Quantization Impact

Explore how different quantization levels affect model size and accuracy:

Deployment Pipeline

Step-by-step process for optimizing and deploying your model:

Model Export

model.save_pretrained("optimized-model")

Export model weights and configuration

Performance Metrics

Compare key performance metrics before and after optimization:

Inference Time (ms)

Standard100

Optimized40

↑ 60% faster

Memory Usage (GB)

Standard16

Optimized4

↑ 75% reduced

Throughput (req/s)

Standard10

Optimized25

↑ 150% increased

Implementation Example

# Load the model
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b")

# Quantize the model
quantized_model = torch.quantization.quantize_dynamic(
    model,
    {torch.nn.Linear},
    dtype=torch.qint8
)

# Convert to GGUF format
!python convert.py \
    --model-path ./quantized_model \
    --outfile model.gguf \
    --outtype q4_0

# Load for inference
from llama_cpp import Llama

llm = Llama(
    model_path="model.gguf",
    n_gpu_layers=32,
    n_ctx=2048
)

Model Optimization & Deployment