Instructions to use UCSC-Admire/Admire-llava-v1.6-mistral-7b-hf-bnb-4bit-Finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UCSC-Admire/Admire-llava-v1.6-mistral-7b-hf-bnb-4bit-Finetuned with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UCSC-Admire/Admire-llava-v1.6-mistral-7b-hf-bnb-4bit-Finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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Download README.md from UCSC-Admire/Admire-llava-v1.6-mistral-7b-hf-bnb-4bit-Finetuned: direct link, hf CLI and curl.
- Browser
- Download file 616 Bytes
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https://huggingface.co/UCSC-Admire/Admire-llava-v1.6-mistral-7b-hf-bnb-4bit-Finetuned/resolve/main/README.md
- Command line
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hf download hf://UCSC-Admire/Admire-llava-v1.6-mistral-7b-hf-bnb-4bit-Finetuned/README.md
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curl -L -o README.md https://huggingface.co/UCSC-Admire/Admire-llava-v1.6-mistral-7b-hf-bnb-4bit-Finetuned/resolve/main/README.md
616 Bytes
metadata
base_model: unsloth/llava-v1.6-mistral-7b-hf-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llava_next
- trl
license: apache-2.0
language:
- en
Uploaded model
- Developed by: UCSC-Admire
- License: apache-2.0
- Finetuned from model : unsloth/llava-v1.6-mistral-7b-hf-bnb-4bit
This llava_next model was trained 2x faster with Unsloth and Huggingface's TRL library.
