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Model Optimization & Deployment

Making large language models efficient and deployable

Model Size Reduction

See how quantization and optimization techniques can dramatically reduce model size while maintaining performance:

LLAMA-7B
7B params
LLAMA-13B
13B params
LLAMA-70B
70B params

Deployment Pipeline

Follow the step-by-step process of preparing a model for deployment:

Model Export

Quantization

Optimization

Deployment

Convert model to GGUF format

Optimization Techniques

Quantization

Reduce numerical precision from 32-bit to 4-bit while maintaining accuracy through careful calibration.

GGUF Conversion

Convert models to GGUF format for efficient CPU/GPU inference across different architectures.

Performance Monitoring

Track inference speed, memory usage, and model accuracy to ensure optimal deployment.

Practical Implementation

4-bit Quantization Example

Model Conversion

# Convert to GGUF and quantize
python convert.py \
--model llama-7b \
--output model-q4.gguf \
--quantization q4_0

Deployment Code

from llama_cpp import Llama

# Load the quantized model
llm = Llama(
model_path="model-q4.gguf",
n_threads=4,  # CPU threads
n_gpu_layers=32  # Offload to GPU
)

Performance Metrics

  • • Memory usage reduced by 75%
  • • 2-3x faster inference speed
  • • Minimal impact on accuracy
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