Text Classification
Transformers
Safetensors
llama
reward-model
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Jennny/llama3_8b_truth_rm_full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jennny/llama3_8b_truth_rm_full with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jennny/llama3_8b_truth_rm_full")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jennny/llama3_8b_truth_rm_full") model = AutoModelForSequenceClassification.from_pretrained("Jennny/llama3_8b_truth_rm_full", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5c3c8cad79f9e7e7b52f9b883f9933c3a680b264c60af31bf7f1cc8322a362fd
- Size of remote file:
- 17.2 MB
- SHA256:
- e8485b8900f2ef3a891091dbe5ef0edc56f6922c0a23c7dfbb4baf64854f7de4
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