Agent Skills
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knowledge-distillation
GitHub通过知识蒸馏将大模型能力迁移至小模型，降低推理成本并保留性能。涵盖温度缩放、软标签、反向KLD等技术，支持从GPT-4等专有模型向开源模型的能力转移及领域知识压缩。
触发场景
需要将大语言模型压缩为更小版本以降低部署成本
希望将专有模型（如GPT-4）的能力迁移到开源模型上
需要利用合成数据提升小型模型在特定领域的表现
安装
npx skills add synthetic-sciences/openscience --skill knowledge-distillation -g -y
SKILL.md
Frontmatter
{
"name": "knowledge-distillation",
"tags": [
"Emerging Techniques",
"Knowledge Distillation",
"Model Compression",
"Teacher-Student",
"MiniLLM",
"Reverse KLD",
"Soft Targets",
"Temperature Scaling",
"Logit Distillation",
"Model Transfer"
],
"author": "Synthetic Sciences",
"license": "MIT",
"version": "1.0.0",
"category": "ml-training",
"description": "Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.",
"dependencies": [
"transformers",
"torch",
"datasets"
]
}
Knowledge Distillation: Compressing LLMs
When to Use This Skill
Use Knowledge Distillation when you need to:
- Compress models from 70B → 7B while retaining 90%+ performance
- Transfer capabilities from proprietary models (GPT-4) to open-source (LLaMA, Mistral)
- Reduce inference costs by deploying smaller student models
- Create specialized models by distilling domain-specific knowledge
- Improve small models using synthetic data from large teachers
Key Techniques: Temperature scaling, soft targets, reverse KLD (MiniLLM), logit distillation, response distillation
Papers: Hinton et al. 2015 (arXiv 1503.02531), MiniLLM (arXiv 2306.08543), KD Survey (arXiv 2402.13116)
Installation
# Standard transformers
pip install transformers datasets accelerate
# For training
pip install torch deepspeed wandb
# Optional: MiniLLM implementation
git clone https://github.com/microsoft/LMOps
cd LMOps/minillm
pip install -e .
Quick Start
Basic Knowledge Distillation
import torch
import torch.nn.functional as F
from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
# 1. Load teacher (large) and student (small) models
teacher = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-2-70b-hf", # Large teacher
torch_dtype=torch.float16,
device_map="auto"
)
student = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-2-7b-hf", # Small student
torch_dtype=torch.float16,
device_map="cuda:0"
)
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-70b-hf")
# 2. Define distillation loss
def distillation_loss(student_logits, teacher_logits, labels, temperature=2.0, alpha=0.5):
"""
Combine hard loss (cross-entropy) with soft loss (KL divergence).
Args:
temperature: Softens probability distributions (higher = softer)
alpha: Weight for distillation loss (1-alpha for hard loss)
"""
# Hard loss: Standard cross-entropy with true labels
hard_loss = F.cross_entropy(student_logits.view(-1, student_logits.size(-1)), labels.view(-1))
# Soft loss: KL divergence between student and teacher
soft_targets = F.softmax(teacher_logits / temperature, dim=-1)
soft_student = F.log_softmax(student_logits / temperature, dim=-1)
soft_loss = F.kl_div(soft_student, soft_targets, reduction='batchmean') * (temperature ** 2)
# Combined loss
return alpha * soft_loss + (1 - alpha) * hard_loss
# 3. Training loop
for batch in dataloader:
# Teacher forward (no grad)
with torch.no_grad():
teacher_outputs = teacher(**batch)
teacher_logits = teacher_outputs.logits
# Student forward
student_outputs = student(**batch)
student_logits = student_outputs.logits
# Compute distillation loss
loss = distillation_loss(
student_logits,
teacher_logits,
batch['labels'],
temperature=2.0,
alpha=0.7 # 70% soft, 30% hard
)
# Backward and optimize
loss.backward()
optimizer.step()
optimizer.zero_grad()
MiniLLM (Reverse KLD)
Source: arXiv 2306.08543 (2024)
Innovation: Use reverse KLD instead of forward KLD for better generative model distillation.
def reverse_kl_loss(student_logits, teacher_logits, temperature=1.0):
"""
Reverse KL divergence: KL(Teacher || Student)
Better for generative models than forward KL.
"""
# Teacher distribution (target)
p_teacher = F.softmax(teacher_logits / temperature, dim=-1)
# Student distribution (model)
log_p_student = F.log_softmax(student_logits / temperature, dim=-1)
# Reverse KL: Sum over teacher, student learns to cover teacher's modes
reverse_kl = -(p_teacher * log_p_student).sum(dim=-1).mean()
return reverse_kl * (temperature ** 2)
# Training with MiniLLM
for batch in dataloader:
with torch.no_grad():
teacher_logits = teacher(**batch).logits
student_logits = student(**batch).logits
# Reverse KLD (better for generation)
loss = reverse_kl_loss(student_logits, teacher_logits, temperature=1.0)
loss.backward()
optimizer.step()
Why reverse KL?
- Forward KL (standard): Student learns to match teacher's mean
- Reverse KL (MiniLLM): Student learns to cover all teacher's modes
- Better for diverse text generation
Response Distillation
# Generate synthetic data from teacher, train student to imitate
# 1. Generate synthetic responses from teacher
prompts = ["Explain AI:", "What is ML?", "Define NLP:"]
teacher_responses = []
for prompt in prompts:
inputs = tokenizer(prompt, return_tensors='pt').to(teacher.device)
outputs = teacher.generate(**inputs, max_new_tokens=256, do_sample=True, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
teacher_responses.append(response)
# 2. Train student on teacher's responses (standard fine-tuning)
train_dataset = [
{"text": f"{prompt}\n{response}"}
for prompt, response in zip(prompts, teacher_responses)
]
# 3. Fine-tune student
trainer = Trainer(
model=student,
args=TrainingArguments(output_dir="./student", num_train_epochs=3, learning_rate=2e-5),
train_dataset=train_dataset,
)
trainer.train()
Core Concepts
1. Temperature Scaling
Purpose: Soften probability distributions to expose teacher's uncertainty.
# Low temperature (T=1): Sharp distribution
logits = [3.0, 2.0, 1.0]
probs_T1 = softmax(logits / 1.0) # [0.67, 0.24, 0.09]
# High temperature (T=4): Soft distribution
probs_T4 = softmax(logits / 4.0) # [0.42, 0.34, 0.24]
# Higher T reveals more information about relative rankings
Rule: Use T=2-5 for distillation (2 is common default).
2. Loss Function Components
# Total loss = alpha * soft_loss + (1 - alpha) * hard_loss
# Soft loss: Learn from teacher's knowledge
soft_loss = KL(student || teacher)
# Hard loss: Learn from ground truth labels
hard_loss = CrossEntropy(student_output, true_labels)
# Typical values:
alpha = 0.5 # Balanced
alpha = 0.7 # More emphasis on teacher
alpha = 0.3 # More emphasis on labels
3. Forward vs Reverse KLD
# Forward KL: KL(Student || Teacher)
# - Student matches teacher's average behavior
# - Mode-seeking: Student focuses on teacher's highest probability modes
# - Good for classification
# Reverse KL: KL(Teacher || Student)
# - Student covers all of teacher's behaviors
# - Mode-covering: Student learns diverse behaviors
# - Good for generation (MiniLLM)
Training Strategies
Strategy 1: Logit Distillation
# Train student to match teacher's logits directly
def logit_distillation_trainer(student, teacher, dataloader, temperature=2.0):
optimizer = torch.optim.AdamW(student.parameters(), lr=2e-5)
for epoch in range(3):
for batch in dataloader:
# Get logits
with torch.no_grad():
teacher_logits = teacher(**batch).logits
student_logits = student(**batch).logits
# MSE on logits (alternative to KLD)
loss = F.mse_loss(student_logits, teacher_logits)
# Or use KLD
# loss = F.kl_div(
# F.log_softmax(student_logits/temperature, dim=-1),
# F.softmax(teacher_logits/temperature, dim=-1),
# reduction='batchmean'
# ) * (temperature ** 2)
loss.backward()
optimizer.step()
optimizer.zero_grad()
return student
Strategy 2: Two-Stage Distillation
# Stage 1: Distill from teacher
student = distill(teacher, student, epochs=5)
# Stage 2: Fine-tune on task-specific data
student = fine_tune(student, task_data, epochs=3)
# Results in better task performance than single-stage
Strategy 3: Multi-Teacher Distillation
# Learn from multiple expert teachers
def multi_teacher_distillation(student, teachers, batch):
"""Distill from ensemble of teachers."""
teacher_logits_list = []
# Get logits from all teachers
with torch.no_grad():
for teacher in teachers:
logits = teacher(**batch).logits
teacher_logits_list.append(logits)
# Average teacher predictions
avg_teacher_logits = torch.stack(teacher_logits_list).mean(dim=0)
# Student learns from ensemble
student_logits = student(**batch).logits
loss = F.kl_div(
F.log_softmax(student_logits, dim=-1),
F.softmax(avg_teacher_logits, dim=-1),
reduction='batchmean'
)
return loss
Production Deployment
Complete Training Script
from transformers import Trainer, TrainingArguments, DataCollatorForLanguageModeling
def train_distilled_model(
teacher_name="meta-llama/Llama-2-70b-hf",
student_name="meta-llama/Llama-2-7b-hf",
output_dir="./distilled-llama-7b",
temperature=2.0,
alpha=0.7,
):
# Load models
teacher = AutoModelForCausalLM.from_pretrained(teacher_name, torch_dtype=torch.float16, device_map="auto")
student = AutoModelForCausalLM.from_pretrained(student_name, torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained(teacher_name)
# Custom trainer with distillation
class DistillationTrainer(Trainer):
def compute_loss(self, model, inputs, return_outputs=False):
# Student forward
outputs_student = model(**inputs)
student_logits = outputs_student.logits
# Teacher forward (no grad)
with torch.no_grad():
outputs_teacher = teacher(**inputs)
teacher_logits = outputs_teacher.logits
# Distillation loss
soft_targets = F.softmax(teacher_logits / temperature, dim=-1)
soft_student = F.log_softmax(student_logits / temperature, dim=-1)
soft_loss = F.kl_div(soft_student, soft_targets, reduction='batchmean') * (temperature ** 2)
# Hard loss
hard_loss = outputs_student.loss
# Combined
loss = alpha * soft_loss + (1 - alpha) * hard_loss
return (loss, outputs_student) if return_outputs else loss
# Training arguments
training_args = TrainingArguments(
output_dir=output_dir,
num_train_epochs=3,
per_device_train_batch_size=4,
gradient_accumulation_steps=8,
learning_rate=2e-5,
warmup_steps=500,
logging_steps=100,
save_steps=1000,
bf16=True,
gradient_checkpointing=True,
)
# Train
trainer = DistillationTrainer(
model=student,
args=training_args,
train_dataset=train_dataset,
data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False),
)
trainer.train()
student.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
# Usage
train_distilled_model(
teacher_name="meta-llama/Llama-2-70b-hf",
student_name="meta-llama/Llama-2-7b-hf",
temperature=2.0,
alpha=0.7
)
Best Practices
1. Hyperparameter Selection
# Temperature
T = 1.0 # Sharp (less knowledge transfer)
T = 2.0 # Standard (good balance)
T = 5.0 # Soft (more knowledge transfer)
# Alpha (weight)
alpha = 0.5 # Balanced
alpha = 0.7 # Emphasize teacher knowledge
alpha = 0.9 # Strong distillation
# Rule: Higher T + higher alpha = stronger distillation
2. Model Size Ratio
# Good ratios (teacher/student)
70B / 7B = 10× # Excellent
13B / 1B = 13× # Good
7B / 1B = 7× # Acceptable
# Avoid too large gap
70B / 1B = 70× # Too large, ineffective
3. Data Quality
# Best: Use teacher-generated data + real data
train_data = {
"teacher_generated": 70%, # Diverse, high-quality
"real_data": 30% # Ground truth
}
# Avoid: Only real data (doesn't utilize teacher fully)
Evaluation
from transformers import pipeline
# Compare student vs teacher
teacher_pipe = pipeline("text-generation", model=teacher)
student_pipe = pipeline("text-generation", model=student)
prompts = ["Explain quantum computing:", "What is AI?"]
for prompt in prompts:
teacher_out = teacher_pipe(prompt, max_new_tokens=100)
student_out = student_pipe(prompt, max_new_tokens=100)
print(f"Prompt: {prompt}")
print(f"Teacher: {teacher_out[0]['generated_text']}")
print(f"Student: {student_out[0]['generated_text']}")
print(f"Match quality: {calculate_similarity(teacher_out, student_out):.2f}")
Resources
- Hinton et al. 2015 (Foundational): https://arxiv.org/abs/1503.02531
- MiniLLM (Reverse KLD): https://arxiv.org/abs/2306.08543
- KD Survey for LLMs (2024): https://arxiv.org/abs/2402.13116
- MiniLLM GitHub: https://github.com/microsoft/LMOps/tree/main/minillm
版本历史
- e9844a4 当前 2026-07-11 17:29
依赖关系
- required transformers
-
required
torch -
required
datasets
