Image-Text-to-Text
LiteRT-LM
LiteRT
English
decision-model
vision
structured-output
hybrid
gated-deltanet
Instructions to use litert-community/decider-2b-vision-LiteRT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use litert-community/decider-2b-vision-LiteRT with LiteRT-LM:
# LiteRT-LM runs on various platforms (Android, iOS, Windows, Linux, macOS, IoT, Web/WASM) # and supports many APIs (C++, Python, Kotlin, Swift, JavaScript, Flutter). # For platform-specific integration guides, please refer to the official developer website: # https://ai.google.dev/edge/litert-lm # To try LiteRT-LM, the easiest way is to use our CLI tool. # 1. Install the LiteRT-LM CLI tool: pip install -U litert-lm # 2. Download and run this model locally: # See: https://ai.google.dev/edge/litert-lm/cli # A single .litertlm file in the repo is picked automatically; otherwise the CLI asks which one to run # (or pass its name right after the repo id). litert-lm run \ --from-huggingface-repo=litert-community/decider-2b-vision-LiteRT \ --prompt="Write me a poem"
- LiteRT
How to use litert-community/decider-2b-vision-LiteRT with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 7,537 Bytes
6c024e9 | 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 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 | """Read the sections of a .litertlm bundle without the LiteRT-LM package, and keep them in a local cache.
The file layout is the one LiteRT-LM reads (schema/core/litertlm_header_schema.fbs, litertlm_read.cc):
bytes 0..7 b'LITERTLM'
bytes 8..19 major, minor, patch version (uint32 little endian); major must be 1
bytes 20..23 padding
bytes 24..31 header end offset (uint64)
bytes 32..end a FlatBuffer LiteRTLMMetaData: section_metadata.objects[i] = (items, begin_offset, end_offset, data_type)
The TFLite sections are copied out byte for byte; the HF tokenizer section (8-byte uncompressed size + zlib stream)
is decompressed to tokenizer.json. The cache folder is named by the bundle's SHA-256 and is written once.
"""
import hashlib
import json
import os
import struct
import zlib
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
DEFAULT_CACHE = ROOT / '.cache/readout'
DATA_TYPES = {0: 'NONE', 1: 'GenericBinaryData', 2: 'Deprecated', 3: 'TFLiteModel', 4: 'SP_Tokenizer',
5: 'LlmMetadataProto', 6: 'HF_Tokenizer_Zlib', 7: 'TFLiteWeights', 8: 'EmbeddingMetadataProto',
9: 'ExecutorMetadataProto'}
STRING_VALUE = 9 # VData union index of StringValue
def sha256(path):
with open(path, 'rb') as stream:
return hashlib.file_digest(stream, 'sha256').hexdigest()
class _Table:
"""Minimal FlatBuffer table reader (little endian, offsets relative to the buffer start)."""
def __init__(self, buf, pos):
self.buf, self.pos = buf, pos
vtable = pos - struct.unpack_from('<i', buf, pos)[0]
self.vt_len = struct.unpack_from('<H', buf, vtable)[0]
self.vtable = vtable
def _field(self, index):
entry = 4 + 2 * index
if entry >= self.vt_len:
return 0
return struct.unpack_from('<H', self.buf, self.vtable + entry)[0]
def scalar(self, index, fmt, default=0):
off = self._field(index)
return default if off == 0 else struct.unpack_from('<' + fmt, self.buf, self.pos + off)[0]
def _indirect(self, index):
off = self._field(index)
if off == 0:
return None
at = self.pos + off
return at + struct.unpack_from('<I', self.buf, at)[0]
def table(self, index):
at = self._indirect(index)
return None if at is None else _Table(self.buf, at)
def string(self, index):
at = self._indirect(index)
if at is None:
return None
n = struct.unpack_from('<I', self.buf, at)[0]
return bytes(self.buf[at + 4:at + 4 + n]).decode('utf-8')
def tables(self, index):
at = self._indirect(index)
if at is None:
return []
n = struct.unpack_from('<I', self.buf, at)[0]
out = []
for i in range(n):
el = at + 4 + 4 * i
out.append(_Table(self.buf, el + struct.unpack_from('<I', self.buf, el)[0]))
return out
def _string_items(obj):
items = {}
for kv in obj.tables(0): # SectionObject.items: [KeyValuePair]
key = kv.string(0)
if kv.scalar(1, 'B') == STRING_VALUE: # KeyValuePair.value_type
items[key] = kv.table(2).string(0) # StringValue.value
return items
def read_sections(bundle):
"""[(index, data_type, begin, end, string items)] from the bundle header."""
with open(bundle, 'rb') as f:
head = f.read(32)
if head[:8] != b'LITERTLM':
raise ValueError(f'{bundle}: not a .litertlm file')
major, minor, patch = struct.unpack_from('<III', head, 8)
if major != 1:
raise ValueError(f'{bundle}: unsupported .litertlm major version {major}')
header_end = struct.unpack_from('<Q', head, 24)[0]
buf = f.read(header_end - 32)
root = _Table(buf, struct.unpack_from('<I', buf, 0)[0]) # LiteRTLMMetaData
objects = root.table(1).tables(0) # section_metadata.objects
return (major, minor, patch), [
(i, DATA_TYPES.get(o.scalar(3, 'B'), 'UNKNOWN'), o.scalar(1, 'Q'), o.scalar(2, 'Q'), _string_items(o))
for i, o in enumerate(objects)]
def _copy_range(src, dst, begin, end, chunk=64 << 20):
with open(src, 'rb') as f, open(dst, 'wb') as g:
f.seek(begin)
left = end - begin
while left:
data = f.read(min(chunk, left))
if not data:
raise IOError(f'{src}: truncated section')
g.write(data)
left -= len(data)
def read_tokenizer_json(bundle):
"""The bundle's HF tokenizer.json as text, read straight from its section (no cache folder)."""
_, sections = read_sections(bundle)
(_, _, begin, end, _), = [s for s in sections if s[1] == 'HF_Tokenizer_Zlib']
with open(bundle, 'rb') as f:
f.seek(begin)
raw = f.read(end - begin)
data = zlib.decompress(raw[8:])
assert len(data) == struct.unpack_from('<Q', raw, 0)[0]
return data.decode('utf-8')
def unpack_bundle(bundle, cache_dir=None):
"""Extract once; returns (folder, {'embedder'|'prefill_decode'|'vision_encoder'|'vision_adapter': path,
'tokenizer': path}, record)."""
bundle = Path(bundle).resolve()
digest = sha256(bundle)
folder = Path(cache_dir or os.environ.get('DECIDER_LITERT_CACHE') or DEFAULT_CACHE) / digest
marker = folder / 'complete.json'
if not marker.exists():
if folder.exists() and any(folder.iterdir()):
raise RuntimeError(f'Incomplete bundle cache, remove it and retry: {folder}')
folder.mkdir(parents=True, exist_ok=True)
version, sections = read_sections(bundle)
files = {}
for i, kind, begin, end, items in sections:
if kind == 'TFLiteModel':
name = items.get('model_type', f'section{i}').removeprefix('tf_lite_')
path = folder / f'{name}.tflite'
_copy_range(bundle, path, begin, end)
elif kind == 'HF_Tokenizer_Zlib':
name, path = 'tokenizer', folder / 'tokenizer.json'
with open(bundle, 'rb') as f:
f.seek(begin)
raw = f.read(end - begin)
size = struct.unpack_from('<Q', raw, 0)[0]
data = zlib.decompress(raw[8:])
assert len(data) == size, (len(data), size)
path.write_bytes(data)
else:
continue
files[name] = dict(file=path.name, section=i, data_type=kind, begin=begin, end=end, bytes=path.stat().st_size,
sha256=sha256(path))
record = dict(bundle=bundle.name, bundle_bytes=bundle.stat().st_size, bundle_sha256=digest,
litertlm_version='.'.join(map(str, version)), files=files)
marker.write_text(json.dumps(record, indent=1) + '\n')
record = json.loads(marker.read_text())
paths = {k: folder / v['file'] for k, v in record['files'].items()}
missing = {'embedder', 'prefill_decode', 'vision_encoder', 'vision_adapter', 'tokenizer'} - set(paths)
if missing:
raise ValueError(f'{bundle.name}: sections missing: {sorted(missing)}')
return folder, paths, record
if __name__ == '__main__':
import sys
version, sections = read_sections(sys.argv[1])
print('litertlm', '.'.join(map(str, version)))
for i, kind, begin, end, items in sections:
print(i, kind, begin, end, end - begin, items)
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