Introducing llm model training services for you and implementing rag
Data Preparation:
- Gather and clean relevant dataset
- Format data to align with LLaMA's input requirements
- Divide data into training and validation sets
Environment Setup:
- Install essential libraries such as transformers and accelerate
- Configure GPU environment with CUDA if needed
- Download pre-trained LLaMA model
Model Configuration:
- Select appropriate model size (7B, 13B, etc.)
- Define hyperparameters including learning rate and batch size
- Specify training parameters like epochs and gradient accumulation
Fine-Tuning Process:
- Apply efficient fine-tuning techniques like LoRA and QLoRA
- Train model using prepared dataset
- Monitor training progress and metrics
Evaluation:
- Evaluate model performance on validation set
- Compare results with baseline metrics
- Conduct qualitative analysis of model outputs
Iteration and Optimization:
- Adjust hyperparameters based on outcomes
- Experiment with various fine-tuning methods
- Refine dataset if needed
Model Deployment:
- Export fine-tuned model
- Optimize for inference, including quantization if required
- Prepare model for deployment environment
| Shop Location | Auckland, New Zealand |
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