Text Classification
Transformers
Safetensors
mistral
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
Instructions to use rlhf-and-friends/TLDR-Mistral-7B-SmallSFT-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-SmallSFT-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-SmallSFT-RM")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rlhf-and-friends/TLDR-Mistral-7B-SmallSFT-RM") model = AutoModelForSequenceClassification.from_pretrained("rlhf-and-friends/TLDR-Mistral-7B-SmallSFT-RM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7961617f80bdd7c1d5607eeb183b2ef1bdaa0396629a3689a927f5b6d9e83451
- Size of remote file:
- 5.56 kB
- SHA256:
- 81910333d3c9a75a344fde26db27dfb3d372bcc4f89c0d81061a1ac75b8ca799
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.