# Embedding Model Construction

The process of creating an embedding model using LLaMA-2-7B and the Hugging Face libraries with PyTorch. 

We'll follow the LLM2Vec approach described in the paper.

<mark style="color:green;">**Step 1: Install the necessary libraries First, make sure you have the required libraries installed:**</mark>

```bash
pip install torch transformers
```

<mark style="color:green;">**Step 2: Load the pre-trained LLaMA-2-7B model Load the pre-trained LLaMA-2-7B model using the Hugging Face Transformers library:**</mark>

```python
from transformers import LlamaForCausalLM, LlamaTokenizer

model_name = "facebook/llama-7b"
tokenizer = LlamaTokenizer.from_pretrained(model_name)
model = LlamaForCausalLM.from_pretrained(model_name)
```

<mark style="color:green;">Step 3: Enable bidirectional attention To enable bidirectional attention, you need to modify the attention mask in the model's forward pass.</mark>&#x20;

One way to do this is to create a custom model class that inherits from `LlamaForCausalLM` and overrides the `forward` method:

```python
import torch
from transformers.models.llama.modeling_llama import LlamaAttention

class LlamaBidirectionalAttention(LlamaForCausalLM):
    def forward(self, input_ids, attention_mask=None, **kwargs):
        if attention_mask is not None:
            attention_mask = torch.ones_like(attention_mask)
        return super().forward(input_ids, attention_mask=attention_mask, **kwargs)
```

<mark style="color:green;">**Step 4: Masked Next Token Prediction (MNTP) Implement the MNTP training objective to adapt the model to use bidirectional attention.**</mark>&#x20;

You can create a custom training loop or modify an existing language modelling training script to mask a fraction of the input tokens and compute the loss based on the logits obtained from the token representation at the previous position.

```python
def mntp_loss(model, input_ids, attention_mask):
    # Mask a fraction of the input tokens
    masked_input_ids, labels = mask_tokens(input_ids)
    
    # Forward pass with the masked input
    outputs = model(masked_input_ids, attention_mask=attention_mask)
    logits = outputs.logits
    
    # Compute the loss based on the logits at the previous position
    shifted_logits = logits[..., :-1, :].contiguous()
    shifted_labels = labels[..., 1:].contiguous()
    loss = torch.nn.functional.cross_entropy(shifted_logits.view(-1, shifted_logits.size(-1)), shifted_labels.view(-1))
    
    return loss
```

<mark style="color:green;">**Step 5: Unsupervised Contrastive Learning (SimCSE)**</mark>&#x20;

Apply unsupervised contrastive learning using the SimCSE approach.&#x20;

Pass the input sequence through the model twice with independently sampled dropout masks to obtain two different representations for the same sequence.&#x20;

Maximise the similarity between these two representations while minimizing the similarity with representations of other sequences in the batch.

```python
def simcse_loss(model, input_ids, attention_mask):
    # Forward pass with different dropout masks
    outputs1 = model(input_ids, attention_mask=attention_mask)
    outputs2 = model(input_ids, attention_mask=attention_mask)
    
    # Apply pooling to get sequence representations
    pooled_outputs1 = mean_pooling(outputs1, attention_mask)
    pooled_outputs2 = mean_pooling(outputs2, attention_mask)
    
    # Compute the contrastive loss
    loss = contrastive_loss(pooled_outputs1, pooled_outputs2)
    
    return loss
```

<mark style="color:green;">**Step 6: Training Combine the MNTP and SimCSE losses and train the model on a suitable dataset, such as English Wikipedia.**</mark>&#x20;

You can use a dataset like Wikitext-103 for the MNTP step and a subset of Wikipedia sentences for the unsupervised SimCSE step.

```python
# Training loop
for batch in dataloader:
    input_ids, attention_mask = batch
    
    # Compute MNTP loss
    mntp_loss_value = mntp_loss(model, input_ids, attention_mask)
    
    # Compute SimCSE loss
    simcse_loss_value = simcse_loss(model, input_ids, attention_mask)
    
    # Combine the losses
    total_loss = mntp_loss_value + simcse_loss_value
    
    # Backward pass and optimization step
    total_loss.backward()
    optimizer.step()
    optimizer.zero_grad()
```

After training, you will have an LLaMA-2-7B model that has been transformed into a text embedding model using the LLM2Vec approach.&#x20;

You can then use this model to generate embeddings for various downstream tasks.

Note: This is a high-level overview of the process, and you may need to adapt the code snippets to fit your specific requirements and environment.