README.md

---
license: llama2
base_model: meta-llama/Llama-3.1-70B-Instruct
tags:
- maritime
- navigation
- llama
- merged
- maritime-navigation
- seamanship
- nautical
language:
- en
pipeline_tag: text-generation
library_name: transformers
---

# 🌊 Llamarine - Maritime Navigation Model

Llamarine is a specialized large language model fine-tuned for maritime navigation and seamanship. This is a merged version combining the base Llama-3.1-70B-Instruct model with maritime-specific LoRA adapters.

## 🚢 Model Details

- **Base Model**: meta-llama/Llama-3.1-70B-Instruct
- **Specialization**: Maritime navigation, seamanship, and nautical operations
- **Model Type**: Merged (base + LoRA adapters)
- **Model Size**: ~140B parameters
- **Precision**: bfloat16
- **Context Length**: 2048 tokens

## ⚓ Maritime Capabilities

This model excels in:

### 🧭 Navigation & Piloting
- Celestial navigation principles
- GPS and electronic navigation
- Dead reckoning and position fixing
- Chart reading and interpretation
- Compass navigation and deviation
- Tide and current calculations

### 🛥️ Ship Operations
- Anchoring procedures and techniques
- Docking and undocking maneuvers  
- Ship handling in various conditions
- Cargo operations and stability
- Emergency procedures

### 📡 Maritime Communications
- Radio protocols and procedures
- Distress and safety communications
- Port communications
- International signal codes

### ⚖️ Maritime Law & Regulations
- International collision regulations (COLREGS)
- Maritime traffic separation schemes
- Port state control requirements
- International maritime conventions

### 🌊 Weather & Oceanography
- Weather routing and planning
- Ocean currents and their effects
- Storm avoidance techniques
- Barometric pressure interpretation

## 🚀 Usage

### Using Transformers
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("nguyennm1024/llamarine")
model = AutoModelForCausalLM.from_pretrained(
    "nguyennm1024/llamarine",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Generate response
prompt = "What is dead reckoning navigation?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_new_tokens=200,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

### Using vLLM (Recommended for Production)
```python
from vllm import LLM, SamplingParams

# Initialize model
llm = LLM(
    model="nguyennm1024/llamarine",
    tensor_parallel_size=2,  # Adjust based on your GPU setup
    dtype="bfloat16"
)

# Configure sampling
sampling_params = SamplingParams(
    temperature=0.7,
    top_p=0.9,
    max_tokens=200
)

# Generate response
prompt = "How do you anchor a ship in rough weather?"
outputs = llm.generate([prompt], sampling_params)
print(outputs[0].outputs[0].text)
```

## 📊 Performance

- **Response Speed**: 15-20 tokens/second (vLLM on 2x A100)
- **Context Awareness**: Maintains conversation history
- **Maritime Accuracy**: Specialized knowledge in nautical operations
- **Safety Focus**: Emphasizes safe maritime practices

## 💡 Example Prompts

### Navigation Questions
```
"What is celestial navigation?"
"How do you plot a course using GPS?"
"Explain magnetic compass deviation and variation"
"What are the principles of dead reckoning?"
```

### Ship Operations
```
"What are the steps for anchoring in emergency conditions?"
"How do you perform a man overboard maneuver?"
"What is the proper procedure for docking in strong winds?"
"How do you calculate cargo stability?"
```

### Safety & Regulations
```
"What are the COLREGS rules for overtaking?"
"How do you signal distress at sea?"
"What are the requirements for crossing traffic separation schemes?"
"What should you do if you encounter a vessel not under command?"
```

## ⚠️ Important Notes

- **Specialized Domain**: This model is optimized for maritime topics and may not perform as well on general tasks
- **Safety Critical**: Always verify navigation and safety information with official sources
- **Professional Use**: Intended for maritime professionals and educational purposes
- **Real-time Operations**: Not a substitute for official navigation equipment or procedures

## 🔧 Hardware Requirements

### Minimum Requirements
- **RAM**: 80GB+ system RAM
- **VRAM**: 80GB+ GPU memory (A100 recommended)
- **Storage**: 200GB+ available space

### Recommended Setup
- **GPUs**: 2x NVIDIA A100 (80GB each)
- **RAM**: 128GB+ system RAM
- **Storage**: NVMe SSD for optimal loading speed

## 📚 Training Data

This model was fine-tuned on maritime navigation data including:
- Navigation textbooks and manuals
- Maritime regulations and procedures
- Ship handling guides
- Weather routing resources
- Emergency response protocols

## 🤝 Contributing

This model is part of the Llamarine project aimed at advancing AI assistance in maritime operations. For questions or contributions, please reach out through the Hugging Face community.

## 📄 License

This model inherits the Llama 2 license from the base model. Please review the license terms before commercial use.

## 🌊 Fair Winds and Following Seas!

*"The sea, once it casts its spell, holds one in its net of wonder forever."* - Jacques Cousteau