Instructions to use flock-io/Flock_Web3_Agent_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flock-io/Flock_Web3_Agent_Model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("flock-io/Flock_Web3_Agent_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from flock-io/Flock_Web3_Agent_Model: direct link, hf CLI and curl.
- Browser
- Download file 7.36 kB
-
https://huggingface.co/flock-io/Flock_Web3_Agent_Model/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://flock-io/Flock_Web3_Agent_Model/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/flock-io/Flock_Web3_Agent_Model/resolve/main/tokenizer_config.json
7.36 kB
- Xet hash:
- 39c126c6caadd19b17d25fc0938fe2cda0f48ed3b0d3863dc2f2677e2874cbc1
- Size of remote file:
- 7.36 kB
- SHA256:
- 056f2c058091cdc7af49f6dc7cc43e0440f1b7d2a76d95d1daea085db11db491
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.