Text Classification
Transformers
Safetensors
English
nli
cross-encoder
qwen3.5
reranker
image-text-to-text
Instructions to use AlexWortega/openjev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlexWortega/openjev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AlexWortega/openjev")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AlexWortega/openjev", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download code/serving/patches/dynamic-paged.patch from AlexWortega/openjev: direct link, hf CLI and curl.
- Browser
- Download file 2.11 kB
-
https://huggingface.co/AlexWortega/openjev/resolve/main/code/serving/patches/dynamic-paged.patch
- Command line
-
hf download hf://AlexWortega/openjev/code/serving/patches/dynamic-paged.patch
-
curl -L -o dynamic-paged.patch https://huggingface.co/AlexWortega/openjev/resolve/main/code/serving/patches/dynamic-paged.patch
2.11 kB
| --- a/python/sglang/srt/layers/attention/tilelang_fa_v100/_kernels_paged.py | |
| +++ b/python/sglang/srt/layers/attention/tilelang_fa_v100/_kernels_paged.py | |
| which doubles warp count from 2 to 4, improving V100 occupancy. | |
| """ | |
| import math | |
| +import os | |
| import torch | |
| import tilelang | |
| import tilelang.language as T | |
| max_blocks_per_seq, num_pages, is_causal, | |
| sliding_window_size=-1, | |
| block_M=32, block_N=128, num_stages=0, threads=256, | |
| - num_splits=1): | |
| + num_splits=1, dynamic_shapes=False): | |
| scale = (1.0 / dim) ** 0.5 | |
| nt = T.dynamic("nt") | |
| + if dynamic_shapes: | |
| + batch = T.dynamic("batch") | |
| + max_blocks_per_seq = T.dynamic("max_blocks_per_seq") | |
| + num_pages = T.dynamic("num_pages") | |
| use_kv_union = dim > _USE_KV_UNION_FOR_DIM | |
| if num_splits > 1: | |
| max_blocks, causal, sliding_window_size=-1): | |
| """Return compiled kernel.""" | |
| cfg = _BEST_CONFIGS.get(dim, dict(block_M=32, block_N=128, threads=256, num_stages=0, num_splits=1)) | |
| + dynamic = os.environ.get("SGLANG_V100_DYNAMIC_PAGED", "0") == "1" | |
| + shape_key = (0, 0, 0) if dynamic else (batch, max_blocks, num_pages) | |
| key = (heads, heads_kv, dim, block_size, causal, sliding_window_size, | |
| cfg["block_M"], cfg["block_N"], cfg["threads"], cfg["num_stages"], cfg["num_splits"], | |
| - batch, max_blocks, num_pages) | |
| + dynamic, *shape_key) | |
| if key not in _KERNEL_CACHE: | |
| kt = _paged_kernel_func( | |
| - batch=batch, heads=heads, heads_kv=heads_kv, dim=dim, | |
| + batch=shape_key[0], heads=heads, heads_kv=heads_kv, dim=dim, | |
| page_block_size=block_size, | |
| - max_blocks_per_seq=max_blocks, | |
| - num_pages=num_pages, | |
| + max_blocks_per_seq=shape_key[1], | |
| + num_pages=shape_key[2], | |
| + dynamic_shapes=dynamic, | |
| is_causal=causal, | |
| sliding_window_size=sliding_window_size, | |
| **cfg, | |