Visual Question Answering
Transformers
Safetensors
English
llama
text-generation
multimodal
KBVQA
VQA
Finetuning
text-generation-inference
Instructions to use m7mdal7aj/fine_tuned_llama_2_7b_chat_OKVQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use m7mdal7aj/fine_tuned_llama_2_7b_chat_OKVQA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="m7mdal7aj/fine_tuned_llama_2_7b_chat_OKVQA")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("m7mdal7aj/fine_tuned_llama_2_7b_chat_OKVQA") model = AutoModelForCausalLM.from_pretrained("m7mdal7aj/fine_tuned_llama_2_7b_chat_OKVQA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 91bf184ab12793d0754344f9095332759432e666320cc6c07f637af50e36db6f
- Size of remote file:
- 500 kB
- SHA256:
- 9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
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