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https://github.com/deepseek-ai/DeepSeek-VL
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feat: init code
This commit is contained in:
Benjamin-eecs
commit
e24fd228ab
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||||
rev: 6.1.0
|
||||
hooks:
|
||||
- id: flake8
|
||||
additional_dependencies:
|
||||
- flake8-bugbear
|
||||
- flake8-comprehensions
|
||||
- flake8-docstrings
|
||||
- flake8-pyi
|
||||
- flake8-simplify
|
||||
exclude: |
|
||||
(?x)(
|
||||
^images/
|
||||
)
|
||||
- repo: local
|
||||
hooks:
|
||||
- id: pylint
|
||||
name: pylint
|
||||
entry: pylint
|
||||
language: system
|
||||
types: [python]
|
||||
require_serial: true
|
||||
exclude: |
|
||||
(?x)(
|
||||
^images/
|
||||
)
|
||||
629
.pylintrc
Normal file
629
.pylintrc
Normal file
@ -0,0 +1,629 @@
|
||||
[MAIN]
|
||||
|
||||
# Analyse import fallback blocks. This can be used to support both Python 2 and
|
||||
# 3 compatible code, which means that the block might have code that exists
|
||||
# only in one or another interpreter, leading to false positives when analysed.
|
||||
analyse-fallback-blocks=no
|
||||
|
||||
# Load and enable all available extensions. Use --list-extensions to see a list
|
||||
# all available extensions.
|
||||
#enable-all-extensions=
|
||||
|
||||
# In error mode, messages with a category besides ERROR or FATAL are
|
||||
# suppressed, and no reports are done by default. Error mode is compatible with
|
||||
# disabling specific errors.
|
||||
#errors-only=
|
||||
|
||||
# Always return a 0 (non-error) status code, even if lint errors are found.
|
||||
# This is primarily useful in continuous integration scripts.
|
||||
#exit-zero=
|
||||
|
||||
# A comma-separated list of package or module names from where C extensions may
|
||||
# be loaded. Extensions are loading into the active Python interpreter and may
|
||||
# run arbitrary code.
|
||||
extension-pkg-allow-list=
|
||||
|
||||
# A comma-separated list of package or module names from where C extensions may
|
||||
# be loaded. Extensions are loading into the active Python interpreter and may
|
||||
# run arbitrary code. (This is an alternative name to extension-pkg-allow-list
|
||||
# for backward compatibility.)
|
||||
extension-pkg-whitelist=
|
||||
|
||||
# Return non-zero exit code if any of these messages/categories are detected,
|
||||
# even if score is above --fail-under value. Syntax same as enable. Messages
|
||||
# specified are enabled, while categories only check already-enabled messages.
|
||||
fail-on=
|
||||
|
||||
# Specify a score threshold under which the program will exit with error.
|
||||
fail-under=10
|
||||
|
||||
# Interpret the stdin as a python script, whose filename needs to be passed as
|
||||
# the module_or_package argument.
|
||||
#from-stdin=
|
||||
|
||||
# Files or directories to be skipped. They should be base names, not paths.
|
||||
ignore=CVS,.vscode,.history
|
||||
|
||||
# Add files or directories matching the regular expressions patterns to the
|
||||
# ignore-list. The regex matches against paths and can be in Posix or Windows
|
||||
# format. Because '\' represents the directory delimiter on Windows systems, it
|
||||
# can't be used as an escape character.
|
||||
ignore-paths=^images/$
|
||||
|
||||
# Files or directories matching the regular expression patterns are skipped.
|
||||
# The regex matches against base names, not paths. The default value ignores
|
||||
# Emacs file locks
|
||||
ignore-patterns=^\.#
|
||||
|
||||
# List of module names for which member attributes should not be checked
|
||||
# (useful for modules/projects where namespaces are manipulated during runtime
|
||||
# and thus existing member attributes cannot be deduced by static analysis). It
|
||||
# supports qualified module names, as well as Unix pattern matching.
|
||||
ignored-modules=
|
||||
|
||||
# Python code to execute, usually for sys.path manipulation such as
|
||||
# pygtk.require().
|
||||
#init-hook=
|
||||
|
||||
# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the
|
||||
# number of processors available to use, and will cap the count on Windows to
|
||||
# avoid hangs.
|
||||
jobs=0
|
||||
|
||||
# Control the amount of potential inferred values when inferring a single
|
||||
# object. This can help the performance when dealing with large functions or
|
||||
# complex, nested conditions.
|
||||
limit-inference-results=100
|
||||
|
||||
# List of plugins (as comma separated values of python module names) to load,
|
||||
# usually to register additional checkers.
|
||||
load-plugins=
|
||||
|
||||
# Pickle collected data for later comparisons.
|
||||
persistent=yes
|
||||
|
||||
# Minimum Python version to use for version dependent checks. Will default to
|
||||
# the version used to run pylint.
|
||||
py-version=3.8 # the lowest version we support (sync with requires-python in pyproject.toml)
|
||||
|
||||
# Discover python modules and packages in the file system subtree.
|
||||
recursive=no
|
||||
|
||||
# When enabled, pylint would attempt to guess common misconfiguration and emit
|
||||
# user-friendly hints instead of false-positive error messages.
|
||||
suggestion-mode=yes
|
||||
|
||||
# Allow loading of arbitrary C extensions. Extensions are imported into the
|
||||
# active Python interpreter and may run arbitrary code.
|
||||
unsafe-load-any-extension=no
|
||||
|
||||
# In verbose mode, extra non-checker-related info will be displayed.
|
||||
#verbose=
|
||||
|
||||
|
||||
[BASIC]
|
||||
|
||||
# Naming style matching correct argument names.
|
||||
argument-naming-style=snake_case
|
||||
|
||||
# Regular expression matching correct argument names. Overrides argument-
|
||||
# naming-style. If left empty, argument names will be checked with the set
|
||||
# naming style.
|
||||
#argument-rgx=
|
||||
|
||||
# Naming style matching correct attribute names.
|
||||
attr-naming-style=snake_case
|
||||
|
||||
# Regular expression matching correct attribute names. Overrides attr-naming-
|
||||
# style. If left empty, attribute names will be checked with the set naming
|
||||
# style.
|
||||
#attr-rgx=
|
||||
|
||||
# Bad variable names which should always be refused, separated by a comma.
|
||||
bad-names=foo,
|
||||
bar,
|
||||
baz,
|
||||
toto,
|
||||
tutu,
|
||||
tata
|
||||
|
||||
# Bad variable names regexes, separated by a comma. If names match any regex,
|
||||
# they will always be refused
|
||||
bad-names-rgxs=
|
||||
|
||||
# Naming style matching correct class attribute names.
|
||||
class-attribute-naming-style=any
|
||||
|
||||
# Regular expression matching correct class attribute names. Overrides class-
|
||||
# attribute-naming-style. If left empty, class attribute names will be checked
|
||||
# with the set naming style.
|
||||
#class-attribute-rgx=
|
||||
|
||||
# Naming style matching correct class constant names.
|
||||
class-const-naming-style=UPPER_CASE
|
||||
|
||||
# Regular expression matching correct class constant names. Overrides class-
|
||||
# const-naming-style. If left empty, class constant names will be checked with
|
||||
# the set naming style.
|
||||
#class-const-rgx=
|
||||
|
||||
# Naming style matching correct class names.
|
||||
class-naming-style=PascalCase
|
||||
|
||||
# Regular expression matching correct class names. Overrides class-naming-
|
||||
# style. If left empty, class names will be checked with the set naming style.
|
||||
#class-rgx=
|
||||
|
||||
# Naming style matching correct constant names.
|
||||
const-naming-style=UPPER_CASE
|
||||
|
||||
# Regular expression matching correct constant names. Overrides const-naming-
|
||||
# style. If left empty, constant names will be checked with the set naming
|
||||
# style.
|
||||
#const-rgx=
|
||||
|
||||
# Minimum line length for functions/classes that require docstrings, shorter
|
||||
# ones are exempt.
|
||||
docstring-min-length=-1
|
||||
|
||||
# Naming style matching correct function names.
|
||||
function-naming-style=snake_case
|
||||
|
||||
# Regular expression matching correct function names. Overrides function-
|
||||
# naming-style. If left empty, function names will be checked with the set
|
||||
# naming style.
|
||||
#function-rgx=
|
||||
|
||||
# Good variable names which should always be accepted, separated by a comma.
|
||||
good-names=i,
|
||||
j,
|
||||
k,
|
||||
ex,
|
||||
Run,
|
||||
_,
|
||||
op,
|
||||
fn,
|
||||
f,
|
||||
g,
|
||||
p,
|
||||
u,
|
||||
t,
|
||||
lr,
|
||||
mu,
|
||||
nu,
|
||||
x,
|
||||
y
|
||||
|
||||
# Good variable names regexes, separated by a comma. If names match any regex,
|
||||
# they will always be accepted
|
||||
good-names-rgxs=
|
||||
|
||||
# Include a hint for the correct naming format with invalid-name.
|
||||
include-naming-hint=no
|
||||
|
||||
# Naming style matching correct inline iteration names.
|
||||
inlinevar-naming-style=any
|
||||
|
||||
# Regular expression matching correct inline iteration names. Overrides
|
||||
# inlinevar-naming-style. If left empty, inline iteration names will be checked
|
||||
# with the set naming style.
|
||||
#inlinevar-rgx=
|
||||
|
||||
# Naming style matching correct method names.
|
||||
method-naming-style=snake_case
|
||||
|
||||
# Regular expression matching correct method names. Overrides method-naming-
|
||||
# style. If left empty, method names will be checked with the set naming style.
|
||||
#method-rgx=
|
||||
|
||||
# Naming style matching correct module names.
|
||||
module-naming-style=snake_case
|
||||
|
||||
# Regular expression matching correct module names. Overrides module-naming-
|
||||
# style. If left empty, module names will be checked with the set naming style.
|
||||
#module-rgx=
|
||||
|
||||
# Colon-delimited sets of names that determine each other's naming style when
|
||||
# the name regexes allow several styles.
|
||||
name-group=
|
||||
|
||||
# Regular expression which should only match function or class names that do
|
||||
# not require a docstring.
|
||||
no-docstring-rgx=^_
|
||||
|
||||
# List of decorators that produce properties, such as abc.abstractproperty. Add
|
||||
# to this list to register other decorators that produce valid properties.
|
||||
# These decorators are taken in consideration only for invalid-name.
|
||||
property-classes=abc.abstractproperty
|
||||
|
||||
# Regular expression matching correct type variable names. If left empty, type
|
||||
# variable names will be checked with the set naming style.
|
||||
#typevar-rgx=
|
||||
|
||||
# Naming style matching correct variable names.
|
||||
variable-naming-style=snake_case
|
||||
|
||||
# Regular expression matching correct variable names. Overrides variable-
|
||||
# naming-style. If left empty, variable names will be checked with the set
|
||||
# naming style.
|
||||
#variable-rgx=
|
||||
|
||||
|
||||
[CLASSES]
|
||||
|
||||
# Warn about protected attribute access inside special methods
|
||||
check-protected-access-in-special-methods=no
|
||||
|
||||
# List of method names used to declare (i.e. assign) instance attributes.
|
||||
defining-attr-methods=__init__,
|
||||
__new__,
|
||||
setUp,
|
||||
__post_init__
|
||||
|
||||
# List of member names, which should be excluded from the protected access
|
||||
# warning.
|
||||
exclude-protected=_asdict,
|
||||
_fields,
|
||||
_replace,
|
||||
_source,
|
||||
_make
|
||||
|
||||
# List of valid names for the first argument in a class method.
|
||||
valid-classmethod-first-arg=cls
|
||||
|
||||
# List of valid names for the first argument in a metaclass class method.
|
||||
valid-metaclass-classmethod-first-arg=cls
|
||||
|
||||
|
||||
[DESIGN]
|
||||
|
||||
# List of regular expressions of class ancestor names to ignore when counting
|
||||
# public methods (see R0903)
|
||||
exclude-too-few-public-methods=
|
||||
|
||||
# List of qualified class names to ignore when counting class parents (see
|
||||
# R0901)
|
||||
ignored-parents=
|
||||
|
||||
# Maximum number of arguments for function / method.
|
||||
max-args=5
|
||||
|
||||
# Maximum number of attributes for a class (see R0902).
|
||||
max-attributes=7
|
||||
|
||||
# Maximum number of boolean expressions in an if statement (see R0916).
|
||||
max-bool-expr=5
|
||||
|
||||
# Maximum number of branch for function / method body.
|
||||
max-branches=12
|
||||
|
||||
# Maximum number of locals for function / method body.
|
||||
max-locals=15
|
||||
|
||||
# Maximum number of parents for a class (see R0901).
|
||||
max-parents=7
|
||||
|
||||
# Maximum number of public methods for a class (see R0904).
|
||||
max-public-methods=20
|
||||
|
||||
# Maximum number of return / yield for function / method body.
|
||||
max-returns=6
|
||||
|
||||
# Maximum number of statements in function / method body.
|
||||
max-statements=50
|
||||
|
||||
# Minimum number of public methods for a class (see R0903).
|
||||
min-public-methods=2
|
||||
|
||||
|
||||
[EXCEPTIONS]
|
||||
|
||||
# Exceptions that will emit a warning when caught.
|
||||
overgeneral-exceptions=builtins.BaseException,
|
||||
builtins.Exception
|
||||
|
||||
|
||||
[FORMAT]
|
||||
|
||||
# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
|
||||
expected-line-ending-format=
|
||||
|
||||
# Regexp for a line that is allowed to be longer than the limit.
|
||||
ignore-long-lines=^\s*(# )?<?https?://\S+>?$
|
||||
|
||||
# Number of spaces of indent required inside a hanging or continued line.
|
||||
indent-after-paren=4
|
||||
|
||||
# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1
|
||||
# tab).
|
||||
indent-string=' '
|
||||
|
||||
# Maximum number of characters on a single line.
|
||||
max-line-length=120
|
||||
|
||||
# Maximum number of lines in a module.
|
||||
max-module-lines=1000
|
||||
|
||||
# Allow the body of a class to be on the same line as the declaration if body
|
||||
# contains single statement.
|
||||
single-line-class-stmt=no
|
||||
|
||||
# Allow the body of an if to be on the same line as the test if there is no
|
||||
# else.
|
||||
single-line-if-stmt=no
|
||||
|
||||
|
||||
[IMPORTS]
|
||||
|
||||
# List of modules that can be imported at any level, not just the top level
|
||||
# one.
|
||||
allow-any-import-level=
|
||||
|
||||
# Allow wildcard imports from modules that define __all__.
|
||||
allow-wildcard-with-all=no
|
||||
|
||||
# Deprecated modules which should not be used, separated by a comma.
|
||||
deprecated-modules=
|
||||
|
||||
# Output a graph (.gv or any supported image format) of external dependencies
|
||||
# to the given file (report RP0402 must not be disabled).
|
||||
ext-import-graph=
|
||||
|
||||
# Output a graph (.gv or any supported image format) of all (i.e. internal and
|
||||
# external) dependencies to the given file (report RP0402 must not be
|
||||
# disabled).
|
||||
import-graph=
|
||||
|
||||
# Output a graph (.gv or any supported image format) of internal dependencies
|
||||
# to the given file (report RP0402 must not be disabled).
|
||||
int-import-graph=
|
||||
|
||||
# Force import order to recognize a module as part of the standard
|
||||
# compatibility libraries.
|
||||
known-standard-library=
|
||||
|
||||
# Force import order to recognize a module as part of a third party library.
|
||||
known-third-party=enchant
|
||||
|
||||
# Couples of modules and preferred modules, separated by a comma.
|
||||
preferred-modules=
|
||||
|
||||
|
||||
[LOGGING]
|
||||
|
||||
# The type of string formatting that logging methods do. `old` means using %
|
||||
# formatting, `new` is for `{}` formatting.
|
||||
logging-format-style=old
|
||||
|
||||
# Logging modules to check that the string format arguments are in logging
|
||||
# function parameter format.
|
||||
logging-modules=logging
|
||||
|
||||
|
||||
[MESSAGES CONTROL]
|
||||
|
||||
# Only show warnings with the listed confidence levels. Leave empty to show
|
||||
# all. Valid levels: HIGH, CONTROL_FLOW, INFERENCE, INFERENCE_FAILURE,
|
||||
# UNDEFINED.
|
||||
confidence=HIGH,
|
||||
CONTROL_FLOW,
|
||||
INFERENCE,
|
||||
INFERENCE_FAILURE,
|
||||
UNDEFINED
|
||||
|
||||
# Disable the message, report, category or checker with the given id(s). You
|
||||
# can either give multiple identifiers separated by comma (,) or put this
|
||||
# option multiple times (only on the command line, not in the configuration
|
||||
# file where it should appear only once). You can also use "--disable=all" to
|
||||
# disable everything first and then re-enable specific checks. For example, if
|
||||
# you want to run only the similarities checker, you can use "--disable=all
|
||||
# --enable=similarities". If you want to run only the classes checker, but have
|
||||
# no Warning level messages displayed, use "--disable=all --enable=classes
|
||||
# --disable=W".
|
||||
disable=duplicate-code,
|
||||
consider-using-from-import
|
||||
|
||||
# Enable the message, report, category or checker with the given id(s). You can
|
||||
# either give multiple identifier separated by comma (,) or put this option
|
||||
# multiple time (only on the command line, not in the configuration file where
|
||||
# it should appear only once). See also the "--disable" option for examples.
|
||||
enable=c-extension-no-member
|
||||
|
||||
|
||||
[METHOD_ARGS]
|
||||
|
||||
# List of qualified names (i.e., library.method) which require a timeout
|
||||
# parameter e.g. 'requests.api.get,requests.api.post'
|
||||
timeout-methods=requests.api.delete,requests.api.get,requests.api.head,requests.api.options,requests.api.patch,requests.api.post,requests.api.put,requests.api.request
|
||||
|
||||
|
||||
[MISCELLANEOUS]
|
||||
|
||||
# List of note tags to take in consideration, separated by a comma.
|
||||
notes=FIXME,
|
||||
XXX,
|
||||
TODO
|
||||
|
||||
# Regular expression of note tags to take in consideration.
|
||||
notes-rgx=
|
||||
|
||||
|
||||
[REFACTORING]
|
||||
|
||||
# Maximum number of nested blocks for function / method body
|
||||
max-nested-blocks=5
|
||||
|
||||
# Complete name of functions that never returns. When checking for
|
||||
# inconsistent-return-statements if a never returning function is called then
|
||||
# it will be considered as an explicit return statement and no message will be
|
||||
# printed.
|
||||
never-returning-functions=sys.exit,argparse.parse_error
|
||||
|
||||
|
||||
[REPORTS]
|
||||
|
||||
# Python expression which should return a score less than or equal to 10. You
|
||||
# have access to the variables 'fatal', 'error', 'warning', 'refactor',
|
||||
# 'convention', and 'info' which contain the number of messages in each
|
||||
# category, as well as 'statement' which is the total number of statements
|
||||
# analyzed. This score is used by the global evaluation report (RP0004).
|
||||
evaluation=max(0, 0 if fatal else 10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10))
|
||||
|
||||
# Template used to display messages. This is a python new-style format string
|
||||
# used to format the message information. See doc for all details.
|
||||
msg-template=
|
||||
|
||||
# Set the output format. Available formats are text, parseable, colorized, json
|
||||
# and msvs (visual studio). You can also give a reporter class, e.g.
|
||||
# mypackage.mymodule.MyReporterClass.
|
||||
#output-format=
|
||||
|
||||
# Tells whether to display a full report or only the messages.
|
||||
reports=no
|
||||
|
||||
# Activate the evaluation score.
|
||||
score=yes
|
||||
|
||||
|
||||
[SIMILARITIES]
|
||||
|
||||
# Comments are removed from the similarity computation
|
||||
ignore-comments=yes
|
||||
|
||||
# Docstrings are removed from the similarity computation
|
||||
ignore-docstrings=yes
|
||||
|
||||
# Imports are removed from the similarity computation
|
||||
ignore-imports=yes
|
||||
|
||||
# Signatures are removed from the similarity computation
|
||||
ignore-signatures=yes
|
||||
|
||||
# Minimum lines number of a similarity.
|
||||
min-similarity-lines=4
|
||||
|
||||
|
||||
[SPELLING]
|
||||
|
||||
# Limits count of emitted suggestions for spelling mistakes.
|
||||
max-spelling-suggestions=4
|
||||
|
||||
# Spelling dictionary name. Available dictionaries: en_AU (hunspell), en_CA
|
||||
# (hunspell), en_GB (hunspell), en_US (hunspell), en_ZA (hunspell).
|
||||
spelling-dict=
|
||||
|
||||
# List of comma separated words that should be considered directives if they
|
||||
# appear at the beginning of a comment and should not be checked.
|
||||
spelling-ignore-comment-directives=fmt: on,fmt: off,noqa:,noqa,nosec,isort:skip,mypy:
|
||||
|
||||
# List of comma separated words that should not be checked.
|
||||
spelling-ignore-words=
|
||||
|
||||
# A path to a file that contains the private dictionary; one word per line.
|
||||
spelling-private-dict-file=docs/source/spelling_wordlist.txt
|
||||
|
||||
# Tells whether to store unknown words to the private dictionary (see the
|
||||
# --spelling-private-dict-file option) instead of raising a message.
|
||||
spelling-store-unknown-words=no
|
||||
|
||||
|
||||
[STRING]
|
||||
|
||||
# This flag controls whether inconsistent-quotes generates a warning when the
|
||||
# character used as a quote delimiter is used inconsistently within a module.
|
||||
check-quote-consistency=no
|
||||
|
||||
# This flag controls whether the implicit-str-concat should generate a warning
|
||||
# on implicit string concatenation in sequences defined over several lines.
|
||||
check-str-concat-over-line-jumps=no
|
||||
|
||||
|
||||
[TYPECHECK]
|
||||
|
||||
# List of decorators that produce context managers, such as
|
||||
# contextlib.contextmanager. Add to this list to register other decorators that
|
||||
# produce valid context managers.
|
||||
contextmanager-decorators=contextlib.contextmanager
|
||||
|
||||
# List of members which are set dynamically and missed by pylint inference
|
||||
# system, and so shouldn't trigger E1101 when accessed. Python regular
|
||||
# expressions are accepted.
|
||||
generated-members=numpy.*,
|
||||
torch.*
|
||||
|
||||
# Tells whether missing members accessed in mixin class should be ignored. A
|
||||
# class is considered mixin if its name matches the mixin-class-rgx option.
|
||||
ignore-mixin-members=yes
|
||||
|
||||
# Tells whether to warn about missing members when the owner of the attribute
|
||||
# is inferred to be None.
|
||||
ignore-none=yes
|
||||
|
||||
# This flag controls whether pylint should warn about no-member and similar
|
||||
# checks whenever an opaque object is returned when inferring. The inference
|
||||
# can return multiple potential results while evaluating a Python object, but
|
||||
# some branches might not be evaluated, which results in partial inference. In
|
||||
# that case, it might be useful to still emit no-member and other checks for
|
||||
# the rest of the inferred objects.
|
||||
ignore-on-opaque-inference=yes
|
||||
|
||||
# List of symbolic message names to ignore for Mixin members.
|
||||
ignored-checks-for-mixins=no-member,
|
||||
not-async-context-manager,
|
||||
not-context-manager,
|
||||
attribute-defined-outside-init
|
||||
|
||||
# List of class names for which member attributes should not be checked (useful
|
||||
# for classes with dynamically set attributes). This supports the use of
|
||||
# qualified names.
|
||||
ignored-classes=optparse.Values,thread._local,_thread._local,argparse.Namespace
|
||||
|
||||
# Show a hint with possible names when a member name was not found. The aspect
|
||||
# of finding the hint is based on edit distance.
|
||||
missing-member-hint=yes
|
||||
|
||||
# The minimum edit distance a name should have in order to be considered a
|
||||
# similar match for a missing member name.
|
||||
missing-member-hint-distance=1
|
||||
|
||||
# The total number of similar names that should be taken in consideration when
|
||||
# showing a hint for a missing member.
|
||||
missing-member-max-choices=1
|
||||
|
||||
# Regex pattern to define which classes are considered mixins.
|
||||
mixin-class-rgx=.*[Mm]ixin
|
||||
|
||||
# List of decorators that change the signature of a decorated function.
|
||||
signature-mutators=
|
||||
|
||||
|
||||
[VARIABLES]
|
||||
|
||||
# List of additional names supposed to be defined in builtins. Remember that
|
||||
# you should avoid defining new builtins when possible.
|
||||
additional-builtins=
|
||||
|
||||
# Tells whether unused global variables should be treated as a violation.
|
||||
allow-global-unused-variables=yes
|
||||
|
||||
# List of names allowed to shadow builtins
|
||||
allowed-redefined-builtins=
|
||||
|
||||
# List of strings which can identify a callback function by name. A callback
|
||||
# name must start or end with one of those strings.
|
||||
callbacks=cb_,
|
||||
_cb
|
||||
|
||||
# A regular expression matching the name of dummy variables (i.e. expected to
|
||||
# not be used).
|
||||
dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_
|
||||
|
||||
# Argument names that match this expression will be ignored.
|
||||
ignored-argument-names=_.*|^ignored_|^unused_
|
||||
|
||||
# Tells whether we should check for unused import in __init__ files.
|
||||
init-import=no
|
||||
|
||||
# List of qualified module names which can have objects that can redefine
|
||||
# builtins.
|
||||
redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io
|
||||
21
LICENSE-CODE
Normal file
21
LICENSE-CODE
Normal file
@ -0,0 +1,21 @@
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2023 DeepSeek
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
91
LICENSE-MODEL
Normal file
91
LICENSE-MODEL
Normal file
@ -0,0 +1,91 @@
|
||||
DEEPSEEK LICENSE AGREEMENT
|
||||
|
||||
Version 1.0, 23 October 2023
|
||||
|
||||
Copyright (c) 2023 DeepSeek
|
||||
|
||||
Section I: PREAMBLE
|
||||
|
||||
Large generative models are being widely adopted and used, and have the potential to transform the way individuals conceive and benefit from AI or ML technologies.
|
||||
|
||||
Notwithstanding the current and potential benefits that these artifacts can bring to society at large, there are also concerns about potential misuses of them, either due to their technical limitations or ethical considerations.
|
||||
|
||||
In short, this license strives for both the open and responsible downstream use of the accompanying model. When it comes to the open character, we took inspiration from open source permissive licenses regarding the grant of IP rights. Referring to the downstream responsible use, we added use-based restrictions not permitting the use of the model in very specific scenarios, in order for the licensor to be able to enforce the license in case potential misuses of the Model may occur. At the same time, we strive to promote open and responsible research on generative models for content generation.
|
||||
|
||||
Even though downstream derivative versions of the model could be released under different licensing terms, the latter will always have to include - at minimum - the same use-based restrictions as the ones in the original license (this license). We believe in the intersection between open and responsible AI development; thus, this agreement aims to strike a balance between both in order to enable responsible open-science in the field of AI.
|
||||
|
||||
This License governs the use of the model (and its derivatives) and is informed by the model card associated with the model.
|
||||
|
||||
NOW THEREFORE, You and DeepSeek agree as follows:
|
||||
|
||||
1. Definitions
|
||||
"License" means the terms and conditions for use, reproduction, and Distribution as defined in this document.
|
||||
"Data" means a collection of information and/or content extracted from the dataset used with the Model, including to train, pretrain, or otherwise evaluate the Model. The Data is not licensed under this License.
|
||||
"Output" means the results of operating a Model as embodied in informational content resulting therefrom.
|
||||
"Model" means any accompanying machine-learning based assemblies (including checkpoints), consisting of learnt weights, parameters (including optimizer states), corresponding to the model architecture as embodied in the Complementary Material, that have been trained or tuned, in whole or in part on the Data, using the Complementary Material.
|
||||
"Derivatives of the Model" means all modifications to the Model, works based on the Model, or any other model which is created or initialized by transfer of patterns of the weights, parameters, activations or output of the Model, to the other model, in order to cause the other model to perform similarly to the Model, including - but not limited to - distillation methods entailing the use of intermediate data representations or methods based on the generation of synthetic data by the Model for training the other model.
|
||||
"Complementary Material" means the accompanying source code and scripts used to define, run, load, benchmark or evaluate the Model, and used to prepare data for training or evaluation, if any. This includes any accompanying documentation, tutorials, examples, etc, if any.
|
||||
"Distribution" means any transmission, reproduction, publication or other sharing of the Model or Derivatives of the Model to a third party, including providing the Model as a hosted service made available by electronic or other remote means - e.g. API-based or web access.
|
||||
"DeepSeek" (or "we") means Beijing DeepSeek Artificial Intelligence Fundamental Technology Research Co., Ltd., Hangzhou DeepSeek Artificial Intelligence Fundamental Technology Research Co., Ltd. and/or any of their affiliates.
|
||||
"You" (or "Your") means an individual or Legal Entity exercising permissions granted by this License and/or making use of the Model for whichever purpose and in any field of use, including usage of the Model in an end-use application - e.g. chatbot, translator, etc.
|
||||
"Third Parties" means individuals or legal entities that are not under common control with DeepSeek or You.
|
||||
|
||||
Section II: INTELLECTUAL PROPERTY RIGHTS
|
||||
|
||||
Both copyright and patent grants apply to the Model, Derivatives of the Model and Complementary Material. The Model and Derivatives of the Model are subject to additional terms as described in Section III.
|
||||
|
||||
2. Grant of Copyright License. Subject to the terms and conditions of this License, DeepSeek hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare, publicly display, publicly perform, sublicense, and distribute the Complementary Material, the Model, and Derivatives of the Model.
|
||||
|
||||
3. Grant of Patent License. Subject to the terms and conditions of this License and where and as applicable, DeepSeek hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this paragraph) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Model and the Complementary Material, where such license applies only to those patent claims licensable by DeepSeek that are necessarily infringed by its contribution(s). If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Model and/or Complementary Material constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for the Model and/or works shall terminate as of the date such litigation is asserted or filed.
|
||||
|
||||
|
||||
Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION
|
||||
|
||||
4. Distribution and Redistribution. You may host for Third Party remote access purposes (e.g. software-as-a-service), reproduce and distribute copies of the Model or Derivatives of the Model thereof in any medium, with or without modifications, provided that You meet the following conditions:
|
||||
a. Use-based restrictions as referenced in paragraph 5 MUST be included as an enforceable provision by You in any type of legal agreement (e.g. a license) governing the use and/or distribution of the Model or Derivatives of the Model, and You shall give notice to subsequent users You Distribute to, that the Model or Derivatives of the Model are subject to paragraph 5. This provision does not apply to the use of Complementary Material.
|
||||
b. You must give any Third Party recipients of the Model or Derivatives of the Model a copy of this License;
|
||||
c. You must cause any modified files to carry prominent notices stating that You changed the files;
|
||||
d. You must retain all copyright, patent, trademark, and attribution notices excluding those notices that do not pertain to any part of the Model, Derivatives of the Model.
|
||||
e. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions - respecting paragraph 4.a. – for use, reproduction, or Distribution of Your modifications, or for any such Derivatives of the Model as a whole, provided Your use, reproduction, and Distribution of the Model otherwise complies with the conditions stated in this License.
|
||||
|
||||
5. Use-based restrictions. The restrictions set forth in Attachment A are considered Use-based restrictions. Therefore You cannot use the Model and the Derivatives of the Model for the specified restricted uses. You may use the Model subject to this License, including only for lawful purposes and in accordance with the License. Use may include creating any content with, finetuning, updating, running, training, evaluating and/or reparametrizing the Model. You shall require all of Your users who use the Model or a Derivative of the Model to comply with the terms of this paragraph (paragraph 5).
|
||||
|
||||
6. The Output You Generate. Except as set forth herein, DeepSeek claims no rights in the Output You generate using the Model. You are accountable for the Output you generate and its subsequent uses. No use of the output can contravene any provision as stated in the License.
|
||||
|
||||
Section IV: OTHER PROVISIONS
|
||||
|
||||
7. Updates and Runtime Restrictions. To the maximum extent permitted by law, DeepSeek reserves the right to restrict (remotely or otherwise) usage of the Model in violation of this License.
|
||||
|
||||
8. Trademarks and related. Nothing in this License permits You to make use of DeepSeek’ trademarks, trade names, logos or to otherwise suggest endorsement or misrepresent the relationship between the parties; and any rights not expressly granted herein are reserved by DeepSeek.
|
||||
|
||||
9. Personal information, IP rights and related. This Model may contain personal information and works with IP rights. You commit to complying with applicable laws and regulations in the handling of personal information and the use of such works. Please note that DeepSeek's license granted to you to use the Model does not imply that you have obtained a legitimate basis for processing the related information or works. As an independent personal information processor and IP rights user, you need to ensure full compliance with relevant legal and regulatory requirements when handling personal information and works with IP rights that may be contained in the Model, and are willing to assume solely any risks and consequences that may arise from that.
|
||||
|
||||
10. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, DeepSeek provides the Model and the Complementary Material on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Model, Derivatives of the Model, and the Complementary Material and assume any risks associated with Your exercise of permissions under this License.
|
||||
|
||||
11. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall DeepSeek be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Model and the Complementary Material (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if DeepSeek has been advised of the possibility of such damages.
|
||||
|
||||
12. Accepting Warranty or Additional Liability. While redistributing the Model, Derivatives of the Model and the Complementary Material thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of DeepSeek, and only if You agree to indemnify, defend, and hold DeepSeek harmless for any liability incurred by, or claims asserted against, DeepSeek by reason of your accepting any such warranty or additional liability.
|
||||
|
||||
13. If any provision of this License is held to be invalid, illegal or unenforceable, the remaining provisions shall be unaffected thereby and remain valid as if such provision had not been set forth herein.
|
||||
|
||||
14. Governing Law and Jurisdiction. This agreement will be governed and construed under PRC laws without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this agreement. The courts located in the domicile of Hangzhou DeepSeek Artificial Intelligence Fundamental Technology Research Co., Ltd. shall have exclusive jurisdiction of any dispute arising out of this agreement.
|
||||
|
||||
END OF TERMS AND CONDITIONS
|
||||
|
||||
Attachment A
|
||||
|
||||
Use Restrictions
|
||||
|
||||
You agree not to use the Model or Derivatives of the Model:
|
||||
|
||||
- In any way that violates any applicable national or international law or regulation or infringes upon the lawful rights and interests of any third party;
|
||||
- For military use in any way;
|
||||
- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
|
||||
- To generate or disseminate verifiably false information and/or content with the purpose of harming others;
|
||||
- To generate or disseminate inappropriate content subject to applicable regulatory requirements;
|
||||
- To generate or disseminate personal identifiable information without due authorization or for unreasonable use;
|
||||
- To defame, disparage or otherwise harass others;
|
||||
- For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
|
||||
- For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics;
|
||||
- To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
|
||||
- For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories.
|
||||
97
Makefile
Normal file
97
Makefile
Normal file
@ -0,0 +1,97 @@
|
||||
print-% : ; @echo $* = $($*)
|
||||
PROJECT_NAME = DeepSeek-LLM
|
||||
COPYRIGHT = "DeepSeek."
|
||||
PROJECT_PATH = evaluation
|
||||
SHELL = /bin/bash
|
||||
SOURCE_FOLDERS = evaluation
|
||||
PYTHON_FILES = $(shell find $(SOURCE_FOLDERS) -type f -name "*.py" -o -name "*.pyi")
|
||||
COMMIT_HASH = $(shell git log -1 --format=%h)
|
||||
PATH := $(HOME)/go/bin:$(PATH)
|
||||
PYTHON ?= $(shell command -v python3 || command -v python)
|
||||
PYTESTOPTS ?=
|
||||
|
||||
.PHONY: default
|
||||
default: install
|
||||
|
||||
# Tools Installation
|
||||
|
||||
check_pip_install = $(PYTHON) -m pip show $(1) &>/dev/null || (cd && $(PYTHON) -m pip install $(1) --upgrade)
|
||||
check_pip_install_extra = $(PYTHON) -m pip show $(1) &>/dev/null || (cd && $(PYTHON) -m pip install $(2) --upgrade)
|
||||
|
||||
pylint-install:
|
||||
$(call check_pip_install_extra,pylint,pylint[spelling])
|
||||
$(call check_pip_install,pyenchant)
|
||||
|
||||
flake8-install:
|
||||
$(call check_pip_install,flake8)
|
||||
$(call check_pip_install,flake8-bugbear)
|
||||
$(call check_pip_install,flake8-comprehensions)
|
||||
$(call check_pip_install,flake8-docstrings)
|
||||
$(call check_pip_install,flake8-pyi)
|
||||
$(call check_pip_install,flake8-simplify)
|
||||
|
||||
py-format-install:
|
||||
$(call check_pip_install,isort)
|
||||
$(call check_pip_install_extra,black,black[jupyter])
|
||||
|
||||
ruff-install:
|
||||
$(call check_pip_install,ruff)
|
||||
|
||||
mypy-install:
|
||||
$(call check_pip_install,mypy)
|
||||
|
||||
pre-commit-install:
|
||||
$(call check_pip_install,pre-commit)
|
||||
$(PYTHON) -m pre_commit install --install-hooks
|
||||
|
||||
go-install:
|
||||
# requires go >= 1.16
|
||||
command -v go || (sudo apt-get install -y golang && sudo ln -sf /usr/lib/go/bin/go /usr/bin/go)
|
||||
|
||||
addlicense-install: go-install
|
||||
command -v addlicense || go install github.com/google/addlicense@latest
|
||||
|
||||
addlicense: addlicense-install
|
||||
addlicense -c $(COPYRIGHT) -ignore tests/coverage.xml -l mit -y 2023-$(shell date +"%Y") -check $(SOURCE_FOLDERS)
|
||||
|
||||
# Python linters
|
||||
|
||||
pylint: pylint-install
|
||||
$(PYTHON) -m pylint $(PROJECT_PATH)
|
||||
|
||||
flake8: flake8-install
|
||||
$(PYTHON) -m flake8 --count --show-source --statistics
|
||||
|
||||
py-format: py-format-install
|
||||
$(PYTHON) -m isort --project $(PROJECT_PATH) --check $(PYTHON_FILES) && \
|
||||
$(PYTHON) -m black --check $(PYTHON_FILES)
|
||||
|
||||
ruff: ruff-install
|
||||
$(PYTHON) -m ruff check .
|
||||
|
||||
ruff-fix: ruff-install
|
||||
$(PYTHON) -m ruff check . --fix --exit-non-zero-on-fix
|
||||
|
||||
mypy: mypy-install
|
||||
$(PYTHON) -m mypy $(PROJECT_PATH) --install-types --non-interactive
|
||||
|
||||
pre-commit: pre-commit-install
|
||||
$(PYTHON) -m pre_commit run --all-files
|
||||
|
||||
# Utility functions
|
||||
|
||||
lint: ruff flake8 py-format mypy pylint addlicense
|
||||
|
||||
format: py-format-install ruff-install addlicense-install
|
||||
$(PYTHON) -m isort --project $(PROJECT_PATH) $(PYTHON_FILES)
|
||||
$(PYTHON) -m black $(PYTHON_FILES)
|
||||
$(PYTHON) -m ruff check . --fix --exit-zero
|
||||
addlicense -c $(COPYRIGHT) -ignore tests/coverage.xml -l mit -y 2023-$(shell date +"%Y") $(SOURCE_FOLDERS)
|
||||
|
||||
clean-py:
|
||||
find . -type f -name '*.py[co]' -delete
|
||||
find . -depth -type d -name "__pycache__" -exec rm -r "{}" +
|
||||
find . -depth -type d -name ".ruff_cache" -exec rm -r "{}" +
|
||||
find . -depth -type d -name ".mypy_cache" -exec rm -r "{}" +
|
||||
|
||||
clean: clean-py
|
||||
324
README.md
Normal file
324
README.md
Normal file
@ -0,0 +1,324 @@
|
||||
<!-- markdownlint-disable first-line-h1 -->
|
||||
<!-- markdownlint-disable html -->
|
||||
<!-- markdownlint-disable no-duplicate-header -->
|
||||
|
||||
<div align="center">
|
||||
<img src="images/logo.svg" width="60%" alt="DeepSeek LLM" />
|
||||
</div>
|
||||
<hr>
|
||||
<div align="center">
|
||||
|
||||
<a href="https://www.deepseek.com/" target="_blank">
|
||||
<img alt="Homepage" src="images/badge.svg" />
|
||||
</a>
|
||||
<a href="https://chat.deepseek.com/" target="_blank">
|
||||
<img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20LLM-536af5?color=536af5&logoColor=white" />
|
||||
</a>
|
||||
<a href="https://huggingface.co/deepseek-ai" target="_blank">
|
||||
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" />
|
||||
</a>
|
||||
|
||||
</div>
|
||||
|
||||
|
||||
<div align="center">
|
||||
|
||||
<a href="https://discord.gg/Tc7c45Zzu5" target="_blank">
|
||||
<img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" />
|
||||
</a>
|
||||
<a href="images/qr.jpeg" target="_blank">
|
||||
<img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" />
|
||||
</a>
|
||||
<a href="https://twitter.com/deepseek_ai" target="_blank">
|
||||
<img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" />
|
||||
</a>
|
||||
|
||||
</div>
|
||||
|
||||
<div align="center">
|
||||
|
||||
<a href="LICENSE-CODE">
|
||||
<img alt="Code License" src="https://img.shields.io/badge/Code_License-MIT-f5de53?&color=f5de53">
|
||||
</a>
|
||||
<a href="LICENSE-MODEL">
|
||||
<img alt="Model License" src="https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53">
|
||||
</a>
|
||||
</div>
|
||||
|
||||
|
||||
<p align="center">
|
||||
<a href="#2-model-downloads">Model Download</a> |
|
||||
<a href="#5-quick-start">Quick Start</a> |
|
||||
<a href="#3-evaluation-results">Evaluation Results</a> |
|
||||
<a href="#8-license">License</a> |
|
||||
<a href="#9-citation">Citation</a>
|
||||
</p>
|
||||
|
||||
|
||||
<p align="center">
|
||||
<a href="https://arxiv.org/abs/2401.02954"><b>Paper Link</b>👁️</a>
|
||||
</p>
|
||||
|
||||
|
||||
## 1. Introduction
|
||||
|
||||
Introducing DeepSeek VL,
|
||||
|
||||
- **Superior General Capabilities:** DeepSeek .
|
||||
|
||||
- **Proficient in Coding and Math:** DeepSeek .
|
||||
|
||||
- **Mastery in Chinese Language:** Based on our evaluation,.
|
||||
|
||||
## 2. Model Downloads
|
||||
|
||||
We release the DeepSeek LLM 7B/67B, including both base and chat models, to the public. To support a broader and more diverse range of research within both academic and commercial communities, we are providing access to the intermediate checkpoints of the base model from its training process. Please **note** that the use of this model is subject to the terms outlined in [License section](#8-license). Commercial usage is permitted under these terms.
|
||||
|
||||
### Huggingface
|
||||
|
||||
| Model | Sequence Length | Download |
|
||||
|:---------------------:|:---------------:|:-----------------------------------------------------------------------:|
|
||||
| DeepSeek VL 7B Base | 4096 | 🤗 [HuggingFace](https://huggingface.co/deepseek-ai/deepseek-llm-7b-base) |
|
||||
| DeepSeek VL 7B Chat | 4096 | 🤗 [HuggingFace](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat) |
|
||||
|
||||
## 3. Evaluation Results
|
||||
|
||||
### Base Model
|
||||
|
||||
We evaluate our models and some baseline models on a series of representative benchmarks, both in English and Chinese. More results can be found in the [`evaluation`](evaluation) folder. In this part, the evaluation results we report are based on the internal, non-open-source hai-llm evaluation framework. Please note that there may be slight discrepancies when using the converted HuggingFace models.
|
||||
|
||||
| model | Hella<br>Swag | Trivia<br>QA | MMLU | GSM8K | Human<br>Eval | BBH | CEval | CMMLU | Chinese<br>QA |
|
||||
|:---------------:|:-------------:|:------------:|:------:|:------:|:-------------:|:------:|:------:|:------:|:-------------:|
|
||||
| | 0-shot | 5-shot | 5-shot | 8-shot | 0-shot | 3-shot | 5-shot | 5-shot | 5-shot |
|
||||
| LLaMA-2<br>-7B | 75.6 | 63.8 | 45.8 | 15.5 | 14.6 | 38.5 | 33.9 | 32.6 | 21.5 |
|
||||
| LLaMA-2<br>-70B | 84.0 | 79.5 | 69.0 | 58.4 | 28.7 | 62.9 | 51.4 | 53.1 | 50.2 |
|
||||
| DeepSeek LLM<br>7B Base| 75.4 | 59.7 | 48.2 | 17.4 | 26.2 | 39.5 | 45.0 | 47.2 | 78.0 |
|
||||
| DeepSeek LLM<br>67B Base| 84.0 | 78.9 | 71.3 | 63.4 | 42.7 | 68.7 | 66.1 | 70.8 | 87.6 |
|
||||
|
||||
**Note:** ChineseQA is an in-house benchmark, inspired by TriviaQA.
|
||||
|
||||
### Chat Model
|
||||
|
||||
#### Never Seen Before Exam
|
||||
|
||||
|
||||
## 4. Pre-Training Details
|
||||
|
||||
### Data
|
||||
Our primary goal is to holistically enhance the dataset's richness and variety. To achieve this, we've implemented multiple methods and established a distributed, frequent-checkpointing batch processing system, named "cc_cleaner", to bolster our data pipeline.
|
||||
|
||||
Our minimal viable solution departs from RefinedWeb + CCNet. We greatly appreciate their selfless dedication to the research of AGI.
|
||||
|
||||
We have also significantly incorporated deterministic randomization into our data pipeline. This approach enables us to continuously enhance our data throughout the lengthy and unpredictable training process.
|
||||
|
||||
- **Data Composition**: Our training data comprises a diverse mix of Internet text, math, code, books, and self-collected data respecting robots.txt. In addition to the diverse content, we place a high priority on personal privacy and copyright protection. All content containing personal information or subject to copyright restrictions has been removed from our dataset.
|
||||
|
||||
- **Dataset Pruning**: Our system employs heuristic rules and models to refine our training data. Our filtering process removes low-quality web data while preserving precious low-resource knowledge. It aims to improve overall corpus quality and remove harmful or toxic content.
|
||||
|
||||
- **Deduplication**: Our advanced deduplication system, using MinhashLSH, strictly removes duplicates both at document and string levels. This rigorous deduplication process ensures exceptional data uniqueness and integrity, especially crucial in large-scale datasets.
|
||||
|
||||
### Pre-Training
|
||||
DeepSeek LM models use the same architecture as LLaMA, an auto-regressive transformer decoder model. The 7B model uses Multi-Head attention (MHA) while the 67B model uses Grouped-Query Attention (GQA).
|
||||
|
||||
We pre-trained DeepSeek language models on a vast dataset of 2 trillion tokens, with a sequence length of 4096 and AdamW optimizer. The 7B model's training involved a batch size of 2304 and a learning rate of 4.2e-4 and the 67B model was trained with a batch size of 4608 and a learning rate of 3.2e-4. We employ a multi-step learning rate schedule in our training process. The learning rate begins with 2000 warmup steps, and then it is stepped to 31.6% of the maximum at 1.6 trillion tokens and 10% of the maximum at 1.8 trillion tokens.
|
||||
|
||||
We release the training loss curve and several benchmark metrics curves, as detailed below.
|
||||
|
||||
<div align="center">
|
||||
<img src="images/pretrain_loss.png" alt="result" width="70%">
|
||||
</div>
|
||||
|
||||
<div align="center">
|
||||
<img src="images/pretrain_metric.png" alt="result" width="80%">
|
||||
</div>
|
||||
|
||||
## 5. Quick Start
|
||||
|
||||
### Installation
|
||||
|
||||
On the basis of `Python >= 3.8` environment, install the necessary dependencies by running the following command:
|
||||
|
||||
```shell
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
### Inference with Huggingface's Transformers
|
||||
|
||||
You can directly employ [Huggingface's Transformers](https://github.com/huggingface/transformers) for model inference.
|
||||
|
||||
**Text Completion**
|
||||
|
||||
```python
|
||||
import torch
|
||||
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
|
||||
|
||||
model_name = "deepseek-ai/deepseek-llm-67b-base"
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||||
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
|
||||
model.generation_config = GenerationConfig.from_pretrained(model_name)
|
||||
model.generation_config.pad_token_id = model.generation_config.eos_token_id
|
||||
|
||||
text = "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is"
|
||||
inputs = tokenizer(text, return_tensors="pt")
|
||||
outputs = model.generate(**inputs.to(model.device), max_new_tokens=100)
|
||||
|
||||
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
|
||||
print(result)
|
||||
```
|
||||
|
||||
**Chat Completion**
|
||||
|
||||
```python
|
||||
import torch
|
||||
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
|
||||
|
||||
model_name = "deepseek-ai/deepseek-llm-67b-chat"
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||||
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
|
||||
model.generation_config = GenerationConfig.from_pretrained(model_name)
|
||||
model.generation_config.pad_token_id = model.generation_config.eos_token_id
|
||||
|
||||
messages = [
|
||||
{"role": "user", "content": "Who are you?"}
|
||||
]
|
||||
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
|
||||
outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)
|
||||
|
||||
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
|
||||
print(result)
|
||||
```
|
||||
|
||||
Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input.
|
||||
|
||||
```
|
||||
User: {messages[0]['content']}
|
||||
|
||||
Assistant: {messages[1]['content']}<｜end▁of▁sentence｜>User: {messages[2]['content']}
|
||||
|
||||
Assistant:
|
||||
```
|
||||
|
||||
**Note:** By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<｜begin▁of▁sentence｜>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input.
|
||||
|
||||
### Inference with vLLM
|
||||
|
||||
You can also employ [vLLM](https://github.com/vllm-project/vllm) for high-throughput inference.
|
||||
|
||||
**Text Completion**
|
||||
|
||||
```python
|
||||
from vllm import LLM, SamplingParams
|
||||
|
||||
tp_size = 4 # Tensor Parallelism
|
||||
sampling_params = SamplingParams(temperature=0.7, top_p=0.9, max_tokens=100)
|
||||
model_name = "deepseek-ai/deepseek-llm-67b-base"
|
||||
llm = LLM(model=model_name, trust_remote_code=True, gpu_memory_utilization=0.9, tensor_parallel_size=tp_size)
|
||||
|
||||
prompts = [
|
||||
"If everyone in a country loves one another,",
|
||||
"The research should also focus on the technologies",
|
||||
"To determine if the label is correct, we need to"
|
||||
]
|
||||
outputs = llm.generate(prompts, sampling_params)
|
||||
|
||||
generated_text = [output.outputs[0].text for output in outputs]
|
||||
print(generated_text)
|
||||
```
|
||||
|
||||
**Chat Completion**
|
||||
|
||||
```python
|
||||
from transformers import AutoTokenizer
|
||||
from vllm import LLM, SamplingParams
|
||||
|
||||
tp_size = 4 # Tensor Parallelism
|
||||
sampling_params = SamplingParams(temperature=0.7, top_p=0.9, max_tokens=100)
|
||||
model_name = "deepseek-ai/deepseek-llm-67b-chat"
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||||
llm = LLM(model=model_name, trust_remote_code=True, gpu_memory_utilization=0.9, tensor_parallel_size=tp_size)
|
||||
|
||||
messages_list = [
|
||||
[{"role": "user", "content": "Who are you?"}],
|
||||
[{"role": "user", "content": "What can you do?"}],
|
||||
[{"role": "user", "content": "Explain Transformer briefly."}],
|
||||
]
|
||||
# Avoid adding bos_token repeatedly
|
||||
prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]
|
||||
|
||||
sampling_params.stop = [tokenizer.eos_token]
|
||||
outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)
|
||||
|
||||
generated_text = [output.outputs[0].text for output in outputs]
|
||||
print(generated_text)
|
||||
```
|
||||
|
||||
## 6. FAQ
|
||||
|
||||
### Could You Provide the tokenizer.model File for Model Quantization?
|
||||
|
||||
DeepSeek LLM utilizes the [HuggingFace Tokenizer](https://huggingface.co/docs/tokenizers/index) to implement the Byte-level BPE algorithm, with specially designed pre-tokenizers to ensure optimal performance. Currently, there is no direct way to convert the tokenizer into a SentencePiece tokenizer. We are contributing to the open-source quantization methods facilitate the usage of HuggingFace Tokenizer.
|
||||
|
||||
#### GGUF(llama.cpp)
|
||||
|
||||
We have submitted a [PR](https://github.com/ggerganov/llama.cpp/pull/4070) to the popular quantization repository [llama.cpp](https://github.com/ggerganov/llama.cpp) to fully support all HuggingFace pre-tokenizers, including ours.
|
||||
|
||||
While waiting for the PR to be merged, you can generate your GGUF model using the following steps:
|
||||
|
||||
```bash
|
||||
git clone https://github.com/DOGEwbx/llama.cpp.git
|
||||
cd llama.cpp
|
||||
git checkout regex_gpt2_preprocess
|
||||
# set up the environment according to README
|
||||
make
|
||||
python3 -m pip install -r requirements.txt
|
||||
# generate GGUF model
|
||||
python convert-hf-to-gguf.py <MODEL_PATH> --outfile <GGUF_PATH> --model-name deepseekllm
|
||||
# use q4_0 quantization as an example
|
||||
./quantize <GGUF_PATH> <OUTPUT_PATH> q4_0
|
||||
./main -m <OUTPUT_PATH> -n 128 -p <PROMPT>
|
||||
```
|
||||
#### GPTQ(exllamav2)
|
||||
|
||||
`UPDATE:`[exllamav2](https://github.com/turboderp/exllamav2) has been able to support HuggingFace Tokenizer. Please pull the latest version and try out.
|
||||
|
||||
### GPU Memory Usage
|
||||
|
||||
We profile the peak memory usage of inference for 7B and 67B models at different batch size and sequence length settings.
|
||||
|
||||
For DeepSeek LLM 7B, we utilize **1 NVIDIA A100-PCIE-40GB GPU** for inference.
|
||||
|
||||
<table><thead><tr><th rowspan="2">Batch Size</th><th colspan="5">Sequence Length</th></tr><tr><th>256</th><th>512</th><th>1024</th><th>2048</th><th>4096</th></tr></thead><tbody><tr><td>1</td><td>13.29 GB</td><td>13.63 GB</td><td>14.47 GB</td><td>16.37 GB</td><td>21.25 GB</td></tr><tr><td>2</td><td>13.63 GB</td><td>14.39 GB</td><td>15.98 GB</td><td>19.82 GB</td><td>29.59 GB</td></tr><tr><td>4</td><td>14.47 GB</td><td>15.82 GB</td><td>19.04 GB</td><td>26.65 GB</td><td>OOM</td></tr><tr><td>8</td><td>15.99 GB</td><td>18.71 GB</td><td>25.14 GB</td><td>35.19 GB</td><td>OOM</td></tr><tr><td>16</td><td>19.06 GB</td><td>24.52 GB</td><td>37.28 GB</td><td>OOM</td><td>OOM</td></tr></tbody></table>
|
||||
|
||||
For DeepSeek LLM 67B, we utilize **8 NVIDIA A100-PCIE-40GB GPUs** for inference.
|
||||
|
||||
<table><thead><tr><th rowspan="2">Batch Size</th><th colspan="5">Sequence Length</th></tr><tr><th>256</th><th>512</th><th>1024</th><th>2048</th><th>4096</th></tr></thead><tbody><tr><td>1</td><td>16.92 GB</td><td>17.11 GB</td><td>17.66 GB</td><td>20.01 GB</td><td>33.23 GB</td></tr><tr><td>2</td><td>17.04 GB</td><td>17.28 GB</td><td>18.55 GB</td><td>25.27 GB</td><td>OOM</td></tr><tr><td>4</td><td>17.20 GB</td><td>17.80 GB</td><td>21.28 GB</td><td>33.71 GB</td><td>OOM</td></tr><tr><td>8</td><td>17.59 GB</td><td>19.25 GB</td><td>25.69 GB</td><td>OOM</td><td>OOM</td></tr><tr><td>16</td><td>18.17 GB</td><td>21.69 GB</td><td>34.54 GB</td><td>OOM</td><td>OOM</td></tr></tbody></table>
|
||||
|
||||
## 7. Limitation
|
||||
|
||||
While DeepSeek LLMs have demonstrated impressive capabilities, they are not without their limitations. Here are some potential drawbacks of such models:
|
||||
|
||||
1. Over-reliance on training data: These models are trained on vast amounts of text data, which can introduce biases present in the data. They may inadvertently generate biased or discriminatory responses, reflecting the biases prevalent in the training data.
|
||||
|
||||
2. Hallucination: The model sometimes generates responses or outputs that may sound plausible but are factually incorrect or unsupported. This can occur when the model relies heavily on the statistical patterns it has learned from the training data, even if those patterns do not align with real-world knowledge or facts.
|
||||
|
||||
3. Repetition: The model may exhibit repetition in their generated responses. This repetition can manifest in various ways, such as repeating certain phrases or sentences, generating redundant information, or producing repetitive structures in the generated text. This issue can make the output of LLMs less diverse and less engaging for users.
|
||||
|
||||
## 8. License
|
||||
|
||||
This code repository is licensed under [the MIT License](https://github.com/deepseek-ai/DeepSeek-LLM/blob/HEAD/LICENSE-CODE). The use of DeepSeek LLM Base/Chat models is subject to [the Model License](https://github.com/deepseek-ai/DeepSeek-LLM/blob/HEAD/LICENSE-MODEL). DeepSeek LLM series (including Base and Chat) supports commercial use.
|
||||
|
||||
## 9. Citation
|
||||
|
||||
```
|
||||
@article{deepseek-llm,
|
||||
author = {DeepSeek-AI},
|
||||
title = {DeepSeek LLM: Scaling Open-Source Language Models with Longtermism},
|
||||
journal = {arXiv preprint arXiv:2401.02954},
|
||||
year = {2024},
|
||||
url = {https://github.com/deepseek-ai/DeepSeek-LLM}
|
||||
}
|
||||
```
|
||||
|
||||
## 10. Contact
|
||||
|
||||
If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
|
||||
1
images/badge.svg
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1
images/badge.svg
Normal file
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|
After Width: | Height: | Size: 6.0 KiB |
BIN
images/logo.png
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BIN
images/logo.png
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|
After Width: | Height: | Size: 8.5 KiB |
22
images/logo.svg
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22
images/logo.svg
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@ -0,0 +1,22 @@
|
||||
<svg width="195.000000" height="41.359375" viewBox="0 0 195 41.3594" fill="none" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
|
||||
<desc>
|
||||
Created with Pixso.
|
||||
</desc>
|
||||
<defs>
|
||||
<clipPath id="clip30_2029">
|
||||
<rect id="_图层_1" width="134.577469" height="25.511124" transform="translate(60.422485 10.022217)" fill="white"/>
|
||||
</clipPath>
|
||||
</defs>
|
||||
<g clip-path="url(#clip30_2029)">
|
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After Width: | Height: | Size: 10 KiB |
BIN
images/qr.jpeg
Normal file
BIN
images/qr.jpeg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 512 KiB |
8
requirements.txt
Normal file
8
requirements.txt
Normal file
@ -0,0 +1,8 @@
|
||||
torch>=2.0
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||||
tokenizers>=0.14.0
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||||
transformers>=4.35.0
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accelerate
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||||
sympy==1.12
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||||
pebble
|
||||
timeout-decorator
|
||||
attrdict
|
||||
Loading…
Reference in New Issue
Block a user