Advanced LLM Model Optimization: Quantization, Pruning, Distillation

Advanced LLM Model Optimization

Quantization

8-bit Quantization

from transformers import AutoModelForCausalLM, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_8bit=True,
    llm_int8_threshold=6.0
)

model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-7b-hf",
    quantization_config=quantization_config,
    device_map="auto"
)

# 2x memory reduction, minimal quality loss

4-bit Quantization

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_quant_type="nf4"
)

# 4x memory reduction

Knowledge Distillation

from transformers import Trainer, TrainingArguments

# Teacher model (large)
teacher = AutoModelForCausalLM.from_pretrained("gpt-3.5-turbo")

# Student model (small)
student = AutoModelForCausalLM.from_pretrained("gpt-2")

# Distillation loss
def distillation_loss(student_logits, teacher_logits, labels, temperature=2.0):
    loss_ce = F.cross_entropy(student_logits, labels)
    loss_kd = F.kl_div(
        F.log_softmax(student_logits / temperature, dim=-1),
        F.softmax(teacher_logits / temperature, dim=-1),
        reduction='batchmean'
    ) * (temperature ** 2)
    return 0.5 * loss_ce + 0.5 * loss_kd

Model Pruning

import torch.nn.utils.prune as prune

def prune_model(model, amount=0.3):
    for name, module in model.named_modules():
        if isinstance(module, torch.nn.Linear):
            prune.l1_unstructured(module, name='weight', amount=amount)

    # Remove pruning reparameterization
    for name, module in model.named_modules():
        if isinstance(module, torch.nn.Linear):
            prune.remove(module, 'weight')

Inference Optimization

Flash Attention

model = AutoModelForCausalLM.from_pretrained(
    "model-name",
    torch_dtype=torch.float16,
    attn_implementation="flash_attention_2"
)

vLLM for Serving

from vllm import LLM, SamplingParams

llm = LLM(model="meta-llama/Llama-2-7b-hf")
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

outputs = llm.generate(prompts, sampling_params)
# 10-20x faster inference

Best Practices

✅ Benchmark before/after ✅ Test quality metrics ✅ Profile memory usage ✅ Monitor latency ✅ Consider accuracy tradeoffs

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