Text Classification
Transformers
Safetensors
mistral
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
Instructions to use rlhf-and-friends/TLDR-Mistral-7B-RM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rlhf-and-friends/TLDR-Mistral-7B-RM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rlhf-and-friends/TLDR-Mistral-7B-RM")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rlhf-and-friends/TLDR-Mistral-7B-RM") model = AutoModelForSequenceClassification.from_pretrained("rlhf-and-friends/TLDR-Mistral-7B-RM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 904459a27e22a74e38a8eadc325c4e4a929d6a6da588031db48f2c865a5f64f8
- Size of remote file:
- 5 GB
- SHA256:
- 4a1cad0a2941e9d953e64059512853f979ec1ec0d7268c71fe3195cca39e417a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.