What is distillation?
Are you thinking about beer or wine? Where, distillation is a process used to separate components of a liquid mixture based on differences in their boiling points? Possibly yes, but in the world of Artificial Intelligence, AI-models and LLMs this has a significant new definition. More importantly, what a teacher model is, and how optimization may take place to create your own model.
“In the context of Artificial Intelligence (AI), distillation refers to a technique used to transfer knowledge from one model to another, typically for the purpose of improving efficiency, reducing computational requirements, or making models more deployable in resource-constrained environments. This process is often metaphorically similar to traditional distillation, where essential components are extracted and concentrated.” – Deepseek r1:32b
In this example, a sample on how to create EGLA-AI model will be shown.
Creating an EGLA AI Model: DeepSeek, Claude AI, GEMINI and GROK3
As an example, in Python you will need PyTorch for processing, in Distillation a “teacher model” is used to handle. We will then:
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Define Teacher Models
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Define the Student model (EGLA-AI)
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Model Training, define loss functions
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Applying the new model and generating a Neural Network Model (e.g. NN.model)
Step 1: Defining the teacher models classes ( Deepseek, Grok3, Claude AI, Gemini) using ollama or API requests.
class Grok3(nn.Module):
# define model architecture …
class ClaudeAI(nn.Module):
#define model architecture …
class Gemini(nn.Module):
#define model architecture …
Step 2: Define the student model (EGLA-model)
A Class model or Studen model can be defined using Python, an be a nn.Module(…) class.
class EGLAModel(nn.Module): def init(self, input_size, hidden_size, output_size): super(EGLAModel, self).init() self.fc1 = nn.Linear(input_size, hidden_size) self.fc2 = nn.Linear(hidden_size, output_size) def forward(self, x): x = torch.relu(self.fc1(x)) return self.fc2(x)
Step 3: Function to compute distillation loss
We need to create a distillation_loss() function to handle the teacher outputs, and use an algorithm such as SOFTMAX (via torch.softmask)., and combine all teacher probabilities and compute a KL Divergence.
def distillation_loss(student_outputs, teacher_outputs):
# Convert outputs to probabilities using softmax
student_probs = torch.softmax(student_outputs, dim=-1)
teacher_probs = [torch.softmax(t_out, dim=-1) for t_out in teacher_outputs]
# Combine teacher probabilities (e.g., average)
combined_teacher_probs = torch.mean(torch.stack(teacher_probs), dim=0)
# Compute KL divergence
return nn.KLDivLoss(reduction='batchmean')(student_probs.log(), combined_teacher_probs)3.1 What is the KL Divergence?
KL Divergence is a measure of how much one probability distribution differs from another. It’s not a true “distance” metric (since it’s not symmetric), but rather a way to quantify the “divergence” or mismatch between two distributions. In simpler terms, it tells you how inefficient it would be to use one distribution to represent another.
3.2 What is SOFTMAX?
The softmax function takes a vector of arbitrary real-valued scores (logits) and converts them into probabilities between 0 and 1, while ensuring they add up to 1. It does this by emphasizing larger values and suppressing smaller ones, making it ideal for interpreting a model’s confidence across different classes.
Step 4: Training loop
We will need a training look use the distillation_loss() function that handles KL Divergence plus SoftMAX to then using the total_loss value with is alpha*dist_loss+(1-alpha)*class_loss, which is based on the criterion, and optimizer class from PyTorch can be used to create the Backward propagation model, or in some papers like in DeepSeeek or OpenAI use Reinforced Learning to improve distillation.
For epoch in range(num_epochs):
for inputs, labels in dataloader:
# Forward pass through teachers
grok3_out = grok3(inputs)
claude_ai_out = claude_ai(inputs)
gemini_out = gemini(inputs)
teacher_outputs = [grok3_out, claude_ai_out, gemini_out]
# Forward pass through student
eglai_out = eglai(inputs)
# Compute loss
dist_loss = distillation_loss(eglai_out, teacher_outputs)
class_loss = criterion(eglai_out, labels)
total_loss = alpha * dist_loss + (1 - alpha) * class_loss
# Backward pass and optimize
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {total_loss.item():.4f}")Conclusions
AI distillation is like teaching a small, simple computer program to act like bigger, smarter ones. Instead of starting from scratch, we use powerful “teacher” models (like Grok3 or Claude AI) to guide a new “student” model (EGLA-AI) so it can learn fast and work on smaller devices. By using tools like SOFTMAX (which turns scores into chances) and KL Divergence (which checks how close the student’s answers are to the teachers’), we train the student to be almost as smart but way more efficient. After training with a loop that mixes the teachers’ wisdom and the student’s own practice, we get a neat, compact AI model ready for real-world jobs—like controlling web apps or games with your phone! This makes AI easier to use everywhere, not just on big computers.
