Zero-Shot Classification
Transformers
Safetensors
English
matilda_jev
feature-extraction
decision-model
typed-decisions
jev
maincode
custom_code
Instructions to use Maincode/matilda-jev-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maincode/matilda-jev-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Maincode/matilda-jev-v1", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Maincode/matilda-jev-v1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update runtime/maincode_jev_serve/model.py
Browse files
runtime/maincode_jev_serve/model.py
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@@ -23,7 +23,7 @@ from transformers.models.qwen3_vl.processing_qwen3_vl import Qwen3VLProcessor
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from maincode_jev_serve.types import Answer, Content, DecisionInput, ImageInput, JSONValue, Question
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# Weights are always read from a local, read-only snapshot; nothing is fetched or cached.
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BASE_MODEL = os.environ.get("
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MAX_OPTIONS = 255
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from maincode_jev_serve.types import Answer, Content, DecisionInput, ImageInput, JSONValue, Question
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# Weights are always read from a local, read-only snapshot; nothing is fetched or cached.
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BASE_MODEL = os.environ.get("MJ_BASE_MODEL", "Qwen/Qwen3.8-27B")
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MAX_OPTIONS = 255
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