README.md

metadata

license: apache-2.0
metrics:
  - perplexity
pipeline_tag: text-generation

Train in 30B Byte. Mode size 353M. Table 2 in MambaByte

To use

import torch
from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel

import numpy as np

model=MambaLMHeadModel.from_pretrained("JunxiongWang/MambaByte_Code", device='cuda', dtype=torch.float32)

text = "import torch"
text_byte = np.frombuffer(text.encode('utf-8'), dtype=np.uint8)
input_ids = torch.from_numpy(text_byte[None, :].copy()).long().cuda()

sample = model.generate(
    input_ids=input_ids,
    max_length=2048,
    cg=True,
    return_dict_in_generate=True,
    output_scores=True,
    enable_timing=True,
    temperature=1,
    top_k=256,
    top_p=0.9,
)

print(bytes(sample.sequences[0].tolist()).decode('utf-8'))

Output

import torch
import numpy as np
import torch.nn.functional as F
from torch.autograd import Variable

from networkx.states import TransientState

def extract_data(num_epochs, epochs, is_last_epoch):

    def get_data(num_features, num_classes):
        data_features = num_features
        data_classes = num_classes
        data_labels = num_epochs

        if num_features == 0 or num_classes == 0:
            return data_features, data_classes
        if is_last_epoch:
            data_features = num_features
            data_classes = num_classes
            data_labels = num_epochs
        return data_features, data_classes

    data_features, data_classes = get_data(num_epochs, epochs, is_last_epoch)
    data_labels = num_epochs * 2
    return data_features, data_classes


class NumChannel:
    def __init__(self, x, y, dx=1, dy=1, idx=1, data_size=2, epoch=None):
        """idx is the channel index with data feature in the first epoch.
        x is the channel of the input data.
        y is the element of the input data.
        dx is the element of the data feature of the input data.
        data_size is the size of the element of the data.
        epoch is the channel of the element of the data.
        """
        self.x = x
        self.y = y
        self.dx = dx
        self.data_size = data_size
        self.epoch = epoch
        self.reference_count = 0
        self.data_features = {}
        self.data_classes = {}

        self._initialize()
        if idx is not None:
            self._start_time = time.time()

    def _initialize(self):
        """idx is the channel index with data feature in the first epoch.
        x is the channel of the input data.
        y is the element of the input data.
        dx is the element of the data feature of the input data.
        data_size is the size of the element of the data.
        epoch is the channel of the element of the data.
        """
        self.idx = idx