Text Classification
Transformers
Safetensors
English
qwen3_5
image-text-to-text
decision-model
classification
calibration
routing
guardrails
vision
multimodal
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
Fix Mammoth’s CLI selecting the wrong engine
#3
by sheppo - opened
I noticed qev serve selects the multimodal engine because Mammoth’s config includes vision_config. That engine misses the block-bidirectional attention handling in serve.patch.
This change routes block-bidirectional checkpoints through the patched text engine, matching the model card’s Python example. Explicit --multimodal requests for these checkpoints fail with a clear error.
Checks: patch applies to the pinned runtime; routing regression fails before and passes after. No new GPU inference run.