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
Commit History
Yesterday
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Fix Mammoth’s CLI selecting the wrong engine
Verified signatureI 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.3881ab5
5 days ago
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Add config, tokenizer and serving bundle
Verified signature8b4a11a