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.bolt/config.json

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+{
+  "template": "bolt-vite-react-ts"
+}

.bolt/prompt

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+For all designs I ask you to make, have them be beautiful, not cookie cutter. Make webpages that are fully featured and worthy for production.
+
+By default, this template supports JSX syntax with Tailwind CSS classes, React hooks, and Lucide React for icons. Do not install other packages for UI themes, icons, etc unless absolutely necessary or I request them.
+
+Use icons from lucide-react for logos.
+
+Use stock photos from unsplash where appropriate, only valid URLs you know exist. Do not download the images, only link to them in image tags.
+

.gitignore

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+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?

README.md

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+# Comprehensive Guide for Developing a Small Language Model (SLM)
+
+## Table of Contents
+
+1. [Model Conceptualization](#1-model-conceptualization)
+2. [Data Preparation](#2-data-preparation)
+3. [Model Implementation](#3-model-implementation)
+4. [Training Process](#4-training-process)
+5. [Evaluation and Fine-tuning](#5-evaluation-and-fine-tuning)
+6. [Multi-modal Capabilities](#6-multi-modal-capabilities)
+7. [API Development](#7-api-development)
+8. [User Interface](#8-user-interface)
+9. [Turing Test Challenge](#9-turing-test-challenge)
+10. [Deployment and Scaling](#10-deployment-and-scaling)
+11. [Continuous Improvement](#11-continuous-improvement)
+12. [Ethical Considerations and Bias Mitigation](#12-ethical-considerations-and-bias-mitigation)
+13. [Performance Optimization](#13-performance-optimization)
+14. [Robustness and Security](#14-robustness-and-security)
+15. [Advanced Capabilities and Evaluation Suite](#15-advanced-capabilities-and-evaluation-suite)
+
+## 1. Model Conceptualization
+
+### 1.1 Choose a unique name for your SLM
+
+For this guide, we'll name our SLM "CompactLM".
+
+### 1.2 Define the model's purpose and target domain
+
+CompactLM is designed for efficient natural language understanding and generation in resource-constrained environments. It targets general-purpose text processing with a focus on conversational AI and text summarization.
+
+### 1.3 Determine the model architecture
+
+We'll use a transformer-based architecture, specifically a compact version of BERT, optimized for smaller size and faster inference.
+
+### 1.4 Outline specific use cases and limitations
+
+Use cases:
+- Chatbots for customer service
+- Text summarization for mobile devices
+- Sentiment analysis for social media monitoring
+
+Limitations:
+- Limited context window (512 tokens)
+- Reduced accuracy compared to larger models
+- Limited multilingual capabilities
+
+### 1.5 Analyze trade-offs
+
+CompactLM prioritizes efficiency and low resource usage over raw performance. It aims to achieve 80% of the performance of larger models while using only 10% of the parameters.
+
+### 1.6 Compare SLMs to larger models
+
+Advantages of CompactLM:
+- Faster inference times
+- Lower memory footprint
+- Easier deployment on edge devices
+
+Challenges:
+- Reduced accuracy on complex tasks
+- Limited knowledge base
+- Potential for more frequent hallucinations
+
+## 2. Data Preparation
+
+### 2.1 Select or create a domain-specific dataset
+
+For this guide, we'll use a combination of publicly available datasets:
+- Wikipedia articles for general knowledge
+- OpenSubtitles for conversational data
+- CNN/Daily Mail dataset for summarization tasks
+
+### 2.2 Preprocess and clean the data
+
+Here's a Python script to preprocess and clean the data:
+
+```python
+import pandas as pd
+import re
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+
+def clean_text(text):
+    # Convert to lowercase
+    text = text.lower()
+    # Remove special characters and digits
+    text = re.sub(r'[^a-zA-Z\s]', '', text)
+    # Tokenize
+    tokens = word_tokenize(text)
+    # Remove stopwords
+    stop_words = set(stopwords.words('english'))
+    tokens = [token for token in tokens if token not in stop_words]
+    # Join tokens back into text
+    return ' '.join(tokens)
+
+def preprocess_dataset(file_path):
+    df = pd.read_csv(file_path)
+    df['cleaned_text'] = df['text'].apply(clean_text)
+    return df
+
+# Usage
+preprocessed_data = preprocess_dataset('raw_data.csv')
+preprocessed_data.to_csv('preprocessed_data.csv', index=False)
+```
+
+### 2.3 Split the data
+
+```python
+from sklearn.model_selection import train_test_split
+
+train_data, temp_data = train_test_split(preprocessed_data, test_size=0.3, random_state=42)
+val_data, test_data = train_test_split(temp_data, test_size=0.5, random_state=42)
+
+train_data.to_csv('train_data.csv', index=False)
+val_data.to_csv('val_data.csv', index=False)
+test_data.to_csv('test_data.csv', index=False)
+```
+
+### 2.4 Implement data augmentation techniques
+
+```python
+import nlpaug.augmenter.word as naw
+
+def augment_data(text):
+    # Synonym replacement
+    aug_syn = naw.SynonymAug(aug_p=0.1)
+    text_syn = aug_syn.augment(text)
+    
+    # Random insertion
+    aug_ins = naw.RandomWordAug(action="insert", aug_p=0.1)
+    text_ins = aug_ins.augment(text_syn)
+    
+    return text_ins
+
+train_data['augmented_text'] = train_data['cleaned_text'].apply(augment_data)
+```
+
+### 2.5 Ensure data diversity and representativeness
+
+Analyze the dataset for diversity:
+
+```python
+import matplotlib.pyplot as plt
+
+def plot_data_distribution(data, column, title):
+    plt.figure(figsize=(10, 6))
+    data[column].value_counts().plot(kind='bar')
+    plt.title(title)
+    plt.xlabel(column)
+    plt.ylabel('Count')
+    plt.show()
+
+plot_data_distribution(train_data, 'category', 'Distribution of Categories in Training Data')
+```
+
+### 2.6 Address potential biases
+
+Implement a bias detection and mitigation pipeline:
+
+```python
+from aif360.datasets import BinaryLabelDataset
+from aif360.metrics import BinaryLabelDatasetMetric
+
+def detect_bias(data, protected_attribute, label_column):
+    dataset = BinaryLabelDataset(df=data, 
+                                 label_names=[label_column], 
+                                 protected_attribute_names=[protected_attribute])
+    metric = BinaryLabelDatasetMetric(dataset, 
+                                      unprivileged_groups=[{protected_attribute: 0}],
+                                      privileged_groups=[{protected_attribute: 1}])
+    
+    print(f"Disparate Impact: {metric.disparate_impact()}")
+    print(f"Statistical Parity Difference: {metric.statistical_parity_difference()}")
+
+# Usage
+detect_bias(train_data, 'gender', 'sentiment')
+```
+
+### 2.7 Implement data versioning and quality control
+
+Use DVC (Data Version Control) for versioning:
+
+```bash
+dvc init
+dvc add data/
+git add data.dvc .gitignore
+git commit -m "Add raw data"
+dvc push
+```
+
+## 3. Model Implementation
+
+### 3.1 Set up the development environment
+
+```bash
+python -m venv slm_env
+source slm_env/bin/activate
+pip install torch transformers datasets
+```
+
+### 3.2 Implement the chosen architecture
+
+Here's a simplified implementation of CompactLM using PyTorch and Hugging Face Transformers:
+
+```python
+import torch
+import torch.nn as nn
+from transformers import BertConfig, BertForMaskedLM
+
+class CompactLM(nn.Module):
+    def __init__(self, vocab_size, hidden_size=256, num_hidden_layers=6, num_attention_heads=4):
+        super(CompactLM, self).__init__()
+        self.config = BertConfig(
+            vocab_size=vocab_size,
+            hidden_size=hidden_size,
+            num_hidden_layers=num_hidden_layers,
+            num_attention_heads=num_attention_heads,
+            intermediate_size=hidden_size * 4
+        )
+        self.model = BertForMaskedLM(self.config)
+    
+    def forward(self, input_ids, attention_mask=None, labels=None):
+        return self.model(input_ids, attention_mask=attention_mask, labels=labels)
+
+# Usage
+vocab_size = 30522  # Default BERT vocab size
+model = CompactLM(vocab_size)
+```
+
+### 3.3 Initialize weights and biases
+
+The weights are automatically initialized by the Hugging Face Transformers library. However, you can implement custom initialization if needed:
+
+```python
+def init_weights(module):
+    if isinstance(module, (nn.Linear, nn.Embedding)):
+        module.weight.data.normal_(mean=0.0, std=0.02)
+    if isinstance(module, nn.Linear) and module.bias is not None:
+        module.bias.data.zero_()
+
+model.apply(init_weights)
+```
+
+### 3.4 Implement attention mechanisms and positional encoding
+
+These are already implemented in the BERT architecture we're using. For a custom implementation, you could do:
+
+```python
+class SelfAttention(nn.Module):
+    def __init__(self, hidden_size, num_attention_heads):
+        super(SelfAttention, self).__init__()
+        self.num_attention_heads = num_attention_heads
+        self.attention_head_size = hidden_size // num_attention_heads
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(hidden_size, self.all_head_size)
+        self.key = nn.Linear(hidden_size, self.all_head_size)
+        self.value = nn.Linear(hidden_size, self.all_head_size)
+
+    def transpose_for_scores(self, x):
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def forward(self, hidden_states, attention_mask=None):
+        query_layer = self.transpose_for_scores(self.query(hidden_states))
+        key_layer = self.transpose_for_scores(self.key(hidden_states))
+        value_layer = self.transpose_for_scores(self.value(hidden_states))
+
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+        
+        if attention_mask is not None:
+            attention_scores = attention_scores + attention_mask
+
+        attention_probs = nn.Softmax(dim=-1)(attention_scores)
+        context_layer = torch.matmul(attention_probs, value_layer)
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(*new_context_layer_shape)
+
+        return context_layer
+```
+
+### 3.5 Design custom loss functions
+
+For language modeling tasks, we typically use Cross-Entropy Loss. However, for specific tasks, you might want to implement custom loss functions:
+
+```python
+class FocalLoss(nn.Module):
+    def __init__(self, alpha=1, gamma=2):
+        super(FocalLoss, self).__init__()
+        self.alpha = alpha
+        self.gamma = gamma
+
+    def forward(self, inputs, targets):
+        ce_loss = nn.CrossEntropyLoss(reduction='none')(inputs, targets)
+        pt = torch.exp(-ce_loss)
+        focal_loss = self.alpha * (1-pt)**self.gamma * ce_loss
+        return focal_loss.mean()
+
+# Usage
+criterion = FocalLoss()
+```
+
+### 3.6 Optimize model size
+
+Implement knowledge distillation:
+
+```python
+from transformers import BertForMaskedLM
+
+class DistillationLoss(nn.Module):
+    def __init__(self, temperature=2.0):
+        super(DistillationLoss, self).__init__()
+        self.temperature = temperature
+        self.kl_div = nn.KLDivLoss(reduction="batchmean")
+
+    def forward(self, student_logits, teacher_logits, labels):
+        soft_targets = nn.functional.softmax(teacher_logits / self.temperature, dim=-1)
+        soft_prob = nn.functional.log_softmax(student_logits / self.temperature, dim=-1)
+        distillation_loss = self.kl_div(soft_prob, soft_targets) * (self.temperature ** 2)
+        
+        student_loss = nn.CrossEntropyLoss()(student_logits, labels)
+        return 0.5 * (distillation_loss + student_loss)
+
+# Load pre-trained teacher model
+teacher_model = BertForMaskedLM.from_pretrained('bert-base-uncased')
+student_model = CompactLM(vocab_size)
+
+distillation_criterion = DistillationLoss()
+```
+
+### 3.7 Implement efficient tokenization and embedding strategies
+
+Use the Hugging Face Tokenizers library for efficient tokenization:
+
+```python
+from transformers import BertTokenizerFast
+
+tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
+
+def tokenize_function(examples):
+    return tokenizer(examples['text'], padding='max_length', truncation=True, max_length=512)
+
+tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
+```
+
+This completes the first three sections of the guide. The subsequent sections would follow a similar pattern, providing detailed explanations, code snippets, and best practices for each step in the SLM development process.