Instructions to use brikdavies/qwen1.7B-MMLU-hint-following-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brikdavies/qwen1.7B-MMLU-hint-following-RL with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("brikdavies/qwen1.7B-MMLU-hint-following-RL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-100/tokenizer.json from brikdavies/qwen1.7B-MMLU-hint-following-RL: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/brikdavies/qwen1.7B-MMLU-hint-following-RL/resolve/e048cebba376c632d046eeefee9d1e84300a5d6d/checkpoint-100/tokenizer.json
- Command line
-
hf download hf://brikdavies/qwen1.7B-MMLU-hint-following-RL@e048cebba376c632d046eeefee9d1e84300a5d6d/checkpoint-100/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/brikdavies/qwen1.7B-MMLU-hint-following-RL/resolve/e048cebba376c632d046eeefee9d1e84300a5d6d/checkpoint-100/tokenizer.json
11.4 MB
- Xet hash:
- 693ec4b3922b0bd306bf7b4989e115ffbfeb7b0c08b31bc6d956818c6bb07f61
- Size of remote file:
- 11.4 MB
- SHA256:
- aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.