Instructions to use HopitAI/hopper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use HopitAI/hopper with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-4B") model = PeftModel.from_pretrained(base_model, "HopitAI/hopper") - Notebooks
- Google Colab
- Kaggle
Card: current JevBench position (#7, v1.4.2.2)
Browse files
README.md
CHANGED
|
@@ -62,10 +62,9 @@ adapter does not change its terms.
|
|
| 62 |
|
| 63 |
## Leaderboards (official)
|
| 64 |
|
| 65 |
-
- **[JevBench](https://benchmarkheaven.com/jev-models)**: **59.43, #
|
| 66 |
-
|
| 67 |
-
|
| 68 |
-
82.3 % on the public items and 34.1 % on the maintainer's sealed set.
|
| 69 |
- **[Jev Decision Index](https://huggingface.co/spaces/multimodalart/jev-decision-index)**: Hopper 1.1.1 scored
|
| 70 |
36.71, #12 of 49 (edition 0.2, 25 Sep 2026), then 39.67, #19 of 67 (edition 0.2.1). Its row has since been replaced by
|
| 71 |
[Hopper (G) 1.2](https://huggingface.co/HopitAI/hopper-g), the general-purpose line served with the same code,
|
|
|
|
| 62 |
|
| 63 |
## Leaderboards (official)
|
| 64 |
|
| 65 |
+
- **[JevBench](https://benchmarkheaven.com/jev-models)**: **59.43, #7** in v1.4.2.2 (27 Sep 2026), measured by the
|
| 66 |
+
maintainer on adapter 1.0.0: Intelligence 48.0, Calibration 79.1, Speed 86.8, Cost 58.7. Accuracy is 82.3 % on the
|
| 67 |
+
public items and 34.1 % on the maintainer's sealed set.
|
|
|
|
| 68 |
- **[Jev Decision Index](https://huggingface.co/spaces/multimodalart/jev-decision-index)**: Hopper 1.1.1 scored
|
| 69 |
36.71, #12 of 49 (edition 0.2, 25 Sep 2026), then 39.67, #19 of 67 (edition 0.2.1). Its row has since been replaced by
|
| 70 |
[Hopper (G) 1.2](https://huggingface.co/HopitAI/hopper-g), the general-purpose line served with the same code,
|