Instructions to use TokenBender/rewardLM_ChaiML_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TokenBender/rewardLM_ChaiML_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TokenBender/rewardLM_ChaiML_v1", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TokenBender/rewardLM_ChaiML_v1") model = AutoModelForSequenceClassification.from_pretrained("TokenBender/rewardLM_ChaiML_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1ce8af32340572b89b0138f4bfa94186885376bc2b092ce7600b60d5d95f4702
- Size of remote file:
- 498 MB
- SHA256:
- 339c6e8fce32209d4acfb33d791473d232b66aecb5c3b6b6b528237b891010bc
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