PEFT
Safetensors
English
llava_next
llava
llava-next
fine-tuned
stack-overflow
qlora
images
vqa
4bit
4-bit precision
bitsandbytes
Instructions to use Narrator5000/llavanext-finetuned-stackoverflow-vqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Narrator5000/llavanext-finetuned-stackoverflow-vqa with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf") model = PeftModel.from_pretrained(base_model, "Narrator5000/llavanext-finetuned-stackoverflow-vqa") - Notebooks
- Google Colab
- Kaggle
Download rng_state.pth from Narrator5000/llavanext-finetuned-stackoverflow-vqa: direct link, hf CLI and curl.
- Browser
- Download file 14.2 kB
-
https://huggingface.co/Narrator5000/llavanext-finetuned-stackoverflow-vqa/resolve/main/rng_state.pth
- Command line
-
hf download hf://Narrator5000/llavanext-finetuned-stackoverflow-vqa/rng_state.pth
-
curl -L -o rng_state.pth https://huggingface.co/Narrator5000/llavanext-finetuned-stackoverflow-vqa/resolve/main/rng_state.pth
14.2 kB
- Xet hash:
- 9be6978816241e199a455e4eb0c64c9a90b09f23b344169b21bc9799b7fef8f7
- Size of remote file:
- 14.2 kB
- SHA256:
- 6a7d4f097a3a9ec7b3660f87ea98b899a158e642e8c4cd18766cc3d04a2f405d
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