Instructions to use minhnguyennnnnn/af1615e2-5e65-478a-8ad3-bd0965cf1489 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use minhnguyennnnnn/af1615e2-5e65-478a-8ad3-bd0965cf1489 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer") model = PeftModel.from_pretrained(base_model, "minhnguyennnnnn/af1615e2-5e65-478a-8ad3-bd0965cf1489") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from minhnguyennnnnn/af1615e2-5e65-478a-8ad3-bd0965cf1489: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/minhnguyennnnnn/af1615e2-5e65-478a-8ad3-bd0965cf1489/resolve/main/tokenizer.json
- Command line
-
hf download hf://minhnguyennnnnn/af1615e2-5e65-478a-8ad3-bd0965cf1489/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/minhnguyennnnnn/af1615e2-5e65-478a-8ad3-bd0965cf1489/resolve/main/tokenizer.json
17.2 MB
- Xet hash:
- 16dbebbf87ec3b264caffa5aae1208c11f26ab9e8b90e5906da24d576c712186
- Size of remote file:
- 17.2 MB
- SHA256:
- 06848e95e34be87e26934cd584d8a7e85433a81f94231c66c2cf780c8dc05eab
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