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callidus
/
good

Text Generation
PyTorch
English
question-answering
faq
codebasics
education
bootcamp
Model card Files
xet
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CodeBasics FAQ & Text Generation System

An intelligent AI system for CodeBasics bootcamp questions with dual capabilities:

  • Smart FAQ retrieval for accurate answers to bootcamp questions
  • Text generation for general AI/ML topics

Model Details

  • Developed by: callidus
  • Model type: Hybrid (TF-IDF FAQ + Transformer)
  • Language: English
  • License: Apache 2.0

Quick Start

Installation 

pip install torch pandas scikit-learn huggingface_hub

Complete Inference Code

Copy and paste this complete code to use the model:

# ============================================================================
# COMBINED INFERENCE: TRANSFORMER MODEL + FAQ SYSTEM
# ============================================================================

!pip install -q torch huggingface_hub pandas scikit-learn

import torch
import torch.nn as nn
import torch.nn.functional as F
import json
import math
from huggingface_hub import hf_hub_download, login
import re
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

# ============================================================================
# CONFIGURATION
# ============================================================================

HF_TOKEN = "hf_your_token_here"  # Replace with your token
REPO_ID = "callidus/good"

login(token=HF_TOKEN, add_to_git_credential=False)

# ============================================================================
# TRANSFORMER MODEL ARCHITECTURE
# ============================================================================

class MultiHeadAttention(nn.Module):
    def __init__(self, d_model, num_heads):
        super().__init__()
        assert d_model % num_heads == 0
        self.d_model = d_model
        self.num_heads = num_heads
        self.d_k = d_model // num_heads
        self.W_q = nn.Linear(d_model, d_model)
        self.W_k = nn.Linear(d_model, d_model)
        self.W_v = nn.Linear(d_model, d_model)
        self.W_o = nn.Linear(d_model, d_model)
    
    def split_heads(self, x, batch_size):
        x = x.view(batch_size, -1, self.num_heads, self.d_k)
        return x.transpose(1, 2)
    
    def forward(self, x, mask=None):
        batch_size = x.size(0)
        Q = self.split_heads(self.W_q(x), batch_size)
        K = self.split_heads(self.W_k(x), batch_size)
        V = self.split_heads(self.W_v(x), batch_size)
        scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(self.d_k)
        if mask is not None:
            scores = scores.masked_fill(mask == 0, -1e9)
        attention_weights = F.softmax(scores, dim=-1)
        attention_output = torch.matmul(attention_weights, V)
        attention_output = attention_output.transpose(1, 2).contiguous()
        attention_output = attention_output.view(batch_size, -1, self.d_model)
        return self.W_o(attention_output), attention_weights

class FeedForward(nn.Module):
    def __init__(self, d_model, d_ff, dropout=0.1):
        super().__init__()
        self.linear1 = nn.Linear(d_model, d_ff)
        self.linear2 = nn.Linear(d_ff, d_model)
        self.dropout = nn.Dropout(dropout)
    
    def forward(self, x):
        return self.linear2(self.dropout(F.relu(self.linear1(x))))

class TransformerBlock(nn.Module):
    def __init__(self, d_model, num_heads, d_ff, dropout=0.1):
        super().__init__()
        self.attention = MultiHeadAttention(d_model, num_heads)
        self.feed_forward = FeedForward(d_model, d_ff, dropout)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
    
    def forward(self, x, mask=None):
        attn_output, attn_weights = self.attention(x, mask)
        x = self.norm1(x + self.dropout1(attn_output))
        ff_output = self.feed_forward(x)
        x = self.norm2(x + self.dropout2(ff_output))
        return x, attn_weights

class PositionalEncoding(nn.Module):
    def __init__(self, d_model, max_len=5000):
        super().__init__()
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len).unsqueeze(1).float()
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * 
                            -(math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0)
        self.register_buffer('pe', pe)
    
    def forward(self, x):
        return x + self.pe[:, :x.size(1)]

class TransformerModel(nn.Module):
    def __init__(self, vocab_size, d_model=512, num_heads=8, 
                 num_layers=6, d_ff=2048, dropout=0.1, max_len=512):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, d_model)
        self.pos_encoding = PositionalEncoding(d_model, max_len)
        self.transformer_blocks = nn.ModuleList([
            TransformerBlock(d_model, num_heads, d_ff, dropout)
            for _ in range(num_layers)
        ])
        self.fc_out = nn.Linear(d_model, vocab_size)
        self.dropout = nn.Dropout(dropout)
        self.d_model = d_model
    
    def forward(self, x, mask=None):
        x = self.embedding(x) * math.sqrt(self.d_model)
        x = self.pos_encoding(x)
        x = self.dropout(x)
        for transformer_block in self.transformer_blocks:
            x, attn_weights = transformer_block(x, mask)
        logits = self.fc_out(x)
        return logits

class Tokenizer:
    def __init__(self, tokenizer_data):
        self.word2idx = tokenizer_data['word2idx']
        self.idx2word = {int(k): v for k, v in tokenizer_data['idx2word'].items()}
        self.vocab_size = tokenizer_data['vocab_size']
        self.special_tokens = tokenizer_data['special_tokens']
    
    def encode(self, text):
        words = re.findall(r'\w+', text.lower())
        return [self.word2idx.get(word, self.word2idx['<UNK>']) for word in words]
    
    def decode(self, indices):
        words = []
        for idx in indices:
            if idx in self.idx2word:
                word = self.idx2word[idx]
                if word not in ['<PAD>', '<SOS>', '<EOS>']:
                    words.append(word)
        return ' '.join(words)

class TransformerInference:
    def __init__(self, repo_id, token=None, device=None):
        self.device = device or ('cuda' if torch.cuda.is_available() else 'cpu')
        self.model = None
        self.tokenizer = None
        self.config = None
        self.token = token
        self.load_from_hub(repo_id)
    
    def load_from_hub(self, repo_id):
        config_path = hf_hub_download(repo_id=repo_id, filename="model_config.json", token=self.token)
        weights_path = hf_hub_download(repo_id=repo_id, filename="model_weights.pt", token=self.token)
        tokenizer_path = hf_hub_download(repo_id=repo_id, filename="tokenizer.json", token=self.token)
        
        with open(config_path, 'r') as f:
            self.config = json.load(f)
        
        with open(tokenizer_path, 'r') as f:
            tokenizer_data = json.load(f)
        self.tokenizer = Tokenizer(tokenizer_data)
        
        self.model = TransformerModel(
            vocab_size=self.config['vocab_size'],
            d_model=self.config['d_model'],
            num_heads=self.config['num_heads'],
            num_layers=self.config['num_layers'],
            d_ff=self.config['d_ff'],
            dropout=self.config.get('dropout', 0.1),
            max_len=self.config.get('max_len', 512)
        )
        
        state_dict = torch.load(weights_path, map_location=self.device, weights_only=True)
        self.model.load_state_dict(state_dict)
        self.model = self.model.to(self.device)
        self.model.eval()
    
    def generate(self, prompt, max_length=50, temperature=0.8, top_k=50, top_p=0.9):
        self.model.eval()
        tokens = self.tokenizer.encode(prompt)
        
        if not tokens or all(t == self.tokenizer.word2idx['<UNK>'] for t in tokens):
            tokens = [self.tokenizer.word2idx['<SOS>']]
        
        generated = tokens.copy()
        
        with torch.no_grad():
            for _ in range(max_length):
                input_tokens = generated[-64:]
                if len(input_tokens) < 64:
                    input_tokens = [self.tokenizer.word2idx['<PAD>']] * (64 - len(input_tokens)) + input_tokens
                
                input_ids = torch.tensor([input_tokens], dtype=torch.long).to(self.device)
                logits = self.model(input_ids)
                next_token_logits = logits[0, -1, :] / temperature
                
                next_token_logits[self.tokenizer.word2idx['<PAD>']] = -float('inf')
                next_token_logits[self.tokenizer.word2idx['<UNK>']] = -float('inf')
                
                if top_k > 0:
                    indices_to_remove = next_token_logits < torch.topk(next_token_logits, top_k)[0][..., -1, None]
                    next_token_logits[indices_to_remove] = -float('inf')
                
                if top_p < 1.0:
                    sorted_logits, sorted_indices = torch.sort(next_token_logits, descending=True)
                    cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
                    sorted_indices_to_remove = cumulative_probs > top_p
                    sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
                    sorted_indices_to_remove[..., 0] = 0
                    indices_to_remove = sorted_indices[sorted_indices_to_remove]
                    next_token_logits[indices_to_remove] = -float('inf')
                
                probs = F.softmax(next_token_logits, dim=-1)
                next_token = torch.multinomial(probs, num_samples=1).item()
                
                if next_token == self.tokenizer.word2idx['<EOS>']:
                    break
                
                generated.append(next_token)
        
        return self.tokenizer.decode(generated)

# ============================================================================
# FAQ SYSTEM
# ============================================================================

class CodeBasicsFAQ:
    def __init__(self, csv_path):
        encodings = ['utf-8', 'latin-1', 'iso-8859-1', 'cp1252']
        df = None
        
        for encoding in encodings:
            try:
                df = pd.read_csv(csv_path, encoding=encoding)
                break
            except:
                continue
        
        if df is None:
            raise Exception("Could not load FAQ CSV")
        
        self.df = df
        self.questions = df['prompt'].tolist()
        self.answers = df['response'].tolist()
        
        self.vectorizer = TfidfVectorizer(
            lowercase=True,
            stop_words='english',
            ngram_range=(1, 2),
            max_features=1000
        )
        
        self.question_vectors = self.vectorizer.fit_transform(self.questions)
    
    def find_best_match(self, query, threshold=0.2):
        query_vector = self.vectorizer.transform([query])
        similarities = cosine_similarity(query_vector, self.question_vectors)[0]
        
        best_idx = np.argmax(similarities)
        best_score = similarities[best_idx]
        
        if best_score >= threshold:
            return {
                'question': self.questions[best_idx],
                'answer': self.answers[best_idx],
                'confidence': best_score
            }
        return None

# ============================================================================
# LOAD BOTH SYSTEMS
# ============================================================================

print("Loading systems...")
transformer = TransformerInference(repo_id=REPO_ID, token=HF_TOKEN)
csv_path = hf_hub_download(repo_id=REPO_ID, filename="codebasics_faqs.csv", token=HF_TOKEN)
faq = CodeBasicsFAQ(csv_path)
print("Ready!")

# ============================================================================
# SMART INFERENCE FUNCTION
# ============================================================================

def smart_inference(query):
    """Automatically chooses FAQ or text generation"""
    faq_match = faq.find_best_match(query)
    
    if faq_match:
        return faq_match['answer']
    else:
        return transformer.generate(query, max_length=50, temperature=0.8)

# ============================================================================
# USAGE
# ============================================================================

# Ask questions - system automatically picks best method
result = smart_inference("Can I take this bootcamp without programming experience?")
print(result)

# Interactive mode
while True:
    user_input = input("Ask me: ").strip()
    if user_input.lower() in ['quit', 'exit']:
        break
    print(smart_inference(user_input))

Usage Examples

FAQ Questions (Returns Accurate Answers) 

result = smart_inference("Can I take this bootcamp without programming experience?")
# Returns: "Yes, this is the perfect bootcamp for anyone..."

result = smart_inference("Why should I trust Codebasics?")
# Returns: "Till now 9000+ learners have benefitted..."

General Topics (Returns Generated Text) 

result = smart_inference("machine learning algorithms")
# Returns: Generated text about ML

result = smart_inference("artificial intelligence")
# Returns: Generated text about AI

Example Questions

Bootcamp Questions (FAQ System)

  • "Can I take this bootcamp without programming experience?"
  • "Why should I trust Codebasics?"
  • "What are the prerequisites?"
  • "Do you provide job assistance?"
  • "Is there lifetime access?"
  • "Can I attend while working full time?"
  • "What is the duration of this bootcamp?"

General Topics (Text Generation)

  • "machine learning"
  • "artificial intelligence"
  • "neural networks"
  • "data science"

Files in Repository

  • codebasics_faqs.csv - FAQ database (50+ Q&A pairs)
  • model_config.json - Transformer configuration
  • model_weights.pt - Transformer weights
  • tokenizer.json - Tokenizer vocabulary
  • README.md - This documentation

Model Architecture

FAQ System

  • Method: TF-IDF + Cosine Similarity
  • Accuracy: ~90% on similar phrasings
  • Threshold: 0.2 similarity score

Transformer Model

  • Layers: 6 transformer blocks
  • Hidden size: 512
  • Attention heads: 8
  • Vocabulary: 229 tokens
  • Max length: 512 tokens

How It Works

The system intelligently routes queries:

  1. FAQ Match? → Returns accurate FAQ answer
  2. No Match? → Falls back to text generation

Users don't need to specify which system to use - it's automatic!

Limitations

  • FAQ requires questions similar to training data
  • Text generation has limited vocabulary (229 tokens)
  • Best for CodeBasics bootcamp questions
  • English language only

Citation 

@misc{codebasics-faq-2024,
  author = {callidus},
  title = {CodeBasics FAQ and Text Generation System},
  year = {2024},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/callidus/good}}
}

License

Apache 2.0

Contact

For CodeBasics courses: codebasics.io

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