Instructions to use Meanblock/JEV-CPU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Meanblock/JEV-CPU with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Meanblock/JEV-CPU")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Meanblock/JEV-CPU", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model card: add HF frontmatter
Browse files
README.md
CHANGED
|
@@ -1,3 +1,17 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
# JEV-CPU
|
| 2 |
|
| 3 |
<div align="center">
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: mit
|
| 3 |
+
base_model:
|
| 4 |
+
- Qwen/Qwen3-0.6B
|
| 5 |
+
tags:
|
| 6 |
+
- semantic-if
|
| 7 |
+
- decision
|
| 8 |
+
- semif
|
| 9 |
+
- cpu
|
| 10 |
+
- zero-shot-classification
|
| 11 |
+
pipeline_tag: zero-shot-classification
|
| 12 |
+
library_name: transformers
|
| 13 |
+
---
|
| 14 |
+
|
| 15 |
# JEV-CPU
|
| 16 |
|
| 17 |
<div align="center">
|