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")# 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 tokenizer.json from Raymond1122/metask-jev-4b-policy-mix: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/Raymond1122/metask-jev-4b-policy-mix/resolve/main/tokenizer.json
- Command line
-
hf download hf://Raymond1122/metask-jev-4b-policy-mix/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Raymond1122/metask-jev-4b-policy-mix/resolve/main/tokenizer.json
20 MB
- Xet hash:
- 777bcaa63794fa47b8f53680be9d6d176f1fcbd7ba03cdc6c3bae2b3d76b323f
- Size of remote file:
- 20 MB
- SHA256:
- 06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.