初始化项目，由ModelHub XC社区提供模型

Model: KurmaAI/AQUA-1B
Source: Original Platform

README.md

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+---
+license: apache-2.0
+tags:
+- text-generation-inference
+- transformers
+language:
+- en
+base_model:
+- google/gemma-3-1b-it
+pipeline_tag: text-generation
+---
+
+<p align="center">
+  <img src="./AQUA-1B.png" alt="AQUA-1B" width="600" style="border-radius: 6px;"/>
+</p>
+
+# Model Information
+
+**AQUA-1B** is Kurma AI’s compact and efficient **1-billion parameter Small Language Model (SLM)**, It is the **first lightweight Aquaculture domain-specific model** purpose-built for real-time aquaculture operations involving IoT sensor data, autonomous systems, and robotic agents.
+
+Designed for edge deployments and low-latency environments, **AQUA-1B** enables on-device decision-making, real-time alert generation, and agentic task execution. It powers intelligent aquaculture systems for Water quality monitoring, Automated feeding routines, Mobile robotic inspections across ponds, tanks, and recirculating aquaculture systems (RAS)
+
+
+Learn more about [Kurma AI](https://kurma.ai/company).
+
+---
+
+# Key Features
+
+- **Edge-Ready Intelligence** Optimized for low-power, real-time inference on embedded devices like Raspberry Pi, Jetson Nano, and Coral TPU.
+- **Agentic Task Execution** Supports multi-step agent-based workflows such as sensor checks, feeding triggers, water exchange scheduling, and autonomous health checks using instruction-following prompts.
+- **IoT-Aware Reasoning** Natively understands and reasons over sensor data inputs (e.g., temperature, pH, TDS, turbidity, DO), enabling rapid decisions in fluctuating aquaculture environments.
+- **Robotic Automation Control** Designed to interact with robotic systems, including underwater, and mobile pond inspectors.
+- **Autonomous Alerting Systems** Powers local alert mechanisms (via SMS, Telegram bots, or MQTT) that notify farmers when water parameters exceed safe thresholds or when interventions are required.
+- **Field-Deployable Decision Engine** Enables fully autonomous operation in remote hatcheries and ponds, ensuring uninterrupted control even in offline conditions or low-connectivity zones.
+
+---
+
+# Training Data Highlights
+
+- Extension worker–farmer dialogues and field advisory logs  
+- FAO, ICAR, NOAA, and peer-reviewed aquaculture research  
+- Synthetic Q&A from 5,000+ aquaculture-focused topics  
+- Climate-resilient practices, hatchery SOPs, and water quality datasets  
+- Carefully curated to support **species-specific culture** methods
+- **Scale:** Trained on approximately **3 million real and synthetic Q&A pairs**, totaling around **1 billion tokens** of high-quality, domain-specific data.
+
+
+---
+
+# Model Specifications
+
+- **Base Model**: Gemma 3 1B (by [Google DeepMind](https://deepmind.google/)) 
+- **Training Tokens**: ~1 Billion
+- **Released On** 4, July 2025
+- **Data Volume**: 3M+ expert-verified and synthetic instructions   
+- **Origin**: Made in America by [Kurma AI](https://kurma.ai/)
+- **Training Technic** Model is trained via Fine-tuning using (LoRA-based) Supervised Fine-Tuning (SFT).
+- **Training Infrastructure**: Trained using **8 NVIDIA H200 GPU Multi Cluster** 
+Special Thanks to [Nebius](https://nebius.com/)
+
+---
+
+# Quickstart
+
+Transformers (Google Colab/ jupyter)
+
+
+- Install dependencies
+```python
+!pip install transformers accelerate
+```
+
+- Log in with your Hugging Face access token
+```python
+from huggingface_hub import login
+```
+
+- Import model from Huggingface
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "KurmaAI/AQUA-1B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",             # Automatically uses GPU if available
+    torch_dtype=torch.float16,     # Use torch.float32 if no GPU
+    trust_remote_code=True
+)
+```
+
+- Test Prompt
+```python
+prompt = "What are the most common diseases in shrimp farming and how can they be prevented?"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=256)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+---
+
+# 🙏 Acknowledgements
+This project was made possible thanks to:
+- [Nebius](https://nebius.com/) for providing a compute grant and access to NVIDIA H200 GPU servers, which powered the model training process.
+- [Google DeepMind](https://deepmind.google/) for sharing their open-source language models, which made this project possible.
+- Kurma AI research team: including aquaculture experts, machine learning engineers, data annotators, and advisors who collaborated to curate, verify, and refine the domain-specific dataset used for fine-tuning this model.
+
+---
+
+# ⚠️ Disclaimer, Bias & Limitations
+
+- **Domain Bias**: The model may reflect inherent biases present in the aquaculture data sources and industry practices on which it was trained.
+- **Temporal Data Limitation**: Climate and environmental recommendations are based on information available up to 2024. Users should cross-check any climate-related advice against the latest advisories (e.g., IMD or NOAA updates).
+- **Potential Hallucinations**: Like all large language models, Aqua-1B may occasionally generate inaccurate or misleading responses ("hallucinations").
+- **Always validate critical, regulatory, or high-impact decisions with a qualified aquaculture professional.**
+
+---
+
+# Citation
+
+```bibtex
+@article{narisetty2025aqua,
+  title={AQUA: A Large Language Model for Aquaculture \& Fisheries},
+  author={Narisetty, Praneeth and Kattamanchi, Uday Kumar Reddy and Nimma, Lohit Akshant and Karnati, Sri Ram Kaushik and Kore, Shiva Nagendra Babu and Golamari, Mounika and Nageshreddy, Tejashree},
+  journal={arXiv preprint arXiv:2507.20520},
+  year={2025},
+  doi={10.48550/arXiv.2507.20520}
+}
+```