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60 lines
1.6 KiB
60 lines
1.6 KiB
import torch
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from transformers import (
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AutoTokenizer,
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BitsAndBytesConfig,
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LlamaForCausalLM,
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)
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from swarms import Agent
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class Lumo:
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"""
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A class for generating text using the Lumo model with 4-bit quantization.
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"""
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def __init__(self):
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"""
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Initializes the Lumo model with 4-bit quantization and a tokenizer.
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"""
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# Configure 4-bit quantization
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.float16,
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llm_int8_enable_fp32_cpu_offload=True
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)
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self.model = LlamaForCausalLM.from_pretrained(
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"lumolabs-ai/Lumo-70B-Instruct",
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device_map="auto",
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quantization_config=bnb_config,
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use_cache=False,
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attn_implementation="sdpa"
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)
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self.tokenizer = AutoTokenizer.from_pretrained("lumolabs-ai/Lumo-70B-Instruct")
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def run(self, task: str) -> str:
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"""
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Generates text based on the given prompt using the Lumo model.
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Args:
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prompt (str): The input prompt for the model.
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Returns:
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str: The generated text.
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"""
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inputs = self.tokenizer(task, return_tensors="pt").to(self.model.device)
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outputs = self.model.generate(**inputs, max_new_tokens=100)
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return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
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Agent(
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agent_name="Solana-Analysis-Agent",
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model_name=Lumo(),
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max_loops="auto",
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interactive=True,
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streaming_on=True,
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).run("How do i create a smart contract in solana?")
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