Visual Question Answering
Transformers
Safetensors
English
videollama3_qwen2
text-generation
multi-modal
large-language-model
video-language-model
custom_code
Instructions to use DAMO-NLP-SG/VideoLLaMA3-2B-Image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DAMO-NLP-SG/VideoLLaMA3-2B-Image with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="DAMO-NLP-SG/VideoLLaMA3-2B-Image", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("DAMO-NLP-SG/VideoLLaMA3-2B-Image", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update modeling_videollama3_encoder.py
#3
by nday - opened
modeling_videollama3_encoder.py
CHANGED
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@@ -343,7 +343,7 @@ class VisionSdpaAttention(VisionAttention):
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attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attention_mask, dropout_p=0.0)
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attn_output = attn_output.transpose(0, 1)
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attn_output = attn_output.reshape(seq_length, -1)
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attn_output = self.
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return attn_output
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attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attention_mask, dropout_p=0.0)
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attn_output = attn_output.transpose(0, 1)
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attn_output = attn_output.reshape(seq_length, -1)
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attn_output = self.out_proj(attn_output)
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return attn_output
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