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swordfish7412/Amihan_1.2
Amihan_1.2 is a machine learning model from swordfish7412. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as mit.
Downloads · 30 days
11
38% of all-time downloads
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From the Hugging Face model README
Created by Jan Israel
Amihan 1.2 is an AI-powered orchestrator built on Phi-3-mini with LoRA fine-tuning. It intelligently coordinates between specialized AI models (Bandila and Amigo) to provide comprehensive solutions combining reasoning and coding expertise.
Amihan 1.2 uses a fine-tuned Phi-3-mini (3.8B parameters) as its decision-making brain. Unlike simple keyword-based routers, it performs semantic analysis to:
Analyzes query intent to determine optimal specialist(s):
Combines outputs from multiple specialists into unified, coherent answers for complex hybrid questions.
Handles simple and meta-questions without specialist calls, reducing latency.
⚠️ IMPORTANT: The Space is currently paused to manage cloud costs.
To test the live demo, please message @janisrael on HuggingFace to request Space activation.
The Space runs on T4 Medium GPU ($0.60/hour) and is paused when not actively being showcased. When you request access, I'll resume it immediately. The first request takes 60-90 seconds (model loading), then normal speed afterward.
Live Demo: Amihan 1.2 Space (Orchestrator)
Specialist Spaces: Bandila-v2 | Amigo-v2
For more details about the pause/resume system and cost management, see the Amihan Space README.
from gradio_client import Client
client = Client("swordfish7412/Amihan-1.2-Space")
response = client.predict(
"Design a microservices architecture and write the deployment script",
api_name="/chat"
)
print(response)
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
model_name = "microsoft/Phi-3-mini-4k-instruct"
adapter_name = "swordfish7412/Amihan_1.2"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
base_model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True
)
model = PeftModel.from_pretrained(base_model, adapter_name)
Amihan 1.2 is part of the Swordfish AI Trio:
Jan Israel is a full-stack engineer and AI/ML developer with over 10 years of combined experience in software development and machine learning. Based between the Philippines and Canada, he transitioned from pure full-stack development to specializing in AI-driven solutions.
Notable Projects:
AI Republic - An intelligent coding assistant and debugging platform powered by fine-tuned language models. The platform provides real-time code analysis, automated debugging, and intelligent code suggestions for enterprise development teams. Built with modern web technologies and deployed on scalable cloud infrastructure.
Swordfish Project - A collection of specialized AI models designed for software development and system operations. These models (Bandila, Amigo, and Amihan) are being integrated into AI Republic as the core reasoning and coding engine, providing developers with expert-level assistance in architecture design, code generation, and system debugging.
@software{amihan_1_2,
author = {Jan Israel},
title = {Amihan 1.2: Intelligent AI Orchestrator},
year = {2025},
publisher = {HuggingFace},
url = {https://huggingface.co/swordfish7412/Amihan_1.2}
}
This model is released under the MIT License, consistent with the Phi-3-mini base model. Free for research and commercial use.