File size: 2,122 Bytes
a18c86a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
import torch
from mamba_block import MambaBlock
from mamba_config import MambaConfig
from mamba_layer import MambaLayer
# 創建一個Mamba配置
config = MambaConfig(
hidden_size=512,
num_layers=6,
num_heads=8,
intermediate_size=2048,
max_position_embeddings=1024,
rms_norm=False,
residual_in_fp32=False,
fused_add_norm=False,
)
# 創建一個Mamba模型
class MambaModel(torch.nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layers = torch.nn.ModuleList([MambaBlock(config, MambaLayer) for _ in range(config.num_layers)])
self.norm = torch.nn.LayerNorm(config.hidden_size)
def forward(self, hidden_states: torch.Tensor):
residual = None
for layer in self.layers:
hidden_states, residual = layer(hidden_states, residual)
hidden_states = self.norm(hidden_states + residual if residual is not None else hidden_states)
return hidden_states
# 創建模型實例
mamba_model = MambaModel(config)
mamba_model.eval()
# Function to generate text from a given prompt using the Mamba model
def generate_text(prompt, model, max_length=50):
# 這裡假設你的prompt已經被轉換為嵌入向量
hidden_states = torch.randn(1, len(prompt), config.hidden_size) # 假設你的輸入序列長度是len(prompt)
with torch.no_grad():
output = model(hidden_states)
# 這裡你需要將模型輸出轉換為可讀的文本
# 這只是一個示例，實際上你可能需要一個解碼器來將輸出轉換為文本
generated_text = "這裡是生成的文本" # 這裡應該是你的實際生成的文本
return generated_text
# Function to generate text from a given prompt using the Mamba model
def generate_uncensored_text(prompt, max_length=50):
mamba_text = generate_text(prompt, mamba_model, max_length)
return mamba_text
# Example usage
prompt = "I want to generate some uncensored text."
uncensored_text = generate_uncensored_text(prompt)
print(uncensored_text)
|