Instructions to use kunal89/llama3.2-lora_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kunal89/llama3.2-lora_model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kunal89/llama3.2-lora_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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Download README.md from kunal89/llama3.2-lora_model: direct link, hf CLI and curl.
- Browser
- Download file 630 Bytes
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https://huggingface.co/kunal89/llama3.2-lora_model/resolve/46e4950f2f0b46eb4c5ad093f3e0ade8efcebc5f/README.md
- Command line
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hf download hf://kunal89/llama3.2-lora_model@46e4950f2f0b46eb4c5ad093f3e0ade8efcebc5f/README.md
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curl -L -o README.md https://huggingface.co/kunal89/llama3.2-lora_model/resolve/46e4950f2f0b46eb4c5ad093f3e0ade8efcebc5f/README.md
630 Bytes
metadata
base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mllama
- trl
license: apache-2.0
language:
- en
Uploaded model
- Developed by: kunal89
- License: apache-2.0
- Finetuned from model : unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
This mllama model was trained 2x faster with Unsloth and Huggingface's TRL library.
