Instructions to use niuqimeng/AGR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use niuqimeng/AGR with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("niuqimeng/AGR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Card for v4-20260116-150217
This model is a fine-tuned version of None. It has been trained using TRL.
Quick start
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
Framework versions
- TRL: 0.26.2
- Transformers: 4.57.5
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.22.2
Inference Providers NEW
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