79 lines
2.9 KiB
Python
79 lines
2.9 KiB
Python
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
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import torch
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from . import util
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from .inference_result import InferenceResult
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class LlmEngine:
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def __init__(self, model_path: str):
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"""
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Initialize the LLM Engine with a model path.
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Args:
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model_path: Path to the model weights to be used.
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"""
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self.set_model_path(model_path)
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def set_model_path(self, model_path: str):
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"""
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Load the model from the specified path.
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Args:
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model_path: Path to the model weights to load.
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"""
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self.tokenizer = AutoTokenizer.from_pretrained(model_path)
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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return_dict=True,
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low_cpu_mem_usage=True,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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trust_remote_code=True,
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)
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if self.tokenizer.pad_token_id is None:
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self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
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if model.config.pad_token_id is None:
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model.config.pad_token_id = model.config.eos_token_id
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self.pipeline = pipeline(
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"text-generation",
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model=model,
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tokenizer=self.tokenizer,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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def infer(self, system_prompt: str, main_context: str, action_schema: str) -> InferenceResult:
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"""
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Run inference using the system prompt and main context, while validating actions against the provided XML schema.
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Args:
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system_prompt: The system prompt string
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main_context: The main context string after templating
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action_schema: XML schema to validate the generated actions
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Returns:
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InferenceResult: Tuple containing reasoning and actions that validate against the schema
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"""
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valid_elements = util.get_valid_root_elements(action_schema)
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": main_context}
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]
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prompt = self.tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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outputs = self.pipeline(prompt, max_new_tokens=120, do_sample=True)
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generated_text = outputs[0]["generated_text"]
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response = generated_text.split("<|start_header_id|>assistant<|end_header_id|>",1)[1].strip()
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result = util.split_response(response, valid_elements)
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return result
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def finetune(self, dataset_paths: list, output_dir: str):
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"""
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Fine-tune the model with new datasets and save the updated model weights.
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Args:
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dataset_paths: List of paths to datasets for fine-tuning.
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output_dir: Directory where the updated model weights will be saved.
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"""
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pass |