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:
- 1f351f0190c8ced2d12d9a76fd2c066fef26741dd06186f882c1897a03d4c30e
- Size of remote file:
- 5.97 kB
- SHA256:
- 48290d27647f8088a0c86caac9189913b1c085064faee50ed40819840f67f8ab
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