### Install Llama QRLHF

Source: https://context7.com/lucidrains/llama-qrlhf/llms.txt

Provides instructions for installing the library via pip or from source.

```bash
pip install llama-qrlhf

# Or install from source
git clone https://github.com/lucidrains/llama-qrlhf
cd llama-qrlhf
pip install -e .

# Dependencies
# - torch>=2.4
# - accelerate
# - ema-pytorch
# - einx>=0.3.0
# - einops>=0.8.0
# - jaxtyping
```

--------------------------------

### Initialize Dueling Q-Head

Source: https://context7.com/lucidrains/llama-qrlhf/llms.txt

Implements the Dueling Network Architecture to separately estimate state values and action advantages.

```python
import torch
from llama_qrlhf.llama import DuelingHead

# Initialize dueling head
q_head = DuelingHead(
    dim=512,              # Input dimension from transformer
    num_tokens=32000,     # Number of possible actions (vocab size)
    expansion_factor=2    # Hidden layer expansion
)

# Input embeddings from transformer
embeddings = torch.randn(2, 128, 512)  # [batch, seq_len, dim]

# Get Q-values via dueling architecture
q_values = q_head(embeddings)
print(f"Q-values shape: {q_values.shape}")  # [2, 128, 32000]

# Q = V(s) + A(s,a) - mean(A(s,a))
# Values and advantages are computed separately then combined
```

--------------------------------

### Configure QRLHF Trainer with EMA

Source: https://context7.com/lucidrains/llama-qrlhf/llms.txt

Wraps a language model with an EMA target network to ensure stable Q-value estimation during training.

```python
import torch
from torch.utils.data import Dataset, DataLoader
from llama_qrlhf import QRLHF
from llama_qrlhf.llama import Llama

# Define a simple dataset
class TextDataset(Dataset):
    def __init__(self, num_samples=1000, seq_len=64, vocab_size=32000):
        self.data = torch.randint(0, vocab_size, (num_samples, seq_len))

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        return self.data[idx]

# Initialize model
model = Llama(
    num_tokens=32000,
    dim=256,
    depth=4,
    dueling_q_head=True
)

dataset = TextDataset()

# Create QRLHF trainer with EMA target network
qrlhf = QRLHF(
    model=model,
    dataset=dataset,
    accelerate_kwargs={},
    ema_kwargs={'beta': 0.99}  # EMA decay rate for target model
)

# Access the model and EMA target
print(f"Main model: {qrlhf.lm}")
print(f"EMA target model: {qrlhf.lm_target}")
```

--------------------------------

### Initialize Llama Model with Q-learning Head

Source: https://context7.com/lucidrains/llama-qrlhf/llms.txt

Configures a Llama transformer model with optional dueling Q-value output heads for reinforcement learning tasks.

```python
import torch
from llama_qrlhf.llama import Llama

# Initialize a Llama model with Q-learning head
model = Llama(
    num_tokens=32000,      # Vocabulary size
    dim=512,               # Model dimension
    depth=6,               # Number of transformer layers
    dim_head=64,           # Dimension per attention head
    heads=8,               # Number of attention heads
    ff_mult=4,             # Feedforward expansion multiplier
    dueling_q_head=True,   # Enable dueling Q-network head
    dueling_q_head_expansion_factor=2  # Q-head hidden expansion
)

# Forward pass with input tokens
input_ids = torch.randint(0, 32000, (2, 128))  # batch=2, seq_len=128

# Get logits only (standard language modeling)
logits = model(input_ids)
print(f"Logits shape: {logits.shape}")  # [2, 128, 32000]

# Get both logits and Q-values for RL training
logits, q_values = model(input_ids, return_q_values=True)
print(f"Q-values shape: {q_values.shape}")  # [2, 128, 32000]
```

--------------------------------

### Apply Conservative Regularization Loss

Source: https://context7.com/lucidrains/llama-qrlhf/llms.txt

Calculates CQL-style regularization to penalize out-of-distribution actions and prevent Q-value overestimation.

```python
import torch
from llama_qrlhf.llama_qrlhf import conservative_regularization_loss

batch_size, seq_len, num_actions = 4, 32, 1000

# Q-values from model forward pass
q_values = torch.randn(batch_size, seq_len, num_actions)
```

--------------------------------

### Compute Autoregressive Q-Learning Loss

Source: https://context7.com/lucidrains/llama-qrlhf/llms.txt

Calculates the Q-learning loss using the Q-Transformer formulation, requiring a model, EMA target, and reward signals.

```python
import torch
from llama_qrlhf.llama import Llama
from llama_qrlhf.llama_qrlhf import autoregressive_q_learn
from ema_pytorch import EMA

# Setup model and EMA target
model = Llama(
    num_tokens=1000,
    dim=128,
    depth=2,
    dueling_q_head=True
)
ema_model = EMA(model, beta=0.99)

batch_size, seq_len = 4, 32

# Prepare training batch
states = torch.randint(0, 1000, (batch_size, seq_len))       # Full sequences
prompt_len = torch.randint(5, 15, (batch_size, seq_len))     # Prompt lengths per position
next_states = torch.randint(0, 1000, (batch_size,))          # Next token actions
rewards = torch.zeros(batch_size, seq_len)                    # Sparse rewards
rewards[:, -1] = 1.0  # Reward at end of sequence

# Compute Q-learning loss
loss = autoregressive_q_learn(
    model=model,
    ema_model=ema_model,
    states=states,
    prompt_len=prompt_len,
    next_states=next_states,
    rewards=rewards,
    eos_id=2,              # EOS token ID for terminal detection
    discount_gamma=0.998    # Reward discount factor
)

print(f"Q-learning loss: {loss.item()}")
```

--------------------------------

### Compute Conservative Regularization Loss

Source: https://context7.com/lucidrains/llama-qrlhf/llms.txt

Calculates the CQL loss to prevent overestimation of Q-values for out-of-distribution actions.

```python
# Actual actions taken in dataset
states_and_actions = torch.randint(0, num_actions, (batch_size, seq_len))

# Mask indicating which positions are actions (vs prompts)
action_mask = torch.ones(batch_size, seq_len, dtype=torch.bool)
action_mask[:, :8] = False  # First 8 tokens are prompt

# Compute conservative regularization
cql_loss = conservative_regularization_loss(
    q_values=q_values,
    states_and_actions=states_and_actions,
    action_mask=action_mask,
    reward_min=0.0  # Minimum reward to regularize towards
)

print(f"CQL regularization loss: {cql_loss.item()}")
```

--------------------------------

### Implement Attention with Rotary Embeddings

Source: https://context7.com/lucidrains/llama-qrlhf/llms.txt

Configures multi-head self-attention with RoPE and causal masking for autoregressive modeling.

```python
import torch
from llama_qrlhf.llama import Attention, RotaryEmbedding

# Initialize attention layer
attention = Attention(
    dim=512,
    dim_head=64,
    heads=8
)

# Initialize rotary embeddings
rotary = RotaryEmbedding(dim=64, theta=10000)

# Input sequence
x = torch.randn(2, 128, 512)  # [batch, seq_len, dim]

# Generate rotary embeddings for sequence length
rotary_emb = rotary(seq_len=128, device=x.device)

# Forward pass with rotary embeddings
output = attention(x, rotary_emb=rotary_emb)
print(f"Attention output shape: {output.shape}")  # [2, 128, 512]
```

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