Text Classification
Transformers
Safetensors
English
qwen3_5
image-text-to-text
decision-model
classification
calibration
routing
guardrails
baseten
Instructions to use baselabs/mammoth-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use baselabs/mammoth-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="baselabs/mammoth-1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("baselabs/mammoth-1") model = AutoModelForMultimodalLM.from_pretrained("baselabs/mammoth-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from baselabs/mammoth-1: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/baselabs/mammoth-1/resolve/main/tokenizer.json
- Command line
-
hf download hf://baselabs/mammoth-1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/baselabs/mammoth-1/resolve/main/tokenizer.json
20 MB
- Xet hash:
- e558cdad9a88d177076f9525ed4ef2d55dfd5dafafd6baac7c6105590ab49b7b
- Size of remote file:
- 20 MB
- SHA256:
- a5cd9732badce41de57e6efce8302930ded1c1188c5f81feb2bd6c24c4a1941f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.