---
base_model: unsloth/LFM2-1.2B
tags:
- text-generation-inference
- transformers
- unsloth
- lfm2
license: apache-2.0
language:
- en
datasets:
- ajibawa-2023/Software-Architecture
---

# SoftwareArchitecture-Instruct-v1

<img src="banner.png" width="800"/>

**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

```python
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))
````

---

## &#x20;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)*

---

## &#x20;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.

---

## &#x20;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](https://huggingface.co/yasserrmd)