Reinforcement Learning
Transformers
Safetensors
llama
text-classification
reward-model
rlhf
sparse-autoencoder
interpretability
custom_code
text-generation-inference
Instructions to use Schrieffer/Llama-SARM-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Schrieffer/Llama-SARM-4B with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Schrieffer/Llama-SARM-4B", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("Schrieffer/Llama-SARM-4B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add library_name and pipeline_tag to metadata
#1
by nielsr HF Staff - opened
This PR enhances the model card by adding the library_name and pipeline_tag to the metadata.
library_name: transformersis added as the model is compatible with the Transformers library, enabling the "how to use" widget.pipeline_tag: reinforcement-learningis added to correctly categorize this reward model within the hub's ecosystem.
No changes were made to the content of the model card, as it already includes comprehensive information, including links to the paper and code, and a usage example.
Schrieffer changed pull request status to merged