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# llama_generator.py
from config.config import GenerationConfig, ModelConfig
@observe()
class LlamaGenerator(BaseGenerator):
def __init__(
self,
llama_model_name: str,
prm_model_path: str,
device: Optional[str] = None,
default_generation_config: Optional[GenerationConfig] = None,
model_config: Optional[ModelConfig] = None,
cache_size: int = 1000,
max_batch_size: int = 32,
# self.tokenizer = self.load_tokenizer(llama_model_name)
# self.tokenizer = self.load_tokenizer(llama_model_name) # Add this line to initialize the tokenizer
):
@observe()
def load_model(self, model_name: str):
# Code to load your model, e.g., Hugging Face's transformers library
from transformers import AutoModelForCausalLM
return AutoModelForCausalLM.from_pretrained(model_name)
@observe()
def load_tokenizer(self, model_name: str):
# Load the tokenizer associated with the model
from transformers import AutoTokenizer
return AutoTokenizer.from_pretrained(model_name)
self.tokenizer = load_tokenizer(llama_model_name) # Add this line to initialize the tokenizer
super().__init__(
llama_model_name,
device,
default_generation_config,
model_config,
cache_size,
max_batch_size
)
# Initialize models
self.model_manager.load_model(
"llama",
llama_model_name,
"llama",
self.model_config
)
self.model_manager.load_model(
"prm",
prm_model_path,
"gguf",
self.model_config
)
self.prompt_builder = LlamaPromptTemplate()
self._init_strategies()
def _init_strategies(self):
self.strategies = {
"default": DefaultStrategy(),
"majority_voting": MajorityVotingStrategy(),
"best_of_n": BestOfN(),
"beam_search": BeamSearch(),
"dvts": DVT(),
}
def _get_generation_kwargs(self, config: GenerationConfig) -> Dict[str, Any]:
"""Get generation kwargs based on config."""
return {
key: getattr(config, key)
for key in [
"max_new_tokens",
"temperature",
"top_p",
"top_k",
"repetition_penalty",
"length_penalty",
"do_sample"
]
if hasattr(config, key)
}
@observe()
def generate_stream (self):
return " NOt implememnted yet "
@observe()
def generate(
self,
prompt: str,
model_kwargs: Dict[str, Any],
strategy: str = "default",
**kwargs
) -> str:
"""
Generate text based on a given strategy.
Args:
prompt (str): Input prompt for text generation.
model_kwargs (Dict[str, Any]): Additional arguments for model generation.
strategy (str): The generation strategy to use (default: "default").
**kwargs: Additional arguments passed to the strategy.
Returns:
str: Generated text response.
Raises:
ValueError: If the specified strategy is not available.
"""
# Validate that the strategy exists
if strategy not in self.strategies:
raise ValueError(f"Unknown strategy: {strategy}. Available strategies are: {list(self.strategies.keys())}")
# Extract `generator` from kwargs if it exists to prevent duplication
kwargs.pop("generator", None)
# Call the selected strategy with the provided arguments
return self.strategies[strategy].generate(
generator=self, # The generator instance
prompt=prompt, # The input prompt
model_kwargs=model_kwargs, # Arguments for the model
**kwargs # Any additional strategy-specific arguments
)
@observe()
def generate_with_context(
self,
context: str,
user_input: str,
chat_history: List[Tuple[str, str]],
model_kwargs: Dict[str, Any],
max_history_turns: int = 3,
strategy: str = "default",
num_samples: int = 5,
depth: int = 3,
breadth: int = 2,
) -> str:
"""Generate a response using context and chat history.
Args:
context (str): Context for the conversation
user_input (str): Current user input
chat_history (List[Tuple[str, str]]): List of (user, assistant) message pairs
model_kwargs (dict): Additional arguments for model.generate()
max_history_turns (int): Maximum number of history turns to include
strategy (str): Generation strategy
num_samples (int): Number of samples for applicable strategies
depth (int): Depth for DVTS strategy
breadth (int): Breadth for DVTS strategy
Returns:
str: Generated response
"""
prompt = self.prompt_builder.format(
context,
user_input,
chat_history,
max_history_turns
)
return self.generate(
generator=self,
prompt=prompt,
model_kwargs=model_kwargs,
strategy=strategy,
num_samples=num_samples,
depth=depth,
breadth=breadth
)
def check_health(self) -> HealthStatus:
"""Check the health status of the generator."""
return self.health_check.check_system_resources() # TODO add model status
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