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:
- ae48100244b5ccf9d0c2b09b8e3bef8daa21fca2a8ec499131b24fc3e99db4d8
- Size of remote file:
- 5.39 kB
- SHA256:
- d5bcd49bd6d1025adc5d4385da3c333196b4008baf24052fee3d16f53db65152
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.