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---
language:
- hi
tags:
- hindi
- text-generation
- causal-lm
- lm
- rope
license: mit
datasets:
- custom_hindi_corpus
---
# Hindi-CausalLM
A Hindi language generation model with the following specifications:
## Model Architecture
- **Type**: Causal Language Model with Transformer architecture
- **Hidden size**: 768
- **Layers**: 12
- **Attention heads**: 16
- **Key-value heads**: 4 (using grouped-query attention)
- **Position encoding**: Rotary Position Embeddings (RoPE)
- **Vocabulary size**: 16000
- **Parameters**: ~100M
- **Context window**: 512 tokens
- **Trained on**: Large corpus of Hindi text
## Training
The model was trained on a large corpus of Hindi text using a cosine learning rate schedule with warmup. Training utilized mixed-precision and distributed data parallel across multiple GPUs.
## Usage
You can use this model with the following code:
```python
import torch
import math
import os
from hindi_embeddings import SentencePieceTokenizerWrapper
from safetensors.torch import load_file
from torch import nn
from transformers import PreTrainedModel, PretrainedConfig
class ConvaiCausalLMConfig(PretrainedConfig):
model_type = "convaicausallm"
def __init__(
self,
vocab_size=16000,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=16,
num_key_value_heads=4,
intermediate_size=3072,
hidden_act="silu",
max_position_embeddings=512,
rope_theta=10000.0, # Base parameter for RoPE
**kwargs
):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.max_position_embeddings = max_position_embeddings
self.rope_theta = rope_theta
def precompute_freqs_cis(dim, end, theta=10000.0):
"""Precompute the frequency tensor for complex exponentials (cos, sin)"""
# Ensure dim is even for complex numbers
assert dim % 2 == 0, "Dimension must be even"
# Create position indices for caching
freqs = 1.0 / (theta ** (torch.arange(0, dim, 2).float() / dim))
t = torch.arange(end).float()
freqs = torch.outer(t, freqs) # [end, dim/2]
# Create complex exponentials (cos, sin pairs)
cos, sin = torch.cos(freqs), torch.sin(freqs)
return cos, sin
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None):
"""Apply rotary position embeddings to q and k tensors"""
# Extract shapes
batch, seq_len, n_heads, head_dim = q.shape
_, kv_seq_len, n_kv_heads, _ = k.shape
# Handle position IDs or use sequential positions
if position_ids is None:
# Default: Just use sequential positions
position_ids = torch.arange(seq_len, device=q.device)
position_ids = position_ids.unsqueeze(0).expand(batch, -1)
# Get the cosine and sine for the positions we're using
cos = cos[position_ids].unsqueeze(-2) # [batch, seq, 1, dim/2]
sin = sin[position_ids].unsqueeze(-2) # [batch, seq, 1, dim/2]
# q and k must be arranged in pairs for rotation
q_embed_dim = q.shape[-1]
q_half_dim = q_embed_dim // 2
# Split the embedding dimensions into pairs
q_half1, q_half2 = q[..., :q_half_dim], q[..., q_half_dim:]
k_half1, k_half2 = k[..., :q_half_dim], k[..., q_half_dim:]
# Apply rotary embeddings to each pair of dimensions
# For each pair (a, b), we compute (a*cos - b*sin, a*sin + b*cos)
q_out_half1 = q_half1 * cos - q_half2 * sin
q_out_half2 = q_half1 * sin + q_half2 * cos
k_out_half1 = k_half1 * cos - k_half2 * sin
k_out_half2 = k_half1 * sin + k_half2 * cos
# Concatenate back to original shape
q_out = torch.cat([q_out_half1, q_out_half2], dim=-1)
k_out = torch.cat([k_out_half1, k_out_half2], dim=-1)
return q_out, k_out
class GroupedQueryAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.num_kv_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // config.num_attention_heads
# For MQA/GQA support
self.num_key_value_groups = self.num_heads // self.num_kv_heads
self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.head_dim)
self.k_proj = nn.Linear(config.hidden_size, self.num_kv_heads * self.head_dim)
self.v_proj = nn.Linear(config.hidden_size, self.num_kv_heads * self.head_dim)
self.o_proj = nn.Linear(config.hidden_size, config.hidden_size)
# Precompute rotary position encoding frequencies
max_seq_len = config.max_position_embeddings
self.max_seq_len = max_seq_len
# Register frequencies as buffers
cos, sin = precompute_freqs_cis(self.head_dim, max_seq_len, config.rope_theta)
self.register_buffer("cos", cos) # [max_seq_len, dim/2]
self.register_buffer("sin", sin) # [max_seq_len, dim/2]
# Create causal mask for attention
self.register_buffer(
"causal_mask",
torch.triu(torch.ones(max_seq_len, max_seq_len) * -1e9, diagonal=1)
)
def forward(self, hidden_states, attention_mask=None):
batch_size, seq_len, _ = hidden_states.size()
# Project queries, keys, values
q = self.q_proj(hidden_states)
k = self.k_proj(hidden_states)
v = self.v_proj(hidden_states)
# Reshape for attention computation
q = q.view(batch_size, seq_len, self.num_heads, self.head_dim)
k = k.view(batch_size, seq_len, self.num_kv_heads, self.head_dim)
v = v.view(batch_size, seq_len, self.num_kv_heads, self.head_dim)
# Apply rotary position embeddings
q_rotary, k_rotary = apply_rotary_pos_emb(q, k, self.cos, self.sin)
# Reshape for attention computation
q_rotary = q_rotary.transpose(1, 2) # [batch, heads, seq, dim]
k_rotary = k_rotary.transpose(1, 2) # [batch, kv_heads, seq, dim]
v = v.transpose(1, 2) # [batch, kv_heads, seq, dim]
# Handle Multi-Query Attention / Grouped-Query Attention
if self.num_key_value_groups > 1:
# Repeat k, v for each query in the group
k_rotary = k_rotary.repeat_interleave(self.num_key_value_groups, dim=1)
v = v.repeat_interleave(self.num_key_value_groups, dim=1)
# Compute attention scores
attn_scores = torch.matmul(q_rotary, k_rotary.transpose(-1, -2)) / (self.head_dim ** 0.5)
# Apply causal mask - only attend to previous tokens
causal_mask = self.causal_mask[:seq_len, :seq_len]
attn_scores = attn_scores + causal_mask
# Apply attention mask if provided
if attention_mask is not None:
attn_scores = attn_scores + attention_mask
# Normalize the attention scores to probabilities
attn_probs = torch.softmax(attn_scores, dim=-1)
# Apply attention to values
context = torch.matmul(attn_probs, v) # [b, n_heads, seq, head_dim]
# Reshape back to [batch_size, seq_length, hidden_size]
context = context.transpose(1, 2).contiguous()
context = context.view(batch_size, seq_len, -1)
# Final projection
output = self.o_proj(context)
return output
class ConvaiCausalLM(PreTrainedModel):
config_class = ConvaiCausalLMConfig
def __init__(self, config):
super().__init__(config)
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
self.layers = nn.ModuleList([
nn.ModuleDict({
"self_attn": GroupedQueryAttention(config),
"mlp": nn.Sequential(
nn.Linear(config.hidden_size, config.intermediate_size),
nn.SiLU(),
nn.Linear(config.intermediate_size, config.hidden_size)
),
"input_layernorm": nn.LayerNorm(config.hidden_size),
"post_attention_layernorm": nn.LayerNorm(config.hidden_size)
}) for _ in range(config.num_hidden_layers)
])
self.norm = nn.LayerNorm(config.hidden_size)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights
self.apply(self._init_weights)
def _init_weights(self, module):
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
def _prepare_attention_mask(self, attention_mask, input_shape, device):
# Prepare masks for attention
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=device)
# Make broadcastable shape: [batch, 1, 1, seq_len]
extended_mask = attention_mask.unsqueeze(1).unsqueeze(2)
# Convert to additive mask (0 for valid, -10000 for masked)
extended_mask = (1.0 - extended_mask) * -10000.0
return extended_mask
def forward(self, input_ids, attention_mask=None):
batch_size, seq_len = input_ids.shape
device = input_ids.device
# Prepare attention mask
if attention_mask is not None:
attention_mask = self._prepare_attention_mask(
attention_mask, (batch_size, seq_len), device
)
# Get embeddings
hidden_states = self.embed_tokens(input_ids)
# Apply each layer
for layer in self.layers:
residual = hidden_states
# First norm and attention
hidden_states = layer["input_layernorm"](hidden_states)
hidden_states = layer["self_attn"](hidden_states, attention_mask)
hidden_states = residual + hidden_states
# Second norm and MLP
residual = hidden_states
hidden_states = layer["post_attention_layernorm"](hidden_states)
hidden_states = layer["mlp"](hidden_states)
hidden_states = residual + hidden_states
# Final norm
hidden_states = self.norm(hidden_states)
# Compute logits
logits = self.lm_head(hidden_states)
return logits
class HindiLLMGenerator:
def __init__(self, model_path, device=None):
# Set device
if device is None:
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
else:
self.device = torch.device(device)
print(f"Using device: {self.device}")
# Load tokenizer
tokenizer_path = os.path.join(model_path, "tokenizer.model")
self.tokenizer = SentencePieceTokenizerWrapper(tokenizer_path)
# Load model config
config_path = os.path.join(model_path, "config.json")
import json
with open(config_path, 'r') as f:
config_dict = json.load(f)
self.config = ConvaiCausalLMConfig(**config_dict)
# Load model - try safetensors first, fall back to PyTorch bin if needed
safetensors_path = os.path.join(model_path, "model.safetensors")
pytorch_path = os.path.join(model_path, "pytorch_model.bin")
self.model = ConvaiCausalLM(self.config)
# Check which format is available and load accordingly
if os.path.exists(safetensors_path):
print(f"Loading model from SafeTensors")
state_dict = load_file(safetensors_path, device="cpu")
self.model.load_state_dict(state_dict)
elif os.path.exists(pytorch_path):
print(f"Loading model from PyTorch bin")
self.model.load_state_dict(torch.load(pytorch_path, map_location="cpu"))
# Move model to device and set to evaluation mode
self.model.to(self.device)
self.model.eval()
def generate(self, prompt, max_length=100, temperature=0.8, top_k=50, top_p=0.9,
repetition_penalty=1.1, do_sample=True):
# Tokenize the prompt
input_ids = self.tokenizer.sp_model.EncodeAsIds(prompt)
input_tensor = torch.tensor([input_ids], dtype=torch.long).to(self.device)
# Start with the input tensor
output_sequence = input_tensor.clone()
# Generate tokens one by one
for _ in range(max_length - len(input_ids)):
with torch.no_grad():
# Get the model's output for the current sequence
outputs = self.model(output_sequence)
next_token_logits = outputs[0, -1, :]
# Apply temperature
if temperature > 0:
next_token_logits = next_token_logits / temperature
# Apply repetition penalty
if repetition_penalty > 1.0:
for token_id in output_sequence[0].tolist():
next_token_logits[token_id] /= repetition_penalty
# Filter with top-k sampling
if top_k > 0:
top_k_values, top_k_indices = torch.topk(next_token_logits, top_k)
next_token_logits = torch.full_like(next_token_logits, float('-inf'))
next_token_logits.scatter_(0, top_k_indices, top_k_values)
# Filter with top-p/nucleus sampling
if top_p < 1.0 and do_sample:
sorted_logits, sorted_indices = torch.sort(next_token_logits, descending=True)
cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
# Remove tokens with cumulative probability above the threshold
sorted_indices_to_remove = cumulative_probs > top_p
# Shift the indices to the right to keep the first token above the threshold
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')
# Sample or choose the next token
if do_sample:
probs = torch.softmax(next_token_logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1)
else:
next_token = torch.argmax(next_token_logits, dim=-1).unsqueeze(0)
# Add the next token to the sequence
output_sequence = torch.cat([output_sequence, next_token.unsqueeze(0)], dim=1)
# Check if we've generated an end token
if next_token.item() == self.tokenizer.eos_token_id:
break
# Decode the generated sequence
generated_ids = output_sequence[0].tolist()
generated_text = self.tokenizer.sp_model.DecodeIds(generated_ids)
return generated_text
# Example usage
if __name__ == "__main__":
generator = HindiLLMGenerator("path/to/model")
result = generator.generate("भारत एक विशाल देश है")
print(result)
```
## Example Prompts
Try the model with these example prompts:
```
भारत एक विशाल देश है
मुझे हिंदी में एक कहानी सुनाओ
आज का मौसम बहुत अच्छा है
हिंदी साहित्य की प्रमुख विशेषताएं
```
## Capabilities
This model can:
- Generate coherent Hindi text
- Continue text from a given prompt
- Create stories, explanations, and other content in Hindi
## Limitations
- Performance varies based on the similarity of the input to the training data
- May occasionally generate repetitive content for longer texts
- May produce grammatically incorrect Hindi in some contexts
- Has no knowledge of events beyond its training corpus
## Intended Use
This model is intended for Hindi language generation tasks, creative writing assistance, and as a foundation for fine-tuning on specific tasks.
## Ethical Considerations
Users should be aware that like all language models, this model may reproduce biases or generate problematic content in certain contexts.
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