Text Classification
Transformers
Safetensors
English
gemma4
image-text-to-text
system-one
system-two
adaptive-thinking
typed-decisions
decision-model
calibrated-probabilities
jev
noul
choice
score
lora
mixture-of-experts
multimodal
vllm
Eval Results (legacy)
Instructions to use autotrust/GEV-26B-Decide with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autotrust/GEV-26B-Decide with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autotrust/GEV-26B-Decide")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("autotrust/GEV-26B-Decide") model = AutoModelForMultimodalLM.from_pretrained("autotrust/GEV-26B-Decide", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download judge_config.json from autotrust/GEV-26B-Decide: direct link, hf CLI and curl.
- Browser
- Download file 1.5 kB
-
https://huggingface.co/autotrust/GEV-26B-Decide/resolve/main/judge_config.json
- Command line
-
hf download hf://autotrust/GEV-26B-Decide/judge_config.json
-
curl -L -o judge_config.json https://huggingface.co/autotrust/GEV-26B-Decide/resolve/main/judge_config.json
1.5 kB
| { | |
| "base_model_path": ".", | |
| "hidden_size": 2816, | |
| "slots": { | |
| "num_slots": 24, | |
| "ranges": { | |
| "noul": [ | |
| 0, | |
| 2 | |
| ], | |
| "score": [ | |
| 2, | |
| 8 | |
| ], | |
| "choice": [ | |
| 8, | |
| 24 | |
| ] | |
| }, | |
| "verbalizers": [ | |
| "false", | |
| "true", | |
| "0", | |
| "1", | |
| "2", | |
| "3", | |
| "4", | |
| "5", | |
| "A", | |
| "B", | |
| "C", | |
| "D", | |
| "E", | |
| "F", | |
| "G", | |
| "H", | |
| "I", | |
| "J", | |
| "K", | |
| "L", | |
| "M", | |
| "N", | |
| "O", | |
| "P" | |
| ], | |
| "template_version": "bare-v1" | |
| }, | |
| "verbalizer_ids": [ | |
| 4530, | |
| 3397, | |
| 236771, | |
| 236770, | |
| 236778, | |
| 236800, | |
| 236812, | |
| 236810, | |
| 236776, | |
| 236799, | |
| 236780, | |
| 236796, | |
| 236788, | |
| 236811, | |
| 236823, | |
| 236814, | |
| 236777, | |
| 236863, | |
| 236855, | |
| 236798, | |
| 236792, | |
| 236797, | |
| 236806, | |
| 236791 | |
| ], | |
| "kinds": [ | |
| "noul", | |
| "choice", | |
| "score" | |
| ], | |
| "softcap": 30.0, | |
| "readout": { | |
| "type": "last_token", | |
| "canvas_len": 1, | |
| "canvas_token": 0, | |
| "prefix_token": 2, | |
| "no_pad_mask": true | |
| }, | |
| "stage": "s2", | |
| "lora": { | |
| "r": 32, | |
| "alpha": 64, | |
| "dropout": 0.05, | |
| "target_modules": "model\\.language_model\\.layers\\.\\d+\\.(self_attn\\.(q|k|v|o)_proj|mlp\\.(gate|up|down)_proj)" | |
| }, | |
| "model_name": "gev-26b-a4b", | |
| "model_version": "0.1.0", | |
| "adapter_subfolder": "adapter", | |
| "weights_mode": "dual", | |
| "base_model": "google/gemma-4-26B-A4B-it", | |
| "temperatures": "jev" | |
| } |