GENERAL-FUNCTION REGULARITY: require 0 < Delta G_s < infinity and all transformed scores f_is in L1([0,1]); positive bounded coefficients on compact activity intervals suffice. The rational step ...
gLM2 is a mixed-modality genomic language model, trained on the OMG Dataset. The model encodes a genomic scaffold with both both amino-acid and DNA tokens. gLM2 is trained at two scales: 150M ...
How to use saidutta69/EuroLLM-9B-Instruct-heretic with Transformers: # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text ...
Free, with instant access: log in to Hugging Face, answer 3 quick questions in the form on this page, and the files open right away. 225 free, sim-ready warehouse objects for robot learning: 75 ...
Face image quality assessment methods vary widely in detecting diffusion-based and other morphing attacks, with performance depending heavily on attack type and dataset, and high quality scores for ...
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Umranz/SD1.5-Uncensored-Photoreal ...
How to use unsloth/Qwen3.6-35B-A3B-GGUF with Transformers: # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to ...
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Umranz/SD1.5-Uncensored-Photoreal ...
How to use plzsay/dual_pick_and_place with LeRobot: # No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help?
This repository stores the Oxford 102 Flowers image archive used by FG-BMK. It is a companion repository for SEU-VIPGroup/FG-BMK, which contains the benchmark metadata, questions, splits, and ...
How to use Guilherme34/LFM3-Omni-2B-Looped-Latent-v2.1-adapter with Transformers: # pip install -U transformers accelerate # Load model directly from transformers ...
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from ...
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