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9 result(s) for “okapi bm25 site terms com pp site does could good com site pass uses gov site:huggingface.co” · 1170 ms
Scope: Nishi’s own index (our crawl + Common Crawl) — not the whole live web. Some sites block all automated crawlers (anti-bot walls), so their pages may be absent here even though we never filter lawful content.
Freshness: no result on this page carries a fetch time yet (indexed before the fetch epoch was stored beside the doc)
com/stockeh/canine-embedded-ml","githubRepoAddedBy":"auto","ai_summary":"A machine learning-based automatic dog treat dispenser uses image classification to identify and reward specific behaviors in real-time using a NVIDIA Jetson Nano
from the top result below
https://huggingface.co/papers?q=companion
com/YutingHe-list/GEMINI","githubRepoAddedBy":"auto","ai_summary":"GEMINI improves dense contrastive representation learning for medical images by incorporating deformable homeomorphism learning and geometric semantic similarity to enhance correspondence discovery, performance, and learning efficiency
https://huggingface.co/datasets/microsoft/benchpress-score-matrix
com/eval-sys/mcpmark"},"canonical_setting_json":{"kind":"string","value":"{\"higher_is_better\": true, \"judge\": \"programmatic verification scripts\", \"metric_type\": \"pct\", \"multimodal_input\": false, \"notes\": \"Official sources are arXiv:2509
https://huggingface.co/Mozilla/Meta-Llama-3.1-405B-llamafile
com/Mozilla-Ocho/llamafile/releases/download/0
https://huggingface.co/papers?q=reflective%20essays
com/rosewang2008/sight","githubRepoAddedBy":"auto","ai_summary":"A dataset and automated methods using large language models classify online student feedback efficiently, enhancing teacher improvement from lectures
https://huggingface.co/Mozilla/Llama-3.2-3B-Instruct-llamafile
If you have the CUDA SDK installed on your system, then you can pass the --recompile flag to build a GGML CUDA library just for your system that uses cuBLAS
https://huggingface.co/Mozilla/Llama-3.2-1B-Instruct-llamafile
If you have the CUDA SDK installed on your system, then you can pass the --recompile flag to build a GGML CUDA library just for your system that uses cuBLAS
https://huggingface.co/docs/hub/jobs-configuration
Alternatively, pass a Hugging Face token manually with --token in the CLI, or the token argument in Python
https://huggingface.co/zai-org/GLM-5.3
- set effective_reasoning_effort = reasoning_effort if reasoning_effort is defined and reasoning_effort in ['low', 'high'] else 'max' -%}\n{%- if effective_reasoning_effort is not none -%}<|system|>Reasoning Effort:
https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B
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