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Fix processor output for chat inference
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Use available multimodal auto model class
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Use CPU-safe multimodal default
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Repair Space runtime and improve documentation
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Correctly format developer attribution in README.md
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Update model card with developer attribution
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Update app.py
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Update app.py
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Update app.py
Verified signatureimport gradio as gr from transformers import AutoModelForImageTextToText, AutoProcessor import torch import os # 1. Setup Model & Token model_id = "google/gemma-3n-E2B-it" hf_token = os.getenv("HF_TOKEN") device = "cpu" print("Loading Gemma 3n (10GB)... This takes a few minutes.") # We add low_cpu_mem_usage=True to prevent crashing on load processor = AutoProcessor.from_pretrained(model_id, token=hf_token) model = AutoModelForImageTextToText.from_pretrained( model_id, token=hf_token, torch_dtype=torch.float32, low_cpu_mem_usage=True, device_map="auto" ) def chat_function(message, history): # Prepare history for the model msgs = [] for user_msg, assistant_msg in history: if user_msg: msgs.append({"role": "user", "content": [{"type": "text", "text": user_msg}]}) if assistant_msg: msgs.append({"role": "model", "content": [{"type": "text", "text": assistant_msg}]}) # Add new message msgs.append({"role": "user", "content": [{"…cd49dba -
Update requirements.txt
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Create app.py
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Create requirements.txt
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initial commit
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