Feature Extraction
Transformers
Safetensors
internvl_chat
Generated from Trainer
trl
sft
custom_code
Instructions to use THP2903/Vintern-1B-v3_5_info with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use THP2903/Vintern-1B-v3_5_info with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="THP2903/Vintern-1B-v3_5_info", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("THP2903/Vintern-1B-v3_5_info", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Model Card for Vintern-1B-v3_5_info
This model is a fine-tuned version of 5CD-AI/Vintern-1B-v3_5. 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="THP2903/Vintern-1B-v3_5_info", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
Training procedure
This model was trained with SFT.
Framework versions
- TRL: 0.16.0
- Transformers: 4.50.1
- Pytorch: 2.6.0
- Datasets: 3.4.0
- Tokenizers: 0.21.1
Citations
Cite TRL as:
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
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