Instructions to use MaoXun/llava-lora-vicuna-7b-v1.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use MaoXun/llava-lora-vicuna-7b-v1.3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("lmsys/vicuna-7b-v1.3") model = PeftModel.from_pretrained(base_model, "MaoXun/llava-lora-vicuna-7b-v1.3") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from MaoXun/llava-lora-vicuna-7b-v1.3: direct link, hf CLI and curl.
- Browser
- Download file 320 MB
-
https://huggingface.co/MaoXun/llava-lora-vicuna-7b-v1.3/resolve/main/adapter_model.bin
- Command line
-
hf download hf://MaoXun/llava-lora-vicuna-7b-v1.3/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/MaoXun/llava-lora-vicuna-7b-v1.3/resolve/main/adapter_model.bin
320 MB
- Xet hash:
- b69d3c3e679c9187afd2b6f6ecfb6e5ca09c990bd719b9be5d86d02c1ce57cdb
- Size of remote file:
- 320 MB
- SHA256:
- 3bb1aeeb58b57c46c742a003d5fc1153da0136a0df7df16bb4254a3db62150e8
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