DeepSeek-R1-Distill-Llama-8B-aviation

DeepSeek-R1 Fine-Tuned on Aviation SOPs ✈️

🚀 Model Overview

This model is a fine-tuned version of DeepSeek-R1-Distill-Llama-8B, optimized for generating aviation-related Standard Operating Procedures (SOPs), flight manuals, and maintenance guidelines. It has been fine-tuned using LoRA (Low-Rank Adaptation) and 4-bit quantization for efficient performance.

🔍 Key Features

• 🛫 Aviation-Specific SOP Generation: Generates structured procedures for aircraft refueling, emergency handling, maintenance, and more.
• 🔬 Fine-Tuned on Realistic Aviation Documents: Trained on aviation SOPs, flight manuals, and safety protocols.
• ⚡ Efficient 4-bit Quantization: Optimized using Unsloth to run on consumer GPUs.
• 🧠 LoRA-Based Adaptation: Fine-tuned with LoRA to adapt without modifying the base model.

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📌 Model Details

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⏳ Fine-Tuning Process

This model was fine-tuned using LoRA (r=8, alpha=32, dropout=0.05) and optimized using Unsloth 4-bit quantization. The training dataset consists of aviation-related Standard Operating Procedures (SOPs), flight manuals, and safety regulations.

📌 Hyperparameters Used

• Batch Size: 2
• Learning Rate: 2e-5
• Epochs: 3
• LoRA Rank: 8
• LoRA Alpha: 32
• LoRA Dropout: 0.05
• Gradient Checkpointing: Enabled

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📖 How to Use This Model

🚀 Using with Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "expertopsai/aviation-llm-v1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Describe the safety precautions during aircraft maintenance."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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💡 Example Outputs

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📌 Limitations

• The model is not a real-world aviation authority and should not be used for critical flight operations.
• It may hallucinate some procedural details—always verify against official documentation.
• It is fine-tuned on aviation-specific data, so responses may be biased toward that domain.

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📜 License

This model is released under the Apache 2.0 License. You are free to use and modify it but must attribute the original work.

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🤝 Contributing & Support

We welcome contributions and feedback!

• GitHub Issues: GitHub
• Hugging Face Discussions: ExpertOps AI.