Implementing Llms Litgpt

# Custom Models

Guide to implementing custom model architectures in LitGPT.

## Overview

LitGPT's clean, single-file implementations make it easy to create custom architectures. You can extend the base `GPT` class or create entirely new models.

**Use cases**:
- Implementing new research architectures
- Adapting models for specific domains
- Experimenting with attention mechanisms
- Adding custom layers or components

## Key Files and Classes

### Core Architecture (`litgpt/model.py`)

**Main classes**:
- `GPT`: Top-level model class
- `Block`: Transformer block (attention + MLP)
- `CausalSelfAttention`: Attention mechanism
- `MLP`: Feed-forward network
- `RMSNorm` / `LayerNorm`: Normalization layers

**Configuration** (`litgpt/config.py`):
- `Config`: Base configuration dataclass
- Model-specific configs: `LlamaConfig`, `MistralConfig`, `PhiConfig`, etc.

## Custom Architecture Workflow

### Step 1: Define Configuration

Create a `Config` dataclass with your model's hyperparameters:

```python
from dataclasses import dataclass
from litgpt.config import Config

@dataclass
class MyModelConfig(Config):
    """Configuration for my custom model."""
    # Standard parameters
    name: str = "my-model-7b"
    block_size: int = 4096
    vocab_size: int = 32000
    n_layer: int = 32
    n_head: int = 32
    n_embd: int = 4096

    # Custom parameters
    custom_param: float = 0.1
    use_custom_attention: bool = True

    # Optional: override defaults
    rope_base: int = 10000
    intermediate_size: int = 11008
```

### Step 2: Implement Custom Components

#### Option A: Custom Attention

```python
from litgpt.model import CausalSelfAttention
import torch
import torch.nn as nn

class CustomAttention(CausalSelfAttention):
    """Custom attention mechanism."""

    def __init__(self, config):
        super().__init__(config)
        # Add custom components
        self.custom_proj = nn.Linear(config.n_embd, config.n_embd)
        self.custom_param = config.custom_param

    def forward(self, x, mask=None, input_pos=None):
        B, T, C = x.size()

        # Standard Q, K, V projections
        q = self.attn(x)
        k = self.attn(x)
        v = self.attn(x)

        # Custom modification
        q = q + self.custom_proj(x) * self.custom_param

        # Rest of attention computation
        q = q.view(B, T, self.n_head, self.head_size)
        k = k.view(B, T, self.n_query_groups, self.head_size)
        v = v.view(B, T, self.n_query_groups, self.head_size)

        # Scaled dot-product attention
        y = self.scaled_dot_product_attention(q, k, v, mask=mask)

        y = y.reshape(B, T, C)
        return self.proj(y)
```

#### Option B: Custom MLP

```python
from litgpt.model import MLP

class CustomMLP(MLP):
    """Custom feed-forward network."""

    def __init__(self, config):
        super().__init__(config)
        # Add custom layers
        self.custom_layer = nn.Linear(config.intermediate_size, config.intermediate_size)

    def forward(self, x):
        x = self.fc_1(x)
        x = self.act(x)
        x = self.custom_layer(x)  # Custom modification
        x = self.fc_2(x)
        return x
```

#### Option C: Custom Block

```python
from litgpt.model import Block

class CustomBlock(Block):
    """Custom transformer block."""

    def __init__(self, config):
        super().__init__(config)
        # Replace attention or MLP
        self.attn = CustomAttention(config)
        # Or: self.mlp = CustomMLP(config)

        # Add custom components
        self.custom_norm = nn.LayerNorm(config.n_embd)

    def forward(self, x, input_pos=None, mask=None):
        # Custom forward pass
        h = self.norm_1(x)
        h = self.attn(h, mask=mask, input_pos=input_pos)
        x = x + h

        # Custom normalization
        x = x + self.custom_norm(x)

        x = x + self.mlp(self.norm_2(x))
        return x
```

### Step 3: Create Custom GPT Model

```python
from litgpt.model import GPT
import torch.nn as nn

class CustomG