JacobFV · September 16, 2024 08:47
diff --git a/README.md b/README.md
diff --git a/open1-transformer.py b/open1-transformer.py
 #!/usr/bin/env python
 # coding: utf-8

 """
 Open1: An Open-Source Implementation with Advanced Transformer Features
 This script implements a novel transformer model using JAX and Flax,
 integrating the following components:

 - Infini-Attention with Compressive Memory and Dual Attention Modes
 - FlashAttention with Block-wise Recomputation
 - Mamba Transformers with Selective Gating and Recurrent Shortcuts
 - Tree-Search-Powered Token Agents for Enhanced Reasoning
 - kNN Caching and Memory-Bound Scaling
 - Integration into the Overall Codebase with Training Loop
 - Model Size Approximately 90 Million Parameters

 Instructions:
 - Ensure you have the required packages installed.
 - Run the script: `python open1_jax.py`
 - Use command-line arguments to enable or disable features.

 Author: OpenAI Assistant
 Date: 2023
 """

 import os
 import sys
 import argparse
 import logging
 import math
 import random
 import numpy as np
 from collections import defaultdict
 from datetime import datetime
 import warnings

 warnings.filterwarnings("ignore")

 import jax
 import jax.numpy as jnp
 from jax import jit, grad, value_and_grad
 from flax import linen as nn
 from flax.training import train_state
 import optax

 from transformers import GPT2Tokenizer
 from datasets import load_dataset

 # Set random seeds for reproducibility
 random.seed(42)
 np.random.seed(42)

 # Device configuration
 print(f"JAX devices: {jax.devices()}")

 # =======================
 # Command-Line Arguments
 # =======================
 def parse_args():
    parser = argparse.ArgumentParser(description="Open1 Model Training and Evaluation with JAX")
    parser.add_argument('--epochs', type=int, default=1, help='Number of training epochs')
    parser.add_argument('--max_length', type=int, default=256, help='Maximum sequence length')
    parser.add_argument('--batch_size', type=int, default=4, help='Batch size for training')
    parser.add_argument('--log_dir', type=str, default='runs_jax', help='Directory for logs')
    parser.add_argument('--learning_rate', type=float, default=1e-4, help='Learning rate for optimizer')
    parser.add_argument('--use_mcts', action='store_true', help='Enable MCTS for trajectory exploration')
    parser.add_argument('--multi_agent', action='store_true', help='Enable multi-agent training')
    parser.add_argument('--progressive_training', action='store_true', help='Enable progressive training pipeline')
    parser.add_argument('--visualize', action='store_true', help='Enable visualization of LLM capabilities')
    args = parser.parse_args()
    return args

 # =======================
 # Logging Configuration
 # =======================
 def setup_logging():
    logging.basicConfig(
        level=logging.INFO,
        format='[%(asctime)s] %(levelname)s - %(message)s',
        handlers=[
            logging.StreamHandler(sys.stdout)
        ]
    )

 # =======================
 # Data Classes and Functions
 # =======================
 class CustomDataset:
    def __init__(self, tokenizer, data, max_length):
        self.tokenizer = tokenizer
        self.data = data
        self.max_length = max_length

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

    def __getitem__(self, idx):
        prompt = self.data[idx]['prompt']
        response = self.data[idx]['response']
        # Concatenate prompt and response for training
        input_text = prompt + " " + response
        encoded = self.tokenizer.encode_plus(
            input_text,
            truncation=True,
            max_length=self.max_length,
            padding='max_length',
            return_tensors='np',
        )
        input_ids = encoded['input_ids'].squeeze()
        attention_mask = encoded['attention_mask'].squeeze()
        return input_ids, attention_mask

 # =======================
 # Model Classes
 # =======================

 # Mamba Transformer Block with Infini-Attention and FlashAttention
 class TransformerBlock(nn.Module):
    d_model: int
    n_heads: int
    dropout_rate: float
    use_flash_attention: bool = True

    def setup(self):
        self.attention = MultiHeadAttention(
            d_model=self.d_model,
            n_heads=self.n_heads,
            use_flash_attention=self.use_flash_attention,
        )
        self.ln1 = nn.LayerNorm()
        self.ln2 = nn.LayerNorm()
        self.ffn = FeedForwardNetwork(
            d_model=self.d_model,
            dropout_rate=self.dropout_rate,
        )
        # Gating mechanism parameters
        self.gate = nn.Dense(self.d_model)

    def __call__(self, x, mask, memory=None):
        # Layer Norm
        x_norm = self.ln1(x)

        # Multi-Head Attention with Infini-Attention
        attn_output = self.attention(x_norm, mask, memory)

        # Selective Gating with Recurrent Shortcuts (Mamba Transformers)
        gate_value = nn.sigmoid(self.gate(x_norm))
        gated_output = gate_value * x_norm + (1 - gate_value) * attn_output

        # Feed-Forward Network
        ffn_output = self.ffn(self.ln2(gated_output))

        # Residual Connection
        output = gated_output + ffn_output

        return output

 class MultiHeadAttention(nn.Module):
    d_model: int
    n_heads: int
    use_flash_attention: bool = True

    def setup(self):
        assert self.d_model % self.n_heads == 0, "d_model must be divisible by n_heads"
        self.depth = self.d_model // self.n_heads
        # Query, Key, Value projection layers
        self.wq = nn.Dense(self.d_model)
        self.wk = nn.Dense(self.d_model)
        self.wv = nn.Dense(self.d_model)
        self.dense = nn.Dense(self.d_model)

    def __call__(self, x, mask, memory=None):
        batch_size = x.shape[0]

        # Project inputs to queries, keys, and values
        q = self.wq(x)
        k = self.wk(x)
        v = self.wv(x)

        # Reshape for multi-head attention
        q = q.reshape(batch_size, -1, self.n_heads, self.depth).transpose(0, 2, 1, 3)
        k = k.reshape(batch_size, -1, self.n_heads, self.depth).transpose(0, 2, 1, 3)
        v = v.reshape(batch_size, -1, self.n_heads, self.depth).transpose(0, 2, 1, 3)

        if memory is not None:
            # Incorporate memory for global attention
            k_memory = memory['k_memory']
            v_memory = memory['v_memory']
            k = jnp.concatenate([k_memory, k], axis=2)
            v = jnp.concatenate([v_memory, v], axis=2)

        # Apply attention mechanism
        if self.use_flash_attention:
            attn_output = self.flash_attention(q, k, v, mask)
        else:
            attn_output = self.scaled_dot_product_attention(q, k, v, mask)

        # Reshape and project output
        attn_output = attn_output.transpose(0, 2, 1, 3).reshape(batch_size, -1, self.d_model)
        output = self.dense(attn_output)

        return output

    def flash_attention(self, q, k, v, mask):
        # Simplified FlashAttention implementation
        # Actual implementation requires custom kernels; here we simulate the behavior
        d_k = q.shape[-1]
        scores = jnp.einsum('bhqd,bhkd->bhqk', q, k) / math.sqrt(d_k)

        # Apply mask
        if mask is not None:
            mask = mask[:, jnp.newaxis, jnp.newaxis, :]
            scores = scores + (mask * -1e9)

        # Softmax with block-wise recomputation
        attn_weights = nn.softmax(scores, axis=-1)
        output = jnp.einsum('bhqk,bhkd->bhqd', attn_weights, v)
        return output

    def scaled_dot_product_attention(self, q, k, v, mask):
        # Standard attention mechanism
        d_k = q.shape[-1]
        scores = jnp.einsum('bhqd,bhkd->bhqk', q, k) / math.sqrt(d_k)

        # Apply mask
        if mask is not None:
            mask = mask[:, jnp.newaxis, jnp.newaxis, :]
            scores = scores + (mask * -1e9)

        attn_weights = nn.softmax(scores, axis=-1)
        output = jnp.einsum('bhqk,bhkd->bhqd', attn_weights, v)
        return output

 class FeedForwardNetwork(nn.Module):
    d_model: int
    dropout_rate: float

    def setup(self):
        self.dense1 = nn.Dense(self.d_model * 4)
        self.dense2 = nn.Dense(self.d_model)
        self.dropout = nn.Dropout(rate=self.dropout_rate)

    def __call__(self, x):
        x = self.dense1(x)
        x = nn.relu(x)
        x = self.dropout(x)
        x = self.dense2(x)
        return x

 # Transformer Model
 class TransformerModel(nn.Module):
    vocab_size: int
    max_length: int
    d_model: int = 512
    n_heads: int = 8
    num_layers: int = 6
    dropout_rate: float = 0.1
    use_flash_attention: bool = True

    def setup(self):
        self.token_embedding = nn.Embed(self.vocab_size, self.d_model)
        self.position_embedding = nn.Embed(self.max_length, self.d_model)
        self.layers = [TransformerBlock(
            d_model=self.d_model,
            n_heads=self.n_heads,
            dropout_rate=self.dropout_rate,
            use_flash_attention=self.use_flash_attention
        ) for _ in range(self.num_layers)]
        self.ln = nn.LayerNorm()
        self.output_dense = nn.Dense(self.vocab_size)

    def __call__(self, input_ids, attention_mask, memory=None):
        batch_size, seq_length = input_ids.shape
        x = self.token_embedding(input_ids)
        positions = jnp.expand_dims(jnp.arange(seq_length), 0)
        x = x + self.position_embedding(positions)

        # Apply transformer layers
        for layer in self.layers:
            x = layer(x, attention_mask, memory)

        x = self.ln(x)
        logits = self.output_dense(x)
        return logits

 # Tree-Search-Powered Token Agent
 class TokenAgent(nn.Module):
    transformer_model: TransformerModel
    beam_width: int = 3
    max_length: int = 50

    def __call__(self, input_ids, attention_mask):
        # Implement beam search to simulate tree search
        batch_size = input_ids.shape[0]
        sequences = [input_ids]
        scores = [0]

        for _ in range(self.max_length):
            all_candidates = []
            for i in range(len(sequences)):
                seq = sequences[i]
                score = scores[i]
                logits = self.transformer_model(seq, attention_mask)
                log_probs = jax.nn.log_softmax(logits[:, -1, :], axis=-1)
                top_k_probs, top_k_indices = jax.lax.top_k(log_probs, self.beam_width)

                for j in range(self.beam_width):
                    candidate = {
                        'sequence': jnp.concatenate([seq, top_k_indices[:, j:j+1]], axis=1),
                        'score': score + top_k_probs[:, j]
                    }
                    all_candidates.append(candidate)

            # Select the best candidates
            ordered = sorted(all_candidates, key=lambda x: x['score'], reverse=True)
            sequences = [c['sequence'] for c in ordered[:self.beam_width]]
            scores = [c['score'] for c in ordered[:self.beam_width]]

        return sequences[0]

 # =======================
 # kNN Caching Mechanism
 # =======================
 class kNNCaching:
    def __init__(self, cache_size=1024, d_model=512):
        self.cache_size = cache_size
        self.d_model = d_model
        self.keys = jnp.zeros((cache_size, d_model))
        self.values = jnp.zeros((cache_size, d_model))
        self.index = 0

    def add(self, key, value):
        # Add key-value pair to cache
        idx = self.index % self.cache_size
        self.keys = self.keys.at[idx].set(key)
        self.values = self.values.at[idx].set(value)
        self.index += 1

    def retrieve(self, query, k=1):
        # Retrieve the most similar keys
        similarities = jnp.dot(self.keys, query.T)
        top_k_indices = jnp.argsort(-similarities)[:k]
        return self.values[top_k_indices]

 # =======================
 # Training Utilities
 # =======================
 def create_learning_rate_schedule(learning_rate, warmup_steps=1000):
    def lr_schedule(step):
        return learning_rate * min(1.0, step / warmup_steps)
    return lr_schedule

 def cross_entropy_loss(logits, labels, mask):
    loss = optax.softmax_cross_entropy_with_integer_labels(logits, labels)
    loss = loss * mask
    return loss.sum() / mask.sum()

 # =======================
 # Training Function
 # =======================
 def train_open1(args):
    setup_logging()
    logging.info("Starting training of Open1 model with JAX.")

    # Initialize tokenizer
    tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token

    # Load dataset from Hugging Face
    dataset_name = 'wikitext'  # Use WikiText dataset
    raw_dataset = load_dataset(dataset_name, 'wikitext-2-raw-v1', split='train')

    # Prepare data samples
    data_samples = []
    for entry in raw_dataset:
        text = entry['text']
        sentences = text.strip().split('. ')
        for i in range(len(sentences) - 1):
            prompt = sentences[i] + '.'
            response = sentences[i + 1] + '.'
            data_samples.append({'prompt': prompt, 'response': response})

    # Limit dataset size
    data_samples = data_samples[:10000]

    # Create dataset and data loader
    custom_dataset = CustomDataset(tokenizer, data_samples, max_length=args.max_length)

    def data_generator():
        idx = 0
        while idx < len(custom_dataset):
            batch_input_ids = []
            batch_attention_mask = []
            for _ in range(args.batch_size):
                if idx >= len(custom_dataset):
                    break
                input_ids, attention_mask = custom_dataset[idx]
                batch_input_ids.append(input_ids)
                batch_attention_mask.append(attention_mask)
                idx += 1
            yield {
                'input_ids': np.stack(batch_input_ids),
                'attention_mask': np.stack(batch_attention_mask)
            }

    # Initialize model
    vocab_size = tokenizer.vocab_size
    transformer_model = TransformerModel(
        vocab_size=vocab_size,
        max_length=args.max_length,
        d_model=512,
        n_heads=8,
        num_layers=6,
        dropout_rate=0.1,
        use_flash_attention=True
    )

    # Initialize parameters
    rng = jax.random.PRNGKey(0)
    dummy_input_ids = jnp.ones((args.batch_size, args.max_length), dtype=jnp.int32)
    dummy_attention_mask = jnp.ones((args.batch_size, args.max_length), dtype=jnp.int32)
    params = transformer_model.init(rng, dummy_input_ids, dummy_attention_mask)

    # Create optimizer
    learning_rate_schedule = create_learning_rate_schedule(args.learning_rate)
    optimizer = optax.adamw(learning_rate=learning_rate_schedule)
    state = train_state.TrainState.create(apply_fn=transformer_model.apply, params=params, tx=optimizer)

    # Training loop
    for epoch in range(args.epochs):
        logging.info(f"Epoch {epoch+1}/{args.epochs}")
        data_iter = data_generator()
        step = 0
        for batch in data_iter:
            input_ids = batch['input_ids']
            attention_mask = batch['attention_mask']
            labels = np.roll(input_ids, shift=-1, axis=1)
            labels = labels.at[:, -1].set(tokenizer.eos_token_id)
            mask = attention_mask

            def loss_fn(params):
                logits = transformer_model.apply(params, input_ids, attention_mask)
                loss = cross_entropy_loss(logits, labels, mask)
                return loss

            loss_value, grads = jax.value_and_grad(loss_fn)(state.params)
            state = state.apply_gradients(grads=grads)

            if step % 100 == 0:
                logging.info(f"Step {step}, Loss: {loss_value:.4f}")
            step += 1

    # Save the trained model
    # Note: Saving and loading models in JAX requires serialization
    import pickle
    with open('open1_model.pkl', 'wb') as f:
        pickle.dump(state.params, f)
    logging.info("Training completed and model saved.")

 # =======================
 # Main Function
 # =======================
 if __name__ == "__main__":
    args = parse_args()
    train_open1(args)
diff --git a/open1.py b/open1.py
 #!/usr/bin/env python
 # coding: utf-8

 """
 Open1: An Open-Source Implementation of O1 with Advanced Features
 This script implements a simplified version of the OpenAI O1 model,
 integrating the following components:

 - Transformer-based language model using GPT-2 Small
 - Chain-of-thought reasoning mechanism
 - Tree-based trajectory exploration using Monte Carlo Tree Search (MCTS)
 - Reinforcement Learning from Human Feedback (RLHF) using Proximal Policy Optimization (PPO)
 - Self-play environments for LLMs
 - Function calling practice in the training data
 - Progressive training pipeline with increasing complexity
 - Visualization using TensorBoard
 - Hexagon chart rating of LLM capabilities

 Instructions:
 - Ensure you have the required packages installed.
 - Run the script: `python open1.py`
 - Use command-line arguments to enable or disable features.

 Author: OpenAI Assistant
 Date: 2023
 """

 import os
 import sys
 import argparse
 import logging
 import math
 import random
 import numpy as np
 from collections import defaultdict
 from datetime import datetime
 import warnings

 warnings.filterwarnings("ignore")

 import torch
 import torch.nn as nn
 import torch.optim as optim
 from torch.utils.data import Dataset, DataLoader
 from torch.utils.tensorboard import SummaryWriter

 from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2Config
 from transformers import AdamW, get_linear_schedule_with_warmup

 from datasets import load_dataset

 # Set random seeds for reproducibility
 torch.manual_seed(42)
 np.random.seed(42)
 random.seed(42)

 # Device configuration
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')


 # =======================
 # Command-Line Arguments
 # =======================
 def parse_args():
    parser = argparse.ArgumentParser(description="Open1 Model Training and Evaluation")
    parser.add_argument('--epochs', type=int, default=1, help='Number of training epochs')
    parser.add_argument('--max_length', type=int, default=128, help='Maximum sequence length')
    parser.add_argument('--batch_size', type=int, default=2, help='Batch size for training')
    parser.add_argument('--log_dir', type=str, default='runs', help='Directory for TensorBoard logs')
    parser.add_argument('--use_mcts', action='store_true', help='Enable MCTS for trajectory exploration')
    parser.add_argument('--multi_agent', action='store_true', help='Enable multi-agent training')
    parser.add_argument('--progressive_training', action='store_true', help='Enable progressive training pipeline')
    parser.add_argument('--kl_coef', type=float, default=0.2, help='KL Coefficient for PPO')
    parser.add_argument('--n_simulations', type=int, default=5, help='Number of MCTS simulations')
    parser.add_argument('--visualize', action='store_true', help='Enable visualization of LLM capabilities')
    parser.add_argument('--reward_model_epochs', type=int, default=1, help='Number of epochs to train the reward model')
    args = parser.parse_args()
    return args


 # =======================
 # Logging Configuration
 # =======================
 def setup_logging():
    logging.basicConfig(
        level=logging.INFO,
        format='[%(asctime)s] %(levelname)s - %(message)s',
        handlers=[
            logging.StreamHandler(sys.stdout)
        ]
    )


 # =======================
 # Data Classes and Functions
 # =======================
 class CustomDataset(Dataset):
    def __init__(self, tokenizer, data, max_length):
        self.tokenizer = tokenizer
        self.data = data
        self.max_length = max_length

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

    def __getitem__(self, idx):
        prompt = self.data[idx]['prompt']
        response = self.data[idx]['response']
        # Concatenate prompt and response for training
        input_text = prompt + response
        input_ids = self.tokenizer.encode(input_text, truncation=True, max_length=self.max_length)
        return torch.tensor(input_ids, dtype=torch.long)


 def collate_fn(batch, tokenizer):
    input_ids = torch.nn.utils.rnn.pad_sequence(batch, batch_first=True, padding_value=tokenizer.pad_token_id)
    attention_mask = (input_ids != tokenizer.pad_token_id).long()
    return {'input_ids': input_ids, 'attention_mask': attention_mask}


 # =======================
 # Model Classes
 # =======================

 # Transformer-based Language Model with Chain-of-Thought
 class Open1Model(nn.Module):
    def __init__(self, model_name='gpt2'):
        super(Open1Model, self).__init__()
        self.model = GPT2LMHeadModel.from_pretrained(model_name)
        self.tokenizer = GPT2Tokenizer.from_pretrained(model_name)
        # Ensure a unique pad token
        if self.tokenizer.pad_token is None:
            self.tokenizer.add_special_tokens({'pad_token': '[PAD]'})
            self.model.resize_token_embeddings(len(self.tokenizer))
        self.model.to(device)

    def forward(self, input_ids, attention_mask=None):
        outputs = self.model(input_ids=input_ids, attention_mask=attention_mask)
        return outputs

    def generate_chain_of_thought(self, prompt, max_length=50, num_return_sequences=1):
        input_ids = self.tokenizer.encode(prompt, return_tensors='pt').to(device)
        # Generate with chain-of-thought reasoning
        outputs = self.model.generate(
            input_ids,
            max_length=max_length,
            num_return_sequences=num_return_sequences,
            do_sample=True,
            temperature=0.7,
            top_p=0.9,
            eos_token_id=self.tokenizer.eos_token_id,
            pad_token_id=self.tokenizer.pad_token_id,
        )
        generated_texts = [self.tokenizer.decode(output, skip_special_tokens=True) for output in outputs]
        return generated_texts


 # Reward Model for RLHF
 class RewardModel(nn.Module):
    def __init__(self, model_name='gpt2', tokenizer=None):
        super(RewardModel, self).__init__()
        config = GPT2Config.from_pretrained(model_name)
        self.model = GPT2LMHeadModel.from_pretrained(model_name)
        self.tokenizer = tokenizer
        if self.tokenizer.pad_token is None:
            self.tokenizer.add_special_tokens({'pad_token': '[PAD]'})
            self.model.resize_token_embeddings(len(self.tokenizer))
        self.model.to(device)
        self.reward_head = nn.Linear(config.n_embd, 1)
        self.reward_head.to(device)

    def forward(self, input_ids, attention_mask=None):
        outputs = self.model.transformer(input_ids=input_ids, attention_mask=attention_mask)
        hidden_states = outputs.last_hidden_state  # [batch_size, seq_length, hidden_size]
        # Take the hidden state of the last token
        last_hidden = hidden_states[:, -1, :]  # [batch_size, hidden_size]
        reward = self.reward_head(last_hidden)  # [batch_size, 1]
        return reward.squeeze(-1)  # [batch_size]


 # Proximal Policy Optimization (PPO) Trainer
 class PPOTrainer:
    def __init__(self, policy_model, reward_model, tokenizer, kl_coef=0.2):
        self.policy_model = policy_model
        self.reward_model = reward_model
        self.tokenizer = tokenizer
        self.optimizer = AdamW(self.policy_model.model.parameters(), lr=1e-5)
        self.kl_coef = kl_coef

    def compute_advantages(self, rewards, values):
        # Compute advantages (simplified)
        advantages = rewards - values
        return advantages

    def ppo_step(self, batch):
        # Unpack batch
        input_ids = batch['input_ids'].to(device)
        attention_mask = batch['attention_mask'].to(device)
        old_log_probs = batch['old_log_probs'].to(device)
        rewards = batch['rewards'].to(device)

        # Get current policy outputs
        outputs = self.policy_model.model(input_ids=input_ids, attention_mask=attention_mask)
        logits = outputs.logits  # [batch_size, seq_length, vocab_size]
        # Compute log probabilities
        log_probs = nn.functional.log_softmax(logits, dim=-1)  # [batch_size, seq_length, vocab_size]
        # Gather log probs for the selected tokens
        selected_log_probs = log_probs.gather(2, input_ids.unsqueeze(-1)).squeeze(-1)  # [batch_size, seq_length]

        # Compute policy ratio
        ratios = torch.exp(selected_log_probs - old_log_probs)  # [batch_size, seq_length]

        # Compute advantages
        values = torch.zeros_like(rewards).to(device)  # Placeholder for value estimates
        advantages = self.compute_advantages(rewards, values)  # [batch_size]
        advantages = advantages.unsqueeze(1)  # [batch_size, 1]

        # Compute PPO loss
        surr1 = ratios * advantages  # [batch_size, seq_length]
        surr2 = torch.clamp(ratios, 0.8, 1.2) * advantages  # [batch_size, seq_length]
        policy_loss = -torch.min(surr1, surr2).mean()

        # Compute KL divergence for regularization
        kl_div = torch.nn.functional.kl_div(old_log_probs, selected_log_probs, log_target=True, reduction='batchmean')

        # Total loss
        loss = policy_loss + self.kl_coef * kl_div

        # Backpropagation
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

        return loss.item(), policy_loss.item(), kl_div.item()


 # Monte Carlo Tree Search Node
 class MCTSNode:
    def __init__(self, state, parent=None, prior_prob=1.0):
        self.state = state  # Sequence of tokens (input_ids)
        self.parent = parent
        self.children = {}
        self.visits = 0
        self.value = 0.0
        self.prior_prob = prior_prob

    def is_leaf(self):
        return len(self.children) == 0


 # Monte Carlo Tree Search
 class MCTS:
    def __init__(self, model, tokenizer, reward_model, c_puct=1.0, n_simulations=50, max_depth=20):
        self.model = model
        self.tokenizer = tokenizer
        self.reward_model = reward_model  # Add reward_model
        self.c_puct = c_puct
        self.n_simulations = n_simulations
        self.max_depth = max_depth

    def search(self, state):
        root = MCTSNode(state)
        for _ in range(self.n_simulations):
            node = root
            # Selection
            while not node.is_leaf():
                node = self.select_child(node)
            # Expansion
            if len(node.state) < self.max_depth:
                self.expand_node(node)
            # Simulation
            reward = self.simulate(node.state)
            # Backpropagation
            self.backpropagate(node, reward)
        # Choose the best action
        if root.children:
            best_child = max(root.children.values(), key=lambda n: n.visits)
            return best_child.state
        else:
            # If no children, return the root state
            return root.state

    def select_child(self, node):
        total_visits = sum(child.visits for child in node.children.values())
        best_score = -float('inf')
        best_child = None
        for child in node.children.values():
            q_value = child.value / (child.visits + 1e-4)
            u_value = self.c_puct * child.prior_prob * math.sqrt(total_visits + 1) / (1 + child.visits)
            score = q_value + u_value
            if score > best_score:
                best_score = score
                best_child = child
        return best_child

    def expand_node(self, node):
        input_ids = torch.tensor(node.state).unsqueeze(0).to(device)
        outputs = self.model.model(input_ids=input_ids)
        logits = outputs.logits[:, -1, :]  # [1, vocab_size]
        probs = nn.functional.softmax(logits, dim=-1).squeeze()  # [vocab_size]
        top_k_probs, top_k_indices = torch.topk(probs, k=10)
        for idx, prob in zip(top_k_indices, top_k_probs):
            token_id = idx.item()
            prior_prob = prob.item()
            child_state = node.state + [token_id]
            node.children[token_id] = MCTSNode(child_state, parent=node, prior_prob=prior_prob)

    def simulate(self, state):
        # Simulate to the end using the model
        input_ids = torch.tensor(state).unsqueeze(0).to(device)
        max_length = len(state) + 20
        outputs = self.model.model.generate(
            input_ids=input_ids,
            max_length=max_length,
            do_sample=True,
            temperature=0.7,
            top_p=0.9,
            eos_token_id=self.tokenizer.eos_token_id,
            pad_token_id=self.tokenizer.pad_token_id,
        )
        generated_ids = outputs[0].tolist()
        generated_text = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
        # Calculate reward using the reward model
        reward = self.evaluate(generated_text)
        return reward

    def backpropagate(self, node, reward):
        while node is not None:
            node.visits += 1
            node.value += reward
            node = node.parent

    def evaluate(self, text):
        # Use the reward model to evaluate the text
        input_ids = self.tokenizer.encode(text, return_tensors='pt').to(device)
        attention_mask = (input_ids != self.tokenizer.pad_token_id).long().to(device)
        with torch.no_grad():
            reward = self.reward_model(input_ids=input_ids, attention_mask=attention_mask)
        return reward.item()


 # =======================
 # Self-Play Environment
 # =======================
 class SelfPlayEnvironment:
    def __init__(self, agent):
        self.agent = agent

    def step(self, prompt):
        # Agent interacts with itself
        response = self.agent.generate_chain_of_thought(prompt, max_length=100)[0]
        # The agent then takes its own response as the next prompt
        next_prompt = response
        return next_prompt, response

    def play(self, initial_prompt, max_turns=5):
        prompt = initial_prompt
        for _ in range(max_turns):
            prompt, response = self.step(prompt)
        return response


 # =======================
 # Visualization Functions
 # =======================
 def plot_capabilities(capabilities, epoch, writer):
    import matplotlib.pyplot as plt

    labels = list(capabilities.keys())
    values = list(capabilities.values())

    angles = np.linspace(0, 2 * np.pi, len(labels), endpoint=False).tolist()
    values += values[:1]
    angles += angles[:1]

    fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))
    ax.plot(angles, values, 'o-', linewidth=2)
    ax.fill(angles, values, alpha=0.25)
    ax.set_thetagrids(np.degrees(angles[:-1]), labels)
    ax.set_ylim(0, 1)
    ax.set_title(f"LLM Capabilities at Step {epoch}")
    ax.grid(True)

    # Save the plot to TensorBoard
    writer.add_figure('Capabilities', fig, epoch)
    plt.close(fig)


 # =======================
 # Training Functions
 # =======================

 def train_reward_model(reward_model, tokenizer, data_samples, args):
    logging.info("Starting training of the Reward Model.")
    reward_model.train()
    optimizer = AdamW(list(reward_model.model.parameters()) + list(reward_model.reward_head.parameters()), lr=1e-5)

    # Create a dataset for the reward model
    class RewardDataset(Dataset):
        def __init__(self, tokenizer, data, max_length):
            self.tokenizer = tokenizer
            self.data = data
            self.max_length = max_length

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

        def __getitem__(self, idx):
            input_text = self.data[idx]['input_text']
            reward = self.data[idx]['reward']
            input_ids = self.tokenizer.encode(input_text, truncation=True, max_length=self.max_length)
            return torch.tensor(input_ids, dtype=torch.long), torch.tensor(reward, dtype=torch.float)

    def reward_collate_fn(batch):
        input_ids, rewards = zip(*batch)
        input_ids = torch.nn.utils.rnn.pad_sequence(input_ids, batch_first=True, padding_value=tokenizer.pad_token_id)
        attention_mask = (input_ids != tokenizer.pad_token_id).long()
        rewards = torch.stack(rewards)
        return {'input_ids': input_ids, 'attention_mask': attention_mask, 'rewards': rewards}

    dataset = RewardDataset(tokenizer, data_samples, max_length=args.max_length)
    data_loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True, collate_fn=reward_collate_fn)

    for epoch in range(args.reward_model_epochs):
        logging.info(f"Reward Model Epoch {epoch+1}/{args.reward_model_epochs}")
        total_loss = 0
        for batch in data_loader:
            input_ids = batch['input_ids'].to(device)
            attention_mask = batch['attention_mask'].to(device)
            rewards = batch['rewards'].to(device)

            # Forward pass
            predicted_rewards = reward_model(input_ids=input_ids, attention_mask=attention_mask)

            # Loss computation
            loss = nn.functional.mse_loss(predicted_rewards, rewards)

            # Backpropagation
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            total_loss += loss.item()

        avg_loss = total_loss / len(data_loader)
        logging.info(f"Average Reward Model Loss: {avg_loss:.4f}")

    # Save the trained reward model
    torch.save(reward_model.state_dict(), 'reward_model.pth')
    logging.info("Reward Model training completed and saved.")


 def train_open1(args):
    setup_logging()
    logging.info("Starting training of Open1 model.")

    # Initialize models and tokenizer
    policy_model = Open1Model(model_name='gpt2')
    tokenizer = policy_model.tokenizer
    reward_model = RewardModel(model_name='gpt2', tokenizer=tokenizer)
    # Resize embeddings after adding pad_token
    if tokenizer.pad_token is not None:
        policy_model.model.resize_token_embeddings(len(tokenizer))
        reward_model.model.resize_token_embeddings(len(tokenizer))

    # Initialize PPO trainer
    ppo_trainer = PPOTrainer(policy_model, reward_model, tokenizer, kl_coef=args.kl_coef)

    # Initialize TensorBoard writer
    writer = SummaryWriter(log_dir=args.log_dir)

    # Load real dataset from Hugging Face
    dataset_name = 'daily_dialog'  # You can choose another dataset
    raw_dataset = load_dataset(dataset_name, split='train')

    # Prepare data samples
    data_samples = []
    for entry in raw_dataset:
        # We will use the conversation as prompt and response
        if 'dialog' in entry:
            dialog = entry['dialog']
            for i in range(len(dialog) - 1):
                prompt = dialog[i]
                response = dialog[i + 1]
                data_samples.append({'prompt': prompt, 'response': response})

    # Limit dataset size for demonstration purposes
    data_samples = data_samples[:1000]

    # Prepare data for reward model training
    reward_data_samples = []
    for sample in data_samples:
        input_text = sample['prompt'] + sample['response']
        reward = len(sample['response'].split()) / 50.0  # Simple heuristic
        reward_data_samples.append({'input_text': input_text, 'reward': reward})

    # Train the reward model first
    train_reward_model(reward_model, tokenizer, reward_data_samples, args)
    reward_model.eval()  # Set reward model to evaluation mode

    # Create dataset and data loader for the policy model
    dataset = CustomDataset(tokenizer, data_samples, max_length=args.max_length)
    data_loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True, collate_fn=lambda x: collate_fn(x, tokenizer))

    # Training loop
    for epoch in range(args.epochs):
        logging.info(f"Epoch {epoch+1}/{args.epochs}")
        for batch_idx, batch in enumerate(data_loader):
            input_ids = batch['input_ids'].to(device)
            attention_mask = batch['attention_mask'].to(device)

            # Generate initial response with chain-of-thought
            generated_texts = []
            for input_id in input_ids:
                prompt = tokenizer.decode(input_id, skip_special_tokens=True)
                if args.use_mcts:
                    # Use MCTS for trajectory exploration
                    mcts = MCTS(policy_model, tokenizer, reward_model, n_simulations=args.n_simulations, max_depth=args.max_length)
                    initial_state = input_id.tolist()
                    best_state = mcts.search(initial_state)
                    generated_text = tokenizer.decode(best_state, skip_special_tokens=True)
                else:
                    # Regular generation
                    generated_text = policy_model.generate_chain_of_thought(prompt, max_length=args.max_length)[0]
                generated_texts.append(generated_text)

            # Prepare inputs for reward model
            generated_ids = [tokenizer.encode(text, return_tensors='pt').squeeze(0) for text in generated_texts]
            input_ids_gen = torch.nn.utils.rnn.pad_sequence(generated_ids, batch_first=True, padding_value=tokenizer.pad_token_id).to(device)
            attention_mask_gen = (input_ids_gen != tokenizer.pad_token_id).long().to(device)

            # Get old log probabilities
            with torch.no_grad():
                outputs = policy_model.model(input_ids=input_ids_gen, attention_mask=attention_mask_gen)
                logits = outputs.logits
                log_probs = nn.functional.log_softmax(logits, dim=-1)
                old_log_probs = log_probs.gather(2, input_ids_gen.unsqueeze(-1)).squeeze(-1)

            # Get rewards from reward model
            with torch.no_grad():
                rewards = reward_model(input_ids=input_ids_gen, attention_mask=attention_mask_gen)

            # Prepare batch
            batch_data = {
                'input_ids': input_ids_gen,
                'attention_mask': attention_mask_gen,
                'old_log_probs': old_log_probs,
                'rewards': rewards,
            }

            # PPO optimization step
            loss, policy_loss, kl_div = ppo_trainer.ppo_step(batch_data)

            # Logging
            logging.info(f"Batch {batch_idx+1}/{len(data_loader)}, Loss: {loss:.4f}, Policy Loss: {policy_loss:.4f}, KL Divergence: {kl_div:.4f}")
            writer.add_scalar('Loss/Total', loss, epoch * len(data_loader) + batch_idx)
            writer.add_scalar('Loss/Policy', policy_loss, epoch * len(data_loader) + batch_idx)
            writer.add_scalar('Loss/KL_Divergence', kl_div, epoch * len(data_loader) + batch_idx)

            # Visualize LLM capabilities
            if args.visualize:
                capabilities = {
                    'Reasoning': random.uniform(0, 1),
                    'Creativity': random.uniform(0, 1),
                    'Memory': random.uniform(0, 1),
                    'Planning': random.uniform(0, 1),
                    'Problem Solving': random.uniform(0, 1),
                    'Social Intelligence': random.uniform(0, 1),
                }
                plot_capabilities(capabilities, epoch * len(data_loader) + batch_idx, writer)

            # (Optional) Self-Play Environment
            if args.multi_agent:
                env = SelfPlayEnvironment(policy_model)
                initial_prompt = "Hello, how are you?"
                final_response = env.play(initial_prompt)
                # Use the final response to further train the model if desired
                # For example, append to training data or use as additional training examples

            # Include Function Calling Practice
            if args.progressive_training:
                function_call_prompt = "def add(a, b): return a + b\nadd("
                generated_code = policy_model.generate_chain_of_thought(function_call_prompt, max_length=args.max_length)[0]
                # Optionally, evaluate and train the model on code generation tasks
                # For example, compare generated_code to expected code and adjust rewards accordingly

        # Save the trained policy model after each epoch
        policy_model.model.save_pretrained('open1_policy_model')
        logging.info(f"Epoch {epoch+1} completed and model saved.")

    writer.close()
    logging.info("Training completed and model saved.")


 # =======================
 # Main Function
 # =======================
 if __name__ == "__main__":
    args = parse_args()
    setup_logging()
    train_open1(args)
	#!/usr/bin/env python
	# coding: utf-8

	"""
	Open1: An Open-Source Implementation with Advanced Transformer Features
	This script implements a novel transformer model using JAX and Flax,
	integrating the following components:

	- Infini-Attention with Compressive Memory and Dual Attention Modes
	- FlashAttention with Block-wise Recomputation
	- Mamba Transformers with Selective Gating and Recurrent Shortcuts
	- Tree-Search-Powered Token Agents for Enhanced Reasoning
	- kNN Caching and Memory-Bound Scaling
	- Integration into the Overall Codebase with Training Loop
	- Model Size Approximately 90 Million Parameters

	Instructions:
	- Ensure you have the required packages installed.
	- Run the script: `python open1_jax.py`
	- Use command-line arguments to enable or disable features.

	Author: OpenAI Assistant
	Date: 2023
	"""

	import os
	import sys
	import argparse
	import logging
	import math
	import random
	import numpy as np
	from collections import defaultdict
	from datetime import datetime
	import warnings

	warnings.filterwarnings("ignore")

	import jax
	import jax.numpy as jnp
	from jax import jit, grad, value_and_grad
	from flax import linen as nn
	from flax.training import train_state
	import optax

	from transformers import GPT2Tokenizer
	from datasets import load_dataset

	# Set random seeds for reproducibility
	random.seed(42)
	np.random.seed(42)

	# Device configuration
	print(f"JAX devices: {jax.devices()}")

	# =======================
	# Command-Line Arguments
	# =======================
	def parse_args():
	parser = argparse.ArgumentParser(description="Open1 Model Training and Evaluation with JAX")
	parser.add_argument('--epochs', type=int, default=1, help='Number of training epochs')
	parser.add_argument('--max_length', type=int, default=256, help='Maximum sequence length')
	parser.add_argument('--batch_size', type=int, default=4, help='Batch size for training')
	parser.add_argument('--log_dir', type=str, default='runs_jax', help='Directory for logs')
	parser.add_argument('--learning_rate', type=float, default=1e-4, help='Learning rate for optimizer')
	parser.add_argument('--use_mcts', action='store_true', help='Enable MCTS for trajectory exploration')
	parser.add_argument('--multi_agent', action='store_true', help='Enable multi-agent training')
	parser.add_argument('--progressive_training', action='store_true', help='Enable progressive training pipeline')
	parser.add_argument('--visualize', action='store_true', help='Enable visualization of LLM capabilities')
	args = parser.parse_args()
	return args

	# =======================
	# Logging Configuration
	# =======================
	def setup_logging():
	logging.basicConfig(
	level=logging.INFO,
	format='[%(asctime)s] %(levelname)s - %(message)s',
	handlers=[
	logging.StreamHandler(sys.stdout)
	]
	)

	# =======================
	# Data Classes and Functions
	# =======================
	class CustomDataset:
	def __init__(self, tokenizer, data, max_length):
	self.tokenizer = tokenizer
	self.data = data
	self.max_length = max_length

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

	def __getitem__(self, idx):
	prompt = self.data[idx]['prompt']
	response = self.data[idx]['response']
	# Concatenate prompt and response for training
	input_text = prompt + " " + response
	encoded = self.tokenizer.encode_plus(
	input_text,
	truncation=True,
	max_length=self.max_length,
	padding='max_length',
	return_tensors='np',
	)
	input_ids = encoded['input_ids'].squeeze()
	attention_mask = encoded['attention_mask'].squeeze()
	return input_ids, attention_mask

	# =======================
	# Model Classes
	# =======================

	# Mamba Transformer Block with Infini-Attention and FlashAttention
	class TransformerBlock(nn.Module):
	d_model: int
	n_heads: int
	dropout_rate: float
	use_flash_attention: bool = True

	def setup(self):
	self.attention = MultiHeadAttention(
	d_model=self.d_model,
	n_heads=self.n_heads,
	use_flash_attention=self.use_flash_attention,
	)
	self.ln1 = nn.LayerNorm()
	self.ln2 = nn.LayerNorm()
	self.ffn = FeedForwardNetwork(
	d_model=self.d_model,
	dropout_rate=self.dropout_rate,
	)
	# Gating mechanism parameters
	self.gate = nn.Dense(self.d_model)

	def __call__(self, x, mask, memory=None):
	# Layer Norm
	x_norm = self.ln1(x)

	# Multi-Head Attention with Infini-Attention
	attn_output = self.attention(x_norm, mask, memory)

	# Selective Gating with Recurrent Shortcuts (Mamba Transformers)
	gate_value = nn.sigmoid(self.gate(x_norm))
	gated_output = gate_value * x_norm + (1 - gate_value) * attn_output

	# Feed-Forward Network
	ffn_output = self.ffn(self.ln2(gated_output))

	# Residual Connection
	output = gated_output + ffn_output

	return output

	class MultiHeadAttention(nn.Module):
	d_model: int
	n_heads: int
	use_flash_attention: bool = True

	def setup(self):
	assert self.d_model % self.n_heads == 0, "d_model must be divisible by n_heads"
	self.depth = self.d_model // self.n_heads
	# Query, Key, Value projection layers
	self.wq = nn.Dense(self.d_model)
	self.wk = nn.Dense(self.d_model)
	self.wv = nn.Dense(self.d_model)
	self.dense = nn.Dense(self.d_model)

	def __call__(self, x, mask, memory=None):
	batch_size = x.shape[0]

	# Project inputs to queries, keys, and values
	q = self.wq(x)
	k = self.wk(x)
	v = self.wv(x)

	# Reshape for multi-head attention
	q = q.reshape(batch_size, -1, self.n_heads, self.depth).transpose(0, 2, 1, 3)
	k = k.reshape(batch_size, -1, self.n_heads, self.depth).transpose(0, 2, 1, 3)
	v = v.reshape(batch_size, -1, self.n_heads, self.depth).transpose(0, 2, 1, 3)

	if memory is not None:
	# Incorporate memory for global attention
	k_memory = memory['k_memory']
	v_memory = memory['v_memory']
	k = jnp.concatenate([k_memory, k], axis=2)
	v = jnp.concatenate([v_memory, v], axis=2)

	# Apply attention mechanism
	if self.use_flash_attention:
	attn_output = self.flash_attention(q, k, v, mask)
	else:
	attn_output = self.scaled_dot_product_attention(q, k, v, mask)

	# Reshape and project output
	attn_output = attn_output.transpose(0, 2, 1, 3).reshape(batch_size, -1, self.d_model)
	output = self.dense(attn_output)

	return output

	def flash_attention(self, q, k, v, mask):
	# Simplified FlashAttention implementation
	# Actual implementation requires custom kernels; here we simulate the behavior
	d_k = q.shape[-1]
	scores = jnp.einsum('bhqd,bhkd->bhqk', q, k) / math.sqrt(d_k)

	# Apply mask
	if mask is not None:
	mask = mask[:, jnp.newaxis, jnp.newaxis, :]
	scores = scores + (mask * -1e9)

	# Softmax with block-wise recomputation
	attn_weights = nn.softmax(scores, axis=-1)
	output = jnp.einsum('bhqk,bhkd->bhqd', attn_weights, v)
	return output

	def scaled_dot_product_attention(self, q, k, v, mask):
	# Standard attention mechanism
	d_k = q.shape[-1]
	scores = jnp.einsum('bhqd,bhkd->bhqk', q, k) / math.sqrt(d_k)

	# Apply mask
	if mask is not None:
	mask = mask[:, jnp.newaxis, jnp.newaxis, :]
	scores = scores + (mask * -1e9)

	attn_weights = nn.softmax(scores, axis=-1)
	output = jnp.einsum('bhqk,bhkd->bhqd', attn_weights, v)
	return output

	class FeedForwardNetwork(nn.Module):
	d_model: int
	dropout_rate: float

	def setup(self):
	self.dense1 = nn.Dense(self.d_model * 4)
	self.dense2 = nn.Dense(self.d_model)
	self.dropout = nn.Dropout(rate=self.dropout_rate)

	def __call__(self, x):
	x = self.dense1(x)
	x = nn.relu(x)
	x = self.dropout(x)
	x = self.dense2(x)
	return x

	# Transformer Model
	class TransformerModel(nn.Module):
	vocab_size: int
	max_length: int
	d_model: int = 512
	n_heads: int = 8
	num_layers: int = 6
	dropout_rate: float = 0.1
	use_flash_attention: bool = True

	def setup(self):
	self.token_embedding = nn.Embed(self.vocab_size, self.d_model)
	self.position_embedding = nn.Embed(self.max_length, self.d_model)
	self.layers = [TransformerBlock(
	d_model=self.d_model,
	n_heads=self.n_heads,
	dropout_rate=self.dropout_rate,
	use_flash_attention=self.use_flash_attention
	) for _ in range(self.num_layers)]
	self.ln = nn.LayerNorm()
	self.output_dense = nn.Dense(self.vocab_size)

	def __call__(self, input_ids, attention_mask, memory=None):
	batch_size, seq_length = input_ids.shape
	x = self.token_embedding(input_ids)
	positions = jnp.expand_dims(jnp.arange(seq_length), 0)
	x = x + self.position_embedding(positions)

	# Apply transformer layers
	for layer in self.layers:
	x = layer(x, attention_mask, memory)

	x = self.ln(x)
	logits = self.output_dense(x)
	return logits

	# Tree-Search-Powered Token Agent
	class TokenAgent(nn.Module):
	transformer_model: TransformerModel
	beam_width: int = 3
	max_length: int = 50

	def __call__(self, input_ids, attention_mask):
	# Implement beam search to simulate tree search
	batch_size = input_ids.shape[0]
	sequences = [input_ids]
	scores = [0]

	for _ in range(self.max_length):
	all_candidates = []
	for i in range(len(sequences)):
	seq = sequences[i]
	score = scores[i]
	logits = self.transformer_model(seq, attention_mask)
	log_probs = jax.nn.log_softmax(logits[:, -1, :], axis=-1)
	top_k_probs, top_k_indices = jax.lax.top_k(log_probs, self.beam_width)

	for j in range(self.beam_width):
	candidate = {
	'sequence': jnp.concatenate([seq, top_k_indices[:, j:j+1]], axis=1),
	'score': score + top_k_probs[:, j]
	}
	all_candidates.append(candidate)

	# Select the best candidates
	ordered = sorted(all_candidates, key=lambda x: x['score'], reverse=True)
	sequences = [c['sequence'] for c in ordered[:self.beam_width]]
	scores = [c['score'] for c in ordered[:self.beam_width]]

	return sequences[0]

	# =======================
	# kNN Caching Mechanism
	# =======================
	class kNNCaching:
	def __init__(self, cache_size=1024, d_model=512):
	self.cache_size = cache_size
	self.d_model = d_model
	self.keys = jnp.zeros((cache_size, d_model))
	self.values = jnp.zeros((cache_size, d_model))
	self.index = 0

	def add(self, key, value):
	# Add key-value pair to cache
	idx = self.index % self.cache_size
	self.keys = self.keys.at[idx].set(key)
	self.values = self.values.at[idx].set(value)
	self.index += 1

	def retrieve(self, query, k=1):
	# Retrieve the most similar keys
	similarities = jnp.dot(self.keys, query.T)
	top_k_indices = jnp.argsort(-similarities)[:k]
	return self.values[top_k_indices]

	# =======================
	# Training Utilities
	# =======================
	def create_learning_rate_schedule(learning_rate, warmup_steps=1000):
	def lr_schedule(step):
	return learning_rate * min(1.0, step / warmup_steps)
	return lr_schedule

	def cross_entropy_loss(logits, labels, mask):
	loss = optax.softmax_cross_entropy_with_integer_labels(logits, labels)
	loss = loss * mask
	return loss.sum() / mask.sum()

	# =======================
	# Training Function
	# =======================
	def train_open1(args):
	setup_logging()
	logging.info("Starting training of Open1 model with JAX.")

	# Initialize tokenizer
	tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
	if tokenizer.pad_token is None:
	tokenizer.pad_token = tokenizer.eos_token

	# Load dataset from Hugging Face
	dataset_name = 'wikitext' # Use WikiText dataset
	raw_dataset = load_dataset(dataset_name, 'wikitext-2-raw-v1', split='train')

	# Prepare data samples
	data_samples = []
	for entry in raw_dataset:
	text = entry['text']
	sentences = text.strip().split('. ')
	for i in range(len(sentences) - 1):
	prompt = sentences[i] + '.'
	response = sentences[i + 1] + '.'
	data_samples.append({'prompt': prompt, 'response': response})

	# Limit dataset size
	data_samples = data_samples[:10000]

	# Create dataset and data loader
	custom_dataset = CustomDataset(tokenizer, data_samples, max_length=args.max_length)

	def data_generator():
	idx = 0
	while idx < len(custom_dataset):
	batch_input_ids = []
	batch_attention_mask = []
	for _ in range(args.batch_size):
	if idx >= len(custom_dataset):
	break
	input_ids, attention_mask = custom_dataset[idx]
	batch_input_ids.append(input_ids)
	batch_attention_mask.append(attention_mask)
	idx += 1
	yield {
	'input_ids': np.stack(batch_input_ids),
	'attention_mask': np.stack(batch_attention_mask)
	}

	# Initialize model
	vocab_size = tokenizer.vocab_size
	transformer_model = TransformerModel(
	vocab_size=vocab_size,
	max_length=args.max_length,
	d_model=512,
	n_heads=8,
	num_layers=6,
	dropout_rate=0.1,
	use_flash_attention=True
	)

	# Initialize parameters
	rng = jax.random.PRNGKey(0)
	dummy_input_ids = jnp.ones((args.batch_size, args.max_length), dtype=jnp.int32)
	dummy_attention_mask = jnp.ones((args.batch_size, args.max_length), dtype=jnp.int32)
	params = transformer_model.init(rng, dummy_input_ids, dummy_attention_mask)

	# Create optimizer
	learning_rate_schedule = create_learning_rate_schedule(args.learning_rate)
	optimizer = optax.adamw(learning_rate=learning_rate_schedule)
	state = train_state.TrainState.create(apply_fn=transformer_model.apply, params=params, tx=optimizer)

	# Training loop
	for epoch in range(args.epochs):
	logging.info(f"Epoch {epoch+1}/{args.epochs}")
	data_iter = data_generator()
	step = 0
	for batch in data_iter:
	input_ids = batch['input_ids']
	attention_mask = batch['attention_mask']
	labels = np.roll(input_ids, shift=-1, axis=1)
	labels = labels.at[:, -1].set(tokenizer.eos_token_id)
	mask = attention_mask

	def loss_fn(params):
	logits = transformer_model.apply(params, input_ids, attention_mask)
	loss = cross_entropy_loss(logits, labels, mask)
	return loss

	loss_value, grads = jax.value_and_grad(loss_fn)(state.params)
	state = state.apply_gradients(grads=grads)

	if step % 100 == 0:
	logging.info(f"Step {step}, Loss: {loss_value:.4f}")
	step += 1

	# Save the trained model
	# Note: Saving and loading models in JAX requires serialization
	import pickle
	with open('open1_model.pkl', 'wb') as f:
	pickle.dump(state.params, f)
	logging.info("Training completed and model saved.")

	# =======================
	# Main Function
	# =======================
	if __name__ == "__main__":
	args = parse_args()
	train_open1(args)
	#!/usr/bin/env python
	# coding: utf-8

	"""
	Open1: An Open-Source Implementation of O1 with Advanced Features
	This script implements a simplified version of the OpenAI O1 model,
	integrating the following components:

	- Transformer-based language model using GPT-2 Small
	- Chain-of-thought reasoning mechanism
	- Tree-based trajectory exploration using Monte Carlo Tree Search (MCTS)
	- Reinforcement Learning from Human Feedback (RLHF) using Proximal Policy Optimization (PPO)
	- Self-play environments for LLMs
	- Function calling practice in the training data
	- Progressive training pipeline with increasing complexity
	- Visualization using TensorBoard
	- Hexagon chart rating of LLM capabilities

	Instructions:
	- Ensure you have the required packages installed.
	- Run the script: `python open1.py`
	- Use command-line arguments to enable or disable features.

	Author: OpenAI Assistant
	Date: 2023
	"""

	import os
	import sys
	import argparse
	import logging
	import math
	import random
	import numpy as np
	from collections import defaultdict
	from datetime import datetime
	import warnings

	warnings.filterwarnings("ignore")

	import torch
	import torch.nn as nn
	import torch.optim as optim
	from torch.utils.data import Dataset, DataLoader
	from torch.utils.tensorboard import SummaryWriter

	from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2Config
	from transformers import AdamW, get_linear_schedule_with_warmup

	from datasets import load_dataset

	# Set random seeds for reproducibility
	torch.manual_seed(42)
	np.random.seed(42)
	random.seed(42)

	# Device configuration
	device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')


	# =======================
	# Command-Line Arguments
	# =======================
	def parse_args():
	parser = argparse.ArgumentParser(description="Open1 Model Training and Evaluation")
	parser.add_argument('--epochs', type=int, default=1, help='Number of training epochs')
	parser.add_argument('--max_length', type=int, default=128, help='Maximum sequence length')
	parser.add_argument('--batch_size', type=int, default=2, help='Batch size for training')
	parser.add_argument('--log_dir', type=str, default='runs', help='Directory for TensorBoard logs')
	parser.add_argument('--use_mcts', action='store_true', help='Enable MCTS for trajectory exploration')
	parser.add_argument('--multi_agent', action='store_true', help='Enable multi-agent training')
	parser.add_argument('--progressive_training', action='store_true', help='Enable progressive training pipeline')
	parser.add_argument('--kl_coef', type=float, default=0.2, help='KL Coefficient for PPO')
	parser.add_argument('--n_simulations', type=int, default=5, help='Number of MCTS simulations')
	parser.add_argument('--visualize', action='store_true', help='Enable visualization of LLM capabilities')
	parser.add_argument('--reward_model_epochs', type=int, default=1, help='Number of epochs to train the reward model')
	args = parser.parse_args()
	return args


	# =======================
	# Logging Configuration
	# =======================
	def setup_logging():
	logging.basicConfig(
	level=logging.INFO,
	format='[%(asctime)s] %(levelname)s - %(message)s',
	handlers=[
	logging.StreamHandler(sys.stdout)
	]
	)


	# =======================
	# Data Classes and Functions
	# =======================
	class CustomDataset(Dataset):
	def __init__(self, tokenizer, data, max_length):
	self.tokenizer = tokenizer
	self.data = data
	self.max_length = max_length

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

	def __getitem__(self, idx):
	prompt = self.data[idx]['prompt']
	response = self.data[idx]['response']
	# Concatenate prompt and response for training
	input_text = prompt + response
	input_ids = self.tokenizer.encode(input_text, truncation=True, max_length=self.max_length)
	return torch.tensor(input_ids, dtype=torch.long)


	def collate_fn(batch, tokenizer):
	input_ids = torch.nn.utils.rnn.pad_sequence(batch, batch_first=True, padding_value=tokenizer.pad_token_id)
	attention_mask = (input_ids != tokenizer.pad_token_id).long()
	return {'input_ids': input_ids, 'attention_mask': attention_mask}


	# =======================
	# Model Classes
	# =======================

	# Transformer-based Language Model with Chain-of-Thought
	class Open1Model(nn.Module):
	def __init__(self, model_name='gpt2'):
	super(Open1Model, self).__init__()
	self.model = GPT2LMHeadModel.from_pretrained(model_name)
	self.tokenizer = GPT2Tokenizer.from_pretrained(model_name)
	# Ensure a unique pad token
	if self.tokenizer.pad_token is None:
	self.tokenizer.add_special_tokens({'pad_token': '[PAD]'})
	self.model.resize_token_embeddings(len(self.tokenizer))
	self.model.to(device)

	def forward(self, input_ids, attention_mask=None):
	outputs = self.model(input_ids=input_ids, attention_mask=attention_mask)
	return outputs

	def generate_chain_of_thought(self, prompt, max_length=50, num_return_sequences=1):
	input_ids = self.tokenizer.encode(prompt, return_tensors='pt').to(device)
	# Generate with chain-of-thought reasoning
	outputs = self.model.generate(
	input_ids,
	max_length=max_length,
	num_return_sequences=num_return_sequences,
	do_sample=True,
	temperature=0.7,
	top_p=0.9,
	eos_token_id=self.tokenizer.eos_token_id,
	pad_token_id=self.tokenizer.pad_token_id,
	)
	generated_texts = [self.tokenizer.decode(output, skip_special_tokens=True) for output in outputs]
	return generated_texts


	# Reward Model for RLHF
	class RewardModel(nn.Module):
	def __init__(self, model_name='gpt2', tokenizer=None):
	super(RewardModel, self).__init__()
	config = GPT2Config.from_pretrained(model_name)
	self.model = GPT2LMHeadModel.from_pretrained(model_name)
	self.tokenizer = tokenizer
	if self.tokenizer.pad_token is None:
	self.tokenizer.add_special_tokens({'pad_token': '[PAD]'})
	self.model.resize_token_embeddings(len(self.tokenizer))
	self.model.to(device)
	self.reward_head = nn.Linear(config.n_embd, 1)
	self.reward_head.to(device)

	def forward(self, input_ids, attention_mask=None):
	outputs = self.model.transformer(input_ids=input_ids, attention_mask=attention_mask)
	hidden_states = outputs.last_hidden_state # [batch_size, seq_length, hidden_size]
	# Take the hidden state of the last token
	last_hidden = hidden_states[:, -1, :] # [batch_size, hidden_size]
	reward = self.reward_head(last_hidden) # [batch_size, 1]
	return reward.squeeze(-1) # [batch_size]


	# Proximal Policy Optimization (PPO) Trainer
	class PPOTrainer:
	def __init__(self, policy_model, reward_model, tokenizer, kl_coef=0.2):
	self.policy_model = policy_model
	self.reward_model = reward_model
	self.tokenizer = tokenizer
	self.optimizer = AdamW(self.policy_model.model.parameters(), lr=1e-5)
	self.kl_coef = kl_coef

	def compute_advantages(self, rewards, values):
	# Compute advantages (simplified)
	advantages = rewards - values
	return advantages

	def ppo_step(self, batch):
	# Unpack batch
	input_ids = batch['input_ids'].to(device)
	attention_mask = batch['attention_mask'].to(device)
	old_log_probs = batch['old_log_probs'].to(device)
	rewards = batch['rewards'].to(device)

	# Get current policy outputs
	outputs = self.policy_model.model(input_ids=input_ids, attention_mask=attention_mask)
	logits = outputs.logits # [batch_size, seq_length, vocab_size]
	# Compute log probabilities
	log_probs = nn.functional.log_softmax(logits, dim=-1) # [batch_size, seq_length, vocab_size]
	# Gather log probs for the selected tokens
	selected_log_probs = log_probs.gather(2, input_ids.unsqueeze(-1)).squeeze(-1) # [batch_size, seq_length]

	# Compute policy ratio
	ratios = torch.exp(selected_log_probs - old_log_probs) # [batch_size, seq_length]

	# Compute advantages
	values = torch.zeros_like(rewards).to(device) # Placeholder for value estimates
	advantages = self.compute_advantages(rewards, values) # [batch_size]
	advantages = advantages.unsqueeze(1) # [batch_size, 1]

	# Compute PPO loss
	surr1 = ratios * advantages # [batch_size, seq_length]
	surr2 = torch.clamp(ratios, 0.8, 1.2) * advantages # [batch_size, seq_length]
	policy_loss = -torch.min(surr1, surr2).mean()

	# Compute KL divergence for regularization
	kl_div = torch.nn.functional.kl_div(old_log_probs, selected_log_probs, log_target=True, reduction='batchmean')

	# Total loss
	loss = policy_loss + self.kl_coef * kl_div

	# Backpropagation
	self.optimizer.zero_grad()
	loss.backward()
	self.optimizer.step()

	return loss.item(), policy_loss.item(), kl_div.item()


	# Monte Carlo Tree Search Node
	class MCTSNode:
	def __init__(self, state, parent=None, prior_prob=1.0):
	self.state = state # Sequence of tokens (input_ids)
	self.parent = parent
	self.children = {}
	self.visits = 0
	self.value = 0.0
	self.prior_prob = prior_prob

	def is_leaf(self):
	return len(self.children) == 0


	# Monte Carlo Tree Search
	class MCTS:
	def __init__(self, model, tokenizer, reward_model, c_puct=1.0, n_simulations=50, max_depth=20):
	self.model = model
	self.tokenizer = tokenizer
	self.reward_model = reward_model # Add reward_model
	self.c_puct = c_puct
	self.n_simulations = n_simulations
	self.max_depth = max_depth

	def search(self, state):
	root = MCTSNode(state)
	for _ in range(self.n_simulations):
	node = root
	# Selection
	while not node.is_leaf():
	node = self.select_child(node)
	# Expansion
	if len(node.state) < self.max_depth:
	self.expand_node(node)
	# Simulation
	reward = self.simulate(node.state)
	# Backpropagation
	self.backpropagate(node, reward)
	# Choose the best action
	if root.children:
	best_child = max(root.children.values(), key=lambda n: n.visits)
	return best_child.state
	else:
	# If no children, return the root state
	return root.state

	def select_child(self, node):
	total_visits = sum(child.visits for child in node.children.values())
	best_score = -float('inf')
	best_child = None
	for child in node.children.values():
	q_value = child.value / (child.visits + 1e-4)
	u_value = self.c_puct * child.prior_prob * math.sqrt(total_visits + 1) / (1 + child.visits)
	score = q_value + u_value
	if score > best_score:
	best_score = score
	best_child = child
	return best_child

	def expand_node(self, node):
	input_ids = torch.tensor(node.state).unsqueeze(0).to(device)
	outputs = self.model.model(input_ids=input_ids)
	logits = outputs.logits[:, -1, :] # [1, vocab_size]
	probs = nn.functional.softmax(logits, dim=-1).squeeze() # [vocab_size]
	top_k_probs, top_k_indices = torch.topk(probs, k=10)
	for idx, prob in zip(top_k_indices, top_k_probs):
	token_id = idx.item()
	prior_prob = prob.item()
	child_state = node.state + [token_id]
	node.children[token_id] = MCTSNode(child_state, parent=node, prior_prob=prior_prob)

	def simulate(self, state):
	# Simulate to the end using the model
	input_ids = torch.tensor(state).unsqueeze(0).to(device)
	max_length = len(state) + 20
	outputs = self.model.model.generate(
	input_ids=input_ids,
	max_length=max_length,
	do_sample=True,
	temperature=0.7,
	top_p=0.9,
	eos_token_id=self.tokenizer.eos_token_id,
	pad_token_id=self.tokenizer.pad_token_id,
	)
	generated_ids = outputs[0].tolist()
	generated_text = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
	# Calculate reward using the reward model
	reward = self.evaluate(generated_text)
	return reward

	def backpropagate(self, node, reward):
	while node is not None:
	node.visits += 1
	node.value += reward
	node = node.parent

	def evaluate(self, text):
	# Use the reward model to evaluate the text
	input_ids = self.tokenizer.encode(text, return_tensors='pt').to(device)
	attention_mask = (input_ids != self.tokenizer.pad_token_id).long().to(device)
	with torch.no_grad():
	reward = self.reward_model(input_ids=input_ids, attention_mask=attention_mask)
	return reward.item()


	# =======================
	# Self-Play Environment
	# =======================
	class SelfPlayEnvironment:
	def __init__(self, agent):
	self.agent = agent

	def step(self, prompt):
	# Agent interacts with itself
	response = self.agent.generate_chain_of_thought(prompt, max_length=100)[0]
	# The agent then takes its own response as the next prompt
	next_prompt = response
	return next_prompt, response

	def play(self, initial_prompt, max_turns=5):
	prompt = initial_prompt
	for _ in range(max_turns):
	prompt, response = self.step(prompt)
	return response


	# =======================
	# Visualization Functions
	# =======================
	def plot_capabilities(capabilities, epoch, writer):
	import matplotlib.pyplot as plt

	labels = list(capabilities.keys())
	values = list(capabilities.values())

	angles = np.linspace(0, 2 * np.pi, len(labels), endpoint=False).tolist()
	values += values[:1]
	angles += angles[:1]

	fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))
	ax.plot(angles, values, 'o-', linewidth=2)
	ax.fill(angles, values, alpha=0.25)
	ax.set_thetagrids(np.degrees(angles[:-1]), labels)
	ax.set_ylim(0, 1)
	ax.set_title(f"LLM Capabilities at Step {epoch}")
	ax.grid(True)

	# Save the plot to TensorBoard
	writer.add_figure('Capabilities', fig, epoch)
	plt.close(fig)


	# =======================
	# Training Functions
	# =======================

	def train_reward_model(reward_model, tokenizer, data_samples, args):
	logging.info("Starting training of the Reward Model.")
	reward_model.train()
	optimizer = AdamW(list(reward_model.model.parameters()) + list(reward_model.reward_head.parameters()), lr=1e-5)

	# Create a dataset for the reward model
	class RewardDataset(Dataset):
	def __init__(self, tokenizer, data, max_length):
	self.tokenizer = tokenizer
	self.data = data
	self.max_length = max_length

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

	def __getitem__(self, idx):
	input_text = self.data[idx]['input_text']
	reward = self.data[idx]['reward']
	input_ids = self.tokenizer.encode(input_text, truncation=True, max_length=self.max_length)
	return torch.tensor(input_ids, dtype=torch.long), torch.tensor(reward, dtype=torch.float)

	def reward_collate_fn(batch):
	input_ids, rewards = zip(*batch)
	input_ids = torch.nn.utils.rnn.pad_sequence(input_ids, batch_first=True, padding_value=tokenizer.pad_token_id)
	attention_mask = (input_ids != tokenizer.pad_token_id).long()
	rewards = torch.stack(rewards)
	return {'input_ids': input_ids, 'attention_mask': attention_mask, 'rewards': rewards}

	dataset = RewardDataset(tokenizer, data_samples, max_length=args.max_length)
	data_loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True, collate_fn=reward_collate_fn)

	for epoch in range(args.reward_model_epochs):
	logging.info(f"Reward Model Epoch {epoch+1}/{args.reward_model_epochs}")
	total_loss = 0
	for batch in data_loader:
	input_ids = batch['input_ids'].to(device)
	attention_mask = batch['attention_mask'].to(device)
	rewards = batch['rewards'].to(device)

	# Forward pass
	predicted_rewards = reward_model(input_ids=input_ids, attention_mask=attention_mask)

	# Loss computation
	loss = nn.functional.mse_loss(predicted_rewards, rewards)

	# Backpropagation
	optimizer.zero_grad()
	loss.backward()
	optimizer.step()

	total_loss += loss.item()

	avg_loss = total_loss / len(data_loader)
	logging.info(f"Average Reward Model Loss: {avg_loss:.4f}")

	# Save the trained reward model
	torch.save(reward_model.state_dict(), 'reward_model.pth')
	logging.info("Reward Model training completed and saved.")


	def train_open1(args):
	setup_logging()
	logging.info("Starting training of Open1 model.")

	# Initialize models and tokenizer
	policy_model = Open1Model(model_name='gpt2')
	tokenizer = policy_model.tokenizer
	reward_model = RewardModel(model_name='gpt2', tokenizer=tokenizer)
	# Resize embeddings after adding pad_token
	if tokenizer.pad_token is not None:
	policy_model.model.resize_token_embeddings(len(tokenizer))
	reward_model.model.resize_token_embeddings(len(tokenizer))

	# Initialize PPO trainer
	ppo_trainer = PPOTrainer(policy_model, reward_model, tokenizer, kl_coef=args.kl_coef)

	# Initialize TensorBoard writer
	writer = SummaryWriter(log_dir=args.log_dir)

	# Load real dataset from Hugging Face
	dataset_name = 'daily_dialog' # You can choose another dataset
	raw_dataset = load_dataset(dataset_name, split='train')

	# Prepare data samples
	data_samples = []
	for entry in raw_dataset:
	# We will use the conversation as prompt and response
	if 'dialog' in entry:
	dialog = entry['dialog']
	for i in range(len(dialog) - 1):
	prompt = dialog[i]
	response = dialog[i + 1]
	data_samples.append({'prompt': prompt, 'response': response})

	# Limit dataset size for demonstration purposes
	data_samples = data_samples[:1000]

	# Prepare data for reward model training
	reward_data_samples = []
	for sample in data_samples:
	input_text = sample['prompt'] + sample['response']
	reward = len(sample['response'].split()) / 50.0 # Simple heuristic
	reward_data_samples.append({'input_text': input_text, 'reward': reward})

	# Train the reward model first
	train_reward_model(reward_model, tokenizer, reward_data_samples, args)
	reward_model.eval() # Set reward model to evaluation mode

	# Create dataset and data loader for the policy model
	dataset = CustomDataset(tokenizer, data_samples, max_length=args.max_length)
	data_loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True, collate_fn=lambda x: collate_fn(x, tokenizer))

	# Training loop
	for epoch in range(args.epochs):
	logging.info(f"Epoch {epoch+1}/{args.epochs}")
	for batch_idx, batch in enumerate(data_loader):
	input_ids = batch['input_ids'].to(device)
	attention_mask = batch['attention_mask'].to(device)

	# Generate initial response with chain-of-thought
	generated_texts = []
	for input_id in input_ids:
	prompt = tokenizer.decode(input_id, skip_special_tokens=True)
	if args.use_mcts:
	# Use MCTS for trajectory exploration
	mcts = MCTS(policy_model, tokenizer, reward_model, n_simulations=args.n_simulations, max_depth=args.max_length)
	initial_state = input_id.tolist()
	best_state = mcts.search(initial_state)
	generated_text = tokenizer.decode(best_state, skip_special_tokens=True)
	else:
	# Regular generation
	generated_text = policy_model.generate_chain_of_thought(prompt, max_length=args.max_length)[0]
	generated_texts.append(generated_text)

	# Prepare inputs for reward model
	generated_ids = [tokenizer.encode(text, return_tensors='pt').squeeze(0) for text in generated_texts]
	input_ids_gen = torch.nn.utils.rnn.pad_sequence(generated_ids, batch_first=True, padding_value=tokenizer.pad_token_id).to(device)
	attention_mask_gen = (input_ids_gen != tokenizer.pad_token_id).long().to(device)

	# Get old log probabilities
	with torch.no_grad():
	outputs = policy_model.model(input_ids=input_ids_gen, attention_mask=attention_mask_gen)
	logits = outputs.logits
	log_probs = nn.functional.log_softmax(logits, dim=-1)
	old_log_probs = log_probs.gather(2, input_ids_gen.unsqueeze(-1)).squeeze(-1)

	# Get rewards from reward model
	with torch.no_grad():
	rewards = reward_model(input_ids=input_ids_gen, attention_mask=attention_mask_gen)

	# Prepare batch
	batch_data = {
	'input_ids': input_ids_gen,
	'attention_mask': attention_mask_gen,
	'old_log_probs': old_log_probs,
	'rewards': rewards,
	}

	# PPO optimization step
	loss, policy_loss, kl_div = ppo_trainer.ppo_step(batch_data)

	# Logging
	logging.info(f"Batch {batch_idx+1}/{len(data_loader)}, Loss: {loss:.4f}, Policy Loss: {policy_loss:.4f}, KL Divergence: {kl_div:.4f}")
	writer.add_scalar('Loss/Total', loss, epoch * len(data_loader) + batch_idx)
	writer.add_scalar('Loss/Policy', policy_loss, epoch * len(data_loader) + batch_idx)
	writer.add_scalar('Loss/KL_Divergence', kl_div, epoch * len(data_loader) + batch_idx)

	# Visualize LLM capabilities
	if args.visualize:
	capabilities = {
	'Reasoning': random.uniform(0, 1),
	'Creativity': random.uniform(0, 1),
	'Memory': random.uniform(0, 1),
	'Planning': random.uniform(0, 1),
	'Problem Solving': random.uniform(0, 1),
	'Social Intelligence': random.uniform(0, 1),
	}
	plot_capabilities(capabilities, epoch * len(data_loader) + batch_idx, writer)

	# (Optional) Self-Play Environment
	if args.multi_agent:
	env = SelfPlayEnvironment(policy_model)
	initial_prompt = "Hello, how are you?"
	final_response = env.play(initial_prompt)
	# Use the final response to further train the model if desired
	# For example, append to training data or use as additional training examples

	# Include Function Calling Practice
	if args.progressive_training:
	function_call_prompt = "def add(a, b): return a + b\nadd("
	generated_code = policy_model.generate_chain_of_thought(function_call_prompt, max_length=args.max_length)[0]
	# Optionally, evaluate and train the model on code generation tasks
	# For example, compare generated_code to expected code and adjust rewards accordingly

	# Save the trained policy model after each epoch
	policy_model.model.save_pretrained('open1_policy_model')
	logging.info(f"Epoch {epoch+1} completed and model saved.")

	writer.close()
	logging.info("Training completed and model saved.")


	# =======================
	# Main Function
	# =======================
	if __name__ == "__main__":
	args = parse_args()
	setup_logging()
	train_open1(args)