• For \( \text{softmax}(z_1) \): \( \frac{2.155}{78.654} \approx 0.0274 \)
• For \( \text{softmax}(z_2) \): \( \frac{6.236}{78.654} \approx 0.0793 \)
• For \( \text{softmax}(z_3) \): \( \frac{18.040}{78.654} \approx 0.2294 \)
• For \( \text{softmax}(z_4) \): \( \frac{52.223}{78.654} \approx 0.6639 \)

Result for the First Row

The first row of the Attention Weights matrix is approximately:

Notice that these values sum up to approximately 1 (due to rounding).

LoRA (Low-Rank Adaptation)

LoRA (Low-Rank Adaptation) doesn't change the fundamental mathematical operations of the attention mechanism itself (like dot product, scaling, or softmax). Instead, it modifies how the weight matrices within the attention mechanism's linear projection layers are adapted during fine-tuning.

In a Transformer's attention block, Query (Q), Key (K), and Value (V) matrices are derived from the input (\(X\)) by multiplying it with specific weight matrices:

• \( Q = X W^Q \)
• \( K = X W^K \)
• \( V = X W^V \)

Where \( W^Q \), \( W^K \), and \( W^V \) are the projection weight matrices for Queries, Keys, and Values, respectively. There's also typically an output projection matrix \( W^O \).

The Math of LoRA Applied to Attention Projections:

Let's consider one of these weight matrices, for example, the Query projection matrix \( W^Q \).

• Let the original pre-trained weight matrix be \( W^Q_0 \in \mathbb{R}^{d_{model} \times d_k} \). Here, \( d_{model} \) is the dimension of the input embeddings from \( X \), and \( d_k \) is the dimension of the query vectors.

When applying LoRA to this layer, the core idea is to freeze the original pre-trained \( W^Q_0 \) and introduce a small, trainable low-rank decomposition \( \Delta W^Q \) such that:

where \( B^Q \in \mathbb{R}^{d_{model} \times r} \) and \( A^Q \in \mathbb{R}^{r \times d_k} \). The parameter \( r \) is the rank of the update, and it is chosen to be much smaller than both \( d_{model} \) and \( d_k \) (\( r \ll \min(d_{model}, d_k) \)).

The effective weight matrix for queries during fine-tuning then becomes:

This same approach is applied to \( W^K \), \( W^V \), and potentially \( W^O \) as well.

The main benefit of this mathematical setup is a drastic reduction in trainable parameters for adaptation:

• Full Fine-tuning: For a single matrix like \( W^Q \), you would train \( d_{model} \times d_k \) parameters.
• LoRA Fine-tuning: You only train the parameters in \( B^Q \) and \( A^Q \), which amount to \( d_{model} \times r + r \times d_k \) parameters, \( r \) refers to the rank of the low-rank approximation in LoRA. Since \( r \) is typically very small (e.g., 4, 8, 16), this is a significant reduction.

This means that while the attention mechanism still performs its standard operations (dot product, scaling, softmax, multiplication with V), the specific way the input data is projected into the Q, K, and V spaces (and how the final output is projected) is efficiently modified by LoRA's low-rank updates. The modification happens at the level of the projection matrices that feed into the attention computation, not the attention computation steps themselves.

Some Python Code for LoRA

The code was initially generated using OpenAI and Gemini, and I subsequently reviewed and corrected it.

!pip install transformers sentence-transformers torch accelerate peft bitsandbytes
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel
from sentence_transformers import SentenceTransformer
from peft import LoraConfig, PeftModel, PeftConfig, TaskType # Added TaskType and LoraConfig directly
import warnings
from sklearn.metrics.pairwise import cosine_similarity # Added for clarity

# Suppress some common warnings for cleaner output
warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore", category=UserWarning)

# The sentence we'll be working with
sentence = "The cat is black."
print(f"Original Sentence: '{sentence}'\n")

print("--- Step 1: Tokenization & Word Embeddings (from a general LLM) ---\n")

# We'll use a smaller pre-trained LLM called 'gpt2' for this demonstration.
# For more complex or production tasks, you'd typically use larger models like Llama-2 or Mistral.
model_name_llm = "gpt2"
tokenizer_llm = AutoTokenizer.from_pretrained(model_name_llm)
# AutoModelForCausalLM is used for text generation tasks
model_llm = AutoModelForCausalLM.from_pretrained(model_name_llm)

# Add a pad token if the tokenizer doesn't have one. This is common for GPT-like models
# and helps when batching sentences of different lengths.
if tokenizer_llm.pad_token is None:
    tokenizer_llm.pad_token = tokenizer_llm.eos_token # The End-Of-Sequence token is often used as a pad token
    model_llm.config.pad_token_id = tokenizer_llm.eos_token_id

print(f"**Loaded Tokenizer and Model:** {model_name_llm}")

# Tokenize the sentence:
# `return_tensors="pt"` ensures the output is a PyTorch tensor.
# `padding=True` and `truncation=True` handle variable sentence lengths (not critical for one sentence).
inputs = tokenizer_llm(sentence, return_tensors="pt", padding=True, truncation=True)
print(f"**Token IDs:** {inputs['input_ids'].tolist()}")
print(f"**Attention Mask:** {inputs['attention_mask'].tolist()}") # 1s indicate actual tokens, 0s for padding

# Decode the token IDs back to human-readable tokens to see the tokenizer's split
tokens = tokenizer_llm.convert_ids_to_tokens(inputs['input_ids'][0])
print(f"**Tokens:** {tokens}")

# Get word embeddings from the model:
# We tell the model to output its hidden states (internal representations).
# `torch.no_grad()` is used during inference to save memory and computation by not calculating gradients.
with torch.no_grad():
    outputs = model_llm(**inputs, output_hidden_states=True)
    # The last layer's hidden states are typically considered the contextualized word embeddings
    word_embeddings = outputs.hidden_states[-1]

print(f"**Word Embeddings Shape:** {word_embeddings.shape} (meaning {word_embeddings.shape[1]} tokens, each with a {word_embeddings.shape[2]}-dimensional embedding)")
# Print the first few dimensions of embeddings for 'cat' and 'black'
print(f"**Embedding for 'cat' (first 5 dims):** {word_embeddings[0, tokens.index('cat')].cpu().numpy()[:5]}...")
print(f"**Embedding for 'black' (first 5 dims):** {word_embeddings[0, tokens.index('black')].cpu().numpy()[:5]}...\n")

print("--- Step 2: Sentence Embeddings (using Sentence Transformers) ---\n")

# Sentence Transformers are specially designed to create high-quality sentence-level embeddings.
# 'all-MiniLM-L6-v2' is a popular choice for its balance of performance and efficiency.
model_name_st = "all-MiniLM-L6-v2"
model_st = SentenceTransformer(model_name_st)

print(f"**Loaded Sentence Embedder:** {model_name_st}")

# Encode the sentence into a single vector
sentence_embedding = model_st.encode(sentence, convert_to_tensor=True)

print(f"**Sentence Embedding Shape:** {sentence_embedding.shape} (a single vector for the entire sentence)")
print(f"**Sentence Embedding (first 5 dims):** {sentence_embedding.cpu().numpy()[:5]}...\n")

# Let's see how sentence embeddings can be used for semantic similarity:
sentence2 = "A dark feline is present." # Semantically similar
sentence3 = "The car is red."         # Semantically dissimilar

embedding2 = model_st.encode(sentence2, convert_to_tensor=True)
embedding3 = model_st.encode(sentence3, convert_to_tensor=True)

# Cosine similarity measures the angle between two vectors. Closer to 1 means more similar.
similarity_cat_feline = cosine_similarity(sentence_embedding.reshape(1, -1), embedding2.reshape(1, -1))[0][0]
similarity_cat_car = cosine_similarity(sentence_embedding.reshape(1, -1), embedding3.reshape(1, -1))[0][0]

print(f"**Similarity ('{sentence}' vs '{sentence2}'):** {similarity_cat_feline:.4f} (High similarity, as expected)")
print(f"**Similarity ('{sentence}' vs '{sentence3}'):** {similarity_cat_car:.4f} (Low similarity, as expected)\n")

print("--- Step 3: Prompting an LLM (Inference) ---\n")

# We use the 'gpt2' model for text generation.
# A prompt guides the LLM on what kind of text to generate.
prompt = f"Given the sentence '{sentence}', complete the following story: The cat"

# Tokenize the prompt for the LLM
inputs_llm_gen = tokenizer_llm(prompt, return_tensors="pt")

# Generate text:
# `max_new_tokens` controls how long the generated text can be.
# `num_return_sequences` specifies how many different outputs to generate.
# `pad_token_id=tokenizer_llm.eos_token_id` is crucial for GPT-like models.
# `do_sample=True` enables sampling, allowing for more creative (less deterministic) output.
# `top_k` and `temperature` control the randomness and diversity of the sampling process.
generated_ids = model_llm.generate(
    inputs_llm_gen["input_ids"],
    # max_new_tokens=20,
    num_return_sequences=1,
    pad_token_id=tokenizer_llm.eos_token_id,
    do_sample=True,
    top_k=50,
    temperature=0.7
)

# Decode the generated token IDs back into human-readable text
generated_text = tokenizer_llm.decode(generated_ids[0], skip_special_tokens=True)
print(f"Prompt: '{prompt}'")
print(f"Generated Text: '{generated_text}'\n")


print("--- Step 4: LoRA (Low-Rank Adaptation) - Conceptual Understanding ---\n")


# This configuration is for illustration and wouldn't be directly executable here without a full fine-tuning setup.
lora_config_example = LoraConfig(
    r=8,              # The rank of the update matrices (smaller `r` means fewer parameters to train)
    lora_alpha=16,    # A scaling factor for the LoRA weights
    target_modules=["q_proj", "v_proj"], # Common layers to apply LoRA in attention mechanisms
    lora_dropout=0.1, # Dropout applied to the LoRA layers
    bias="none",      # How bias parameters are handled
    task_type=TaskType.CAUSAL_LM # Specifies the type of task (e.g., text generation)
)
print(lora_config_example)


import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import LoraConfig, get_peft_model, TaskType
import warnings

# Suppress some common warnings for cleaner output
warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore", category=UserWarning)

# --- 0. Setup ---
sentence = "The cat is black."
print(f"Original Sentence: '{sentence}'\n")

# Choose a small pre-trained model for demonstration. 'gpt2' is good for causal language modeling.
base_model_name = "gpt2"

# --- 1. Load the Base LLM and Tokenizer ---
print(f"--- Step 1: Loading Base LLM ({base_model_name}) ---")
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
# AutoModelForCausalLM is for text generation
base_model = AutoModelForCausalLM.from_pretrained(base_model_name)

# Ensure the tokenizer has a pad token, which is often needed for batching and generation
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token
    base_model.config.pad_token_id = tokenizer.eos_token_id

print(f"Base Model Parameters (total): {base_model.num_parameters()}")
print(f"Example: First few weights of a linear layer in base model (e.g., first attention query layer):")
# Accessing an example weight matrix (for visualization of LoRA's effect)
# For GPT-2, the attention layers are in model.transformer.h[layer_idx].attn.c_attn
# c_attn is a Conv1D layer that projects to Q, K, V. Let's pick one part.
# We'll just take the first layer for simplicity.
# The weights are usually stored in 'weight' or 'W' depending on the layer type
# For Conv1D, it's typically 'weight'
if hasattr(base_model.transformer.h[0].attn.c_attn, 'weight'):
    original_weight_qkv = base_model.transformer.h[0].attn.c_attn.weight.data.clone()
    print(f"Original c_attn.weight shape: {original_weight_qkv.shape}")
    print(f"Original c_attn.weight (first row, first 5 cols): {original_weight_qkv[0, :5].tolist()}")
else:
    print("Could not directly access 'weight' attribute of c_attn. Skipping detailed weight inspection.")
print("\n" + "="*80 + "\n")


# --- 2. Configure LoRA ---
print("--- Step 2: Configuring LoRA ---")
# LoRA configuration parameters:
# r (rank): The dimension of the low-rank matrices. A smaller 'r' means fewer trainable parameters.
# lora_alpha: A scaling factor for the LoRA weights.
# target_modules: The names of the modules (linear layers) in the base model to apply LoRA to.
#                 Commonly "q_proj", "v_proj" for attention mechanisms. For GPT2, it's often within c_attn.
#                 PEFT can sometimes infer this, but explicit is clearer.
# bias: 'none', 'all', or 'lora_only'. Controls how bias parameters are handled.
# task_type: Specifies the type of task (e.g., CAUSAL_LM for text generation).
lora_config = LoraConfig(
    r=8, # Rank
    lora_alpha=16, # Scaling factor
    # For GPT-2, 'c_attn' is a Conv1D that projects to Q, K, V. PEFT can automatically handle this.
    # Alternatively, you might target specific sub-modules if they were explicitly named q_proj, v_proj.
    # For GPT2, it's common to target the c_attn and c_proj layers.
    target_modules=["c_attn", "c_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type=TaskType.CAUSAL_LM,
)
print("LoRA Configuration:")
print(lora_config)
print("\n" + "="*80 + "\n")


# --- 3. Apply LoRA to the Base Model ---
print("--- Step 3: Applying LoRA to the Base Model ---")
# `get_peft_model` wraps the base model, injecting LoRA adapters.
# It makes only the LoRA adapter parameters trainable.
lora_model = get_peft_model(base_model, lora_config)

print("LoRA-enabled Model Structure and Trainable Parameters:")
lora_model.print_trainable_parameters()
# This output shows that only a small fraction of parameters are now trainable.
print("\n" + "="*80 + "\n")


# --- 4. Simulate a Small "Update" (Conceptual Training Step) ---
# In a real scenario, you'd have a training loop, a dataset, and an optimizer.
# Here, we'll manually change a small LoRA parameter to show it's trainable.
# This simulates how a LoRA adapter would learn during fine-tuning.

print("--- Step 4: Simulating a Small 'Update' to LoRA Adapters ---")
# Find a LoRA adapter parameter and change it slightly
found_lora_param = False
for name, param in lora_model.named_parameters():
    if "lora" in name and "default.lora_A" in name: # Target 'lora_A' matrix
        print(f"Before 'update': first value of '{name}': {param.data.flatten()[0]:.6f}")
        param.data.fill_(0.01) # Set all values to 0.01 (simulating a learning update)
        print(f"After 'update': first value of '{name}': {param.data.flatten()[0]:.6f}")
        found_lora_param = True
        break # Just update one for demonstration
if not found_lora_param:
    print("Could not find a 'lora_A' parameter to simulate update.")

# Verify that base model parameters are unchanged
print("\nVerifying Base Model Parameters are Frozen:")
if hasattr(base_model.transformer.h[0].attn.c_attn, 'weight'):
    current_weight_qkv = base_model.transformer.h[0].attn.c_attn.weight.data
    # Note: `lora_model.base_model.transformer...` accesses the original frozen weights
    # We compare the original cloned weights to the weights inside the LoRA model's base
    # They should be identical if the base model is frozen.
    are_weights_same = torch.equal(original_weight_qkv, current_weight_qkv)
    print(f"Are original base model weights (c_attn.weight) unchanged? {are_weights_same}")
    print(f"Current c_attn.weight (first row, first 5 cols): {current_weight_qkv[0, :5].tolist()}")
else:
    print("Skipping base weight verification due to direct access issue.")
print("\n" + "="*80 + "\n")


# --- 5. Perform Inference with the LoRA-enabled Model ---
print("--- Step 5: Performing Inference with LoRA-enabled Model ---")

prompt = f"The cat is black. It then"
inputs = tokenizer(prompt, return_tensors="pt")

# Generate text using the LoRA-enabled model
# Use `no_grad()` to ensure no gradients are computed during inference
with torch.no_grad():
    lora_generated_ids = lora_model.generate(
        inputs["input_ids"],
        max_new_tokens=20,
        num_return_sequences=1,
        pad_token_id=tokenizer.eos_token_id,
        do_sample=True,
        top_k=50,
        temperature=0.7
    )
lora_generated_text = tokenizer.decode(lora_generated_ids[0], skip_special_tokens=True)
print(f"Prompt: '{prompt}'")
print(f"Generated by LoRA-enabled model: '{lora_generated_text}'")

# For comparison, generate with the original base model (should produce different output if LoRA was effective)
print("\n--- Comparing with Base Model (without LoRA effects) ---")
with torch.no_grad():
    base_generated_ids = base_model.generate(
        inputs["input_ids"],
        max_new_tokens=20,
        num_return_sequences=1,
        pad_token_id=tokenizer.eos_token_id,
        do_sample=True,
        top_k=50,
        temperature=0.7
    )
base_generated_text = tokenizer.decode(base_generated_ids[0], skip_special_tokens=True)
print(f"Generated by original base model: '{base_generated_text}'")
print("\n" + "="*80 + "\n")


# --- 6. Merging LoRA Weights (for efficient inference) ---
print("--- Step 6: Merging LoRA Weights for Efficient Inference ---")

# After training, you can merge the LoRA adapters into the base model's weights.
# This results in a single model that no longer requires the PEFT structure,
# and performs inference at the same speed as a fully fine-tuned model.
# The `merge_and_unload()` method detaches the LoRA adapters and updates the base model's weights.
# It returns the base model with the merged weights.
try:
    merged_model = lora_model.merge_and_unload()
    print("LoRA adapters merged successfully into the base model.")

    # Verify that the model is no longer a PeftModel
    print(f"Is the merged model still a PeftModel? {isinstance(merged_model, PeftModel)}")

    # Accessing the same weight matrix again to show it has changed after merge
    if hasattr(merged_model.transformer.h[0].attn.c_attn, 'weight'):
        merged_weight_qkv = merged_model.transformer.h[0].attn.c_attn.weight.data
        # Compare with original weights again
        are_weights_still_same_after_merge = torch.equal(original_weight_qkv, merged_weight_qkv)
        print(f"Are original base model weights (c_attn.weight) the same AFTER MERGE? {are_weights_still_same_after_merge}")
        print(f"Merged c_attn.weight (first row, first 5 cols): {merged_weight_qkv[0, :5].tolist()}")
        # You should see that `are_weights_still_same_after_merge` is False,
        # and `merged_weight_qkv` is different from `original_weight_qkv`.
    else:
        print("Skipping merged weight verification due to direct access issue.")

    # You can now save this merged model just like any other Hugging Face model
    # merged_model.save_pretrained("./my_merged_lora_model")
    # tokenizer.save_pretrained("./my_merged_lora_model")

except Exception as e:
    print(f"Error merging LoRA adapters: {e}")
    print("This might happen if the model was already merged or if there's no GPU available for certain operations.")

print("\n")
print("--- Summary ---")
print("This example demonstrated: ")
print("1. Loading a base LLM.")
print("2. Configuring LoRA parameters for efficient fine-tuning.")
print("3. Transforming the base model into a LoRA-enabled model, showing drastically fewer trainable parameters.")
print("4. Conceptually altering a LoRA parameter to show its trainability, while the base model remains frozen.")
print("5. Performing inference to show how the LoRA adapters (even with simulated changes) can affect output.")
print("6. Merging LoRA adapters back into the base model for deployment-ready inference without overhead.")
  

And a bit more...

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel
from sentence_transformers import SentenceTransformer
from peft import PeftModel, PeftConfig # For LoRA conceptual understanding
import warnings

# Suppress some common warnings for cleaner output
warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore", category=UserWarning)

# --- 1. The Sentence ---
sentence = "The cat is black."
print(f"Original Sentence: '{sentence}'\n")

# --- 2. Tokenization & Word Embeddings (from a general LLM) ---
print("--- Tokenization & Word Embeddings (from a general LLM) ---")

# We'll use a smaller pre-trained LLM like 'gpt2' for demonstration.
# For actual complex tasks, you'd use models like Llama-2, Mistral, etc.
# Note: 'gpt2' is a decoder-only model, good for text generation.
model_name_llm = "gpt2"
tokenizer_llm = AutoTokenizer.from_pretrained(model_name_llm)
model_llm = AutoModelForCausalLM.from_pretrained(model_name_llm)

# Add a pad token if the tokenizer doesn't have one (common for GPT-like models)
if tokenizer_llm.pad_token is None:
    tokenizer_llm.pad_token = tokenizer_llm.eos_token # End-of-sequence token often used as pad
    model_llm.config.pad_token_id = tokenizer_llm.eos_token_id

print(f"Tokenizer: {model_name_llm}")

# Tokenize the sentence
# `return_tensors="pt"` returns PyTorch tensors
inputs = tokenizer_llm(sentence, return_tensors="pt", padding=True, truncation=True)
print(f"Token IDs: {inputs['input_ids'].tolist()}")
print(f"Attention Mask: {inputs['attention_mask'].tolist()}")

# Decode tokens back to words to see what the tokenizer did
tokens = tokenizer_llm.convert_ids_to_tokens(inputs['input_ids'][0])
print(f"Tokens: {tokens}")

# Get word embeddings from the model
# We get the hidden states (embeddings) from the last layer
with torch.no_grad(): # Disable gradient calculation for inference
    outputs = model_llm(**inputs, output_hidden_states=True)
    word_embeddings = outputs.hidden_states[-1] # Last layer hidden states are often considered word embeddings

print(f"Word Embeddings Shape (tokens, embedding_dim): {word_embeddings.shape}")
print(f"Embedding for 'cat': {word_embeddings[0, tokens.index('cat')].cpu().numpy()[:5]}...") # First 5 dims
print(f"Embedding for 'black': {word_embeddings[0, tokens.index('black')].cpu().numpy()[:5]}...\n")


# --- 3. Sentence Embeddings (using Sentence Transformers) ---
print("--- Sentence Embeddings (using Sentence Transformers) ---")

# Sentence Transformers are specifically designed to produce good sentence-level embeddings.
# 'all-MiniLM-L6-v2' is a popular and efficient choice.
model_name_st = "all-MiniLM-L6-v2"
model_st = SentenceTransformer(model_name_st)

print(f"Sentence Embedder: {model_name_st}")

# Encode the sentence
sentence_embedding = model_st.encode(sentence, convert_to_tensor=True)

print(f"Sentence Embedding Shape: {sentence_embedding.shape}")
print(f"Sentence Embedding (first 5 dims): {sentence_embedding.cpu().numpy()[:5]}\n")

# Example of comparing sentence embeddings (semantic similarity)
sentence2 = "A dark feline is present."
sentence3 = "The car is red."

embedding2 = model_st.encode(sentence2, convert_to_tensor=True)
embedding3 = model_st.encode(sentence3, convert_to_tensor=True)

from sklearn.metrics.pairwise import cosine_similarity

similarity_cat_feline = cosine_similarity(sentence_embedding.reshape(1, -1), embedding2.reshape(1, -1))[0][0]
similarity_cat_car = cosine_similarity(sentence_embedding.reshape(1, -1), embedding3.reshape(1, -1))[0][0]

print(f"Similarity ('{sentence}' vs '{sentence2}'): {similarity_cat_feline:.4f}")
print(f"Similarity ('{sentence}' vs '{sentence3}'): {similarity_cat_car:.4f}\n")


# --- 4. Prompting an LLM (Inference) ---
print("--- Prompting an LLM (Inference) ---")

# The LLM we loaded earlier (gpt2) can be used for text generation.
# Let's create a simple prompt.
prompt = f"Given the sentence '{sentence}', complete the following story: The cat"

# Tokenize the prompt
inputs_llm_gen = tokenizer_llm(prompt, return_tensors="pt")

# Generate text
# `max_new_tokens` limits the length of the generated output
# `do_sample=True` enables sampling, `top_k` and `temperature` control randomness
generated_ids = model_llm.generate(
    inputs_llm_gen["input_ids"],
    max_new_tokens=20,
    num_return_sequences=1,
    pad_token_id=tokenizer_llm.eos_token_id, # Essential for GPT2
    do_sample=True,
    top_k=50,
    temperature=0.7
)

generated_text = tokenizer_llm.decode(generated_ids[0], skip_special_tokens=True)
print(f"Prompt: '{prompt}'")
print(f"Generated Text: '{generated_text}'\n")


# --- 5. LoRA (Low-Rank Adaptation) - Conceptual Understanding ---
print("--- LoRA (Low-Rank Adaptation) ---")

print("LoRA is a parameter-efficient fine-tuning (PEFT) technique.")
print("It works by injecting small, trainable low-rank matrices into the existing layers of a pre-trained LLM.")
print("Instead of fine-tuning *all* the millions/billions of parameters of the base LLM, LoRA only updates these much smaller new matrices.")
print("This significantly reduces the number of trainable parameters, memory footprint, and training time, while still achieving good performance.")
print("\nKey benefits:")
print("- Much faster training.")
print("- Significantly less VRAM usage.")
print("- Smaller checkpoint sizes (only the LoRA adapter weights are saved).")
print("- Easier to swap and combine different LoRA adapters for a single base model.")

print("\nConceptual Steps for using LoRA (not executable in this example due to complexity):")
print("1. Load a pre-trained base LLM (e.g., Llama-2, Mistral).")
print("2. Import `LoraConfig` and `get_peft_model` from `peft` library.")
print("3. Define `LoraConfig`: specifying which layers to target (e.g., query, value matrices), rank, alpha, dropout.")
print("4. Wrap the base model with `get_peft_model(base_model, lora_config)`. This creates a `PeftModel`.")
print("5. The `PeftModel` will now only train the newly injected LoRA adapter weights.")
print("6. Train this `PeftModel` on your specific dataset (e.g., for sentiment analysis, summarization, etc.) using a `Trainer`.")
print("7. Save only the LoRA adapters: `peft_model.save_pretrained('my_lora_adapters')`.")
print("8. To use the fine-tuned model for inference: Load the base model, load the LoRA adapters, and then merge them or use the `PeftModel` directly.")

print("\nExample LoRA config (conceptual):")
from peft import LoraConfig, TaskType
lora_config_example = LoraConfig(
    r=8, # Rank of the update matrices
    lora_alpha=16, # Scaling factor for LoRA weights
    target_modules=["q_proj", "v_proj"], # Which linear layers to apply LoRA to
    lora_dropout=0.1,
    bias="none",
    task_type=TaskType.CAUSAL_LM # Or TaskType.SEQ_CLS, etc.
)
print(lora_config_example)

# This part is conceptual for merging LoRA adapters, not runnable without prior training:
# try:
#     # This assumes you have trained and saved LoRA adapters
#     lora_model_path = "./my_lora_adapters"
#     peft_config = PeftConfig.from_pretrained(lora_model_path)
#     base_model_for_merge = AutoModelForCausalLM.from_pretrained(peft_config.base_model_name_or_path)
#     lora_model_for_merge = PeftModel.from_pretrained(base_model_for_merge, lora_model_path)
#     merged_model = lora_model_for_merge.merge_and_unload()
#     print(f"\nSuccessfully conceptually loaded and merged LoRA adapters from {lora_model_path}")
# except Exception as e:
#     print(f"\n(Skipping LoRA loading/merging: No pre-trained LoRA adapters found for this example. Error: {e})")

Resources to help you better understand LoRA that you should definitely check:

• LoRA: Low-Rank Adaptation of Large Language Models
  Edward J. Hu*, Yelong Shen*, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen
  Paper Link: https://arxiv.org/abs/2106.09685
• Video explainer: https://www.youtube.com/watch?v=DhRoTONcyZE
• Github Repo: https://github.com/microsoft/LoRA?tab=readme-ov-file