### Install self-rewarding-lm-pytorch

Source: https://github.com/lucidrains/self-rewarding-lm-pytorch/blob/main/README.md

Install the library using pip.

```bash
pip install self-rewarding-lm-pytorch
```

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

### Initialize and Run DPOTrainer

Source: https://context7.com/lucidrains/self-rewarding-lm-pytorch/llms.txt

Sets up and runs the DPOTrainer for Direct Preference Optimization. Supports pre-generated datasets and on-the-fly generation.

```python
from self_rewarding_lm_pytorch import DPOTrainer
from self_rewarding_lm_pytorch.dpo import DPODataset

# Using pre-generated DPO dataset from memmap files
dpo_dataset = DPODataset(
    data_folder='./data',
    preference_seq_memmap_file='preference_seq.memmap.npy',
    prompt_len_memmap_file='prompt_len.memmap.npy'
)
```

```python
# Initialize DPO trainer
dpo_trainer = DPOTrainer(
    model,
    train_dataset=dpo_dataset,
    batch_size=16,
    grad_accum_steps=2,
    num_train_steps=1000,
    start_learning_rate=1e-6,
    end_learning_rate=1e-7,
    dropout=0.1,
    dpo_kwargs=dict(
        beta=0.1,              # DPO temperature parameter
        ref_model_ema_decay=1.0
    )
)
```

```python
# Run DPO training
dpo_trainer()
```

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

### Initialize and Run SPINTrainer

Source: https://github.com/lucidrains/self-rewarding-lm-pytorch/blob/main/README.md

Set up and run the SPINTrainer with a transformer model and SFT dataset. Training parameters like max sequence length and checkpoint frequency can be configured.

```python
import torch

from self_rewarding_lm_pytorch import (
    SPINTrainer,
    create_mock_dataset
)

from x_transformers import TransformerWrapper, Decoder

transformer = TransformerWrapper(
    num_tokens = 256,
    max_seq_len = 1024,
    attn_layers = Decoder(
        dim = 512,
        depth = 6,
        heads = 8
    )
)

sft_dataset = create_mock_dataset(100, lambda: (torch.randint(0, 256, (256,)), torch.tensor(1)))

spin_trainer = SPINTrainer(
    transformer,
    max_seq_len = 16,
    train_sft_dataset = sft_dataset,
    checkpoint_every = 100,
    spin_kwargs = dict(
        λ = 0.1,
    ),
)

spin_trainer()
```

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

### Initialize and Run SelfRewardingTrainer

Source: https://github.com/lucidrains/self-rewarding-lm-pytorch/blob/main/README.md

Set up and run the SelfRewardingTrainer with a transformer model and datasets. Checkpoints are saved to ./checkpoints.

```python
import torch
from torch import Tensor

from self_rewarding_lm_pytorch import (
    SelfRewardingTrainer,
    create_mock_dataset
)

from x_transformers import TransformerWrapper, Decoder

transformer = TransformerWrapper(
    num_tokens = 256,
    max_seq_len = 1024,
    attn_layers = Decoder(
        dim = 512,
        depth = 1,
        heads = 8
    )
)

sft_dataset = create_mock_dataset(100, lambda: (torch.randint(0, 256, (256,)), torch.tensor(1)))
prompt_dataset = create_mock_dataset(100, lambda: 'mock prompt')

def decode_tokens(tokens: Tensor) -> str:
    decode_token = lambda token: str(chr(max(32, token)))
    return ''.join(list(map(decode_token, tokens)))

def encode_str(seq_str: str) -> Tensor:
    return Tensor(list(map(ord, seq_str)))

trainer = SelfRewardingTrainer(
    transformer,
    finetune_configs = dict(
        train_sft_dataset = sft_dataset,
        self_reward_prompt_dataset = prompt_dataset,
        dpo_num_train_steps = 1000
    ),
    tokenizer_decode = decode_tokens,
    tokenizer_encode = encode_str,
    accelerate_kwargs = dict(
        cpu = True
    )
)

trainer(overwrite_checkpoints = True)

# checkpoints after each finetuning stage will be saved to ./checkpoints
```

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

### Wrap Model with DPO

Source: https://context7.com/lucidrains/self-rewarding-lm-pytorch/llms.txt

Initializes the DPO module for computing the DPO loss. Requires a base model and the beta temperature parameter.

```python
import torch
from self_rewarding_lm_pytorch.dpo import DPO

# Wrap model with DPO
dpo_model = DPO(
    model,
    beta=0.1,                 # Temperature parameter
    ref_model_ema_decay=1.0,  # EMA decay for reference model
    pad_id=-1                 # Padding token ID
)
```

```python
# Compute DPO loss
# preferred_seq: Sequences with higher reward
# unpreferred_seq: Sequences with lower reward
# prompt_len: Length of prompt in each sequence
loss = dpo_model(
    preferred_seq=preferred_sequences,      # (batch, seq_len)
    unpreferred_seq=unpreferred_sequences,  # (batch, seq_len)
    prompt_len=prompt_lengths               # (batch,)
)
```

```python
# Update reference model before new training round
dpo_model.update_reference_model_with_policy()
```

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

### Create Mock Datasets

Source: https://context7.com/lucidrains/self-rewarding-lm-pytorch/llms.txt

Generate mock datasets with either dynamic string outputs or fixed data tuples.

```python
# Create prompt dataset with string outputs
prompt_dataset = create_mock_dataset(
    length=100,
    output=lambda: 'What is the meaning of life?'
)

# Create dataset with fixed output
fixed_dataset = create_mock_dataset(
    length=50,
    output=('fixed data', 0)
)
```

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

### Initialize and Run SPINTrainer

Source: https://context7.com/lucidrains/self-rewarding-lm-pytorch/llms.txt

Implements Self-Play Fine-Tuning (SPIN) by training models to distinguish between real human and model-generated responses. Requires a transformer model and SFT dataset. Configure `max_seq_len`, `checkpoint_every`, and `spin_kwargs` as needed.

```python
import torch
from self_rewarding_lm_pytorch import SPINTrainer, create_mock_dataset
from x_transformers import TransformerWrapper, Decoder

# Initialize transformer
transformer = TransformerWrapper(
    num_tokens=256,
    max_seq_len=1024,
    attn_layers=Decoder(
        dim=512,
        depth=6,
        heads=8
    )
)

# SFT dataset with (sequence, prompt_length) tuples
sft_dataset = create_mock_dataset(
    100,
    lambda: (torch.randint(0, 256, (256,)), torch.tensor(1))
)

# Initialize SPIN trainer
spin_trainer = SPINTrainer(
    transformer,
    max_seq_len=16,
    train_sft_dataset=sft_dataset,
    checkpoint_every=100,
    spin_kwargs=dict(
        λ=0.1,  # SPIN regularization parameter
    ),
)

# Run SPIN training
spin_trainer()
```

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

### Initialize and Run SelfRewardingTrainer

Source: https://context7.com/lucidrains/self-rewarding-lm-pytorch/llms.txt

Orchestrates the complete self-rewarding training pipeline, including SFT, self-rewarding DPO, and SPIN. Accepts a model and fine-tuning configurations. Ensure datasets and tokenizer functions are properly defined.

```python
import torch
from torch import Tensor
from self_rewarding_lm_pytorch import (
    SelfRewardingTrainer,
    create_mock_dataset
)
from x_transformers import TransformerWrapper, Decoder

# Initialize transformer model
transformer = TransformerWrapper(
    num_tokens=256,
    max_seq_len=1024,
    attn_layers=Decoder(
        dim=512,
        depth=1,
        heads=8
    )
)

# Create datasets
# SFT dataset returns (sequence, prompt_length) tuples
sft_dataset = create_mock_dataset(100, lambda: (torch.randint(0, 256, (256,)), torch.tensor(1)))

# Prompt dataset returns string prompts
prompt_dataset = create_mock_dataset(100, lambda: 'mock prompt')

# Define tokenizer functions
def decode_tokens(tokens: Tensor) -> str:
    decode_token = lambda token: str(chr(max(32, token)))
    return ''.join(list(map(decode_token, tokens)))

def encode_str(seq_str: str) -> Tensor:
    return Tensor(list(map(ord, seq_str)))

# Initialize trainer with simplified config
trainer = SelfRewardingTrainer(
    transformer,
    finetune_configs=dict(
        train_sft_dataset=sft_dataset,
        self_reward_prompt_dataset=prompt_dataset,
        dpo_num_train_steps=1000
    ),
    tokenizer_decode=decode_tokens,
    tokenizer_encode=encode_str,
    accelerate_kwargs=dict(cpu=True)
)

# Run training - checkpoints saved to ./checkpoints after each stage
trainer(overwrite_checkpoints=True)
```

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

### Create Mock Dataset

Source: https://context7.com/lucidrains/self-rewarding-lm-pytorch/llms.txt

Generates a mock PyTorch Dataset for testing purposes. Can create datasets with random sequences or fixed values.

```python
import torch
from self_rewarding_lm_pytorch import create_mock_dataset

# Create SFT dataset that generates random sequences
sft_dataset = create_mock_dataset(
    length=100,
    output=lambda: (
        torch.randint(0, 256, (256,)),  # Random sequence
        torch.tensor(1)                  # Prompt length
    )
)
```

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

### Sample Sequences with Nucleus and Top-K

Source: https://context7.com/lucidrains/self-rewarding-lm-pytorch/llms.txt

Generate sequences using nucleus (top-p) or top-k filtering strategies.

```python
import torch
from self_rewarding_lm_pytorch.sampling_utils import sample, top_p, top_k

# Generate sequences from a trained model
generated = sample(
    net=model,
    prompts=prompt_tensors,      # Tensor or list of tensors
    seq_len=256,                 # Max generation length
    temperature=0.7,             # Sampling temperature
    filter_fn=top_p,             # Nucleus sampling
    filter_kwargs=dict(thres=0.9),
    pad_id=-1,
    eos_id=None,                 # Optional EOS token
    output_keep_prompt=True      # Include prompt in output
)

# Using top-k sampling instead
generated = sample(
    net=model,
    prompts=prompt_tensors,
    seq_len=256,
    temperature=0.7,
    filter_fn=top_k,
    filter_kwargs=dict(k=50),    # Keep top 50 tokens
    output_keep_prompt=False     # Only return generated tokens
)
```

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

### Flexible Fine-tuning with FinetuneConfig List

Source: https://github.com/lucidrains/self-rewarding-lm-pytorch/blob/main/README.md

Interleave different fine-tuning stages like SPIN, External Rewarding, and Self-Rewarding by passing a list of FinetuneConfig instances to the SelfRewardingTrainer.

```python
# import the configs

from self_rewarding_lm_pytorch import (
    SFTConfig,
    SelfRewardDPOConfig,
    ExternalRewardDPOConfig,
    SelfPlayConfig,
)

trainer = SelfRewardingTrainer(
    model,
    finetune_configs = [
        SFTConfig(...),
        SelfPlayConfig(...),
        ExternalRewardDPOConfig(...),
        SelfRewardDPOConfig(...),
        SelfPlayConfig(...),
        SelfRewardDPOConfig(...)
    ],
    ...
)

trainer()

# checkpoints after each finetuning stage will be saved to ./checkpoints
```

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

### Define Custom Finetuning Pipeline

Source: https://context7.com/lucidrains/self-rewarding-lm-pytorch/llms.txt

Configures a custom fine-tuning pipeline using SelfRewardingTrainer with various configurations like SFT, SelfPlay, ExternalRewardDPO, and SelfRewardDPO.

```python
from self_rewarding_lm_pytorch import (
    SelfRewardingTrainer,
    SFTConfig,
    SelfRewardDPOConfig,
    ExternalRewardDPOConfig,
    SelfPlayConfig,
)

# Define custom fine-tuning pipeline
trainer = SelfRewardingTrainer(
    model,
    finetune_configs=[
        # Stage 1: Supervised fine-tuning
        SFTConfig(
            train_dataset=sft_dataset,
            valid_dataset=valid_dataset,
            dropout=0.1
        ),
        # Stage 2: Self-play training
        SelfPlayConfig(
            train_dataset=sft_dataset,
            max_seq_len=1024,
            spin_λ=0.1,
            dropout=0.1
        ),
        # Stage 3: DPO with external reward model
        ExternalRewardDPOConfig(
            reward_model=external_reward_model,
            dpo_beta=0.1,
            max_seq_len=1024,
            dropout=0.1
        ),
        # Stage 4: Self-rewarding DPO (first iteration)
        SelfRewardDPOConfig(
            prompt_dataset=prompt_dataset,
            num_generated_preference_pairs=3964,
            dpo_beta=0.1,
            num_train_steps=1000,
            dropout=0.1
        ),
        # Stage 5: Self-play again
        SelfPlayConfig(
            train_dataset=sft_dataset,
            max_seq_len=1024,
            spin_λ=0.1
        ),
        # Stage 6: Self-rewarding DPO (second iteration)
        SelfRewardDPOConfig(
            prompt_dataset=prompt_dataset,
            num_generated_preference_pairs=6942,
            dpo_beta=0.1,
            num_train_steps=1000
        )
    ],
    tokenizer_encode=encode_str,
    tokenizer_decode=decode_tokens
)

trainer()
```

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

### Wrap Model with SPIN

Source: https://context7.com/lucidrains/self-rewarding-lm-pytorch/llms.txt

Initializes the SPIN model for self-rewarding language models. Requires a base model and regularization strength (λ).

```python
spin_model = SPIN(
    model,
    λ=0.1,                    # Regularization strength
    pad_id=-1,                # Padding token ID
    ref_model_ema_decay=1.0,  # Reference model EMA decay
)
```

```python
loss = spin_model(
    generated_seq=generated_sequences,  # (batch, seq_len)
    real_seq=real_sequences,            # (batch, seq_len)
    prompt_len=prompt_lengths           # (batch,)
)
```

```python
spin_model.update_reference_model_with_policy()
```

```python
spin_model.update_ema()
```

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

### Configure Custom Reward Prompt

Source: https://github.com/lucidrains/self-rewarding-lm-pytorch/blob/main/README.md

Customize the reward prompt configuration for the SelfRewardingTrainer using RewardConfig. Define a prompt template and a reward regex template for parsing the reward.

```python
# first import

from self_rewarding_lm_pytorch import RewardConfig

# then say you want to try asking the transformer nicely

# reward_regex_template is the string that will be looked for in the LLM response, for parsing out the reward where {{ reward }} is defined as a number

trainer = SelfRewardingTrainer(
    transformer,
    ...,
    self_reward_prompt_config = RewardConfig(
        prompt_template = """
        Pretty please rate the following user prompt and response
        User: {{ prompt }}
        Response: {{ response }}

        Format your score as follows:
        Rating: <rating as integer from 0 - 10>
        """,
        reward_regex_template = """
        Rating: {{ reward }}
        """
    )
)
```

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

### Configure Custom Reward Prompt for Trainer

Source: https://context7.com/lucidrains/self-rewarding-lm-pytorch/llms.txt

Customizes the reward prompt template and parsing logic for LLM-as-Judge. Use this to experiment with different prompts beyond the default scoring system. Ensure the `prompt_template` and `reward_regex_template` are correctly formatted.

```python
from self_rewarding_lm_pytorch import SelfRewardingTrainer, RewardConfig

# Custom reward prompt configuration
custom_reward_config = RewardConfig(
    prompt_template="""
    Pretty please rate the following user prompt and response
    User: {{ prompt }}
    Response: {{ response }}

    Format your score as follows:
    Rating: <rating as integer from 0 - 10>
    """,
    reward_regex_template="""
    Rating: {{ reward }}
    """
)

# Use custom reward config in trainer
trainer = SelfRewardingTrainer(
    model,
    finetune_configs=dict(
        train_sft_dataset=sft_dataset,
        self_reward_prompt_dataset=prompt_dataset,
        dpo_num_train_steps=1000
    ),
    self_reward_prompt_config=custom_reward_config,
    tokenizer_decode=decode_tokens,
    tokenizer_encode=encode_str
)

trainer()
```

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

### Import SPIN Module

Source: https://context7.com/lucidrains/self-rewarding-lm-pytorch/llms.txt

Imports the low-level SPIN module for computing the self-play loss between generated and real sequences. This module maintains a reference model using EMA for stable training.

```python
import torch
from self_rewarding_lm_pytorch.spin import SPIN
```

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

### Implement Early Stopping for DPO

Source: https://context7.com/lucidrains/self-rewarding-lm-pytorch/llms.txt

Monitor validation performance and restore checkpoints using the EarlyStopper class.

```python
from self_rewarding_lm_pytorch.dpo import EarlyStopper

# Define custom evaluation function
class ValidationEvaluator(torch.nn.Module):
    def forward(self, model):
        # Compute validation loss or metric
        total_loss = 0.0
        with torch.no_grad():
            for batch in valid_dataloader:
                loss = compute_loss(model, batch)
                total_loss += loss.item()
        return total_loss / len(valid_dataloader)

# Create early stopper
early_stopper = EarlyStopper(
    model=model,
    evaluator=ValidationEvaluator(),
    accelerator=accelerator,
    calculate_should_stop=lambda scores: len(scores) > 1 and scores[-1] > scores[-2],
    early_stop_checkpoint_folder='./early-stop-checkpoints'
)

# During training loop
for step in range(num_steps):
    # Training logic...

    if step % check_every == 0:
        result = early_stopper()
        if result.should_stop:
            print(f"Early stopping at step {step}, score: {result.score}")
            break
```

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

### Load Datasets with DataLoader

Source: https://context7.com/lucidrains/self-rewarding-lm-pytorch/llms.txt

Integrate datasets with PyTorch DataLoader for batch processing.

```python
# Use with DataLoader
from torch.utils.data import DataLoader
dataloader = DataLoader(sft_dataset, batch_size=16, shuffle=True)

for batch in dataloader:
    sequences, prompt_lengths = batch
    print(f"Batch shape: {sequences.shape}")
    break
```

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