Instructions to use dd101bb/latentRM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dd101bb/latentRM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dd101bb/latentRM")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dd101bb/latentRM") model = AutoModelForTokenClassification.from_pretrained("dd101bb/latentRM") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -5,7 +5,6 @@ tags:
|
|
| 5 |
- latent
|
| 6 |
datasets:
|
| 7 |
- openai/gsm8k
|
| 8 |
-
- ChilleD/MultiArith
|
| 9 |
base_model:
|
| 10 |
- openai-community/gpt2
|
| 11 |
pipeline_tag: token-classification
|
|
@@ -36,4 +35,4 @@ LatentRM provides the missing aggregation signal for parallel test-time scaling
|
|
| 36 |
primaryClass={cs.CL},
|
| 37 |
url={https://arxiv.org/abs/2510.07745},
|
| 38 |
}
|
| 39 |
-
```
|
|
|
|
| 5 |
- latent
|
| 6 |
datasets:
|
| 7 |
- openai/gsm8k
|
|
|
|
| 8 |
base_model:
|
| 9 |
- openai-community/gpt2
|
| 10 |
pipeline_tag: token-classification
|
|
|
|
| 35 |
primaryClass={cs.CL},
|
| 36 |
url={https://arxiv.org/abs/2510.07745},
|
| 37 |
}
|
| 38 |
+
```
|