median Words per Response | ~139 |
| Min / Max Words per Response | 47 / 224 |
| Avg Sentences per Output | ~8.6 |
| Lexical Diversity (TTR) | ~0.73 |
| Readability Complexity | High (professional-level) |
| Accuracy (topic keyword coverage) | Majority ≥ 60% |
| Off-topic Responses | None detected |
Interpretation:
- Responses are substantive and domain-appropriate for technical audiences.
- Coverage is strong—while a few answers could benefit from including extra keywords, the core technical content is accurate.
- Readability intentionally leans into complexity, aligning with expert users.
Intended Use
- Ideal for: Software architects, system designers, engineering leads, and experienced developers seeking architecture guidance.
- Use cases include:
- Exploring architectural patterns (e.g., CQRS, Saga, API Gateway).
- Drafting design docs and decision rationale.
- Architectural interview prep and system design walkthroughs.
Not intended for:
- Non-technical or general-purpose Q&A.
- In-depth code generation or debugging without architectural focus.
Usage Example
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "yasserrmd/SoftwareArchitecture-Instruct-v1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
messages = [
{"role": "user", "content": "Explain the Saga pattern with orchestration and choreography."}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=256,
temperature=0.3,
repetition_penalty=1.05
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Training Details
- Base model:
LiquidAI/LFM2-1.2B, optimized for edge/CPU inference ([ai.plainenglish.io][1], [generativeai.pub][2], [AI Models][3], [marktechpost.com][4], [Hugging Face][5]) - Dataset:
ajibawa‑2023/Software‑Architecture - Fine-tuning: Supervised instruction tuning
- (Optionally include parameters if available—epochs, LR, hardware used)
Limitations
- Answer length is capped by
max_new_tokens. Some responses may truncate mid-explanation—raising this limit improves completeness. - Keyword coverage is strong but not exhaustive. A few responses could benefit from enriching with additional terms.
- Not a replacement for expert-reviewed architectural validation—use as a support tool, not the final authority.
License
- Base model license: LFM Open License v1.0 ([Hugging Face][6])
- Dataset license: (Insert dataset license if known)
Author
Mohamed Yasser – Hugging Face profile