Text Classification
Transformers
Safetensors
English
qwen3_5
image-text-to-text
typed-decisions
calibrated-classification
system-one
classification
structured-prediction
candidate-logit
jev
single-forward-pass
commercial-use
Instructions to use Raymond1122/metask-jev-4b-policy-mix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Raymond1122/metask-jev-4b-policy-mix with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Raymond1122/metask-jev-4b-policy-mix")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Raymond1122/metask-jev-4b-policy-mix") model = AutoModelForMultimodalLM.from_pretrained("Raymond1122/metask-jev-4b-policy-mix", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metask-jev-4b: Qwen3.5-4B LoRA for typed decisions (13-subset 79.6%, JevBench 80.1% @4096)
8a82297 verified Download model.safetensors from Raymond1122/metask-jev-4b-policy-mix: direct link, hf CLI and curl.
- Browser
- Download file 9.08 GB
-
https://huggingface.co/Raymond1122/metask-jev-4b-policy-mix/resolve/main/model.safetensors
- Command line
-
hf download hf://Raymond1122/metask-jev-4b-policy-mix/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Raymond1122/metask-jev-4b-policy-mix/resolve/main/model.safetensors
9.08 GB
- Xet hash:
- 0d947016f1cd783329a3f63ae30128f4bfa343762923cc437b70533b9116d899
- Size of remote file:
- 9.08 GB
- SHA256:
- dcb84510169471a01525a892a49750da1d7d0d2457a84e66d82408837b91e344
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