Instructions to use ECOFRI/CXR-LLAVA-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ECOFRI/CXR-LLAVA-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ECOFRI/CXR-LLAVA-v2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ECOFRI/CXR-LLAVA-v2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update CXR_LLAVA_HF.py
#4
by vivek-69 - opened
- CXR_LLAVA_HF.py +1 -1
CXR_LLAVA_HF.py
CHANGED
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@@ -46,7 +46,7 @@ class CXRLLAVAModel(PreTrainedModel):
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def __init__(self, config):
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super().__init__(config)
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-
self.tokenizer = transformers.LlamaTokenizer.from_pretrained(config._name_or_path
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self.tokenizer.pad_token = self.tokenizer.unk_token
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self.tokenizer.sep_token = self.tokenizer.unk_token
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self.tokenizer.cls_token = self.tokenizer.unk_token
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def __init__(self, config):
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super().__init__(config)
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+
self.tokenizer = transformers.LlamaTokenizer.from_pretrained(config._name_or_path)
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self.tokenizer.pad_token = self.tokenizer.unk_token
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self.tokenizer.sep_token = self.tokenizer.unk_token
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self.tokenizer.cls_token = self.tokenizer.unk_token
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