Image-to-Text
Transformers
Safetensors
English
phi
text-generation
vision-language
llava
clip
qlora
multimodal
text-generation-inference
4-bit precision
bitsandbytes
Instructions to use sagar007/Lava_phi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sagar007/Lava_phi 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="sagar007/Lava_phi")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("sagar007/Lava_phi") model = AutoModelForCausalLM.from_pretrained("sagar007/Lava_phi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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# LLaVA-Phi Model
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language:
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- en
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tags:
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- vision-language
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- phi
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- llava
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- clip
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- qlora
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- multimodal
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license: mit
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datasets:
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- laion/instructional-image-caption-data
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base_model: microsoft/phi-1_5
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library_name: transformers
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pipeline_tag: image-to-text
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# LLaVA-Phi Model
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