Feature Extraction
Transformers
TensorBoard
Safetensors
opensci
llama-factory
full
Generated from Trainer
custom_code
Instructions to use open-sci/sft_ot30k_1.7b-MixtureVitae-300BT-v1-decontaminated-16k-SFT-Tulu3-decontaminated_v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use open-sci/sft_ot30k_1.7b-MixtureVitae-300BT-v1-decontaminated-16k-SFT-Tulu3-decontaminated_v0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="open-sci/sft_ot30k_1.7b-MixtureVitae-300BT-v1-decontaminated-16k-SFT-Tulu3-decontaminated_v0", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("open-sci/sft_ot30k_1.7b-MixtureVitae-300BT-v1-decontaminated-16k-SFT-Tulu3-decontaminated_v0", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 03056479a89f6ec549cb5cda631dcfe4f937b2f21280b4a23cb714f8f2896345
- Size of remote file:
- 8.85 kB
- SHA256:
- 3d4c9aa4f8c0a3baf02f9aa00b16b6f6d2866324f8b9bf8ceb35ad1d056258e0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.