Summarization
Transformers
Safetensors
gpt_bigcode
text-classification
reward-model
reward-trainer
trl
rlhf
preference-learning
text-generation-inference
Instructions to use caffeic/tinystarcoder-reward-tldr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use caffeic/tinystarcoder-reward-tldr with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="caffeic/tinystarcoder-reward-tldr")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("caffeic/tinystarcoder-reward-tldr") model = AutoModelForSequenceClassification.from_pretrained("caffeic/tinystarcoder-reward-tldr") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 83d1df1e1758d4ba8619f1f008694c96feac3eba7d0eec61e29a59e9aca4ef64
- Size of remote file:
- 1.31 GB
- SHA256:
- 8ccd5c5c93605b1293e1d7012c320378be7af14a7baaa76f9d5844e394ca5128
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