Lumo Example¶
Introducing Lumo-70B-Instruct - the largest and most advanced AI model ever created for the Solana ecosystem. Built on Meta's groundbreaking LLaMa 3.3 70B Instruct foundation, this revolutionary model represents a quantum leap in blockchain-specific artificial intelligence. With an unprecedented 70 billion parameters and trained on the most comprehensive Solana documentation dataset ever assembled, Lumo-70B-Instruct sets a new standard for developer assistance in the blockchain space.
from swarms import Agent
from transformers import LlamaForCausalLM, AutoTokenizer
import torch
from transformers import BitsAndBytesConfig
class Lumo:
"""
A class for generating text using the Lumo model with 4-bit quantization.
"""
def __init__(self):
"""
Initializes the Lumo model with 4-bit quantization and a tokenizer.
"""
# Configure 4-bit quantization
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16,
llm_int8_enable_fp32_cpu_offload=True
)
self.model = LlamaForCausalLM.from_pretrained(
"lumolabs-ai/Lumo-70B-Instruct",
device_map="auto",
quantization_config=bnb_config,
use_cache=False,
attn_implementation="sdpa"
)
self.tokenizer = AutoTokenizer.from_pretrained("lumolabs-ai/Lumo-70B-Instruct")
def run(self, task: str) -> str:
"""
Generates text based on the given prompt using the Lumo model.
Args:
prompt (str): The input prompt for the model.
Returns:
str: The generated text.
"""
inputs = self.tokenizer(task, return_tensors="pt").to(self.model.device)
outputs = self.model.generate(**inputs, max_new_tokens=100)
return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
Agent(
agent_name="Solana-Analysis-Agent",
llm=Lumo(),
max_loops="auto",
interactive=True,
streaming_on=True,
).run("How do i create a smart contract in solana?")