Text Classification
Transformers
Safetensors
llama
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
Instructions to use rlhf-and-friends/TLDR-Llama-3.2-1B-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-Llama-3.2-1B-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-Llama-3.2-1B-SmallSFT-RM")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rlhf-and-friends/TLDR-Llama-3.2-1B-SmallSFT-RM") model = AutoModelForSequenceClassification.from_pretrained("rlhf-and-friends/TLDR-Llama-3.2-1B-SmallSFT-RM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ce0c3e9fde053667d3bb6fce023f6852a15279f71b6104864c0b8c112522a66
- Size of remote file:
- 2.47 GB
- SHA256:
- 05115fd2c9d80ed97ecd4720892d4b63109974eb73370ee2d725d12cc3177f23
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