Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,192 @@
+---
+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