Instructions to use Jeong-su/car with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Jeong-su/car with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("llava-hf/LLaVA-NeXT-Video-7B-hf") model = PeftModel.from_pretrained(base_model, "Jeong-su/car") - Notebooks
- Google Colab
- Kaggle
Download chat_template.json from Jeong-su/car: direct link, hf CLI and curl.
- Browser
- Download file 839 Bytes
-
https://huggingface.co/Jeong-su/car/resolve/main/chat_template.json
- Command line
-
hf download hf://Jeong-su/car/chat_template.json
-
curl -L -o chat_template.json https://huggingface.co/Jeong-su/car/resolve/main/chat_template.json
839 Bytes
| { | |
| "chat_template": "{% for message in messages %}{% if message['role'] != 'system' %}{{ message['role'].upper() + ': '}}{% endif %}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>\n' }}{% endfor %}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'video') %}{{ '<video>\n' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ content['text'] + ' '}}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] + ' '}}{% endgeneration %}{% endfor %}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'ASSISTANT:' }}{% endif %}" | |
| } | |