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hangyav / textLSP

Language server for text spell and grammar check with various tools.
GNU General Public License v3.0
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build(deps): bump the python-packages group with 7 updates #41

Open dependabot[bot] opened 2 days ago

dependabot[bot] commented 2 days ago

Bumps the python-packages group with 7 updates:

Package From To
openai 1.43.0 1.50.2
ollama 0.3.2 0.3.3
pytest 8.3.2 8.3.3
coverage-threshold 0.4.4 0.5.0
torch 2.4.0 2.4.1
transformers 4.44.2 4.45.1
bitsandbytes 0.43.3 0.44.1

Updates openai from 1.43.0 to 1.50.2

Release notes

Sourced from openai's releases.

v1.50.2

1.50.2 (2024-09-27)

Full Changelog: v1.50.1...v1.50.2

Bug Fixes

  • audio: correct types for transcriptions / translations (#1755) (76c1f3f)

v1.50.1

1.50.1 (2024-09-27)

Full Changelog: v1.50.0...v1.50.1

Documentation

v1.50.0

1.50.0 (2024-09-26)

Full Changelog: v1.49.0...v1.50.0

Features

  • structured outputs: add support for accessing raw responses (#1748) (0189e28)

Chores

  • pydantic v1: exclude specific properties when rich printing (#1751) (af535ce)

v1.49.0

1.49.0 (2024-09-26)

Full Changelog: v1.48.0...v1.49.0

Features

Chores

v1.48.0

1.48.0 (2024-09-25)

Full Changelog: v1.47.1...v1.48.0

... (truncated)

Changelog

Sourced from openai's changelog.

1.50.2 (2024-09-27)

Full Changelog: v1.50.1...v1.50.2

Bug Fixes

  • audio: correct types for transcriptions / translations (#1755) (76c1f3f)

1.50.1 (2024-09-27)

Full Changelog: v1.50.0...v1.50.1

Documentation

1.50.0 (2024-09-26)

Full Changelog: v1.49.0...v1.50.0

Features

  • structured outputs: add support for accessing raw responses (#1748) (0189e28)

Chores

  • pydantic v1: exclude specific properties when rich printing (#1751) (af535ce)

1.49.0 (2024-09-26)

Full Changelog: v1.48.0...v1.49.0

Features

Chores

1.48.0 (2024-09-25)

Full Changelog: v1.47.1...v1.48.0

Features

  • client: allow overriding retry count header (#1745) (9f07d4d)

... (truncated)

Commits


Updates ollama from 0.3.2 to 0.3.3

Release notes

Sourced from ollama's releases.

v0.3.3

What's Changed

New Contributors

Full Changelog: https://github.com/ollama/ollama-python/compare/v0.3.2...v0.3.3

Commits
  • 89e8b74 Merge pull request #267 from ollama/dependabot/pip/ruff-0.6.3
  • 81edab1 _stream in async client raises RuntimeError processing HTTP errors (#266)
  • 5f51129 Bump ruff from 0.6.2 to 0.6.3
  • See full diff in compare view


Updates pytest from 8.3.2 to 8.3.3

Release notes

Sourced from pytest's releases.

8.3.3

pytest 8.3.3 (2024-09-09)

Bug fixes

  • #12446: Avoid calling @property (and other instance descriptors) during fixture discovery -- by asottile{.interpreted-text role="user"}

  • #12659: Fixed the issue of not displaying assertion failure differences when using the parameter --import-mode=importlib in pytest>=8.1.

  • #12667: Fixed a regression where type change in [ExceptionInfo.errisinstance]{.title-ref} caused [mypy]{.title-ref} to fail.

  • #12744: Fixed typing compatibility with Python 3.9 or less -- replaced [typing.Self]{.title-ref} with [typing_extensions.Self]{.title-ref} -- by Avasam{.interpreted-text role="user"}

  • #12745: Fixed an issue with backslashes being incorrectly converted in nodeid paths on Windows, ensuring consistent path handling across environments.

  • #6682: Fixed bug where the verbosity levels where not being respected when printing the "msg" part of failed assertion (as in assert condition, msg).

  • #9422: Fix bug where disabling the terminal plugin via -p no:terminal would cause crashes related to missing the verbose option.

    -- by GTowers1{.interpreted-text role="user"}

Improved documentation

  • #12663: Clarify that the [pytest_deselected]{.title-ref} hook should be called from [pytest_collection_modifyitems]{.title-ref} hook implementations when items are deselected.
  • #12678: Remove erroneous quotes from [tmp_path_retention_policy]{.title-ref} example in docs.

Miscellaneous internal changes

  • #12769: Fix typos discovered by codespell and add codespell to pre-commit hooks.
Commits


Updates coverage-threshold from 0.4.4 to 0.5.0

Commits


Updates torch from 2.4.0 to 2.4.1

Release notes

Sourced from torch's releases.

PyTorch 2.4.1 Release, bug fix release

This release is meant to fix the following issues (regressions / silent correctness):

Breaking Changes:

  • The pytorch/pytorch docker image now installs the PyTorch package through pip and has switch its conda installation from miniconda to miniforge (#134274)

Windows:

  • Fix performance regression on Windows related to MKL static linking (#130619) (#130697)
  • Fix error during loading on Windows: [WinError 126] The specified module could not be found. (#131662) (#130697)

MPS:

  • Fix tensor.clamp produces wrong values (#130226)
  • Fix Incorrect result from batch norm with sliced inputs (#133610)

ROCM:

  • Fix for launching kernel invalid config error when calling embedding with large index (#130994)
  • Added a check and a warning when attempting to use hipBLASLt on an unsupported architecture (#128753)
  • Fix image corruption with Memory Efficient Attention when running HuggingFace Diffusers Stable Diffusion 3 pipeline (#133331)

Distributed:

  • Fix FutureWarning when using torch.load internally (#130663)
  • Fix FutureWarning when using torch.cuda.amp.autocast internally (#130660)

Torch.compile:

  • Fix exception with torch compile when onnxruntime-training and deepspeed packages are installed. (#131194)
  • Fix silent incorrectness with torch.library.custom_op with mutable inputs and torch.compile (#133452)
  • Fix SIMD detection on Linux ARM (#129075)
  • Do not use C++20 features in cpu_inducotr code (#130816)

Packaging:

  • Fix for exposing statically linked libstdc++ CXX11 ABI symbols (#134494)
  • Fix error while building pytorch from source due to not missing QNNPACK module (#131864)
  • Make PyTorch buildable from source on PowerPC (#129736)
  • Fix XPU extension building (#132847)

Other:

  • Fix warning when using pickle on a nn.Module that contains tensor attributes (#130246)
  • Fix NaNs return in MultiheadAttention when need_weights=False (#130014)
  • Fix nested tensor MHA produces incorrect results (#130196)
  • Fix error when using torch.utils.flop_counter.FlopCounterMode (#134467)

Tracked Regressions:

  • The experimental remote caching feature for Inductor's autotuner (enabled via TORCHINDUCTOR_AUTOTUNE_REMOTE_CACHE) is known to still be broken in this release and actively worked on in main. Following Error is generated: redis.exceptions.DataError: Invalid input of type: 'dict'. Please use nightlies if you need this feature (reported and Fixed by PR: #134032)

Release tracker #132400 contains all relevant pull requests related to this release as well as links to related issues.

Commits


Updates transformers from 4.44.2 to 4.45.1

Release notes

Sourced from transformers's releases.

Patch Release v4.45.1

Patches for v4.45.1

Llama 3.2, mllama, Qwen2-Audio, Qwen2-VL, OLMoE, Llava Onevision, Pixtral, FalconMamba, Modular Transformers

New model additions

mllama

The Llama 3.2-Vision collection of multimodal large language models (LLMs) is a collection of pretrained and instruction-tuned image reasoning generative models in 11B and 90B sizes (text + images in / text out). The Llama 3.2-Vision instruction-tuned models are optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. The models outperform many of the available open source and closed multimodal models on common industry benchmarks.

image

Qwen2-VL

The Qwen2-VL is a major update from the previous Qwen-VL by the Qwen team.

An extract from the Qwen2-VL blogpost available here is as follows:

Qwen2-VL is the latest version of the vision language models based on Qwen2 in the Qwen model familities. Compared with Qwen-VL, Qwen2-VL has the capabilities of:

  • SoTA understanding of images of various resolution & ratio: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc.
  • Understanding videos of 20min+: Qwen2-VL can understand videos over 20 minutes for high-quality video-based question answering, dialog, content creation, etc.
  • Agent that can operate your mobiles, robots, etc.: with the abilities of complex reasoning and decision making, Qwen2-VL can be integrated with devices like mobile phones, robots, etc., for automatic operation based on visual environment and text instructions.
  • Multilingual Support: to serve global users, besides English and Chinese, Qwen2-VL now supports the understanding of texts in different languages inside images, including most European languages, Japanese, Korean, Arabic, Vietnamese, etc.

image

Qwen2-Audio

The Qwen2-Audio is the new model series of large audio-language models from the Qwen team. Qwen2-Audio is capable of accepting various audio signal inputs and performing audio analysis or direct textual responses with regard to speech instructions.

They introduce two distinct audio interaction modes:

  • voice chat: users can freely engage in voice interactions with Qwen2-Audio without text input
  • audio analysis: users could provide audio and text instructions for analysis during the interaction

image

OLMoE

OLMoE is a series of Open Language Models using sparse Mixture-of-Experts designed to enable the science of language models. The team releases all code, checkpoints, logs, and details involved in training these models.

... (truncated)

Commits


Updates bitsandbytes from 0.43.3 to 0.44.1

Release notes

Sourced from bitsandbytes's releases.

0.44.1

What's Changed

Full Changelog: https://github.com/bitsandbytes-foundation/bitsandbytes/compare/0.44.0...0.44.1

0.44.0: New AdEMAMix optimizer, Embeddings quantization, and more!

New optimizer: AdEMAMix

The AdEMAMix optimizer is a modification to AdamW which proposes tracking two EMAs to better leverage past gradients. This allows for faster convergence with less training data and improved resistance to forgetting.

We've implemented 8bit and paged variations: AdEMAMix, AdEMAMix8bit, PagedAdEMAMix, and PagedAdEMAMix8bit. These can be used with a similar API to existing optimizers.

import bitsandbytes as bnb

optimizer = bnb.optim.PagedAdEMAMix8bit(

model.parameters(),

lr=1e-4,

betas=(0.9, 0.999, 0.9999),

alpha=5.0,

eps=1e-8,

weight_decay=1e-2,

alpha=5.0,

)

8-bit Optimizers Update

The block size for all 8-bit optimizers has been reduced from 2048 to 256 in this release. This is a change from the original implementation proposed in the paper which improves accuracy.

CUDA Graphs support

A fix to enable CUDA Graphs capture of kernel functions was made in #1330. This allows for performance improvements with inference frameworks like vLLM. Thanks @​jeejeelee!

Quantization for Embeddings

The trend of LLMs to use larger vocabularies continues. The embeddings can take up a significant portion of a quantized model's footprint. We now have an implementation of Embedding4bit and Embedding8bit thanks to @​galqiwi!

Example usage:

import torch
import torch.nn as nn

from bitsandbytes.nn import Embedding4bit


fp16_module = nn.Embedding(128, 64)

quantized_module = Embedding4bit(128, 64)


quantized_module.load_state_dict(fp16_module.state_dict())


quantized_module = quantized_module.to(0)

... (truncated)

Commits


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