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chore(deps): update loader dependencies non-major - #297
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| datasource | package | from | to | | ---------- | ------------ | ------ | ------ | | pypi | ollama | 0.6.2 | 0.6.3 | | pypi | peft | 0.21.0 | 0.21.2 | | pypi | torch | 2.14.0 | 2.14.1 | | pypi | transformers | 5.17.0 | 5.18.0 |
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This PR contains the following updates:
==0.6.2→==0.6.3==0.21.0→==0.21.2==2.14.0→==2.14.1==5.17.0→==5.18.0Release Notes
ollama/ollama-python (ollama)
v0.6.3Compare Source
What's Changed
New Contributors
Full Changelog: ollama/ollama-python@v0.6.2...v0.6.3
huggingface/peft (peft)
v0.21.2Compare Source
This is a PEFT release fixes an issue that prevented encoder-decoder models to work when using Transformers ≥ 5.18.0.
Changes:
v0.21.1Compare Source
This is a small PEFT release to enable Tensor Parallel (TP) to work properly with PEFT. It requires Transformers ≥ 5.17.0 to work.
Changes:
pytorch/pytorch (torch)
v2.14.1: PyTorch 2.14.1 ReleaseCompare Source
This release is meant to fix the following regressions and silent correctness issues:
Silent correctness fixes
torch.linalg.lstsqsolutions on MPS for complex batched underdetermined systems (#196113), fixed by #196128Uand inaccurate small singular values fromtorch.linalg.svdon MPS for rank-deficient and ill-conditioned inputs (#196112), fixed by #196139 and #199063cublasLtMatmul()could ignore tensor-wide scaling for NVFP4 matrix multiplications (introduced in CUDA 13.2 Update 1)Regression fixes
torch.linalg.svd,torch.linalg.svdvalsandtorch.linalg.lstsqfailing on MPS with a Metal pipeline-state error for inputs above 8192 elements (#195937), fixed by #195949 and #195950torch.svd(out=)on MPS for complex inputs (#195822), fixed by #195872huggingface/transformers (transformers)
v5.18.0: Release 5.18.0Compare Source
New Model additions
Nemotron 3 Diarization
Nemotron 3 Diarization is an open-weight streaming speaker diarization model designed to determine "who spoke when" in real-world audio. It supports both streaming and offline inference, handles up to eight speakers, and orders speaker outputs by each speaker's first arrival in the input audio.
The model uses the Arrival-Order Speaker Cache (AOSC) 1 and FIFO queue introduced for Streaming Sortformer 1, 2. A single checkpoint supports configurable latency profiles, from an 80 ms input buffer to a 30.4 s offline-style buffer, and configurable output frame resolution in multiples of 10 ms. With chunked inference, the maximum audio duration is not limited.
Links: Documentation
NemotronH Omni
NemotronH Omni is a multimodal reasoning model from NVIDIA that pairs the NemotronH hybrid
Mamba-Transformer language model with a RADIO vision encoder and an optional Parakeet-based sound encoder.
Image (and video) patches are projected through a RADIO tower and a pixel-shuffle MLP into the language model's
embedding space at the
<image>/<video>context-token positions; audio clips are projected in the same way at<audio>positions. The result is a single autoregressive model that reasons jointly over text, images, video andsound.
Links: Documentation
HyperCLOVAX Vision V2
HyperCLOVAX Vision V2 is a multimodal vision-language model developed by NAVER. It combines the HyperClovaX language model backbone with a Qwen2.5-VL vision encoder. The model supports text, image, and video inputs and is capable of chain-of-thought reasoning via built-in thinking tokens (
<think>...</think>).Links: Documentation
GTE
GTE was proposed in mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval by Xin Zhang, Yanzhao Zhang, Dingkun Long, Wen Xie, Ziqi Dai, Jialong Tang, Huan Lin, Baosong Yang, Pengjun Xie, Fei Huang, Meishan Zhang, Wenjie Li and Min Zhang.
GTE is a BERT-style bidirectional encoder that replaces absolute position embeddings with RoPE, uses a gated MLP, and applies layer normalization after each residual connection. The same architecture backs Alibaba's
gte-*-v1.5,gte-multilingual-*andgte-en-mlm-*checkpoints as well as Snowflake'ssnowflake-arctic-embed-m-v2.0.Links: Documentation
Breaking changes
Kernels] Bump version (#48714) by @vasquBugfixes and improvements
AutoImageProcessorrequiring torchvision when only Pillow is installed (#48616) by @blipbyteMoE] Fix eager EP (#48653) by @vasquminimaxfailing withoutput_mismatch(tensor values differ (2)) (#48515) by @sergereview[bot]mistralfailing withother(other (2)) (#48429) by @sergereview[bot]MemoryCleanupMixinclass for tests (#48681) by @tarekziadeflex_olmofailing withother(other (1)) (#48668) by @sergereview[bot]RTDetrModel/SEWDForCTCloads (wrongbase_model_prefix) (#48744) by @peftversion requirement (#48716) by @shniuboboedgetamfailing withimport_or_config(other (12)) (#48322) by @sergereview[bot]AutoModel.from_pretrainednot restoringmodules_to_saveweights (#48595) by @shniuboboreseton the dynamic cache layers (#48809) by @jiqing-fengStaticCachefor Mllama and enabletorch.compile(#48141) by @jiqing-fengfeat] Allow untyinghidden_states[-1]fromlast_hidden_statevia the model config (#48087) by @tomaarsendeepseek_vlfailing withother(other (3)) (#48536) by @sergereview[bot]DSA] Only save latents on dsa with indexer as well (#48876) by @vasquprefix_allowed_tokens_fnoverride model-infand raise an exception on unsatisfiable generation constraints. (#48927) by @ksh108405update_candidate_strategy(#48982) by @Cyrilvalleztorch.compile(#48975) by @jiqing-fengconfig.output_router_logitsin the MoE VLM wrappers (#48885) by @qgallouedecattention_maskasNonein OPT's causal mask creation (#49002) by @jiqing-fengTrainer.end(#48875) by @qgallouedeccvtfailing withoutput_mismatch(tensor values differ (2)) (#49076) by @sergereview[bot]hy_v3failing withoutput_mismatch(tensor values differ (2)) (#49068) by @sergereview[bot]pvt_v2failing withother(#49067) by @sergereview[bot]jambafailing withother(other (2)) (#49044) by @sergereview[bot]Configuration
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