This guide provides detailed instructions and scripts to enhance the Vishwamai model's capabilities in text generation, evaluation, MMLU, math, and reasoning. Follow the steps below to implement these enhancements in your local development environment.
To improve the text generation capabilities of the Vishwamai model, fine-tune the following parameters in the generate method of the VishwamaiForCausalLM class:
temperature: Controls the randomness of predictions by scaling the logits before applying softmax. Lower values make the model more deterministic.top_p: Probability threshold for top-p (nucleus) sampling. Only the most probable tokens with a cumulative probability above this threshold are considered.top_k: Number of top tokens to consider for top-k sampling. Only the top-k tokens are considered for generation.repetition_penalty: Penalty for repeated tokens. Higher values reduce the likelihood of repeating tokens.no_repeat_ngram_size: Size of n-grams that should not be repeated. Prevents the model from generating repetitive n-grams.
Ensure that the grammar-based token validation using the Earley parser is robust and efficient. This involves validating generated token sequences against a predefined grammar to improve the syntactic correctness and coherence of the output.
Below is an example of how to implement these enhancements in the generate method:
def generate(
self,
prompts: Union[str, Sequence[str]],
device: Any,
output_len: int = 100,
temperature: float = 0.95,
top_p: float = 1.0,
top_k: int = 100,
num_beams: int = 5,
repetition_penalty: float = 1.0,
no_repeat_ngram_size: int = 0,
) -> Union[str, Sequence[str]]:
"""
Generates responses for given prompts using Vishwamai model.
Args:
prompts (Union[str, Sequence[str]]): Input prompts for text generation.
device (Any): Device to run the model on (e.g., 'cpu' or 'cuda').
output_len (int, optional): Length of the generated output. Defaults to 100.
temperature (float, optional): Sampling temperature. Defaults to 0.95.
top_p (float, optional): Probability threshold for top-p (nucleus) sampling. Defaults to 1.0.
top_k (int, optional): Number of top tokens to consider for top-k sampling. Defaults to 100.
num_beams (int, optional): Number of beams for beam search. Defaults to 5.
repetition_penalty (float, optional): Penalty for repeated tokens. Defaults to 1.0.
no_repeat_ngram_size (int, optional): Size of n-grams that should not be repeated. Defaults to 0.
Returns:
Union[str, Sequence[str]]: Generated text responses.
"""
# Implement text generation logic here
# ...
# Grammar masking: validate sequences using Earley parser
valid_tokens = []
for token in next_token_ids:
sequence = beam_token_ids[0, :output_index.item()].tolist() + [token]
sequence_str = ' '.join([self.tokenizer.decode([t]) for t in sequence])
try:
self.earley_parser.parse(sequence_str)
valid_tokens.append(token)
except:
continue
# Return generated text
# ...To evaluate the performance of the Vishwamai model's text generation capabilities, implement the following evaluation metrics in the calculate_metrics method:
- BLEU
- ROUGE
- METEOR
- CIDEr
Below is an example of how to implement these evaluation metrics:
def calculate_metrics(self, generated_texts: List[str], reference_texts: List[str]) -> dict:
"""
Calculate evaluation metrics for generated texts.
Args:
generated_texts (List[str]): List of generated text sequences.
reference_texts (List[str]): List of reference text sequences.
Returns:
dict: Dictionary containing evaluation metrics (e.g., BLEU, ROUGE, METEOR, CIDEr).
"""
from datasets import load_metric
# Load evaluation metrics
bleu_metric = load_metric('bleu')
rouge_metric = load_metric('rouge')
meteor_metric = load_metric('meteor')
cider_metric = load_metric('cider')
# Compute BLEU score
bleu_score = bleu_metric.compute(predictions=generated_texts, references=reference_texts)
# Compute ROUGE score
rouge_score = rouge_metric.compute(predictions=generated_texts, references=reference_texts)
# Compute METEOR score
meteor_score = meteor_metric.compute(predictions=generated_texts, references=reference_texts)
# Compute CIDEr score
cider_score = cider_metric.compute(predictions=generated_texts, references=reference_texts)
# Return evaluation metrics
return {
'BLEU': bleu_score,
'ROUGE': rouge_score,
'METEOR': meteor_score,
'CIDEr': cider_score,
}To enhance the Vishwamai model's capabilities in MMLU, math, and reasoning, integrate specialized modules or layers designed for these tasks. This may involve adding new neural network components or fine-tuning the model on specific datasets.
Fine-tune the Vishwamai model on datasets specific to MMLU, math, and reasoning tasks to improve its performance in these areas. Ensure that the datasets are properly preprocessed and formatted for training.
Below is an example of how to integrate specialized modules and fine-tune the model:
# Example of integrating a specialized module for math reasoning
class MathReasoningModule(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
# Define neural network components for math reasoning
# ...
def forward(self, input_ids, attention_mask=None):
# Implement forward pass for math reasoning
# ...
return output
# Integrate the MathReasoningModule into the Vishwamai model
class VishwamaiModel(nn.Module):
def __init__(self, config: vishwamai_config.VishwamaiConfig):
super().__init__()
self.config = config
self.math_reasoning_module = MathReasoningModule(config)
# Initialize other components of the Vishwamai model
# ...
def forward(self, input_ids, attention_mask=None):
# Implement forward pass for the Vishwamai model
# ...
math_reasoning_output = self.math_reasoning_module(input_ids, attention_mask)
# Combine outputs from different modules
# ...
return final_outputBy following the steps and implementing the example code provided in this guide, you can enhance the Vishwamai model's capabilities in text generation, evaluation, MMLU, math, and reasoning. These enhancements will improve the model's performance and make it more versatile for various natural language processing tasks.