Instructions to use photonmz/llava-roco-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use photonmz/llava-roco-8bit with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="photonmz/llava-roco-8bit", device_map="auto")# Load model directly from transformers import AutoProcessor, AutoModelForCausalLM processor = AutoProcessor.from_pretrained("photonmz/llava-roco-8bit") model = AutoModelForCausalLM.from_pretrained("photonmz/llava-roco-8bit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload LlavaLlamaForCausalLM
Browse files- config.json +1 -1
- pytorch_model-00001-of-00002.bin +1 -1
- pytorch_model-00002-of-00002.bin +1 -1
config.json
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{
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"_name_or_path": "/
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"architectures": [
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"LlavaLlamaForCausalLM"
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{
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"_name_or_path": "kaelee/llava-llama-2-7b-chat-finetuning",
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"architectures": [
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"LlavaLlamaForCausalLM"
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pytorch_model-00001-of-00002.bin
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pytorch_model-00002-of-00002.bin
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