Spaces:
Running
on
Zero
Running
on
Zero
backup
Browse files
app.py
CHANGED
@@ -28,35 +28,19 @@ check_environment()
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login(token=os.environ["HF_TOKEN"], add_to_git_credential=True)
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# Load model and processor (do this outside the inference function to avoid reloading)
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base_model_path =
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# lora_weights_path = "taesiri/BugsBunny-LLama-3.2-11B-Vision-Base-Medium-LoRA"
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# )
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from transformers import AutoModelForCausalLM, AutoProcessor, LlamaTokenizer
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import torch
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model_path = "taesiri/BugsBunny-LLama-3.2-11B-Vision-Instruct-Medium-FullModel"
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# Load the processor (handles both text and vision inputs)
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processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
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# Load the model
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model = AutoModelForCausalLM.from_pretrained(
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model_path, torch_dtype=torch.bfloat16, device_map="cuda", trust_remote_code=True
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# If you specifically need the tokenizer
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tokenizer = LlamaTokenizer.from_pretrained(model_path)
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model.tie_weights()
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# model = PeftModel.from_pretrained(model, lora_weights_path)
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def describe_image_in_JSON(json_string):
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login(token=os.environ["HF_TOKEN"], add_to_git_credential=True)
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# Load model and processor (do this outside the inference function to avoid reloading)
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base_model_path = (
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"taesiri/BugsBunny-LLama-3.2-11B-Vision-BaseCaptioner-Medium-FullModel"
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)
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# lora_weights_path = "taesiri/BugsBunny-LLama-3.2-11B-Vision-Base-Medium-LoRA"
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processor = AutoProcessor.from_pretrained(base_model_path)
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model = MllamaForConditionalGeneration.from_pretrained(
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base_model_path,
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torch_dtype=torch.bfloat16,
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device_map="cuda",
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)
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# model = PeftModel.from_pretrained(model, lora_weights_path)
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model.tie_weights()
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def describe_image_in_JSON(json_string):
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