datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
base_model_sprint
Base Model Metadata Sprint
Description
Join us in improving the discoverability and understanding of models on the Hugging Face Hub by adding base_model metadata! This sprint aims to enhance the information available for models derived from, fine-tuned on, or quantized versions of existing base models.
🤗 Strong contributions will win prizes!! 🤗
Why It Matters
Adding base_model metadata helps users:
Easily find models derived from specific architectures… See the full description on the dataset page: https://huggingface.co/datasets/librarian-bots/base_model_sprint.CNNOV_Eng_BaseModelbase_model_eval
Base Model Blind Spots: Zero-Shot Instruction Failures
Model Name: Qwen3-4b-base.
The model was loaded using a standard Kaggle environment with a P100 GPU using the transformers library.
from transformers import AutoModelForCausalLM, AutoTokenizer
import kagglehub
model_name = kagglehub.model_download("qwen-lm/qwen-3/transformers/4b-base")
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(… See the full description on the dataset page: https://huggingface.co/datasets/dd42/base_model_eval.